no .bbl file generated mac Latex

Remember that it’s not LaTeX that generates the BBL, it’s BibTeX.

  1. Run LaTeX to generate the AUX file. It adds a line every time it finds a cite.
  2. Then run BibTeX (executable: bibtex) to generate the BBL (from the AUX lines).
  3. Then run LaTeX again, which will include the BBL.
  4. Then run LaTeX again to make any updates corresponding to including the possibly massive BBL (e.g., updating your final page count on each of your pages, updating your ToC, etc.).

Android 4.4.2 root issue


1. driver issue- on media device:

download Google USB driver, connecting with usb debugging and media device, then update the failed automatic driver installation.

2. root issue: the previous root doesn’t work


What’s installed
– SuperSU binary and APK
– Nothing else, that’s it.

Installation and usage

– Download the ZIP file (see post below for link)
– Extract the ZIP file
– Boot your device in bootloader/fastboot mode. Usually this can be done by turning your device off, then holding VolUp+VolDown+Power to turn it on.
– Connect your device to your computer using USB

– Windows:
— Run root-windows.bat

– Linux
chmod +x
— Run

– Mac OS X
chmod +x
— Run

– Follow the on-screen instructions – watch both the computer and the device !

谷歌发布了安卓4.4.2 KOT49H,也更新了相应的CF-Auto-root工具,支持nexus4安卓4.4.2 KOT49H一键root。解决了fastboot,adb命令需要的工具包问题,也就是说在这个版本我们不必担心fastboot和adb驱动问题了,CF-Auto-root包里已经自带了。再次感谢大神chainfire的辛苦工作,他同时也是SuperSU的作者,是业界真正的大神使用这个工具您不需要刷第三方recovery就可以root了,并且工具非常干净,只是写入supersu而已,真正的一键root操作。

nexus 4 安卓4.4.2 KOT49H root包下载:



1、将您的nexus 4进入fastboot模式,在关机状态下,同时按音量+/-和电源键进入fastboot模式,然后用USB数据线连接手机和电脑





由于我在升4.4的时候已经解锁BL了,清过一次数据,所以升4.4.2的时候不想再清一次数据,据说bat的批处理不会重复解锁BL,但我还是不放心,用记事本打开root-windows.bat进行修改,删除“tools\fastboot-windows.exe oem unlock”,这一行就是用来解锁的,不知道fastboot-windows.exe会不会判断机器的解锁状态,所以删除这条批处理是绝对保险的。


判断BL解锁的方法是开机后下面有个白色的打开锁的图标,或者进入fastboot 最后一行会显示UNLOCK.


colors in Android

Android uses standard RGB (red, green and blue) color model. Each primary color value is usually represented by hexadecimal number.  At the beginning of such a color definition you have to put a pound character (#).

The simplest is just #RGB format, where #000 is black and #FFF is white. But in this format we have only 16 values per color so it gives 4096 combinations. That’s why #RRGGBB format is mainly used. In this format we have 256 values per primary color, so 16 777 216 colors in total. Black is #000000 and white is #FFFFFF.

decide which test to use

One-Way ANOVA: An ANOVA hypothesis tests the difference in population means based on one characteristic or factor. a—–>b

“An example of when a one-way ANOVA could be used is if you want to determine if there is a difference in the mean height of stalks of three different types of seeds.  Since there is more than one mean, you can use a one-way ANOVA since there is only one factor that could be making the heights different.

Two-Way ANOVA: An ANOVA hypothesis tests comparisons between populations based on multiple characteristics. a—->c<—-b “Suppose that there are three different types of seeds, and the possibility that four different types of fertilizer is used, then you would want to use a two-way ANOVA. The mean height of the stalks could be different for a combination of several reasons

Multivariate analysis of variance (MANOVA): it is simply an ANOVA with several dependent variables. That is to say, ANOVA tests for the difference in means between two or more groups, while MANOVA tests for the difference in two or more vectors of means. a—–>c, b——>d, a——d, b—–>c


gridview -image+text/keep selected item highlight

 public static class ViewHolder
        public ImageView imgViewFlag;
        public TextView txtViewTitle;
    public View getView(int position, View convertView, ViewGroup parent) {
        // TODO Auto-generated method stub
        ViewHolder view;
        LayoutInflater inflator = activity.getLayoutInflater();
            view = new ViewHolder();
            convertView = inflator.inflate(R.layout.gridview_row, null);
            view.txtViewTitle = (TextView) convertView.findViewById(;
            view.imgViewFlag = (ImageView) convertView.findViewById(;
            view = (ViewHolder) convertView.getTag();
        return convertView;

Gridview have option to modify it’s selector (listSelector). When we click on item in Gridview, we saw blink blue and then dissapear after we release it.

