                           ˿µϣ20101223գ

10.12.22ûи
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10.12.23, йѧԺϺоłƳ䷹ٱ
10.12.23, ѡеġٵǡѧ2010ʮѧͻơ
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뿿ˡ

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õͳɱĹǽðۣһƭΪȻӦǴ𡢳ʹ
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۾ǺܺõķƷȻ۵Ľո͡۷
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һµӤܳԷۣԹǽû⡣ȻĶδؾͱ
ĺãҲδؾͱİȫ

2010.12.20

й걨2010.12.22.

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2010ӦЩ֣

2010-12-22

ӣ



2010˵Ƿḻʵһ꣬ӦǶصһꡣ˺
飬ҲУҲСһ飬˺ܶ٣Ҳ˴ˡ

Ǹܵ΢ӶƿٿʼһĴٶǴ
΢ϷֵģҲǴ΢Ͽʼģ˵ǷḻʣϢǳ
ͨʱǴĿ٣ͬܶණҲ޷

Щϵʵһͬ㣬Ǻͳ
йأѧٲƷĵȡ˵ڵһȱʧ
ʱܶԭӦøгŵˣƫƫȴûгšҳΣ
˵飬ҲǸ˵飬ÿ˶Ҫٵ⡣
Σԭܶ࣬ġġ˵ġ

Σ֮£ͬӦнǾӦýһĳƣ
ǹϵģӦƶϵģӦû𾴺ͳϣ
ӦõõͷۣҲŻὲš

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йũУŽǼѧMBAҵ

ӰѧȻû̫ƽѧʵͬšй
ҵңϴˮϰ壩ƾҲˣôƴѧ쵼
ҲԩǮԽأ

http://www.cau.edu.cn/home//jieshao/zjh.htm

ŽУ壬ˣ19601³19832²μӹ19851
뵳ѧMBAҵоԱ
1979.021983.02ũҵѧũѧϵѧƣ 
1983.021995.08ũҵѧ ʵҹ񸱴

1995.082002.07йũҵѧ,ڴ.
2002.072005.07йũҵѧУɲ½ũҵѧίίУ
2005.072008.04 йũҵѧ̨УУУ
2008.04 ййũҵѧίίԱίУ

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ѧѧԺ갱ڿϮ

𾴵ķ

Ķʱָѧ갱ڵĴڴ
ֽ갱һƪĺͱƪϢʲο

갱ģ Binyu Zang , Yinsheng Li , Wei Xie , Zhuangjian Chen , 
Chen-Fang Tsai ,Christopher LaingAn ontological engineering approach 
for automating inspection and quarantine at airports, Journal of 
Computer and System Sciences, Elsevier 74 (2008) 196C210

ԭ1 Jingshan Huang, Jiangbo Dang, Michael N. Huhns, Yongzhen 
Shao, "Ontology Alignment as a Basis for Mobile Service Integration 
and Invocation," International Journal of Pervasive Computing and 
Communications, vol. 3, no. 2, pp. 138-158, Emerald, 2007 

ԭ2 Jingshan Huang, Jiangbo Dang, and Michael N. Huhns, 
Reconciling Ontologies for Coordination among E-business Agents, 
Proc. AAMAS Workshop on Business Agents and the Semantic Web, Hakodate, 
Japan, May 2006 

갱:

