prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: When did Born resign as chairperson of the CFTC?

Read the following text and answer briefly.

Context 1: Brooksley Elizabeth Born (born August 27, 1940) is an American attorney and former public official who, from August 26, 1996, to June 1, 1999, was chair of the Commodity Futures Trading Commission (CFTC), the federal agency which oversees the U S  futures and commodity options markets  During her tenure on the CFTC, Born lobbied Congress and the President to give the CFTC oversight of off-exchange markets for derivatives, in addition to its role with respect to exchange-traded derivatives, but her warnings were ignored or dismissed, and her calls for reform resisted by other regulators <ref name="nytimes">Goodman, Peter S  The Reckoning - Taking Hard New Look at a Greenspan Legacy, The New York Times, October 9, 2008 </ref> Born resigned as chairperson on June 1, 1999, shortly after Congress passed legislation prohibiting her agency from regulating derivatives 

In 2009, Born received the John F  Kennedy Profiles in Courage Award, along with Sheila Bair of the Federal Deposit Insurance Corporation, in recognition of the "political courage she demonstrated in sounding early warnings about conditions that contributed" to the 2007-08 financial crisis 

Early life and education
Born graduated from Abraham Lincoln High School (San Francisco, California) at the age of 16  She then attended Stanford University, where she majored in English and was graduated with the class of 1961   She initially wanted to become a doctor, but a guidance counsellor at Stanford advised her against medicine, so she majored in English literature instead 

She then attended Stanford Law School, one of only seven women in her class   She was the first female student ever to be named president of the Stanford Law Review  She received the "Outstanding Senior" award and graduated as valedictorian of the class of 1964 

Legal career
Immediately after law school Born was selected as a law clerk to judge Henry Edgerton of the U S  Court of Appeals for the District of Columbia Circuit  It was during this time that she met her first husband, Jacob C  Landau, who was a journalist covering the Federal courts at the time  Following her clerkship, she became an associate at the Washington, D C -based international law firm of Arnold & Porter  Born was attracted to Arnold & Porter because it was one of the few major law firms to have a woman partner at that time, Carolyn Agger, who was the head of the tax practice  Born took a two-year leave of absence from Arnold & Porter to accompany her first husband to Boston, where he had received a fellowship  During that time she worked as a research assistant to law professor Alan Dershowitz 

Born's early career at Arnold & Porter focused on international trade law, in which she represented a number of Swiss industries and the government of Switzerland  She developed a practice representing clients in numerous complex litigation and arbitration cases involving financial market transactions  Among her high-profile cases was the matter of the Hunt Brothers attempt to corner the market in silver in the 1970s  She made partner at Arnold & Porter, after moving to a three-day schedule to help raise her second child, and eventually rose to be the head of the firm's derivatives practice 

Born was among the first female attorneys to systematically address inequities regarding how the laws treated women  Born and another female lawyer, Marna Tucker, taught what is considered to have been the first "Women and the Law" course at Catholic University’s Columbus School of Law  The class exclusively concerned prejudicial treatment of women under the laws of the United States, past and present   Born and Tucker were surprised to discover that there was no textbook on the issue at the time   Born is also one of the co-founders of the National Women's Law Center  Born also helped rewrite the American Bar Association rules to make it possible for more women and minorities to sit on federal bench 

During her long legal career, and into her retirement, Born did much pro bono and other types of volunteer work  She was active in the American Bar Association, the largest professional organization of lawyers in the United States   Initially Born was named a member of the governing council of the ABA's Individual Rights Section, eventually becoming chairperson   Born and Tucker founded the ABA Women's Caucus, the first organization of female lawyers in the ABA   She held several other senior positions in the ABA, including being named the first woman member of the ABA's Standing Committee on the Federal Judiciary   As a member of the Judiciary Committee, Born provided testimony and opinion on persons nominated for federal judgeships  In 1980 she was named chair of the committee   As chair of the committee, Born was invited to address the U S  Congress regarding the nomination of Judge Sandra Day O'Connor to the U S  Supreme Court 

In 1993, Born's name was floated as a possible candidate for Attorney General of the United States, but Janet Reno was nominated 

