Anomalous pinning behavior in an incommensurate two-chain model of friction

tpo32三篇托福阅读TOEFL原文译文题目答案译文背景知识阅读-1 (2)原文 (2)译文 (5)题目 (7)答案 (16)背景知识 (16)阅读-2 (25)原文 (25)译文 (28)题目 (31)答案 (40)背景知识 (41)阅读-3 (49)原文 (49)译文 (53)题目 (55)答案 (63)背景知识 (64)阅读-1原文Plant Colonization①Colonization is one way in which plants can change the ecology of a site.Colonization is a process with two components:invasion and survival.The rate at which a site is colonized by plants depends on both the rate at which individual organisms(seeds,spores,immature or mature individuals)arrive at the site and their success at becoming established and surviving.Success in colonization depends to a great extent on there being a site available for colonization–a safe site where disturbance by fire or by cutting down of trees has either removed competing species or reduced levels of competition and other negative interactions to a level at which the invading species can become established.For a given rate of invasion,colonization of a moist,fertile site is likely to be much more rapid than that of a dry, infertile site because of poor survival on the latter.A fertile,plowed field is rapidly invaded by a large variety of weeds,whereas a neighboring construction site from which the soil has been compacted or removed to expose a coarse,infertile parent material may remain virtually free of vegetation for many months or even years despite receiving the same input of seeds as the plowed field.②Both the rate of invasion and the rate of extinction vary greatly among different plant species.Pioneer species-those that occur only in the earliest stages of colonization-tend to have high rates of invasion because they produce very large numbers of reproductive propagules(seeds,spores,and so on)and because they have an efficient means of dispersal(normally,wind).③If colonizers produce short-lived reproductive propagules,they must produce very large numbers unless they have an efficient means of dispersal to suitable new habitats.Many plants depend on wind for dispersal and produce abundant quantities of small,relatively short-lived seeds to compensate for the fact that wind is not always a reliable means If reaching the appropriate type of habitat.Alternative strategies have evolved in some plants,such as those that produce fewer but larger seeds that are dispersed to suitable sites by birds or small mammals or those that produce long-lived seeds.Many forest plants seem to exhibit the latter adaptation,and viable seeds of pioneer species can be found in large numbers on some forest floors. For example,as many as1,125viable seeds per square meter were found in a100-year-old Douglas fir/western hemlock forest in coastal British Columbia.Nearly all the seeds that had germinated from this seed bank were from pioneer species.The rapid colonization of such sites after disturbance is undoubtedly in part a reflection of the largeseed band on the forest floor.④An adaptation that is well developed in colonizing species is a high degree of variation in germination(the beginning of a seed’s growth). Seeds of a given species exhibit a wide range of germination dates, increasing the probability that at least some of the seeds will germinate during a period of favorable environmental conditions.This is particularly important for species that colonize an environment where there is no existing vegetation to ameliorate climatic extremes and in which there may be great climatic diversity.⑤Species succession in plant communities,i.e.,the temporal sequence of appearance and disappearance of species is dependent on events occurring at different stages in the life history of a species. Variation in rates of invasion and growth plays an important role in determining patterns of succession,especially secondary succession. The species that are first to colonize a site are those that produce abundant seed that is distributed successfully to new sites.Such species generally grow rapidly and quickly dominate new sites, excluding other species with lower invasion and growth rates.The first community that occupies a disturbed area therefore may be composed of specie with the highest rate of invasion,whereas the community of the subsequent stage may consist of plants with similar survival ratesbut lower invasion rates.译文植物定居①定居是植物改变一个地点生态环境的一种方式。

详细解析新GRE阅读出题点今天三立在线老师带同学们通过几个具体例子来感性体会GRE阅读所谓隐蔽和出题点的概念。

先从一道No题中的难题开始。

在一篇关于有氧代谢和无氧糖酵解的生命科学文章中，第三段有这样一个长达数行的难句子：With the conclusion of a burst of activity, the lactic acid level is high in the body fluids, leaving the large animal vulnerable to attack until the acid is reconverted, via oxidative metabolism, by the liver into glucose, which is then sent (in part) back to the muscles for glycogen resynthesis.面对这样一个充斥着烦难专业名词的难句，很多同学读完之后几乎当场晕倒。

这里重点强调的不是句子本身的结构和各因素相互之间的制约联系。

而是因为这句难句对应了一道非常有代表性的罗马数字类型题。

It can be inferred from the passage that the time required to replenish muscle glycogen following anaerobicglycolysis is determined by which of the following factors?I. Rate of oxidative metabolismII. Quantity of lactic acid in the body fluidsIII. Percentage of glucose that is returned to the muscles(A) I only(B) III only(C) I and II only(D) I and III only(E) I, II, and III题目问及无氧糖酵解过程转化糖原时间和哪些因素有关?有氧代谢率和乳酸本身含量是比较明显的因素。

小学上册英语第3单元真题英语试题一、综合题(本题有100小题，每小题1分，共100分.每小题不选、错误，均不给分)1.What do you call a person who studies the Earth?A. GeographerB. GeologistC. MeteorologistD. BiologistA2.The witch has a ___ (black) cat.3. A __________ is a large landform that stretches across a region.4. A _______ (小刺猬) curls up when it feels threatened.5.The chemical symbol for nihonium is ______.6.The capital of the Netherlands is _______.7.What is 2 x 3?A. 5B. 6C. 7D. 8B8. A __________ can tell us about the Earth's temperature.9.What do you call a person who fixes cars?A. MechanicB. PlumberC. ElectricianD. CarpenterA10.The _____ (小鸟) finds food among the trees.11.The __________ is a large area of grassy plains.12.The __________ (生态研究) informs public policy.13.The __________ is a famous area known for its underwater attractions.14.What is 5 + 3?A. 6B. 8C. 9D. 10B15.The capital of Vanuatu is ________ (瓦努阿图的首都是________).16.The _______ (The Roman Republic) preceded the Roman Empire.17.My family goes ________ every summer.18.Acids can change the color of indicators to ______.19.What is the name of the famous volcano in Italy?A. Mount EtnaB. Mount VesuviusC. Mount St. HelensD. Mount Fuji20.I have a _____ (玩具火车) that makes sounds while it moves. 我有一辆会发出声音的玩具火车。