To make it stay, all we need just declare it at “android:listSelector” in GridView XML.

<?xml version=”1.0″ encoding=”utf-8″?>
<GridView xmlns:android=”;
android:verticalSpacing=”1dp” />

To make selector background not overriden / set in bottom side, use “setDrawSelectorOnTop(false)” :

gridView = (GridView) inflater.inflate(R.layout.date_grid_fragment, container, false);
gridView.setDrawSelectorOnTop(false); //work for gridview activity, but not the dialog.
焦点不见了是因为设置了background颜色,而selector的被background挡住了,设置这个属性即可                gridView.setDrawSelectorOnTop(true);

 在adapter getView() 中设置 v.setSelected(true); 不起作用,而在点击的时候设置就有用?而一定要通过



我认为是当adapter初始化View之前已经设置select position ,通过onItemSelect 可以看到select 在 getView() 之前已经触发。

for (int i = 0; i < adatper.colorList.size(); i++) {
if (currentPenColor == adatper.colorList.get(i)) {
// gridViewColors.setDrawSelectorOnTop(true);
pos = i; Runnable() {
public void run() {

sorting colors

Different people will sort a color palette in different ways. Our goal was to find a sorting mechanism that looked pleasing to the eye, but of course the results are completely subjective.

  • For aesthetics, we prefer the YIQ sort. It highlights trends in value change, but information about hue variation is hidden and sometimes invisible.
  • To communicate more information about hues, HSV sorting is by far superior, but it lacks the pleasant gradient-effect of the YIQ sort.
  • RGB sorting proves inferior to HSV and YIQ, though the results can still be interesting.
  • HSV sort is excellent for grouping colors of similar hues, but the IYQ and YIQ sorts also do this well and they can look cleaner than their HSV counterpart.


“web” palette sorted by saturation. see the sortpal pages for fuller versions.  It shows a set of color palettes, sorted by the various attributes like it’s redness, or brightness, or saturation. For example, in the “red” row, the reddest colors are at the far left and decreasingly red colors to the right. The width of the color depends on the number of colors in the palette.

Lots of ways to sort them mathematically. Some map well to what people perceive as correct. Some do not. e.g. web palette sorted by proximity in rgb colorspace:

Different color palettes can be chosen, including the 216 “web safe” palette (wiki), theX11/css “named” colors (wiki), the xkcd color survey (xkcd color survey), a rough approximation of the spot colors often used in print, a Hilbert curve through rgb space(wiki), misc artistic palettes, etc.

sorting methods:

  • Sorting by one component of a color space, the most obvious being the red, blue, green values of RGB:
  • Hue, saturation, and value (HSL and HSV) is another color space, that maps a lot more naturally to how people understand colors. e.g. web palette sorted by hue, saturation, value, lightness, respectively.

  • sorting by proximity in the 3d color space. The idea being to start at the origin, and find the closest colors in 3d.

The code is up at github. No promises to it’s correctness or functionality. secondary and tertiary parameters sort as well (hsv3d, and chroma ) mostly just to stabilize the sorts.

Color Spectrum   Digital Image Basics


The color spectrum is a 1D representation of the 3D color information in an image. The spectrum represents all the color information associated with that image or a region of the image in the HSL space.

HSL Color Space

If the input image is in RGB format, the image is first converted to HSL format and the color spectrum is computed from the HSL space. Using HSL images directly—those acquired with an image acquisition device with an onboard RGB to HSL conversion for color matching—improves the operation speed.

Colors represented in the HSL model space are easy for humans to quantify. The luminance—or intensity—component in the HSL space is separated from the color information. This feature leads to a more robust color representation independent of light intensity variation. However, the chromaticity—or hue and saturationplane cannot be used to represent the black and white colors that often comprise the background colors in many machine vision applications. Refer to the color pattern matching section for more information about color spaces.

Each element in the color spectrum array corresponds to a bin of colors in the HSL space. The last two elements of the array represent black and white colors, respectively.

how the HSL color space is divided into bins

The hue space is divided into a number of equal sectors, and each sector is further divided into two parts: one part representing high saturation values and another part representing low saturation values. Each of these parts corresponds to a color bin—an element in the color spectrum array.