In July 2009, Nancy Pelosi appointed Brooksley Born as a commissioner to the Financial Crisis Inquiry Commission (FCIC) 

Born and the OTC derivatives market
Born was appointed to the CFTC on April 15, 1994, by President Bill Clinton  Due to litigation against Bankers Trust Company by Procter and Gamble and other corporate clients, Born and her team at the CFTC sought comments on the regulation of over-the-counter derivatives, a first step in the process of writing CFTC regulations to supplement the existing regulations of the Federal Reserve System,  the Options Clearing Corporation, and the National Association of Insurance Commissioners  Born was particularly concerned about swaps, financial instruments that are traded over the counter between banks, insurance companies or other funds or companies, and thus have no transparency except to the two counterparties and the counterparties' regulators, if any   CFTC regulation was strenuously opposed by Federal Reserve chairman Alan Greenspan, and by Treasury Secretaries Robert Rubin and Lawrence Summers  On May 7, 1998, former SEC Chairman Arthur Levitt joined Rubin and Greenspan in objecting to the issuance of the CFTC's concept release  Their response dismissed Born's analysis and focused on the hypothetical possibility that CFTC regulation of swaps and other OTC derivative instruments could create a "legal uncertainty" regarding such financial instruments,  hypothetically reducing the value of the instruments  They argued that the imposition of regulatory costs would "stifle financial innovation" and encourage financial capital to transfer its  transactions offshore  The disagreement between Born and the Executive Office's top economic policy advisors has been described not only as a classic Washington turf war, but also a war of ideologies,  insofar as it is possible to argue that Born's actions were consistent with Keynesian and neoclassical economics while Greenspan, Rubin, Levitt, and Summers consistently espoused neoliberal, and neoconservative policies 

In 1998, a trillion-dollar hedge fund called Long Term Capital Management (LTCM) was near collapse   Using mathematical models to calculate debt risk, LTCM used derivatives to leverage $5 billion into more than $1 trillion, doing business with fifteen of Wall Street's largest financial institutions   The derivative transactions were not regulated, nor were investors able to evaluate LTCM's exposures   Born stated, "I thought that LTCM was exactly what I had been worried about"   In the last weekend of September 1998, the President's working group was told that the entire American economy hung in the balance   After intervention by the Federal Reserve, the crisis was averted   In congressional hearings into the crisis, Greenspan acknowledged that language had been introduced into an agriculture bill that would prevent CFTC from regulating the derivatives which were at the center of the crisis that threatened the US economy   U S  Representative Maurice Hinchey (D-NY) asked "How many more failures do you think we'd have to have before some regulation in this area might be appropriate?"  In response, Greenspan brushed aside the substance of Born's warnings with the simple assertion that "the degree of supervision of regulation of the over-the-counter derivatives market is quite adequate to maintain a degree of stability in the system"  Born's warning was that there wasn't any regulation of them   Born's chief of staff, Michael Greenberger summed up Greenspan's position this way: "Greenspan didn't believe that fraud was something that needed to be enforced, and he assumed she probably did  And of course, she did "  Under heavy pressure from the financial lobby, legislation prohibiting regulation of derivatives by Born's agency was passed by the Congress   Born resigned on June 1, 1999 

The derivatives market continued to grow yearly throughout both terms of George W  Bush's administration  On September 15, 2008, the bankruptcy of Lehman Brothers forced a broad recognition of a financial crisis in both the US and world capital markets   As Lehman Brothers' failure temporarily reduced financial capital's confidence, a number of newspaper articles and television programs suggested that the failure's possible causes included the conflict between the CFTC and the other regulators Faiola, Anthony, Nakashima, Ellen and Drew, Jill  The Crash: Risk and Regulation - What Went Wrong, The Washington Post, October 15, 2008 

Born declined to publicly comment on the unfolding 2008 crisis until March 2009, when she said: "The market grew so enormously, with so little oversight and regulation, that it made the financial crisis much deeper and more pervasive than it otherwise would have been " She also lamented the influence of Wall Street lobbyists on the process and the refusal of regulators to discuss even modest reforms 