北京市西城区2023-2024学年高一下学期期末考试英语试题15小题；每小题1.5分，共22.5分）阅读下面短文，掌握其大意，从每题所给的A、B、C、D四个选项中，选出可以填入空白处的最佳选项。

It's a gift more than 50 years in the making.In 1972, Barbara Rieco 1． a heartfelt children's book. It was based on a true story—a little boy she had met while she was a teenager living in Appalachia briefly. It was Barbara's first encounter with poverty (贫困). She came from a middle-class family. She got to see another side of life that she'd heard of, but never seen. It 2．her and had a lasting influence on her life.3．her best efforts, the manuscript (手稿) faced repeated refusal from publishers, leading her to eventually 4．her dreams of becoming a published author.Little did she know that, many years later, her grandson would turn those dreams into reality. Chad Cooper, a design and production professional based in New York City, decided to give his grandmother the perfect Christmas gift—the 5．of her treasured book. Visiting her for the holidays, he 6．took her original manuscript, along with the illustrations she had an artist draw years ago, and turned them into a beautifully printed book.On Christmas morning, as Barbara 7．the carefully wrapped gift, she was overcome with emotion. The moment, 8．by Chad, quickly became popular with viewers, and the video went viral online."This is probably the nicest thing anyone's ever done for me in my entire life," Barbara says in the video.Chad's 9．act didn't just end with the surprise. He also included a(n) 10．to the book on Amazon, making it accessible to the public. The 11．was nothing short of a miracle (奇迹)—within days, Barbara's 12．book skyrocketed to the top of the bestseller list.As the world 13．this unexpected Christmas miracle, Barbara Rieco's book, now 14．by a new generation of readers, continues to spread joy and inspiration, proving that sometimes the most extraordinary 15．are the ones we never knew we needed.1．A．edited B．adapted C．penned D．translated 2．A．worried B．touched C．supported D．challenged 3．A．Despite B．Through C．Without D．For4．A．take on B．live up toC．hold on to D．set aside5．A．promotion B．revisionC．publication D．recommendation6．A．curiously B．secretly C．calmly D．shyly 7．A．bought B．held C．opened D．showed 8．A．photographed B．filmed C．written D．described 9．A．respectful B．faithful C．thankful D．thoughtful 10．A．link B．introduction C．approach D．guide 11．A．comment B．impression C．response D．prediction 12．A．once-forgotten B．badly-neededC．well-known D．fully-prepared13．A．accepts B．presents C．ignores D．celebrates 14．A．sold B．donated C．rated D．loved 15．A．videos B．gifts C．belongings D．materials阅读下面短文，从每题所给的A、B、C、D四个选项中选出最佳选项。

中考英语阅读理解推理判断题单选题30题1. The man in the story always smiles at people. We can infer that he is _____.A. friendlyB. shyC. angryD. sad答案：A。

本题考查对人物性格的推理判断。

文中提到这个人总是对人微笑，微笑通常表示友好，A 选项friendly 意为友好的，符合语境。

B 选项shy 意为害羞的，总是微笑的人不太可能是害羞的。

C 选项angry 意为生气的，与总是微笑不符。

D 选项sad 意为悲伤的，也与总是微笑相矛盾。

2. The woman in the passage helps an old man cross the street. We can guess she is _____.A. selfishB. kindC. cruelD. lazy答案：B。

根据文中女人帮助老人过马路的行为，可以推断她是善良的。

B 选项kind 意为善良的，符合推理。

A 选项selfish 意为自私的，与帮助他人的行为相反。

C 选项cruel 意为残忍的，与帮助行为不符。

D 选项lazy 意为懒惰的，与帮助行为无关。

3. The boy in the story studies hard every day. We can think he wants to _____.A. play gamesB. get good gradesC. sleep moreD. watch TV答案：B。

男孩每天努力学习，通常是为了取得好成绩。

B 选项get good grades 意为取得好成绩，符合逻辑。

A 选项play games 意为玩游戏，与努力学习的行为相悖。

C 选项sleep more 意为睡更多觉，不是努力学习的目的。

D 选项watch TV 意为看电视，也不是努力学习的目的。

4. The girl in the text gives her food to a hungry dog. We can assume she is _____.A. meanB. kind-heartedC. coldD. proud答案：B。

2019年春四川省棠湖中学高二第一学月考试英语试题第I卷选择题（100分）第一部分听力第一节（共5小题；每小题1.5分，满分7.5分）请听下面5段对话，选出最佳选项。

1. Why does the man come here?A. To relax.B. To study.C. To borrow books.2. What happens to the speakers?A. They are lost.B. Their car is broken.C. They are running out of gas.3. What sport did the woman watch?A. Football.B. Tennis.C. Swimming.4. When will the woman leave for the airport tomorrow?A. At 5:30.B. At 6:30.C. At 7:30.5. What does the professor suggest the woman do?A. Hand in the final paper.B. Write another paper.C. Revise her paper.第二节（共15小题；每小题1.5分，满分22.5分）请听下面5段对话或独白，选出最佳选项。

请听第6段材料，回答第6、7题。

6. How does the man probably feel now?A. Impatient.B. Regretful.C. Confused.7. What is the man doing?A. Complaining.B. Comforting.C. Encouraging.请听第7段材料，回答第8、9题。

