These are my recent Pin​board​.in links:

• [1110.1462] Dynamic Clus­ter­ing of His­togram Data Based on Adap­tive Squared Wasser­stein Distances

  “…To clus­ter sets of his­togram data, we pro­pose to use Dynamic Clus­ter­ing Algo­rithm, (based on adap­tive squared Wasser­stein dis­tances) that is a k-​​means-​​like algo­rithm for clus­ter­ing a set of indi­vid­u­als into K classes that are apri­ori fixed. The main aim of this research is to pro­vide a tool for clus­ter­ing his­tograms, empha­siz­ing the dif­fer­ent con­tri­bu­tions of the his­togram vari­ables, and their com­po­nents, to the def­i­n­i­tion of the clus­ters. We demon­strate that this can be achieved using adap­tive dis­tances. Two kind of adap­tive dis­tances are con­sid­ered: the first takes into account the vari­abil­ity of each com­po­nent of each descrip­tor for the whole set of indi­vid­u­als; the sec­ond takes into account the vari­abil­ity of each com­po­nent of each descrip­tor in each clus­ter. We fur­nish inter­pre­ta­tive tools of the obtained par­ti­tion based on an exten­sion of the clas­si­cal mea­sures (indexes) to the use of adap­tive dis­tances in the clus­ter­ing cri­te­rion func­tion. Appli­ca­tions on syn­thetic and real-​​world data cor­rob­o­rate the pro­posed procedure.”
  
  clas­si­fi­ca­tion sta­tis­tics his­tograms met­rics clus­ter­ing

• [1110.1412] Quan­ti­fy­ing loopy net­work architectures

  “Biol­ogy presents many exam­ples of pla­nar dis­tri­b­u­tion and struc­tural net­works hav­ing dense sets of closed loops. An arche­type of this form of net­work orga­ni­za­tion is the vas­cu­la­ture of dicotyle­do­nous leaves, which show­cases a hierarchically-​​nested archi­tec­ture con­tain­ing closed loops at many dif­fer­ent lev­els. Although a num­ber of meth­ods have been pro­posed to mea­sure aspects of the struc­ture of such net­works, a robust met­ric to quan­tify their hier­ar­chi­cal orga­ni­za­tion is still lack­ing. We present an algo­rith­mic frame­work, the hier­ar­chi­cal loop decom­po­si­tion, that allows map­ping loopy net­works to binary trees, pre­serv­ing in the con­nec­tiv­ity of the trees the archi­tec­ture of the orig­i­nal graph. We apply this frame­work to inves­ti­gate com­puter gen­er­ated graphs, such as arti­fi­cial mod­els and opti­mal dis­tri­b­u­tion net­works, as well as nat­ural graphs extracted from dig­i­tized images of dicotyle­do­nous leaves and vas­cu­la­ture of rat cere­bral neo­cor­tex. We cal­cu­late var­i­ous met­rics based on the Asym­me­try, the cumu­la­tive size dis­tri­b­u­tion and the Strahler bifur­ca­tion ratios of the cor­re­spond­ing trees and dis­cuss the rela­tion­ship of these quan­ti­ties to the archi­tec­tural orga­ni­za­tion of the orig­i­nal graphs. This algo­rith­mic frame­work decou­ples the geo­met­ric infor­ma­tion (exact loca­tion of edges and nodes) from the met­ric topol­ogy (con­nec­tiv­ity and edge weight) and it ulti­mately allows us to per­form a quan­ti­ta­tive sta­tis­ti­cal com­par­i­son between pre­dic­tions of the­o­ret­i­cal mod­els and nat­u­rally occur­ring loopy graphs.”
  
  com­plex­ol­ogy bio­physics network-​​theory met­rics

• [1110.1393] High-​​Precision Tun­ing of State for Mem­ris­tive Devices by Adapt­able Variation-​​Tolerant Algorithm