An October 2009 Frontline documentary titled "The Warning"  described Born's thwarted efforts to regulate and bring transparency to the derivatives market, and the continuing opposition thereto  The program concluded with an excerpted interview with Born sounding another warning: "I think we will have continuing danger from these markets and that we will have repeats of the financial crisis -- may differ in details but there will be significant financial downturns and disasters attributed to this regulatory gap, over and over, until we learn from experience "

In 2009 Born, along with Sheila Bair of the FDIC, was awarded the John F  Kennedy Profiles in Courage Award in recognition of the "political courage she demonstrated in sounding early warnings about conditions that contributed" to the 2007-08 financial crisis   According to Caroline Kennedy, "Brooksley Born recognized that the financial security of all Americans was being put at risk by the greed, negligence and opposition of  powerful and well connected interests     The catastrophic financial events of recent months have  proved them [Born and Sheila Bair] right "  One member of the President's working group had a change of heart about Brooksley Born   SEC Chairman Arthur Levitt stated "I've come to know her as one of the most capable, dedicated, intelligent and committed public servants that I have ever come to know", adding that "I could have done much better  I could have made a difference" in response to her warnings 

In 2010, a documentary film Inside Job further alleged that derivatives regulation was ineffective from the Clinton administration on  Along with fellow whistleblower, former IMF Chief Economist Raghuram Rajan, who was also scorned by the economic establishment, Brooksley Born was cited as one of the authorities arguing that financial derivatives increase economic risk 

 Personal life 
Born is married to Alexander E  Bennett (also retired from Arnold & Porter)   She has five adult children - two from a previous marriage to Jacob Landau and three stepchildren  Notably, Born was named a partner at Arnold & Porter while working part-time so she could raise her two young children   When both of her children were school-age, Born returned to practice full-time 

References

External links
Attorney profile at Arnold & Porter
Brooksley Born (2009 Winner) of the Profiles in Courage Award, with acceptance speech transcript and NECN video

Profile at MarketsWiki
Speeches and statements
"Testimony Of Brooksley Born Chairperson of the CFTC Concerning The Over-The-Counter Derivatives Market", before the House Committee On Banking And Financial Services, July 24, 1998 
"The Lessons of Long Term Capital Management L P ", Remarks of Brooksley Born, Chairperson of the CFTC, Chicago-Kent-IIT Commodities Law Institute, Chicago, Illinois, October 15, 1998 
 Interview: Brooksley Born for "PBS Frontline: The Warning", PBS, (streaming VIDEO 1 hour), October 20, 2009 
Articles
Manuel Roig-Franzia  "Credit Crisis Cassandra:Brooksley Born's Unheeded Warning Is a Rueful Echo 10 Years On", The Washington Post, May 26, 2009
 Taibbi, Matt  "The Great American Bubble Machine", Rolling Stone'', July 9–23, 2009

1940 births
American women lawyers
Arnold & Porter people
Clinton administration personnel
Columbus School of Law faculty
Commodity Futures Trading Commission personnel
Heads of United States federal agencies
Lawyers from San Francisco
Living people
Stanford Law School alumni
21st-century American women
Stanford University alumni
Context 2: Paper Info

Title: Force Feedback Control For Dexterous Robotic Hands Using Conditional Postural Synergies
Publish Date: Unkown
Author List: Dimitrios Dimou, José Santos-Victor, Plinio Moreno

Figure

Fig  1 Example of modeling the contacts and friction during manipulation 
Fig  2  Schematic representation of the proposed force controller The input is the state (GRASP or RELEASE) and the force readings Based on that the grasp size is adjusted by a value C and is given to the posture mapping function along with the desired grasp type A finger configuration is then generated and commanded to the robot 
Fig  3  Our control algorithm in Python-like pseudocode 
Fig  4  Our first experiment The robot picks up a bottle, transports it, and places down on the desk In the bottom part of the figure, you can see the control signals during this task 
Fig  5 The household objects used in our experiments 
Under the pictures of the execution you can see the signals recorded by the controller: the average normal force applied by all fingers (blue line), the thresholds f threshold high n  (purple dashed line) and f threshold low n (yellow dashed line), the average tangential force (green), and the grasp size used in each time-step (red) The task is divided four stages: 1) (red part) the initial grasp of the object, in this stage the force controller closes the grasp until the applied normal
Fig 6 In the upper row of images, you can see our second experiment The robot picks up the chips can, rotates it 90 degrees, and places back down In the middle row, for our third experiment, the robot picks up the chips can, rotates it 90 degrees, and hands it over to a person In the bottom row, for our forth experiment, the robot picks up a foam brick, rotates it 180 degrees, and hands it over to a person, using a pinch grasp 

