Battery Problem with Nikon Coolpix L100

Nikon Coolpix L100 is a compact Megazoom digital camera, coming with 15x optical zooming capacity and wide view angle – ideal for amateur photo-takers like me . The price is also not bad, at about $400 Singapore dollars when I bought one in 2009. The only pitfall is with its batteries: at the outset, it simply doesn’t accept rechargeable batteries, such as the typical Ni-MH’s – even you’re not a doctrine environmentalist, it may bother you a lot whenever you have to head for stores for batteries.

In fact, Nikon has added in rechargeable support not long after this camera was released. You can follow these steps to get your L100 live well with the rechargeable:

  1. Firmware Version. Press the MENU botton, and go to Set Up –> Firmware version. If the firmware is COOLPIX L100 Ver.1.0, you will need to update your firmware to V1.1. following these instructions (Coolpix L100 1.1 firmware update from Nikon website).
  2. Battery Type. After the updating, one has to select Set up –> Battery type –> COOLPIX (Ni-MH) to enable this lovely feature.

Now you should be able to power your L100 with your rechargeable batteries (and also still be able to use the disposable)!

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My Picasa Albums Updated!

This includes some of my activities in the past several months!

My Albums 

Stay tuned!

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Useful Tips on Running External Programs from Matlab on Linux

Most of these problems stem from bad association of Matlab’s C++ (or Fortran) libraries. The solution is often to create a soft-link and point to the corresponding system libraries. This blog article provides a very nice summary of this!

Matlab: Running External Programs

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Shapiro and Mudd …

冒 个泡:CU 本办公楼叫Shapiro, 旁边那个叫Mudd。某西北女将前者念的像“ 傻x 楼”, 后者回”妈的“ 。。。 对掐中。。。

PS: 今天presentation 把without loss of generality 说成 without loss of generosity 。。。被John 掐住好,很肿的补了句 Anyway we cannot lose it …

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A Tight Lower Bound for Finite Sum of Arctangents

Days ago we ran into a need to lower bound {\sum_{j=1}^k \pi/2 - \arctan j = \sum_{j=1}^k \arctan 1/j}.

One possibility is observe the non-negativity and continuity of the function {\arctan x} over {j \in {\mathbb Z}_+} and apply an integral lower bound:

\displaystyle \begin{array}{rcl} \sum_{j=1}^k \arctan \frac{1}{j} &\geq & \int_{1}^{k+1} \arctan 1/x \; dx = \left. x\arctan 1/x + \frac{1}{2} \ln \left(1+x^2\right) \right|_{x = 1}^{k+1} \\ & = & \frac{1}{2}\ln \left[\left(k+1\right)^2 +1\right] + \left(k+1\right) \arctan \frac{1}{k+1} - \frac{\pi}{4} - \frac{1}{2} \ln 2 \\ & \geq & \frac{1}{2}\ln \left[\left(k+1\right)^2 +1\right] + \left(k+1\right) \left(\frac{1}{k+1} - \frac{1}{3} \frac{1}{\left(k+1\right)^3}\right) - 1.132 \\ & = & \frac{1}{2}\ln \left[\left(k+1\right)^2 +1\right] - 0.132 - \frac{1}{3} \frac{1}{\left(k+1\right)^2}, \end{array}

where in the third line we have retained the first two terms of series expansion for {\arctan x} for {|x|\leq 1}. The integral approximation gives very messy terms and a somewhat loose lower bound. We can obtain a much neater one:

\displaystyle \sum_{j=1}^k \arctan \frac{1}{j} \geq \log \left(k+1\right). \ \ \ \ \ (1)

Before the proof, we may look at a plot to see how tight the lower bound is. Here it goes!

Sum of arctangents and its logarithm lower bound.

The trick of proof lies with series expansion.

Proof: It is true for {k=1} as {\pi/4 > \log(2)}. Now suppose the claim holds for {k-1}, i.e., {\sum_{j=1}^{k-1} \arctan\left(1/j\right) \geq \log\left(k\right)}, we need to show it holds for {k}. It suffices to show {\arctan(1/k) \geq \log\left(1+1/k\right)}. Now we consider the series expansions of {\arctan\left(x\right)} and {\log\left(1+x\right)}:

\displaystyle \begin{array}{rcl} \arctan\left(x\right) & = x - \frac{1}{3}x^3 + \frac{1}{5}x^5 - \frac{1}{7}x^7 + \frac{1}{9}x^9 + \cdots, \forall \left|x\right|\leq 1, \\ \log\left(x+1\right) & = x - \frac{1}{2}x^2 + \frac{1}{3}x^3 - \frac{1}{4}x^4 + \frac{1}{5}x^5 + \cdots, \forall -1 < x \leq 1. \end{array}

So we have

\displaystyle \begin{array}{rcl} \arctan\left(x\right) - \log\left(1+x\right) & = \left(\frac{1}{2}x^2 + \frac{1}{4}x^4 + \frac{1}{6}x^6 + \cdots\right) - \left(\frac{2}{3}x^3 + \frac{2}{7}x^7 + \frac{2}{11}x^{11} + \cdots \right) \\ & = \frac{2x^2}{3}\left(\frac{3}{4}- x\right) + \frac{2x^4}{7}\left(\frac{7}{8}-x^3\right) + \frac{2x^6}{11}\left(\frac{11}{12} - x^5\right) + \cdots \geq 0 \end{array}

if {0< x \leq \frac{3}{4}}, and {1/k, \forall k>1} satisfies the condition. \Box

[Updated to the Proof] There is a simpler way to see {\arctan \left(1/k\right) \geq \log\left(1+1/k\right)} (Thanks to Dai Liang! See first comment below). Basically it follows from the fact that {\arctan x \geq \log \left(1+x\right), \forall x\leq 1}, where the latter can be observed as follows: {\arctan 0 = \log \left(1+0\right)} and {\left(\arctan x\right)' \geq \left[\log \left(1+x\right)\right]'} for {x \leq 1}. (03/07/2012)

Recall that {\arctan x \leq x}. Thus we have

\displaystyle \boxed{\sum_{j=1}^{k} \frac{1}{j} \geq \sum_{j=1}^{k} \arctan \frac{1}{j} \geq \log \left(k+1\right)}. \ \ \ \ \ (2)

Acknowledgement: Dr. Tewodros Amdeberhan, at Math Department of Tulane University, has kindly provided the original proof and permitted me to share this on my blog. You may want to read their interesting paper on techniques of evaluating sum of arctangent: Sum of Arctangents and Some Formula of Ramanujan.