  “Using mem­ris­tive prop­er­ties com­mon for the tita­nium diox­ide thin film devices, we designed a sim­ple write algo­rithm to tune device con­duc­tance at a spe­cific bias point to 1% rel­a­tive accu­racy (which is roughly equiv­a­lent to 7-​​bit pre­ci­sion) within its dynamic range even in the pres­ence of large vari­a­tions in switch­ing behav­ior. The high pre­ci­sion state is non­volatile and the results are likely to be sus­tained for nanoscale mem­ris­tive devices because of the inher­ent fil­a­men­tary nature of the resis­tive switch­ing. The pro­posed func­tion­al­ity of mem­ris­tive devices is espe­cially attrac­tive for ana­log com­put­ing with low pre­ci­sion data. As one rep­re­sen­ta­tive exam­ple we demon­strate hybrid cir­cuitry con­sist­ing of CMOS sum­ming ampli­fier and two mem­ris­tive devices to per­form ana­log mul­ti­ply and accu­mu­late com­pu­ta­tion, which is a typ­i­cal bot­tle­neck oper­a­tion in infor­ma­tion processing.”
  
  mem­ris­tors engineering-​​design sim­u­la­tion control-​​systems nudge-​​targets

• [1110.1521] Nodal domains of a non-​​separable prob­lem — the right angled isosce­les triangle

  “Our result may be gen­er­al­ized to other domains where sim­i­lar algo­rithms may apply. Our algo­rithm is based on the fact that the eigen­func­tions are pre­sented as a lin­ear com­bi­na­tion of sim­ple plane waves. It is there­fore tempt­ing to try and gen­er­al­ize it for other drums with sim­i­lar prop­erty. The equi­lat­eral tri­an­gle is an imme­di­ate can­di­date (see [29] and ref­er­ences within). A fur­ther, and quite sur­pris­ing, result is the recur­sive for­mula for the num­ber of nodal loops. To our knowl­edge this is the first known exact for­mula for the nodal count of a non-​​separable pla­nar man­i­fold (for cer­tain eigen­func­tions of tori exact for­mu­las have been given in [22]). The for­mula was found by direct inspec­tion of large tables and has been ver­i­fied for a large bulk of data com­pu­ta­tion­ally. An obvi­ous chal­lenge is to prove this for­mula. In par­tic­u­lar, the recur­sive part of the for­mula resem­bles the famous Euclid algo­rithm for the great­est com­mon divi­sor. A fur­ther inves­ti­ga­tion of the men­tioned for­mula might there­fore expose some new num­ber the­o­ret­i­cal prop­er­ties of the nodal count.”
  
  physics algo­rithms analytical-​​results open-​​questions geom­e­try acoustics exact-​​form nudge-​​targets

• [1110.1485] A Face Recog­ni­tion Scheme using Wavelet Based Dom­i­nant Features

  “In this paper, a multi-​​resolution fea­ture extrac­tion algo­rithm for face recog­ni­tion is pro­posed based on two-​​dimensional dis­crete wavelet trans­form (2D-​​DWT), which effi­ciently exploits the local spa­tial vari­a­tions in a face image. For the pur­pose of fea­ture extrac­tion, instead of con­sid­er­ing the entire face image, an entropy-​​based local band selec­tion cri­te­rion is devel­oped, which selects high-​​informative hor­i­zon­tal seg­ments from the face image. In order to cap­ture the local spa­tial vari­a­tions within these high­in­for­ma­tive hor­i­zon­tal bands pre­cisely, the hor­i­zon­tal band is seg­mented into sev­eral small spa­tial mod­ules. Dom­i­nant wavelet coef­fi­cients cor­re­spond­ing to each local region resid­ing inside those hor­i­zon­tal bands are selected as fea­tures. In the selec­tion of the dom­i­nant coef­fi­cients, a thresh­old cri­te­rion is pro­posed, which not only dras­ti­cally reduces the fea­ture dimen­sion but also pro­vides high within-​​class com­pact­ness and high between-​​class sep­a­ra­bil­ity. A prin­ci­pal com­po­nent analy­sis is per­formed to fur­ther reduce the dimen­sion­al­ity of the fea­ture space. Exten­sive exper­i­men­ta­tion is car­ried out upon stan­dard face data­bases and a very high degree of recog­ni­tion accu­racy is achieved by the pro­posed method in com­par­i­son to those obtained by some of the exist­ing methods.”
  
  face-​​recognition algo­rithms image-​​processing wavelets nudge-​​targets

• [1110.1553] Hier­ar­chi­cal QR fac­tor­iza­tion algo­rithms for multi-​​core clus­ter systems