8. When does the conversation take place?A. In the morning.B. At noon.C. In the evening.9. What did the man attend?A. A party.B. A discussion.C. The opening ceremony. 听第8段材料，回答第10至12题。

a r X i v :c o n d -m a t /9511007v 1 2 N o v 1995Anomalous diffusion in the presence of external forces:exacttime-dependent solutions and entropyConstantino TsallisDepartment of Chemistry,Baker Laboratory,and Materials Science CenterCornell University,Ithaca,NY 14853-1301and Centro Brasileiro de Pesquisas F´ısicas Rua Xavier Sigaud 150,22290-180–Rio de Janeiro –RJ,Brazil ∗Dirk Jan Bukman Department of Chemistry,Baker Laboratory,Cornell University,Ithaca,NY 14853-1301(February 1,2008)Abstract The optimization of the usual entropy S 1[p ]=− du p (u )ln p (u )un-der appropriate constraints is closely related to the Gaussian form of the exact time-dependent solution of the Fokker-Planck equation describing animportant class of normal diffusions.We show here that the optimizationof the generalized entropic form S q [p ]={1− du [p (u )]q }/(q −1)(withq =1+µ−ν∈R )is closely related to the calculation of the exact time-dependent solutions of a generalized,nonlinear,Fokker Planck equation,namely ∂∂x [F (x )p µ]+D ∂2∗Permanent address (tsallis@cat.cbpf.br)a great variety of physical situations become unified in a single picture.05.20.-y;05.40.+j;05.60.+w;66.10.CbTypeset using REVT E XAnomalous diffusion is intensively studied nowadays,both theoretically and experimen-tally.It is observed,for instance,in CTAB micelles dissolved in salted water[1],the analysis of heartbeat histograms in a healthy individual[2],chaotic transport in laminarfluidflow of a water-glycerol mixture in a rapidly rotating annulus[3],subrecoil laser cooling[4],particle chaotic dynamics along the stochastic web associated with a d=3Hamiltonianflow with hexagonal symmetry in a plane[5],conservative motion in a d=2periodic potential[6], transport offluid in porous media(see[7]and references therein),surface growth[7],and many other interesting physical systems.Its thermostatistical foundation(as it is known for normal diffusion)is naturally highly desirable and has,since long,been looked for(see, for instance,[8]and references therein).This goal was recently achieved by Alemany and Zanette[9](see also[10])for L´e vy-like anomalous diffusion,in the context of a generalized, not necessarily extensive(additive),thermostatistics that has been recently proposed[11,12]. This thermostatistics(already applied to a considerable variety of physical systems[13]and optimization techniques[14])is based upon the entropic formS q[p]≡1− du[p(u)]q∂t [p(x,t)]µ=−∂∂x2[p(x,t)]ν(3)where(µ,ν)∈R2,D>0is a(dimensionless)diffusion constant,F(x)≡−dV(x)/dx is a(dimensionless)external force(drift)associated with the potential V(x),and(x,t)is a (dimensionless)1+1space-time.Let us mention that,in variance with the correlated type we are focusing on here,the L´e vy-like anomalous diffusion is associated with a linear equation, though in fractional derivatives[5].We intend to consider here a specific(but very common)drift,namely characterized by F(x)=k1−k2x(k1∈R and k2≥0;k2=0corresponds to the important case of constant external force,and k1=0corresponds to the so called Uhlenbeck-Ornstein process[15]). The particular caseµ=ν=1corresponds to the standard Fokker-Planck equation,i.e.,to normal diffusion.The particular case F(x)=0(no drift)has been considered by Spohn[7] forµ=1and arbitraryν(for instance,ν=3satisfactorily describes a standard solid-on-solid model for surface growth),and has been extended by Duxbury[16]for arbitraryµand ν.The case(µ,k1)=(1,0)has been considered by Plastino and Plastino[17].Our present discussion recovers all of these as particular instances.First,let us illustrate the procedure we intend to follow,by briefly reviewing normal diffusion(µ=ν=1).We wish to optimize S1(given by Eq.(2))with the constraintsdu p(u)=1,(4)u−u M 1≡ du(u−u M)p(u)=0(5) and(u−u M)2 1≡ du(u−u M)2p(u)=σ2,(6) u M andσbeingfixedfinite real quantities.The optimization straightforwardly yields the solutione−β(u−u M)2p1(u)=e−β(t)[x−x M(t)]2p1(x,t)== Z1(0)β(0)= Z1(0)[1−2Dβ(0)k2(11)β(0)anddx M(t)+ x M(0)−k1k2essential)fact that by doing so we preserve[12]the Legendre structure of Thermodynamics and(through the nonnegativity of C q/q[18],where C q denotes the specific heat)guarantee thermodynamic stability.Let us consistently stress that the constraint(14)is equivalent to u q= u M q,but not to u q=u M(since,unless q=1, u M q=u M).All these peculiarities are of course originated by the essential nonextensivity that the index q introduces in the theory.For example,if we have two independent systems A and B(i.e.,p A∗B(u A,u B)= p A(u A)p B(u B)),we immediately verify that S q(A∗B)=S q(A)+S q(B)+(1−q)S q(A)S q(B).It is straightforward to see that the above described optimization of S q yieldsp q(u)=[1−β(1−q)(u−u M)2]1Z q(16)withZ q= du[1−β(1−q)(u−u M)2]11−qβ(0)= Z q(0)µ+νd[Z q(t)]µ+νZ q(t)=Z q(0) 1−1K2 1(22)2νDβ(0)[Z q(0)]µ−νandµτ≡t 1µArrayµ+ν.As we see,µ/ν=1,>1and<1respectively imply that[x(t)−x M(t)]2 scales like t(normal diffusion),faster than t(superdiffusion)and slower than t(subdiffu-sion).The limitsµ/ν=0andµ/ν=∞correspond to“no diffusion”and ballistic motion, respectively.For(µ,k1)=(1,0),the present set of equations reduces to that of Plastino and Plastino[17].Let us mention that the general solution given in Eq.(21)can be derived from that forµ=1(and arbitrary k1)by defining p(x,t)=[p(x,t)]µ,and ν=ν/µ,as can easily be seenfrom Eq.(3).Finally,by using Eq.(19)withλ=2µ,we can verify thatdx p q(x,t)=[Z q(t)/Z q(0)]µ−1 dx p q(x,0).