GLUE [30] is schema-based and introduces well-founded notions of 
semantic similarity, applies multiple machine learning strategies, and 
can find not only one-to-one mappings, but also complex mappings. 
However, it depends heavily on the availability of instance data. 
Therefore, it is not practical for cases where there is an 
insignificant number of instances or no instance at all.  PROMPT [13] 
is a tool making use of linguistic similarity matches between concepts 
for initiating the merging or alignment process, and then use the 
underlying ontological structures of the Protege-2000 environment to 
inform a set of heuristics for identifying further matches between the 
ontologies. PROMPT has a good performance in terms of precision and 
recall. However, user intervention is required, which is not always 
available in real world application. COMA [14] provides an extensible 
library of matching algorithms, a framework for combining results, and 
evaluation platform as well. According to their evaluation, COMA is 
performing well in terms of  precision, recall and overall measures. 
Although being a composite schema-matching tool, COMA does not 
integrate reasoning and machine learning techniques. Similarity 
Flooding [12] utilizes a hybrid matching technique based on the idea 
that similarity spreading from similar nodes to the adjacent neighbours. 
Before a fix-point is reached, alignments between nodes are refined 
iteratively. This algorithm only considers the simple linguistic 
similarity between node names, leaving behind the node property and 
inter-node relationship. Cupid [15] combines linguistic and structural 
schema matching techniques, as well as the help of a precompiled 
dictionary.

But it can only work with a tree-structured ontology instead of a 
more general graph-structured one. As a result, there are many 
limitations to its application, because a tree cannot represent 
multiple-inheritance, an important characteristic in ontologies. 
S-Match [11] is a modular system  into which individual components can 
be plugged and unplugged. The core of the system is the computation of 
relations. Five possible relations are defined between nodes: 
equivalence, more general, less general, mismatch, and overlapping. 
Giunchiglia et al. claim that S-Match outperforms Cupid, COMA, and SF 
in measurements of precision, recall, overall, and  F-measure.  However, 
like Cupid, S-Match uses a tree-structured ontology.

Ϯԭ1:

GLUE introduces well-founded notions of semantic similarity, 
applies multiple machine learning strategies, and can find not only 
one-to-one mappings, but also complex mappings. However, it depends 
heavily on the availability of instance data. Therefore, it is not 
practical for cases where there is an insignificant number of 
instances or no instances at all.PROMPT (Noy and Musen, 2001) is a 
tool making use of linguistic similarity matches between concepts for 
initiating the merging or alignment process, and then use the 
underlying ontological structures of the Protege-2000 environment 
to inform a set of heuristics for identifying further matches between 
the ontologies. PROMPT has a good performance in terms of  precision 
and recall. However, user intervention is required, which is not 
always available in real world applications. COMA (Do et al., 2002) 
provides an extensible library of matching  algorithms, a framework 
for combining results, and evaluation platform as well. According to 
their evaluation, COMA is performing well in terms of precision, recall, 
and overall measures. Although being a composite schema matching tool, 
COMA does not integrate reasoning and machine learning techniques. 
Similarity Flooding (Melnik et al., 2002) utilizes a hybrid  matching 
technique based on the idea that similarity spreading from similar 
nodes to the adjacent neighbors. Before a fix-point is reached, 
alignments between nodes are refined iteratively. This algorithm only 
considers the simple linguistic similarity between node names, leaving 
behind the node property and inter-node relationship. Cupid (Madhavan 
et al., 2001) combines linguistic and structural schema matching 
techniques, as well as the help of a precompiled dictionary. But it  
can only work with a tree-structured ontology instead of a more 
general graph-structured one. As a result, there are many limitations 
to its application, because a tree cannot represent multipleinheritance, 
an important characteristic in ontologies. S-Match (Giunchiglia et al., 
2004) is a modular system into which individual components can be 
plugged and unplugged. The core of the system is the computation of 
relations. Five possible relations are defined between nodes:  
equivalence, more general, less general, mismatch, and overlapping. 
Giunchiglia et al. claim that S-Match outperforms Cupid, COMA, and 
Similarity Flooding in measurements of precision, recall, overall, and 
F-measure. However, like Cupid, S-Match uses a tree-structured ontology. 
 

갱ģ

An ontology-based information retrieval model was presented for 
Semantic Web in the literature [7]. The authors generate ontology 
through translating and integrating domain ontologies. The terms 
defined in ontology are used as metadata to mark up the Web content; 
these semantic mark-ups are semantic index terms for information 
retrieval. The equivalent classes of semantic index terms are obtained 
by using description logic reasoner. They claim that the logical views 
of documents and user information needs, generated in terms of the 
equivalent classes of semantic index terms, can represent documents 
and user information needs well, so the performance of  information 
retrieval can be improved effectively when suitable ranking function 
is chosen.