abstract

We present a force feedback controller for a dexterous robotic hand equipped with force sensors on its fingertips  Our controller uses the conditional postural synergies framework to generate the grasp postures, i e  the finger configuration of the robot, at each time step based on forces measured on the robot's fingertips 
Using this framework we are able to control the hand during different grasp types using only one variable, the grasp size, which we define as the distance between the tip of the thumb and the index finger  Instead of controlling the finger limbs independently, our controller generates control signals for all the hand joints in a (lowdimensional) shared space (i e 
synergy space)  In addition, our approach is modular, which allows to execute various types of precision grips, by changing the synergy space according to the type of grasp  We show that our controller is able to lift objects of various weights and materials, adjust the grasp configuration during changes in the object's weight, and perform object placements and object handovers 

INTRODUCTION

To perform complex manipulation tasks in unstructured environments, humans use tactile feedback from their fingers  This feedback is provided by tactile afferents located in the skin of the hand  Particularly, for handling small objects with precise movements, the afferents located in the fingertips are used, which have high density and adapt fast to pressure changes  
These afferents provide information about the characteristics of the exerted contact forces, such as the magnitude and the direction  For anthropomorphic robots to be able to perform dexterous tasks similar force feedback signals must be used to alleviate problems arising from uncertainty in measurements, and handle external perturbations 
For example, using open-loop position control to lift a heavy object may fail due to slip without any feedback mechanism to provide tactile information  Previous works have used tactile sensors to design force controllers that use slip prediction to update the desired normal forces applied by the fingertips 
The slip predictors are based on machine learning models such as neural networks and random forests to classify multi-modal signals from a tactile sensor  In all previous works, each finger was separately controlled by an independent force controller  In addition, they required labeled data to train the slip predictors and because each finger is controlled independently is not obvious how to implement different anthropomorphic grasp types 
In this work we develop a force controller that takes as input the force readings of the fingertips and computes the grasp size which is then used along with a grasp type label to generate a grasp posture with the desired characteristics  To avoid slippage the desired normal contact force is calculated to be proportional to the tangential contact forces 
The applied normal force is then controlled using the size of the grasp as a control variable  Larger grasp sizes mean less force is applied to the object  So the grasp size is calculated from the error between the desired normal force and the actual measured normal force  The grasp size is then given to the posture sampler that generates a grasp posture, i e  the finger joint angles 
The posture sampler is modeled with a conditional Variational Auto-Encoder (cVAE) based on the framework proposed in   With this framework we abstract away the low-level control of the fingers and generate hand postures based on high-level properties such as the type and the size of the grasp  So it works as a mapping function that takes as input a low-dimensional vector and the grasp type and size as conditional variables and maps them to a set of joint angles 
We show that with our controller we can control a dexterous robotic hand to lift objects of different weights using three precision grasps  Our controller is also able to compensate and retain a stable grasp during changes in the objects' weight, for example when filling up a cup or emptying it  In addition we show how with the addition of the hand pose information we can use the controller to calculate if the tangential force is due to gravity or due to a support surface and use this information to perform handovers and place down objects on surfaces 
We perform several real-world experiments with a dexterous robotic hand to showcase the capabilities of our controller and support our design choices  To sum up our main contributions are • We develop a controller for a dexterous robotic hand that uses force feedback and the conditional synergies framework to perform dexterous manipulation tasks 
• We show that with our controller we can easily use different precision grasp types, by changing only the grasp type variable which is given to the grasp posture mapping function  • We demonstrate by incorporating information about the world pose of the hand we can use our controller to perform additional tasks such as placing down and handing over objects 