(25)Consequently,the norm(”total mass”)is generically conserved for all times only ifµ=1 (∀K2)or if K2=1(∀µ).For0≤K2<1(a common case),the norm monotonically increases(decreases)with time ifµ>1(µ<1).If K2>1,it is the other way around.Before ending let us mention that,also when t grows to infinity,the solutions we have found must be physically meaningful.This imposesµ/ν>−1.Indeed,if k2=0,τin Eq.(21)must be positive,which impliesµ/ν>−1.Also,if k2=0,x must scale with an increasing function of t;hence,β(t)must decrease with t,which implies(through Eqs.(19) and(24))2µ/(µ+ν)>0,hence,the already mentioned restriction applies once again.The entire picture which emerges is indicated in Fig.2(we have not focused theµ<0region because that would force us to discuss the stability of the solutions with respect to small departures,and this lies outside of the scope of the present work).Summarizing,on general grounds,we have shown that thermostatistics allowing for nonextensivity constitute a theoretical framework within which a rather nice unification of normal and correlated anomalous diffusions can be achieved.Both types of diffusions have been founded,on equal footing,on primary concepts of(appropriately generalized) Thermodynamics and Information Theory.On specific grounds,we have obtained,for a generic linear force F(x),the physically relevant exact(space,time)-dependent solutions of a considerably generalized Fokker-Planck equation,namely Eq.(3).It is with pleasure that one of us(C.T.)acknowledges warm hospitality by B.Widom at the Baker Laboratory.This work was carried out in the research group of B.Widom, and was supported by the National Science Foundation and the Cornell University Materials Science Center.REFERENCES[1]A.Ott,J.P.Bouchaud,ngevin and W.Urbach,Phys.Rev.Lett.65,2201(1990);J.P.Bouchaud,A.Ott,ngevin and W.Urbach,J.Phys.II France1,1465(1991).[2]C.-K.Peng,J.Mietus,J.M.Hausdorff,S.Havlin,H.E.Stanley and A.L.Goldberger,Phys.Rev.Lett.70,1343(1993).[3]T.H.Solomon,E.R.Weeks and H.L.Swinney,Phys.Rev.Lett.71,3975(1993).[4]F.Bardou,J.P.Bouchaud,O.Emile,A.Aspect and C.Cohen-Tannoudji,Phys.Rev.Lett.72,203(1994).[5]G.M.Zaslavsky,D.Stevens and H.Weitzner,Phys.Rev.E48,1683(1993);G.M.Zaslavsky,Physica D76,110(1994)and Chaos4,25(1994)and references therein.[6]J.Klafter and G.Zumofen,Phys.Rev.E49,4873(1994).[7]H.Spohn,J.Phys.I France3,69(1993).[8]M.F.Shlesinger and B.D.Hughes,Physica A109,597(1981);E.W.Montroll and M.F.Shlesinger,J.Stat.Phys.32,209(1983);E.W.Montroll and M.F.Shlesinger,in”Non-equilibrium Phenomena II:from Stochastic to Hydrodynamics”,eds.J.L.Lebowitz andE.W.Montroll(North-Holland,Amsterdam,1984).[9]P.A.Alemany and D.H.Zanette,Phys.Rev.E49,956(1994).[10]C.Tsallis, A.M.C.Souza and R.Maynard,in”L´e vyflights and related topics inPhysics”,eds.M.F.Shlesinger,G.M.Zaslavsky and U.Frisch(Springer,Berlin,1995), p.269;D.H.Zanette and P.A.Alemany,Phys.Rev.Lett.75,366(1995);C.Tsallis, S.V.F.Levy,A.M.C.Souza and R.Maynard,Phys.Rev.Lett.75(Oct/Nov1995),in press.[11]C.Tsallis,J.Stat.Phys.52,479(1988).[12]E.M.F.Curado and C.Tsallis,J.Phys.A24,L69(1991);Corrigenda:24,3187(1991)and25,1019(1992).[13]A.R.Plastino and A.Plastino,Phys.Lett.A174,384(1993);J.J.Aly,in”N-Body Prob-lems and Gravitational Dynamics”,Proc.of the Meeting held at Aussois-France(21-25 March1993),bes and E.Athanassoula(Publications de l’Observatoire de Paris,1993),p.19;A.R.Plastino and A.Plastino,Phys.Lett.A193,251(1994);L.S.Lucena,L.R.da Silva and C.Tsallis,Phys.Rev.E51,6247(1995);P.Jund,S.G.Kim and C.Tsallis,Phys.Rev.B52,50(1995);C.Tsallis,F.C.S´a Barreto and E.D.Loh,Phys.Rev.E52,1447(1995);B.M.Boghosian,preprint(1995)[E-mail to chao-dyn@ with”get chao-dyn/9505012”on subject line];M.Portesi,A.Plastino and C.Tsallis,Phys Rev E52,3317(1995).[14]T.J.P.Penna,Phys.Rev.E51,R1(1995)and Computer in Physics9,341(1995);D.A.Stariolo and C.Tsallis,p.Phys.,vol.II,ed.D.Stauffer(World Scientific, Singapore,1995),p.343;K.C.Mundim and C.Tsallis,Int.J.Quantum Chem.(1996), in press.[15]G.E.Uhlenbeck and L.S.Ornstein,Phys.Rev.36,823(1930).[16]P.M.Duxbury,private communication(1994);see[13]of P.Jund,S.G.Kim and C.Tsallis,Phys.Rev.B52,50(1995),and[18]of C.Tsallis,F.C.S´a Barreto and E.D.Loh,Phys.Rev.E52,1447(1995).[17]A.R.Plastino and A.Plastino,Physica A(1995),in press.[18]E.P.da Silva,C.Tsallis and E.M.F.Curado,Physica A199,137(1993);Erratum:Physica A203,160(1994).FIGURESFIG.1.Theµ/ν=1/3example:(a)Time dependence ofβ(0)/β(t)=[Z q(t)/Z q(0)]2µfor Z q(0)=0and typical values of K2(indicated at the right of each curve).The curve for K2=0lies on the vertical axis.For K2=0.25,0.5,and2the asymptotic values for t/τ→∞are shown by the dashed lines.(τis defined in Eq.(23).);(b)Time dependence of{β(0)[Z q(0)]2µ}/β(t)=[Z q(t)]2µfor Z q(0)=0,β(0)[Z q(0)]2µ=0,and typical values of K′2≡k2/{2νDβ(0)[Z q(0)]2µ}(indicated at the right of each curve).The curve for K2=∞coincides with the horizontal axis.All curves saturate at afinite value as t→∞,except that for K′2=0,which is proportional to t2µ/(µ+ν)for all t.FIG.2.”Norm conservation”means that N≡ dx p q(x,t)is time-invariant;”Norm creation”means that N monotonically increases(decreases)with time if K2<1(K2>1);”Norm dissipa-tion”means that N monotonically decreases(increases)with time if K2<1(K2>1).”Normal diffusion”,”Superdiffusion”and”Subdiffusion”refer to the fact that,for k2=0,(x−x M)2scales like t,faster than t and slower than t,respectively.The standard Fokker-Planck equation corre-sponds toµ=ν=q=1.For the precise meaning of”unphysical”,see the text.On theµ=1 line we have q=2−ν;consequently,whenνvaries from∞to−1,q varies from−∞to3,which precisely is the interval within which Eq.(4)(and,consistently, dx p q(x,0)=1)can be satisfied.11。