  “This paper describes a new QR fac­tor­iza­tion algo­rithm which is espe­cially designed for mas­sively par­al­lel plat­forms com­bin­ing par­al­lel dis­trib­uted multi-​​core nodes. These plat­forms make the present and the fore­see­able future of high-​​performance com­put­ing. Our new QR fac­tor­iza­tion algo­rithm falls in the cat­e­gory of the tile algo­rithms which nat­u­rally enables good data local­ity for the sequen­tial ker­nels exe­cuted by the cores (high sequen­tial per­for­mance), low num­ber of mes­sages in a par­al­lel dis­trib­uted set­ting (small latency term), and fine gran­u­lar­ity (high parallelism).”
  
  parallel-​​computing operations-​​research fac­tor­iza­tion algo­rithms nudge-​​targets meta-​​algorithms

• [1110.1560] On the Col­or­ing of Grid Wire­less Sen­sor Net­works: the Vector-​​Based Col­or­ing Method

  “Graph col­or­ing is used in wire­less net­works to opti­mize net­work resources: band­width and energy. Nodes access the medium accord­ing to their color. It is the respon­si­bil­ity of the col­or­ing algo­rithm to ensure that inter­fer­ing nodes do not have the same color. In this research report, we focus on wire­less sen­sor net­works with grid topolo­gies. How does a col­or­ing algo­rithm take advan­tage of the reg­u­lar­ity of grid topol­ogy to pro­vide an opti­mal peri­odic col­or­ing, that is a col­or­ing with the min­i­mum num­ber of col­ors? We pro­pose the Vector-​​Based Col­or­ing Method, denoted VCM, a new method that is able to pro­vide an opti­mal peri­odic col­or­ing for any radio trans­mis­sion range and for any h-​​hop col­or­ing, h>=1. This method con­sists in deter­min­ing at which grid nodes a color can be repro­duced with­out cre­at­ing inter­fer­ences between these nodes while min­i­miz­ing the num­ber of col­ors used. We com­pare the num­ber of col­ors pro­vided by VCM with the num­ber of col­ors obtained by a dis­trib­uted col­or­ing algo­rithm with line and col­umn pri­or­ity assign­ments. We also pro­vide bounds on the num­ber of col­ors of opti­mal gen­eral col­or­ings of the infi­nite grid, and show that peri­odic col­or­ings (and thus VCM) are asymp­tot­i­cally opti­mal. Finally, we dis­cuss the applic­a­bil­ity of this method to a real wire­less network.”
  
  graph-​​theory algo­rithms operations-​​research nudge-​​targets

• [1110.1580] A Polylogarithmic-​​Competitive Algo­rithm for the k-​​Server Problem

  “We give the first polylogarithmic-​​competitive ran­dom­ized online algo­rithm for the $k$-server prob­lem on an arbi­trary finite met­ric space. In par­tic­u­lar, our algo­rithm achieves a com­pet­i­tive ratio of O(log^3 n log^2 k log log n) for any met­ric space on n points. Our algo­rithm improves upon the deter­min­is­tic (2k-1)-competitive algo­rithm of Kout­sou­pias and Papadim­itriou [J.ACM’95] when­ever n is sub-​​exponential in k.”
  
  sched­ul­ing operations-​​research algo­rithms nudge-​​targets

• [1110.1590] PSA: The Packet Sched­ul­ing Algo­rithm for Wire­less Sen­sor Networks

  “The main cause of wasted energy con­sump­tion in wire­less sen­sor net­works is packet col­li­sion. The packet sched­ul­ing algo­rithm is there­fore intro­duced to solve this prob­lem. Some packet sched­ul­ing algo­rithms can also influ­ence and delay the data trans­mit­ting in the real-​​time wire­less sen­sor net­works. This paper presents the packet sched­ul­ing algo­rithm (PSA) in order to reduce the packet con­ges­tion in MAC layer lead­ing to reduce the over­all of packet col­li­sion in the sys­tem The PSA is com­pared with the sim­ple CSMA/​CA and other approaches using net­work topol­ogy bench­marks in math­e­mat­i­cal method. The per­for­mances of our PSA are bet­ter than the stan­dard (CSMA/​CA). The PSA pro­duces bet­ter through­put than other algo­rithms. On other hand, the aver­age delay of PSA is higher than pre­vi­ous works. How­ever, the PSA uti­lizes the chan­nel bet­ter than all algorithms.”
  
  sensor-​​networks distributed-​​processing sched­ul­ing rout­ing operations-​​research algo­rithms nudge-​​targets