Roboticists have looked for inspiration in humans for developing methods for complex object manipulation   Neuroscientists have studied for a long time the processes that allow humans to use tactile feedback to perform complex manipulation tasks  Humans tend to adjust the grip force according to the object's weight, its friction and they use a safety margin to account for uncertainties  
To gather information about the tactile states they use multiple afferents that are located in the skin of the fingers   There are different afferents in different parts of the hand depending on their usage, e g  fast adapting afferents in the fingertips for precise manipulation  Based on signals from these afferents, humans encode simple contact events into action phases, such as grasping, lifting or releasing, which they combine in order to perform more complex and long-horizon manipulation tasks  
In robotics tactile sensors have been used for object stabilization and slip prediction in a variety of settings  For example, in , a compliant anthropomorphic prosthetic hand was controlled using force sensing to maintain object stability and avoid slip  In , they develop a control approach that uses integrated force and spatial tactile signals to avoid slip with unknown objects in real world settings 
In , , grasp quality metrics are computed based on the tactile feedback from the robots fingertips  In these works, simple two or three fingered grippers were considered for simple grasping tasks  Force control with anthropomorphic robotic hands has also been explored in more recent works  In , they employ three slip prediction methods to estimate when slip starts and based on the force signals at that moment they calculate the friction coefficient value 
Based on the calculated friction coefficient, they design a force controller that independently controls each finger to achieve a desired normal force  The desired normal contact force is set to be proportional to the tangential contact force and a safety margin based on the evidence found in   In , they train a random forest to classify the contact states into the classes: no contact, contact, slip 
Based on this classification signal, when slip is detected they increase the desired normal contact force to avoid it  In they train a recurrent neural network to estimate slip and the object material from the readings of a Biotac sensor  The force controller is increasing the desired normal contact force when slip is detected 
All these works , , use tactile feedback sensors to predict slip  They collect labeled data, on which they train their models  This approach is based on complex and expensive tactile sensors, and the process of collecting data is cumbersome  In addition, the data do not cover all possible hand poses, which would be impractical 
In contrast, in our work we do not rely on slip prediction, we avoid slip by defining a tangential force gain and a safety margin that work for a large number of objects  Furthermore, instead of independently controlling each finger we use a synergistic framework to generate grasp postures, that is conditioned on two variables: the grasp type and the grasp size 
This way, instead of controlling the values of each joint of each finger, we control only the two conditional variables greatly simplifying the control pipeline  This also, gives us the ability to use different grasp types in our manipulation tasks by changing only the grasp type variable  In also a synergistic framework was used to prevent an object from slipping from a humanoid hand, but they modeled only one synergy for a tripod grasp and they used the forces on the robotic arm as feedback, while we use force feedback from the fingertips 
Our control algorithm could also be applied to different hands as it does not depend on the hands configuration  Finally, in previous approaches only lifting tasks had been considered  In our work we demonstrate that our approach can be used to perform more complex tasks, such as placing objects on surfaces and performing handovers, which was not done in previous works 
Our goal in this work is to design a control algorithm for an anthropomorphic robotic hand to perform dexterous manipulation skills such as lifting and placing down objects  Our control algorithm will use tactile feedback from the force sensors on the fingertips of the hand to decide the forces that need to be applied to the object in each step of the task 
Given the desired forces to be applied, the size of the grasp will be computed  Given the grasp size and a desired grasp type, the posture generator will generate a grasp posture, i e  the hand configuration, such that the force constraints are satisfied  To model the contacts and friction we use Coulombs' law, which states that in order to avoid slip, the normal contact force f n to the contact surface of an object, times the fiction coefficient µ, has to be larger than the tangential force f t :
µf n ≥ f t You can see an example in Figure , where an object is pressed against a wall by an applied normal force f n , and we have the tangential force f t = mg due to gravity
Context 3: 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



ground_truth: June 1, 1999.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How many experiments were demonstrated to test the capabilities of the controller?

Read the following text and answer briefly.