GRE填空复习题之动物篇1.青蛙内外和分子内外题coincide.。

.differ分子的相似和外表的相似does not necessarily____， for example，几乎所有的青蛙都长的差不多， but他们的内部分子是____.2.青蛙/蜥蜴/两栖动物皮肤和毒物题permeable/porous.。

.absorbed#青蛙/amphibian的皮肤是如此的____，所以大自然中的有毒物怎样____皮肤毒害青蛙。

#一种蜥蜴extinct是因为他们的皮如此的____，以至于很容易____poisonous gas.(no5)【注解】amphibian：两栖动物。

permeable：可渗透的'porous：多孔的，可渗透的3.人的眼睛聚焦细节能力把周围转为背景题focus on.。

.background#The human eyes have the ability to ____on object. So when it happens， the environment will diminish to____.4.苍蝇死而复生对野营者骚扰题resurgence一个地方已经很多年没有受到什么(mxx)fly害虫骚扰了，但(mxx)fly最近变多了，导致了很多camper很不爽，指责the____of the fly is due to the purification of 当地的水源。

5.萤火虫发光和人造光能源效率比较题outstrips.。

.rivals/ approach#Natre‘s energy efficiency often____human technology： despite the intensity of the light fireflies produce，the amount of heat is negligible;only recently have humans developed chemical light-producing system whose efficiency____the firefly’s system.【注解】engender：[v]产生，导致，生育manipulate：[v]操纵outstrip：[v]比。