Ϯԭ2

An ontology-based information retrieval model for the Semantic Web 
is presented in [13]. The authors generate an ontology through 
translating and integrating domain ontologies. The terms defined in 
the ontology are used as metadata to markup the Web's content; these 
semantic markups are semantic index terms for information retrieval. 
The equivalent classes of semantic index terms are obtained by using 
description logic reasoner. It is claimed that the logical views of 
documents and user information needs, generated in terms of the 
equivalent classes of semantic index terms, can represent documents 
and user information needs well, so the performance of information  
retrieval can be improved effectively when suitable ranking function 
is chosen.

갱ģ             

Tijerino et al. introduce an approach (TANGO) to generate 
ontologies based on table analysis [9]. TANGO aims to understand a 
tables structure and conceptual content; discover the constraints 
that hold between concepts  extracted from the table; match the 
recognized concepts with ones from a more general specification of 
related concepts; and merge the resulting structure with other similar 
knowledge representations. The authors claim that TANGO is a 
formalized method of processing the format and content of tables that 
can serve to incrementally build a relevant reusable conceptual 
ontology.

Ϯԭ2                

Tijerino et al. introduce an approach (TANGO) to generate 
ontologies based on table analysis [14]. TANGO aims to understand a 
tables structure and conceptual content; discover the constraints 
that hold between concepts extracted from the table; match the 
recognized concepts with ones from a more general specification of 
related concepts; and merge the resulting structure with other similar 
knowledge representations. The authors claim that TANGO is a 
formalized method of processing the format and content of tables that 
can serve to incrementally build a relevant reusable conceptual 
ontology.

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йѧԺϺоłƳ䷹ٱ

ߣjames_hussein_bond

Chemical & Pharmaceutical Bulletin·ݷһƪ֪ͨй
ѧԺϺоłǰ2008귢һƪ£

http://www.jstage.jst.go.jp/article/cpb/58/6/58_884/_article
ã
Vol./Page: Chem. Pharm. Bull. 56, 993994 (2008).
Title: Purine and Pyrimidine Derivatives from the South China Sea 
Gorgonian Subergorgia suberosa
Authors: Shu-Hua QI, Si ZHANG, Cheng-Hai GAO, and Qin-Xing LI
Comments by Editorial Committee:
Editorial Committee of Chemical & Pharmaceutical Bulletin decided 
to retract the above paper. The two new compounds in Chem. Pharm. 
Bull. had already been reported by the same authors with a different 
name in their previous paper (J. Nat. Prod., 71, 716718 (2008)). In 
addition, descriptions of the structure elucidations of two new 
compounds are word-for-word the same as those used for compounds in the 
J. Nat. Prod. paper. These facts are apparent evidences of self-plagiarism, 
and the bEditorial Committee made this decision.

룺
Chemical & Pharmaceutical Bulletinί¡е
""֮ǰͬԲͬĻһƪ(J. Nat. 
Prod., 71, 716718 (2008))бˡ⣬""ṹĲ
ҲJ. Nat. Prod.еĻͬЩʵ"ҳϮ"֤ݣ
˱ί˳

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еġٵǡѧ2010ʮѧͻơ

ߣhuangmin

գһĵϢ ѧ־ǰ
2010ʮѧͻơ񵥣ͻڻоԺ
ʿѧ͹֪ĿѧĪ׷ɣڶ෴֮
ӶĹع˾κɾˡΪ󣬼ߵһʱ
ѧ־ԭıоͻԼԭõԭʼ֧ſ
ѧУûлЩѧ׵һߵĹλ
ŷޡ˿ѧͻϢĳֶԻоԺ֮֡