Context 2:  In order for the object to remain stable we need to apply a normal force: where µ is the friction coefficient between the object and the wall 
In the case of a dexterous hand manipulating an object, we want the normal forces applied by all fingers to be greater than the tangential force divided by the friction coefficient of the materials of the object and the fingertip  Since it is hard to accurately compute the friction coefficient between all possible object materials previous works have used multi-modal tactile sensors like the BioTac sensor, which provides information about the pressure, skin deformation, and temperature, to predict slip and based on that signal to increase the applied normal force 
In our work we use the FTS3 sensors which is a low-cost sensor that measures the 3D force applied in each fingertip  In addition, previous works gathered labeled datasets in order to train their slip prediction models which is time-consuming and limits the possible orientations of the hand, because gathering labeled data for all possible orientations is impractical 
To overcome this we experimentally selected the parameters that determine the value of the applied normal force such that we avoid slip for all objects in our dataset, from the lightest to the heaviest  In order to guarantee contact between the fingertip and the object, in the beginning of the grasping phase, we use an offset f of f set n as the minimum normal force applied by each finger 
In they also suggest that humans use an additional safety margin which is proportional to the tangential force, f margin n ∝ f t   So the final desired normal contact force becomes: where G is the gain that includes the friction coefficient and the additional safety margin  To alleviate the effects of noise in the sensors, the running average of the measured normal force f n and tangential force f t is used, as a low pass filter 
So for each force measurement we have the following relation: where α ∈ (0, 1) is a parameter that determines how much new measurements affect the value, and is experimentally selected  Given the measured normal force f n from the fingertip sensors we can compute the error f err n = f des n − f n   We use this error signal to control the grasp size variable g size , that we use as a conditional variable in our posture mapping function 
The grasp size represents the distance between the thumb and the index finger in a grasp posture  So a smaller grasp size will result in a tighter grasp and greater normal force applied to the surface of the object  We use a linear controller for the grasp size variable that is implemented as follows: where K is a parameter that controls the rate of decrease of the grasp size, and is experimentally selected 
So when the error between the desired normal force and the actual normal force is large the grasp size decreases so tighter grasp postures are generated in order to apply more normal force  In practice, in order to avoid oscillations in the grasp size we use the desired normal force as a high threshold that we want the measured normal force to be below:
If the normal force is below that threshold the grasp size does not change even if there are small oscillations in the measured tangential and normal forces  Also, in order to avoid the hand applying too much force that damages the hardware or the object we use a low threshold, that is: where w threshold is the width of the threshold in mN  
If the measured normal force is below the grasp size increases in order to apply less force  So the final grasp size variable for grasping is calculated as follows: where This is similar to the deadband control method , where instead of having a fixed reference point, an operating range is set  If the response is in this range, the controller does not exert any correction 
In our case, the operating range changes according to the force signals from the robot's fingertips  The grasp posture mapping function is based on the conditional postural synergies model presented in   It uses a conditional Variational Auto-Encoder model to generate grasps postures conditioned on additional variables such as the grasp size 
In this work we augment this model to also generate grasp postures conditioned on the grasp type  The model is trained on a set of labeled grasp samples acquired by teleoperating a robotic hand using a data-glove  Using this model we are able to abstract away the low-level control of each joint of each finger and generate grasps based on more general characteristics such as the type and the size of the grasp 
In this way we can control all the fingers jointly by a single value, the grasp size, thus greatly reducing the control parameters  In addition we are able to use the same control algorithm for different precision grasp types, by changing the grasp type conditional variable  Finally, we can modify our controller to release objects instead of grasping them 
Given the pose of the hand in the world coordinate frame, which we can acquire from the robotic arm that is attached to, we can use the forward kinematics of the hand to compute the poses of each fingertip  Then using the force readings of each fingertip we can calculate the global direction of the net tangential force 
If the angle between the direction of the net tangential force and the direction of gravity is less than 90 degrees, i e  the net tangential force's direction is towards the ground, we assume that the tangential force is due to gravity pulling the object, so the force controller tries to grasp it  If the angle is more than 90 degrees, i e  the net tangential force's direction is upward, it means that something is pushing (or pulling) the object upward, in which case we assume that the object is touching on a support surface or someone is pulling the object so the controller increases the grasp size given to the posture mapping function proportionally to the normal force measured thus slowly releasing the object 
Opening the grasp is done by controlling the grasp size variable as follows: That way we can place objects on surfaces but also perform robot to human handovers, where the robot holds the object and the human grasps the object and slightly pushes or pulls it up, signaling to the robot that there is a support surface 
The robot then slowly releases the object by opening its grasp  We showcase these scenarios in the experiments' section  Based on these observations, we present our force controller in Figure   The hand starts in an open pre-grasp position, a latent point is sampled from the prior distribution of the posture mapping function, and given the desired grasp type and the grasp size a grasp posture, i e  the joint angles of the fingers, is sampled 
The initial grasp size is set to the maximum value, and when the force controller comes into effect and depending on the state of the system and the forces on the fingertips grasp size changes by some value C, according to equations 1,2, until the desired normal force is achieved  To choose between grasping or releasing an object we use a finite state machine formulation 
When the hand reaches the desired grasp pose, which we assume is provided, the GRASP state is activated, in which the controller tries to grasp the object  When the controller detects that the tangential force applied to the object is coming from a support surface the state changes to the RELEASE state, in which the controller releases the object by opening the grasp 
You can see the full algorithm in Python-like pseudocode in Figure   To summarize, the advantages of our controller compared with previous approaches are threefold: 1) instead of controlling each joint of each finger of the hand we use only two variables, the grasp size and the grasp type, which allows us to perform multiple grasp types by changing only one variable while the grasp size variable is common among all grasp types, that greatly reduces the complexity of the control process compared to independently controlling a 21 DoF hand to perform different grasp types, 2) we do not rely on slip prediction for controlling the desired normal force, which involves gathering labeled data and works only for the hand poses in the training dataset, and 3) we can use our controller to also release objects instead of only grasping them 