A modified version of this technical report will appear in ACM Computing Surveys,September2009. Anomaly Detection:A SurveyVARUN CHANDOLAUniversity of MinnesotaARINDAM BANERJEEUniversity of MinnesotaandVIPIN KUMARUniversity of MinnesotaAnomaly detection is an important problem that has been researched within diverse research areas and application domains.Many anomaly detection techniques have been specifically developed for certain application domains,while others are more generic.This survey tries to provide a structured and comprehensive overview of the research on anomaly detection.We have grouped existing techniques into different categories based on the underlying approach adopted by each technique.For each category we have identified key assumptions,which are used by the techniques to differentiate between normal and anomalous behavior.When applying a given technique to a particular domain,these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain.For each category,we provide a basic anomaly detection technique,and then show how the different existing techniques in that category are variants of the basic tech-nique.This template provides an easier and succinct understanding of the techniques belonging to each category.Further,for each category,we identify the advantages and disadvantages of the techniques in that category.We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains.We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic,and how techniques developed in one area can be applied in domains for which they were not intended to begin with.Categories and Subject Descriptors:H.2.8[Database Management]:Database Applications—Data MiningGeneral Terms:AlgorithmsAdditional Key Words and Phrases:Anomaly Detection,Outlier Detection1.INTRODUCTIONAnomaly detection refers to the problem offinding patterns in data that do not conform to expected behavior.These non-conforming patterns are often referred to as anomalies,outliers,discordant observations,exceptions,aberrations,surprises, peculiarities or contaminants in different application domains.Of these,anomalies and outliers are two terms used most commonly in the context of anomaly detection; sometimes interchangeably.Anomaly detectionfinds extensive use in a wide variety of applications such as fraud detection for credit cards,insurance or health care, intrusion detection for cyber-security,fault detection in safety critical systems,and military surveillance for enemy activities.The importance of anomaly detection is due to the fact that anomalies in data translate to significant(and often critical)actionable information in a wide variety of application domains.For example,an anomalous traffic pattern in a computerTo Appear in ACM Computing Surveys,092009,Pages1–72.2·Chandola,Banerjee and Kumarnetwork could mean that a hacked computer is sending out sensitive data to an unauthorized destination[Kumar2005].An anomalous MRI image may indicate presence of malignant tumors[Spence et al.2001].Anomalies in credit card trans-action data could indicate credit card or identity theft[Aleskerov et al.1997]or anomalous readings from a space craft sensor could signify a fault in some compo-nent of the space craft[Fujimaki et al.2005].Detecting outliers or anomalies in data has been studied in the statistics commu-nity as early as the19th century[Edgeworth1887].Over time,a variety of anomaly detection techniques have been developed in several research communities.Many of these techniques have been specifically developed for certain application domains, while others are more generic.This survey tries to provide a structured and comprehensive overview of the research on anomaly detection.We hope that it facilitates a better understanding of the different directions in which research has been done on this topic,and how techniques developed in one area can be applied in domains for which they were not intended to begin with.1.1What are anomalies?Anomalies are patterns in data that do not conform to a well defined notion of normal behavior.Figure1illustrates anomalies in a simple2-dimensional data set. The data has two normal regions,N1and N2,since most observations lie in these two regions.Points that are sufficiently far away from the regions,e.g.,points o1 and o2,and points in region O3,are anomalies.Fig.1.A simple example of anomalies in a2-dimensional data set. Anomalies might be induced in the data for a variety of reasons,such as malicious activity,e.g.,credit card fraud,cyber-intrusion,terrorist activity or breakdown of a system,but all of the reasons have a common characteristic that they are interesting to the analyst.The“interestingness”or real life relevance of anomalies is a key feature of anomaly detection.Anomaly detection is related to,but distinct from noise removal[Teng et al. 1990]and noise accommodation[Rousseeuw and Leroy1987],both of which deal To Appear in ACM Computing Surveys,092009.Anomaly Detection:A Survey·3 with unwanted noise in the data.Noise can be defined as a phenomenon in data which is not of interest to the analyst,but acts as a hindrance to data analysis. Noise removal is driven by the need to remove the unwanted objects before any data analysis is performed on the data.Noise accommodation refers to immunizing a statistical model estimation against anomalous observations[Huber1974]. Another topic related to anomaly detection is novelty detection[Markou and Singh2003a;2003b;Saunders and Gero2000]which aims at detecting previously unobserved(emergent,novel)patterns in the data,e.g.,a new topic of discussion in a news group.The distinction between novel patterns and anomalies is that the novel patterns are typically incorporated into the normal model after being detected.It should be noted that solutions for above mentioned related problems are often used for anomaly detection and vice-versa,and hence are discussed in this review as well.1.2ChallengesAt an abstract level,an anomaly is defined as a pattern that does not conform to expected normal behavior.A straightforward anomaly detection approach,there-fore,is to define a region representing normal behavior and declare any observation in the data which does not belong to this normal region as an anomaly.But several factors make this apparently simple approach very challenging:—Defining a normal region which encompasses every possible normal behavior is very difficult.In addition,the boundary between normal and anomalous behavior is often not precise.Thus an anomalous observation which lies close to the boundary can actually be normal,and vice-versa.—When anomalies are the result of malicious actions,the malicious adversaries often adapt themselves to make the anomalous observations appear like normal, thereby making the task of defining normal behavior more difficult.—In many domains normal behavior keeps evolving and a current notion of normal behavior might not be sufficiently representative in the future.—The exact notion of an anomaly is different for different application domains.For example,in the medical domain a small deviation from normal(e.g.,fluctuations in body temperature)might be an anomaly,while similar deviation in the stock market domain(e.g.,fluctuations in the value of a stock)might be considered as normal.Thus applying a technique developed in one domain to another is not straightforward.—Availability of labeled data for training/validation of models used by anomaly detection techniques is usually a major issue.—Often the data contains noise which tends to be similar to the actual anomalies and hence is difficult to distinguish and remove.Due to the above challenges,the anomaly detection problem,in its most general form,is not easy to solve.In fact,most of the existing anomaly detection techniques solve a specific formulation of the problem.The formulation is induced by various factors such as nature of the data,availability of labeled data,type of anomalies to be detected,etc.Often,these factors are determined by the application domain inTo Appear in ACM Computing Surveys,092009.4·Chandola,Banerjee and Kumarwhich the anomalies need to be detected.Researchers have adopted concepts from diverse disciplines such as statistics ,machine learning ,data mining ,information theory ,spectral theory ,and have applied them to specific problem formulations.Figure 2shows the above mentioned key components associated with any anomaly detection technique.Anomaly DetectionTechniqueApplication DomainsMedical InformaticsIntrusion Detection...Fault/Damage DetectionFraud DetectionResearch AreasInformation TheoryMachine LearningSpectral TheoryStatisticsData Mining...Problem CharacteristicsLabels Anomaly Type Nature of Data OutputFig.2.Key components associated with an anomaly detection technique.1.3Related WorkAnomaly detection has been the topic of a number of surveys and review articles,as well as books.Hodge and Austin [2004]provide an extensive survey of anomaly detection techniques developed in machine learning and statistical domains.A broad review of anomaly detection techniques for numeric as well as symbolic data is presented by Agyemang et al.