оͻƷֱΪڵλġ/򡱼
ڵڰλġһĻѧЩͻȫǧĻѧ
ĹͬоĳɹɻΪԼĿѧɹܽΪ
ҵл֣ͰѺͻйصĿѧоɹ㵽Լϣ
ˣѧͼˣֻҪȡ˷ĺ־Ϳɡ

ÿǧĲǻڶ໨Ǯνϲ
оǮĺϢɣȴûһԼĳɹ. һ
̻ƭ

Ϊؽѧ־ԭѧ־õ֧ſѧ
׼ѧߵĹλ棺

λ/: ֻͨĳһе
˵ǸСʵʱ뵰ʵĻ鲿֣воŴ
ԼоԱܹ12ּرĻͻ䣻ЩŴԼ
ĳȱݵĻġ 

/֧ſѧ

Homing In on Errant Genes

1ѧ־

J. Kaiser, "Affordable 'Exomes' Fill Gaps in a Catalog of Rare 
Diseases," Science 330, 903 (2010). 

2ʢٴѧDepartment of Genome Sciences, University of 
Washington School of Medicine

S. B. Ng et al., "Massively parallel sequencing and rare disease," Hum. 
Mol. Genet. 19, 119 (2010). 

3Ү³ѧDepartment of Neurosurgery, Yale University 
School of Medicine

K. Bilgvar et al., "Whole-exome sequencing identifies recessive 
WDR62 mutations in severe brain malformations," Nature 467, 207 (2010). 

4ʡҽԺʿٴѧCardiovascular Research Center, 
Massachusetts General Hospital, and Department of Medicine, Boston 
University School of Public Health

K. Musunuru et al., "Exome Sequencing, ANGPTL3 Mutations, and Familial 
Combined Hypolipidemia," N. Engl. J. Med. 363, 2220 (2010). 

5ʢٴѧ

S. B. Ng et al., "Exome sequencing identifies MLL2 mutations as a 
cause of Kabuki syndrome," Nature Genet. 42, 790 (2010).

ڰλһĻѧ: ۵ĲʹܹԶźִ
DNAзǳģо 磬1ǧƻѾ
һ޶Ļ  ڽеļƻһ¶Ļ
鹦ܡ

һĻѧ֧ſѧ

Next-Generation Genomics

1ӢŴѧΤķɣѧԺоȵǧ˻ƻ
Wellcome Trust Sanger InstituteCambridge, UK

The 1000 Genomes Project Consortium, "A map of human genome variation 
from population-scale sequencing," Nature 467, 1061 (2010). 

2˹ʵ Department of Genome Biology, 
Lawrence Berkeley National Laboratory, Berkeley, California, USA

S. E. Celniker et al., "Unlocking the secrets of the genome," Nature 
459, 927 (2009). 

3ѧPennsylvania State University, Center for 
Comparative Genomics and Bioinformatics

S. C. Schuster et al., "Complete Khoisan and Bantu genomes from 
southern Africa," Nature 463, 943 (2010). 

4Ե籾ѧ University of Copenhagen, Denmark

M. Rasmussen et al., "Ancient human genome sequence of an extinct 
Palaeo-Eskimo," Nature 463, 757 (2010). 

5ͼϵͳѧо Institute for Systems Biology, 
Seattle, USA.

J. C. Roach, et al., "Analysis of Genetic Inheritance in a Family 
Quartet by Whole-Genome Sequencing" Science 328, 636 (2010).

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Judeʲôŵ



ã֪ͬͨҿ˿10.12.21Judeʲôŵ
ҷǳлλѡJudeָý屨⡣

λѡJude˵ȫȷ֪ȷûνġŵ
ȻұҲûлùںܶೡϣˣҵѧ
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ܹǵصһ,ʲôط˵һ֪
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Ҳʲô˲õ飬Ҳ޷ȷǷıԶ
Ҳû⣬ѡͬʱҲһЦȥѣý
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ȥɺˡǻ˵˵˹˵Լʮõŵ
ֻƵĹɢǵġ޴ġĪΪˡ