Experimental Set-up 

For our experiments we used the Seed Robotics RH8D Hand , which is a robotic hand with 7 DoFs  The hand is equipped with the FTS-3 force sensors in each fingertip, which are high resolution tactile sensors that provide the 3D force applied in each fingertip  The sensor provides data at a rate of 50Hz  For the experiments the hand was mounted on a Kinova Gen3 7DoF robot 
To train the posture mapping function we used the CyberGlove to teleoperate the hand and collect 468 grasps belonging to three precision grasp  types: tripod, pinch, lateral tripod  The architecture of the cVAE model was the same as in , with the addition of the grasp type as a conditional variable, which was one-hot encoded 
We used 10 household objects shown in Figure   With the heaviest object weighing 380g and the lightest 1g  During the experiments the trajectories of the arm were prerecorded, while the hand was controlled online by our control algorithm 

Parameter tuning 

To select the values of the parameters in our controllers we conducted preliminary experiments where we tested lifting and releasing several objects, with different physical properties  To select the value of the normal offset force f of f set n , we used an empty plastic cup as our test object, and we choose a value such that the fingers do not deform the cup 
The final value of the parameter was set to -50 mN  To select the values of the gain G and the rate of decrease K, of the grasp size, we experimented with the heaviest object in our dataset, which is the mustard bottle and weighs 380g  The gain G was set to 2 0 such that the desired normal force would be enough to hold the object 
The rate of change of the grasp size was set to 100 0, based on the operating frequency of the force sensor and the range of values of the tangential force  For the tangential force averaging process we used a parameter value of α t = 0 7, because we want the controller to be sensitive to fast changes in its value, that can arise for example during lifting an object 
For the normal force averaging process we used a parameter value of α n = 0 5, as we do not want it to be affected by noise that could make the controller overconfident 