 

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Finals, QE and Ahead

 

I’m finished with my finals, after interrupted study and broken rest time for several days — you must pay afterwards even with unmindful procrastination on the move. Time-management

Qualification Exam is ahead within two plus weeks. It’s the time to get refreshed my mind with the concepts of circuits, Fourier transformation, registers, and bit error probability, pretty everything a typical electrical engineering graduate needs to know. Yuh… another fighting! Of course the words your professor would often spend on this issue is ” make sure you pass, but not spend too much time!”. It’s an interesting balance a graduate student most likely always tries to strike… 

Visits to this blog keep roll up, even after my absence in the past few months. I’m pleased to be noticed of the 20,000 hits by the system. I’ve decided to put reasonable amount of time in future to update this blog with more technical expositions and discussions. This would considerably improve my ability to understand technical materials, which I would need to sharpen as soon as possible. 

I was just having a Christmas dinner with John and one master student. It’s pretty fun! We consumed some 1.5 bottles of red wines. And John was asking one theorem for each bottle we had for next semester, jokingly … 

 

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CV Meeting — Frontiers in computer vision

Prof. Alan Yuille (UCLA) in parter with others is organizing a special workshop for exploring frontiers in vision, a good chance for researchers to pause a while and look backward and forward.

Frontiers in Computer Vision

From the cyber-discussion being fired there, some interesting disagreements amongst these top vision researchers are already significant: conservatism versus radicalism, and pragmatism versus idealism. For a research community as ambitious and diverse and young as vision, nevertheless, consensus rarely occurs and ideological debates prevail — it is not surprising; in fact, this signals an active research field in my opinion. But my humble mind is really seeking some fuels the workshop could potentially generate for these topics:

  1. Object recognition
  2. Culture of scholarship in vision
The former has been central on the spot for the past 10 years with least success, while the latter has partially contributed to the dismal stagnation.
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【转】史上最狠的论文评审意见

2010年Environmental Microbiology杂志刊登了一部分杂志的审稿意见,让人在学术研究严肃、认真的前提下充分体会了下,审稿人的幽默与诙谐,有的审稿意见甚至让人啼笑皆非,下面列举了一部分经典的审稿意见,让大家也见识一下史上最狠的评审意见,其他的内容见文后附件,有兴趣的可以继续寻找。

PS:翻译纯属搞笑,不喜勿拍!

This paper is desperate. Please reject it completely and then block the author’s email ID so they can’t use the online system in future.

这文章太不给力了…请万勿发表。此外,建议锁定该作者的电子邮件ID,避免此人日后继续投稿。。。

 

Continue reading

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CVPR11–Tutorial on Activity Analysis; Dataset Biases

There’s a review of the frontiers of human activity analysis in this tutorial at the ongoing CVPR conference. Though it’s obvious the presenters have purposeful selected the highlighted to their own taste, I do like their smart way of dividing existing research efforts into Single-layered and Hierarchical approaches (as shown in the figure below borrowed from their slides), in accordance with the inherent hierarchy associated with human activities (postures –> actions –> interactions –> activities according to them).

image

Image credit: Aggarwal and Ryoo, ACM CSUR 2011.

Another piece of impression is that hierarchical approaches to date can only deal with very constrained and perhaps well-defined activity cases. This may be due to the statistical modeling or grammatical reasoning they’re using. The open question is beyond these explicit modeling of structures and rules, are there ways of dealing with this implicitly? Or perhaps think of this less radically, can we have reliable ways to learn about these structures?

On another lead, Prof. Torralba and Prof. Efros are scrutinizing the use of datasets in vision today and the possible biases in this interesting paper. Though it sounds like they are saying the right words at the right time, I hope this is not the first time they realized this – both have been emerging heroes in vision for a while and is in leading institutes of AI. Anyway, the cross-dataset generalization and negative sample bias are indeed interesting to note (and in fact more or less touched by many authors already, maybe not as systematic as here). I would like to acknowledge Prof. Torralba’s contribution of the new object recognition dataset (I’m not to be credited for the name of the dataset though Smile; meanwhile I doubt part of the motivation of the current paper is to raise awareness of the community to the dataset)

sun_mosaic_logo

Image credit: Jianxiong Xiao et al working on the SUN dataset.

SUN Database: Large-scale scene recognition from abbey to zoo.

and I also love the way they view the different roles of datasets to computer vision and machine learning

… Unlike datasets in machine learning, where the dataset is the world, computer vision datasets are supposed to be a representation of the world.

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Cheong’s Birthday on June 01

IMG_0218June 01 we had a very pleasant seminar (and subspace segmentation) dedicated to Prof. Cheong’s birthday. I just found out our innocent hero was born on Children’s Day (for China only … Oct 1st is the Children’s Day for Singapore). Wish Prof. Cheong happy research and life forever!

 

 

Left: Prof. Cheong happily looking at his birthday cake prepared by us.

Bottom: Prof. Cheong and his wife, with part of the structure-from-X group and some guests.

            

IMG_0220

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