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ѧɽ¼

ߣoldtie

ֻջ¼ڡ˿϶Ϳۣеһ
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wewoxiaoĹ۵Ĳͬ

ߣsnowman

wewoxiaoĹ۵ҺܲΪȻ¿

1 ȫΪĳ˵ȻҪΪԼΪرش
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Ƶӻ2010֪ʶй佱

http://video.sina.com.cn/v/b/44034057-1468424043.html

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Ф봸

ߣŷ
2010-12-22 ɶ̱

ոջðٶķӸһ΢
ҵһЩʻ㣬е죺 ŵ绰
 ڡ ũҰˡ  ƴ
衯

ûСѧҲûС١ѧڼΪ˰һ
ķ˵൱̡

ӰһӣФϣФ
ΪФ˵ĸ˶Թ䶯2010ѧ󰸣ͻΪһ־硣


ߵĻطãչʾФһ棺̿ࡢࡢ棬һѧ˵
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ӰһǴĳ˵˾һӣôĶ
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2011꣬ѧÿζܾѴӵô
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(XYS20101223)

˿(www.xys.org)(xys5.dxiong.com)(www.xinyusi.info)(xys2.dropin.org)

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ġͯҳֻ

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2010ȡ֪ʶйʢ䡱ǰڱУ2010֪ʶй
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ڣͷӡ桱Ϯ¼ӵķ
˽ܹڻӭ괺ҲԷΪزģƳ
ʵĻڣַڡ١ϵִţǹעַ֧ӣ
Ϊӡ١ź룬Էӡ١ش󣬶
Ʒλ˵Ҳġ

(XYS20101223)

˿(www.xys.org)(xys5.dxiong.com)(www.xinyusi.info)(xys2.dropin.org)

ٿCEO˺°ٶCEOĹ

һ

Hi Robin,

    һֱֱӽΪԭδԸ쿴ƽ
жҲ֮ᱻٶȽãΪйҵ
¼չ·һѺȷдһŵ뷨˻
˼˾Ļֵ

    ͬΪعҵ㻳ĳ֮顣ǰDFJĹȰ
ҿƬȱķʱΪйĳɾͶ
ԺͬʱҲΪԼйĻ´ҵһҵ
ѡDFJĴʼϻTim DraperҲҵļ˵ϣܳΪһꡣ
ǣҶ˽Խ࣬԰ٶȵ˽Խ࣬ҪźضҼ
Ͷ˵ԲҹǵĺˣҲΪһꡣΪ
һΰ˾CEO

    ΪǸǳѧϰˡ2000һʱ򣬾
Ӵơ˶һۡաéٵȻӢƾһ
뷨ڷŬͿԳɹ¼ŬΪһΪꡣع
ҵ𽥶Իĳɹʿ˽ΪʶǸؿĲ
ߣǸʿˣ˶ǸֲиǰߣܺtǸ
սߡǣأһɹʿһ׬
Ǯ˾CEOɽ˶ѡ

    һЩýı˵һЩվΪڰٶͶŹɱҿ
ʼǲŵġܶߵҽĲʹʱҳŭˡ
˵վĴʼǸɹĴҵߣҽվΪڰ
ͶŹЧֹͣͶţԱܿϣܼڰٶͶŹ棬
ܾڶ조˵վڰٶȻϾڻҵ꣬
ͨͰٶڲԱۣͨ΢ЩָǶһ
ҵ˾վʧȥٶȵԴվԪˡһζ׷ðٶ
ԱֶΡڶܺվΪȫʧȥӰٶ

    ҲһЩý屨˵һЩվΪͰٶҵھϵɱ
ŵġԱΪٶȷɱòΰٶȵ¼ǻٿƴ
ٶ˻ɰٶǲȫ档ҵһλѣҲ
ǧ˼ƻƸרңڵ֯רݼڼ䣬ҽ
һվΪͰٶĳƷһľϵٶȷɱܷ
ٶȵѹͨһ¡һ൱͵һվΪĳ־
ϵɱڴҵڵŶһĪչ֪ȥδӡ