Experiments 

To explore the capabilities of our controller, we demonstrate five experiments of increasing complexity: 1) we picked and placed a bottle using a tripod grasp, 2) we picked, rotated and placed a chips can on a box using a tripod grasp, 3) we picked, rotated and handed over the chips can to a person using a tripod grasp, 4) we picked, rotated and handed over a brown foam brick to a person using a pinch grasp, 5) a person handed over a plastic cup to the robot, filled it with coins to increase its weight, and the robot then handed it back to the person using a tripod grasp 
You can see the execution of the first experiment in  In the middle row, for our third experiment, the robot picks up the chips can, rotates it 90 degrees, and hands it over to a person  In the bottom row, for our forth experiment, the robot picks up a foam brick, rotates it 180 degrees, and hands it over to a person, using a pinch grasp 
Fig    In our fifth experiment, a person hands over an empty plastic cup to the robot, throws coins in it to increase its weight while the robot adjusts its grip to stabilize the object, and then hand overs the cup back to the person  force is below the offset f of f set n , 2) (green part) the robot lifts the object, as it tries to lift the tangential force increases, increasing the threshold, so the grasp size decreases to apply more normal force, 3) (orange part) the robot transports the object, you can see, in point A in the Figure, a perturbation in the tangential force when the robot begins to move, the controller responds by decreasing the grasp thus stabilizing the object, and 4) (blue part) the robot enters the releasing phase, where it lowers the arm until it detects that the tangential force is due to a support surface, then it stops lowering the arm and increases the grasp size slowly releasing the object 
In point B in the Figure, you can see that there is noise in the tangential force, due to the arm moving to place the object on the table, that is also reflected in the desired normal force  Because we use the desired normal force as a threshold and not as a reference signal this noise is not manifested in the control of the grasp size 
You can see the execution of the second experiment in the upper part of Figure   This experiment demonstrates the ability of the controller to handle arbitrary hand poses  The experiment is divided in four parts: 1) the robot enters the GRASP phase and the force controller generates grasps to achieve a normal contact force below the f of f set n threshold, 2) the robot lifts the object and adjusts the grasp size to avoid the object falling, 3) the hand rotates to place the chips can on the horizontal position, and 4) the robot enters the RELEASE phase, and the arm lowers until the object touches the box, when the hand detects the supporting surface, it starts to slowly release the object 
You can see the execution of the third experiment in the middle part of Figure   This experiment demonstrates the ability of the controller to perform robot to human handovers  The experiment is divided in four parts: 1) the robot enters the GRASP phase and the force controller generates grasps to achieve a normal contact force below the f of f set n threshold, 2) the robot lifts the object and adjusts the grasp size to avoid the object falling, 3) the hand rotates to place the chips can on the vertical position, and 4) the robot enters the RELEASE phase, the arm stays still, the human grasps the object from the bottom and slightly pushes it up, the hand then detects that there is a supporting surface and starts to slowly release the object 
You can see the execution of the fourth experiment in the bottom part of Figure   This experiment is similar to previous one, but the grasp type that the robot uses is a pinch grasp, that involves only the thumb and the index finger  To perform this we only had to alter the grasp type conditional variable that was given to the posture mapping function 
You can see the execution of the fifth experiment in the bottom part of Figure   In the first part (blue) of the experiment the robot closes its grasp, by reducing the grasp size, until the normal force is below the force offset  In the next three parts (pink, green, red) the person throws coins in the cup to increase its weight 
You can see in the signal plots that each time coins are added the tangential force decreases so the normal force threshold decreases too  The grasp sizes then decreases as well in order to apply more normal force  This experiment demonstrates the ability of the controller to handle perturbations in the weight of the object during grasping 

CONCLUSION

In summary, we presented a controller that uses force feedback integrated with conditional synergies to control a dexterous robotic hand to grasp and release objects  We demonstrated that our controller can lift objects of different weights and materials while avoiding slip, react online when the weight of the object changes, place them down on surfaces, and hand them over to humans 
In addition, the control architecture is modular, so the synergy grasp mapping component can be easily changed in order to control several precision grasp types  However, our experiments also revealed various limitations of our controller  For example our method fails to stabilize the object when rotational slip occurs 
In addition hardware limitations such as, slow update rates and noise in the force measurements can create problems that result in the object falling  In future work we plan to incorporate additional sensing modalities, such as vision to alleviate some of these issues 
Context 3: 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



ground_truth: 5.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: 如何安装并启动Ganache?

Read the following text and answer briefly.

Context 3: 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



ground_truth: 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。.