[2006].An extensive review of novelty detection techniques using neural networks and statistical approaches has been presented in Markou and Singh [2003a]and Markou and Singh [2003b],respectively.Patcha and Park [2007]and Snyder [2001]present a survey of anomaly detection techniques To Appear in ACM Computing Surveys,092009.Anomaly Detection:A Survey·5 used specifically for cyber-intrusion detection.A substantial amount of research on outlier detection has been done in statistics and has been reviewed in several books [Rousseeuw and Leroy1987;Barnett and Lewis1994;Hawkins1980]as well as other survey articles[Beckman and Cook1983;Bakar et al.2006].Table I shows the set of techniques and application domains covered by our survey and the various related survey articles mentioned above.12345678TechniquesClassification Based√√√√√Clustering Based√√√√Nearest Neighbor Based√√√√√Statistical√√√√√√√Information Theoretic√Spectral√ApplicationsCyber-Intrusion Detection√√Fraud Detection√Medical Anomaly Detection√Industrial Damage Detection√Image Processing√Textual Anomaly Detection√Sensor Networks√Table parison of our survey to other related survey articles.1-Our survey2-Hodge and Austin[2004],3-Agyemang et al.[2006],4-Markou and Singh[2003a],5-Markou and Singh [2003b],6-Patcha and Park[2007],7-Beckman and Cook[1983],8-Bakar et al[2006]1.4Our ContributionsThis survey is an attempt to provide a structured and a broad overview of extensive research on anomaly detection techniques spanning multiple research areas and application domains.Most of the existing surveys on anomaly detection either focus on a particular application domain or on a single research area.[Agyemang et al.2006]and[Hodge and Austin2004]are two related works that group anomaly detection into multiple categories and discuss techniques under each category.This survey builds upon these two works by significantly expanding the discussion in several directions. We add two more categories of anomaly detection techniques,viz.,information theoretic and spectral techniques,to the four categories discussed in[Agyemang et al.2006]and[Hodge and Austin2004].For each of the six categories,we not only discuss the techniques,but also identify unique assumptions regarding the nature of anomalies made by the techniques in that category.These assumptions are critical for determining when the techniques in that category would be able to detect anomalies,and when they would fail.For each category,we provide a basic anomaly detection technique,and then show how the different existing techniques in that category are variants of the basic technique.This template provides an easier and succinct understanding of the techniques belonging to each category.Further, for each category we identify the advantages and disadvantages of the techniques in that category.We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains.To Appear in ACM Computing Surveys,092009.6·Chandola,Banerjee and KumarWhile some of the existing surveys mention the different applications of anomaly detection,we provide a detailed discussion of the application domains where anomaly detection techniques have been used.For each domain we discuss the notion of an anomaly,the different aspects of the anomaly detection problem,and the challenges faced by the anomaly detection techniques.We also provide a list of techniques that have been applied in each application domain.The existing surveys discuss anomaly detection techniques that detect the sim-plest form of anomalies.We distinguish the simple anomalies from complex anoma-lies.The discussion of applications of anomaly detection reveals that for most ap-plication domains,the interesting anomalies are complex in nature,while most of the algorithmic research has focussed on simple anomalies.1.5OrganizationThis survey is organized into three parts and its structure closely follows Figure 2.In Section2we identify the various aspects that determine the formulation of the problem and highlight the richness and complexity associated with anomaly detection.We distinguish simple anomalies from complex anomalies and define two types of complex anomalies,viz.,contextual and collective anomalies.In Section 3we briefly describe the different application domains where anomaly detection has been applied.In subsequent sections we provide a categorization of anomaly detection techniques based on the research area which they belong to.Majority of the techniques can be categorized into classification based(Section4),nearest neighbor based(Section5),clustering based(Section6),and statistical techniques (Section7).Some techniques belong to research areas such as information theory (Section8),and spectral theory(Section9).For each category of techniques we also discuss their computational complexity for training and testing phases.In Section 10we discuss various contextual anomaly detection techniques.We discuss various collective anomaly detection techniques in Section11.We present some discussion on the limitations and relative performance of various existing techniques in Section 12.Section13contains concluding remarks.2.DIFFERENT ASPECTS OF AN ANOMALY DETECTION PROBLEMThis section identifies and discusses the different aspects of anomaly detection.As mentioned earlier,a specific formulation of the problem is determined by several different factors such as the nature of the input data,the availability(or unavailabil-ity)of labels as well as the constraints and requirements induced by the application domain.This section brings forth the richness in the problem domain and justifies the need for the broad spectrum of anomaly detection techniques.2.1Nature of Input DataA key aspect of any anomaly detection technique is the nature of the input data. Input is generally a collection of data instances(also referred as object,record,point, vector,pattern,event,case,sample,observation,entity)[Tan et al.2005,Chapter 2].Each data instance can be described using a set of attributes(also referred to as variable,characteristic,feature,field,dimension).The attributes can be of different types such as binary,categorical or continuous.Each data instance might consist of only one attribute(univariate)or multiple attributes(multivariate).In To Appear in ACM Computing Surveys,092009.Anomaly Detection:A Survey·7 the case of multivariate data instances,all attributes might be of same type or might be a mixture of different data types.The nature of attributes determine the applicability of anomaly detection tech-niques.For example,for statistical techniques different statistical models have to be used for continuous and categorical data.Similarly,for nearest neighbor based techniques,the nature of attributes would determine the distance measure to be used.Often,instead of the actual data,the pairwise distance between instances might be provided in the form of a distance(or similarity)matrix.In such cases, techniques that require original data instances are not applicable,e.g.,many sta-tistical and classification based techniques.Input data can also be categorized based on the relationship present among data instances[Tan et al.2005].Most of the existing anomaly detection techniques deal with record data(or point data),in which no relationship is assumed among the data instances.In general,data instances can be related to each other.Some examples are sequence data,spatial data,and graph data.In sequence data,the data instances are linearly ordered,e.g.,time-series data,genome sequences,protein sequences.In spatial data,each data instance is related to its neighboring instances,e.g.,vehicular traffic data,ecological data.When the spatial data has a temporal(sequential) component it is referred to as spatio-temporal data,e.g.,climate data.In graph data,data instances are represented as vertices in a graph and are connected to other vertices with ter in this section we will discuss situations where such relationship among data instances become relevant for anomaly detection. 2.2Type of AnomalyAn important aspect of an anomaly detection technique is the nature of the desired anomaly.Anomalies can be classified into following three categories:2.2.1Point Anomalies.If an individual data instance can be considered as anomalous with respect to the rest of data,then the instance is termed as a point anomaly.This is the simplest type of anomaly and is the focus of majority of research on anomaly detection.For example,in Figure1,points o1and o2as well as points in region O3lie outside the boundary of the normal regions,and hence are point anomalies since they are different from normal data points.As a real life example,consider credit card fraud detection.Let the data set correspond to an individual’s credit card transactions.For the sake of simplicity, let us assume that the data is defined using only one feature:amount spent.A transaction for which the amount spent is very high compared to the normal range of expenditure for that person will be a point anomaly.2.2.2Contextual Anomalies.If a data instance is anomalous in a specific con-text(but not otherwise),then it is termed as a contextual anomaly(also referred to as conditional anomaly[Song et al.2007]).The notion of a context is induced by the structure in the data set and has to be specified as a part of the problem formulation.Each data instance is defined using following two sets of attributes:To Appear in ACM Computing Surveys,092009.8·Chandola,Banerjee and Kumar(1)Contextual attributes.The contextual attributes are used to determine thecontext(or neighborhood)for that instance.For example,in spatial data sets, the longitude and latitude of a location are the contextual attributes.In time-series data,time is a contextual attribute which determines the position of an instance on the entire sequence.