    ڰٶȵľΪȺ˺ǳġڰٶ
Ѽҽη˶ʲôˡ΢ЩϵĶȥҽԺ
ֻЩû·ԻǺϤ˲ŻЩе
ҽҩȥдˣĻ̶Ȳ벻ߵˣ
Ⱥ塣ǱŶ԰ٶȵޱΣҽҩվ˴Ѵ
Ǯ˴ѴѲõļҩǲ·ߵ߸ϵС棬
ٶȣû뵽ٶǸС漯еĵطСͨ
Ƽֶη˸ɱ

    ۵Ӱ޼߽죬ҪġҲЦ̸
ϷٶȰ¹谷̷¼ĸϢ
300¶ߣ3000أòʧٿ
ٶȸ߹Ƶ滻ٶΪȨ˾ȵȣΪٶȵĹ
˾Ļ˱Ƚϴ⡣Ҳ͹˾Ļ뷨㽻һ¡ 
  
ףã

PHD
 


Hi Robin,

    ٶյĶǴѧ߲ҵ羫ӢΪʲôǽ뵽ٶȾͿΪ
ǮֶأǸ˼⡣

    һЩٶȵļԱΪʲô໨Щ
㷨Ϊۺ˵Googleʵǿ̫ˡиԱ
ԥ˰콲еʵΪٶȵ˹Ԥ̫ˡ
ٶȵվֵɸ⣬ÿӦͬȨأȼ
ҪԼĲƷҪѹֻ߲ͶŹվֱ
ΪѶӦվӦĿȨȨΪ
㷨õĲӲüԱƲѡ

    ҲһЩٶȵĲƷԱΪʲôɾ͸вǿ
ǲƷĳɹΪƷĴ£Ϊٶȵ󡣶൱
ĲƷڴѹֵĻȡһЩɾ͵ġһ˵Ĺ˾Ϊ
һʮ˼˵Ĺ˾ĲƷϧĵ£ԡ
ԭ

    ҲԱüͷڹ˾˵ٶҪ׬ǮĹ˾
ҲԱյؽڹ˾Ʒ˵Ҫ2009ٿƣҲ
ԱʹĵؽΪ˾WIFI˵ԸڰٶȴͺúøɲԸ
ߣҲԱЦŽԱԹ˵Աܺ
ǻǲˡ

    ҩڲҲûȨһҪ˵Robin̫
ˣ߸ϣңʧҡˮǲ֪ҪѴ
η

    Shawnй̵Ľǳܸɣ׺ܺãҲǫ͡
һֱΪǰٶCEOѡϧӢšҺͰٶȺһλ߹
ҲжݽӴڽƿѧ¼󣬺ܶ˵ٿ޸
ҲյλѷҪʼһʱգ֪Ӻ
˵

    ҽ9·ݲμ˵Уľɽ֮áëСũ
ھɽֻ2ʱ䣬ҵҾķչʷȴǸҪĽ׶Ρ
ëڰ˽¥дָйҪ:йĺɫȨΪʲô
Դڡ֮ԭë칤ũĴҵŶӣ
Եذ˼йľʵǴ£ȡй
ʤΪʲô񵳵ĶȡʤûǮûеλ
ûǹûûأйͬĺļֵۣƽȵƶȣ
йٶҪܺļֵۡʼCEOҪؿ˼
˾ĳԶչ

    ֪ĸҵʼʶ¼衣ٶΪɹĴҵ˾һ·
ԻͲϡҸϣٶߵøԶֻǸ׬ǮĹ˾Ҫһ
ΰ𾴵Ĺ˾

ףã

PHD
 


Hi Robin,

    ڽ죬ҷҵԽԽ˸̾йĻ»ڡ³
Ѹ곫ġFair Playһ̸ֽ

    һֱΪʲôйѳֿȡʤΰĻ˾Googleû
г֧λΪΪҲûĴͶֹС
辶Androidƽ̨˼ʻȴ²Ʒƶ˿Ƽ
׬ǮʵϰٶҲĻᣬͨʱĴ
²ƷΪΪһΰĹ˾춨ǰٶæǮæŰ
ԼվϣæŴССĶ֣Ϊ¡һ
ǰûϧź