The following figure illustrates the correspondence between the color spectrum elements and the bins in the color space.

A color spectrum with a larger number of bins, or elements, represents the color information in an image with more detail, such as a higher color resolution, than a spectrum with fewer bins.

The value of each element in the color spectrum indicates the percentage of image pixels in each color bin. When the number of bins is set according to the color sensitivity parameter, the machine vision software scans the image, counts the number of pixels that fall into each bin, and stores the ratio of the count and total number of pixels in the image in the appropriate element within the color spectrum array.

The color spectrum contains useful information about the color distribution in the image. You can analyze the color spectrum to get information such as the most dominant color in the image, which is the element with the highest value in the color spectrum. You also can use the array of the color spectrum to directly analyze the color distribution and for color matching applications.

If you lighten or darken color images you need to understand how color is represented. Unfortunately there are several models for representing color. The first two should be familiar; the latter two may be new.

  • It is not practical to use RGB or CMY(K) to adjust brightness or color saturation because each of the three color channels would have to be changed, and changing them by the same amount to adjust brightness would usually shift the color (hue).
  • HSV and HSL are practical for editing because the software only needs to change V, L, or S.

Image editing software typically transforms RGB data into one of these representations, performs the adjustment, then transforms the data back to RGB. You need to know which color model is used because the effects on saturation are very different.

HSV – It is not practical to use RGB or CMY(K) to adjust brightness or color saturation because each of the three color channels would have to be changed, and changing them by the same amount to adjust brightness would usually shift the color (hue). HSV and HSL are practical for editing because the software only needs to change V, L, or S. Image editing software typically transforms RGB data into one of these representations, performs the adjustment, then transforms the data back to RGB. You need to know which color model is used because the effects on saturation are very different.

HSL color. Maximum color saturation takes place at L = 0.5 (50%). L = 0 is pure black and L = 1 (100%) is pure white, regardless of H or S. The HSL color model can be depicted as a double cone, widest at the middle (L = 0.5), coming to points at the top (L = 1; pure white) and bottom (L = 0; pure black).

HSV and HSL were developed to represent colors in systems with limited dynamic range (pixel levels 0-255 for 24-bit color). The limitation forces a compromise.

  • HSV represents saturation much better than brightness: V = 1 can be a pure primary color or pure white; hence “Value” is a poor representation of brightness.
  • HSL represents brightness much better than saturation: L = 1 is always pure white, but when L > 0.5, colors with S = 1 contain white, hence aren’t completely saturated.
  • In both models, hue H is unchanged when L, V, or S are adjusted.


Darkening in HSV reduces saturation. Darkening in HSL increases saturation when L > 0.5.
  Lightening in HSV increases saturation. Lightening in HSL reduces saturation when L > 0.5.

Best representation of saturation
Best representation of lightness
V, L, and H illustrated for S = 1 (maximum saturation)
V, L, and S illustrated for H = 0.333 (120º; Green)

HSV: Best representation of saturation

HSL: Best representation of lightness

e the eye can barely distinguish about 200 different gray levels

RGB is the primary color



Vertical biases in scene memory.


The representation of visual scenes


AJAX之父Jesse James Garrett在其《用户体验要素》中提到的5个层级来达成一些共识:








用户界面是交互设计的结果的自然体现,但是不能说交互设计就是用户界面设计。交互设计的出发点在于研究人在和物交流(dialog)时候,人的心理模式和行为模式,并在此研究基础上,设计人工物的可提供的交互方式,来满足人对使用人工物的三个层次的需求(usefulness, usability and emotionality)。从这个角度看来,交互设计是设计方法,而界面设计是交互设计的自然结果。同时界面设计不一定由显意识交互设计驱动,然而界面设计必然自然包含交互设计。


  • 用户调研
  • 概念设计
  • 创建用户模型
  • 创建界面流程
  • 开发原型并进行可用性测试








Balsamiq mockup也是不错的选择,总之,这一阶段你需要设计产品的低保真模型。但千万不要自娱自乐并深陷细节。因为你需要基于低保真模型进行又一轮沟通,如果条件允许,最好进行一次可用性测试


  • 敏捷小团队:直接基于Axure进行开发;
  • 矩阵式项目组:将低保真模型做成高保真模型,并尽可能完善交互细节,便于交付UED或美工进行设计;
  • 跨业务或外包:为了预防变更,需要更多前期可用性测试。并尽可能完善说明和注释信息,输出word等存档。






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