(2)Behavioral attributes.The behavioral attributes define the non-contextual char-acteristics of an instance.For example,in a spatial data set describing the average rainfall of the entire world,the amount of rainfall at any location is a behavioral attribute.The anomalous behavior is determined using the values for the behavioral attributes within a specific context.A data instance might be a contextual anomaly in a given context,but an identical data instance(in terms of behavioral attributes)could be considered normal in a different context.This property is key in identifying contextual and behavioral attributes for a contextual anomaly detection technique.TimeFig.3.Contextual anomaly t2in a temperature time series.Note that the temperature at time t1is same as that at time t2but occurs in a different context and hence is not considered as an anomaly.Contextual anomalies have been most commonly explored in time-series data [Weigend et al.1995;Salvador and Chan2003]and spatial data[Kou et al.2006; Shekhar et al.2001].Figure3shows one such example for a temperature time series which shows the monthly temperature of an area over last few years.A temperature of35F might be normal during the winter(at time t1)at that place,but the same value during summer(at time t2)would be an anomaly.A similar example can be found in the credit card fraud detection domain.A contextual attribute in credit card domain can be the time of purchase.Suppose an individual usually has a weekly shopping bill of$100except during the Christmas week,when it reaches$1000.A new purchase of$1000in a week in July will be considered a contextual anomaly,since it does not conform to the normal behavior of the individual in the context of time(even though the same amount spent during Christmas week will be considered normal).The choice of applying a contextual anomaly detection technique is determined by the meaningfulness of the contextual anomalies in the target application domain. To Appear in ACM Computing Surveys,092009.Anomaly Detection:A Survey·9 Another key factor is the availability of contextual attributes.In several cases defining a context is straightforward,and hence applying a contextual anomaly detection technique makes sense.In other cases,defining a context is not easy, making it difficult to apply such techniques.2.2.3Collective Anomalies.If a collection of related data instances is anomalous with respect to the entire data set,it is termed as a collective anomaly.The indi-vidual data instances in a collective anomaly may not be anomalies by themselves, but their occurrence together as a collection is anomalous.Figure4illustrates an example which shows a human electrocardiogram output[Goldberger et al.2000]. The highlighted region denotes an anomaly because the same low value exists for an abnormally long time(corresponding to an Atrial Premature Contraction).Note that that low value by itself is not an anomaly.Fig.4.Collective anomaly corresponding to an Atrial Premature Contraction in an human elec-trocardiogram output.As an another illustrative example,consider a sequence of actions occurring in a computer as shown below:...http-web,buffer-overflow,http-web,http-web,smtp-mail,ftp,http-web,ssh,smtp-mail,http-web,ssh,buffer-overflow,ftp,http-web,ftp,smtp-mail,http-web...The highlighted sequence of events(buffer-overflow,ssh,ftp)correspond to a typical web based attack by a remote machine followed by copying of data from the host computer to remote destination via ftp.It should be noted that this collection of events is an anomaly but the individual events are not anomalies when they occur in other locations in the sequence.Collective anomalies have been explored for sequence data[Forrest et al.1999; Sun et al.2006],graph data[Noble and Cook2003],and spatial data[Shekhar et al. 2001].To Appear in ACM Computing Surveys,092009.10·Chandola,Banerjee and KumarIt should be noted that while point anomalies can occur in any data set,collective anomalies can occur only in data sets in which data instances are related.In contrast,occurrence of contextual anomalies depends on the availability of context attributes in the data.A point anomaly or a collective anomaly can also be a contextual anomaly if analyzed with respect to a context.Thus a point anomaly detection problem or collective anomaly detection problem can be transformed toa contextual anomaly detection problem by incorporating the context information.2.3Data LabelsThe labels associated with a data instance denote if that instance is normal or anomalous1.It should be noted that obtaining labeled data which is accurate as well as representative of all types of behaviors,is often prohibitively expensive. Labeling is often done manually by a human expert and hence requires substantial effort to obtain the labeled training data set.Typically,getting a labeled set of anomalous data instances which cover all possible type of anomalous behavior is more difficult than getting labels for normal behavior.Moreover,the anomalous behavior is often dynamic in nature,e.g.,new types of anomalies might arise,for which there is no labeled training data.In certain cases,such as air traffic safety, anomalous instances would translate to catastrophic events,and hence will be very rare.Based on the extent to which the labels are available,anomaly detection tech-niques can operate in one of the following three modes:2.3.1Supervised anomaly detection.Techniques trained in supervised mode as-sume the availability of a training data set which has labeled instances for normal as well as anomaly class.Typical approach in such cases is to build a predictive model for normal vs.anomaly classes.Any unseen data instance is compared against the model to determine which class it belongs to.There are two major is-sues that arise in supervised anomaly detection.First,the anomalous instances are far fewer compared to the normal instances in the training data.Issues that arise due to imbalanced class distributions have been addressed in the data mining and machine learning literature[Joshi et al.2001;2002;Chawla et al.2004;Phua et al. 2004;Weiss and Hirsh1998;Vilalta and Ma2002].Second,obtaining accurate and representative labels,especially for the anomaly class is usually challenging.A number of techniques have been proposed that inject artificial anomalies in a normal data set to obtain a labeled training data set[Theiler and Cai2003;Abe et al.2006;Steinwart et al.2005].Other than these two issues,the supervised anomaly detection problem is similar to building predictive models.Hence we will not address this category of techniques in this survey.2.3.2Semi-Supervised anomaly detection.Techniques that operate in a semi-supervised mode,assume that the training data has labeled instances for only the normal class.Since they do not require labels for the anomaly class,they are more widely applicable than supervised techniques.For example,in space craft fault detection[Fujimaki et al.2005],an anomaly scenario would signify an accident, which is not easy to model.The typical approach used in such techniques is to 1Also referred to as normal and anomalous classes.To Appear in ACM Computing Surveys,092009.Anomaly Detection:A Survey·11 build a model for the class corresponding to normal behavior,and use the model to identify anomalies in the test data.A limited set of anomaly detection techniques exist that assume availability of only the anomaly instances for training[Dasgupta and Nino2000;Dasgupta and Majumdar2002;Forrest et al.1996].Such techniques are not commonly used, primarily because it is difficult to obtain a training data set which covers every possible anomalous behavior that can occur in the data.2.3.3Unsupervised anomaly detection.Techniques that operate in unsupervised mode do not require training data,and thus are most widely applicable.The techniques in this category make the implicit assumption that normal instances are far more frequent than anomalies in the test data.If this assumption is not true then such techniques suffer from high false alarm rate.Many semi-supervised techniques can be adapted to operate in an unsupervised mode by using a sample of the unlabeled data set as training data.Such adaptation assumes that the test data contains very few anomalies and the model learnt during training is robust to these few anomalies.2.4Output of Anomaly DetectionAn important aspect for any anomaly detection technique is the manner in which the anomalies are reported.Typically,the outputs produced by anomaly detection techniques are one of the following two types:2.4.1Scores.Scoring techniques assign an anomaly score to each instance in the test data depending on the degree to which that instance is considered an anomaly. Thus the output of such techniques is a ranked list of anomalies.An analyst may choose to either analyze top few anomalies or use a cut-offthreshold to select the anomalies.2.4.2Labels.Techniques in this category assign a label(normal or anomalous) to each test instance.Scoring based anomaly detection techniques allow the analyst to use a domain-specific threshold to select the most relevant anomalies.Techniques that provide binary labels to the test instances do not directly allow the analysts to make such a choice,though this can be controlled indirectly through parameter choices within each technique.3.APPLICATIONS OF ANOMALY DETECTIONIn this section we discuss several applications of anomaly detection.For each ap-plication domain we discuss the following four aspects:—The notion of anomaly.—Nature of the data.—Challenges associated with detecting anomalies.—Existing anomaly detection techniques.To Appear in ACM Computing Surveys,092009.。