    ٶĻдˣɾԼٶеһ
ˣɾԼĽɫ˵ٶʮһֱ벻Ϣ
ֵġʼʹźܶǳǼྭӪŵС
վҵļ£ͬʱҵĽ

    Ȼ㲢ܱ֤Ҳ޷ŷж֮
ΪֵĹߣҸԸŵʵǣƽġж 
԰ٶеҵκԼֻðٶȶӵиݺεͨ
Ҳֵ룬רעsearchƽ̨רע
ļھ򣬲ǮǣƣȥаԵİٶȵ۹
ȫΪйĻ춨һֻšƽȡµ̬;

    Űٶȵҳ棬Ҳ֪ٶȵĻݣ
йĻݡ֣ص˵ٶȣй㲻
йȫ

    йĻҵķչ̣ǹʻ̶ߵһҵ
г֧λѾأսɱΪһĻ
ҵڽϨͬΪԱΪĴҵߣҿԲɹ
ҿ԰ͶʻҵͶˣҿڹƽгԸķ䣬
ͷǣΪйҵĿɳչΪйҵ
ϴ£Ϊ˰ٶȳΪһΰĹ˾ΪǧǧĻҵԱʹ
ҵԱĴºͶ֮һز½飬Ϊ֮ȫ
ϧ

    ز׼ڰٶȵĵһҳֹܳ棬
ָԼʵʿվҳݡ 
  
    ϣͬ 
  
ףã

PHD

(XYS20101223)

˿(www.xys.org)(xys5.dxiong.com)(www.xinyusi.info)(xys2.dropin.org)

ٶ2010ʮ﷢

ߣ
20101223պѶ

˵йʷкΣе塣ھĭٺ80
ռ90ͷǰ꣬ȷʵкܶѧԺʿӹ
ֻڿǶͷˡ

ٶ2010ʮٲȻһϰΪƹԣ
ƿӡ¸١衢ڡղۡɣ桢¹ꡣ
ֻλһƴһǹ¸١λս
һߵҵңλΪԼ붷׵ġʿ

ƹԣϰȻλĺһȫԿƸ
̳㹻ĵʡҵǣƹԣûļ
ͼҳ֮С෴ƹԣȴΧƹĶ»Ȩ
ͱʱ̸Уõ˺̶ܴȵ֧֡

ƹԣֵҵֵ۱ܶоʿ߶ۡǾһҵ
ҡһҵܻ᷸ӦеĺϷƲȨɶ
ȨӦĨɱԵд淨ɣƷһ
ӴĴͳʶҲһȷ򣬶һˣһҵ۸
ȫ滯

ΪƹԣзǸţɿȷʵ
͹жƹԣһûбעصǣưһ
ʼۺ͹۵׷׳֧֡ԻУڡڳ
ֵǰǰ۵Ŀ͹ѲֵûɡҲȷܶ
ʶ֮ʿϣɸ߶ȵͳһӦˡƹԣʹԼĺϷȨ

ƿ鸴ӳ̶ȵڻưǽҲƿҲδܵǷ
ýֻһ£ϵɺͱƿƿԲ
ϴ󣬵ýԽȨȥִСһ仰ģ
ɱ

ǵλֵ۳ͻҵܹϰλʿϰҲ
ˡӼִٺܶ꣬Ҳǽ¶ƿѧŵǡ
˾ܱԸƫٴ򵽱˲㰵塣ɣǹ
塰ʿҪصףϧŽҲѡ

Щսڼֵ۳ͻһЩܹϰܵõ͹۵ۣ
ܲ˵ʵ壬ܿס

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֢֮ޱҪ

ߣSW

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,Ԥ,ʳҪ㡭(Ҳɷֹ˴ôƫ
֢ƭǮ).

ARF, תԡ. ,XYS ݵ, ţǼ˷
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