• [1110.0725] A Sur­vey of Dis­trib­uted Data Aggre­ga­tion Algorithms

  “Dis­trib­uted data aggre­ga­tion has been an active field of research in the last decade, and a huge diverse amount of tech­niques can be found in the lit­er­a­ture. For this rea­sons, this sur­vey intends to be an impor­tant time sav­ing instru­ment, for those that desire to get a quick and com­pre­hen­sive overview of the state of the art on dis­trib­uted data aggre­ga­tion. More­over, by care­fully high­light­ing the strength and lim­i­ta­tions of the more per­ti­nent approaches, this study can pro­vide a use­ful assis­tance to help read­ers choose which tech­nique to apply in spe­cific set­tings. Cur­rently, there is no ideal gen­eral solu­tion to the dis­trib­uted com­pu­ta­tion of an aggre­ga­tion func­tion, all exist­ing tech­niques have its pit­falls (some more than oth­ers). There­fore, more research in this field will be expected in the next few years. In par­tic­u­lar, due to the added value of com­put­ing com­plex aggre­gates, new algo­rithms might arise to esti­mate the sta­tis­ti­cal dis­tri­b­u­tion of val­ues, as the few exist­ing approaches exhibit some lim­i­ta­tions in terms of accu­racy and resource con­sump­tion. Addi­tional research efforts should be made to improve the sup­port to churn, mes­sage loss, and con­tin­u­ous esti­ma­tion of muta­ble input values.”
  
  sta­tis­tics reviews distributed-​​processing com­mu­ni­ca­tion coor­di­na­tion nudge-​​targets

• [0911.3482] Com­plex­ity of Net­works (reprise)

  “Net­work or graph struc­tures are ubiq­ui­tous in the study of com­plex sys­tems. Often, we are inter­ested in com­plex­ity trends of these sys­tem as it evolves under some dynamic. An exam­ple might be look­ing at the com­plex­ity of a food web as species enter an ecosys­tem via migra­tion or spe­ci­a­tion, and leave via extinc­tion. In a pre­vi­ous paper, a com­plex­ity mea­sure of net­works was pro­posed based on the {em com­plex­ity is infor­ma­tion con­tent} par­a­digm. To apply this par­a­digm to any object, one must fix two things: a rep­re­sen­ta­tion lan­guage, in which strings of sym­bols from some alpha­bet describe, or stand for the objects being con­sid­ered; and a means of deter­min­ing when two such descrip­tions refer to the same object. With these two things set, the infor­ma­tion con­tent of an object can be com­puted in prin­ci­ple from the num­ber of equiv­a­lent descrip­tions describ­ing a par­tic­u­lar object. The pre­vi­ously pro­posed rep­re­sen­ta­tion lan­guage had the defi­ciency that the fully con­nected and empty net­works were the most com­plex for a given num­ber of nodes. A vari­a­tion of this mea­sure, called zcom­plex­ity, applied a com­pres­sion algo­rithm to the result­ing bit­string rep­re­sen­ta­tion, to solve this prob­lem. Unfor­tu­nately, zcom­plex­ity proved too com­pu­ta­tion­ally expen­sive to be prac­ti­cal. In this paper, I pro­pose a new rep­re­sen­ta­tion lan­guage that encodes the num­ber of links along with the num­ber of nodes and a rep­re­sen­ta­tion of the lin­klist. This, like zcom­plex­ity, exhibits min­i­mal com­plex­ity for fully con­nected and empty net­works, but is as tractable as the orig­i­nal measure.”
  
  network-​​theory com­plex­ol­ogy complex-​​systems mea­sure­ment per­form structure-​​function-​​relations discrete-​​mathematics

• [1108.4279] Detec­tion and emergence

  “Two dif­fer­ent con­cep­tions of emer­gence are rec­on­ciled as two instances of the phe­nom­e­non of detec­tion. In the process of com­par­ing these two con­cep­tions, we find that the notions of com­plex­ity and detec­tion allow us to form a uni­fied def­i­n­i­tion of emer­gence that clearly delin­eates the role of the observer.”
  
  com­plex­ol­ogy emer­gence pragmatism-it-ain’t but-​​soon

• [1001.4278] Weight Opti­miza­tion for Dis­trib­uted Aver­age Con­sen­sus Algo­rithm in Sym­met­ric, CCS & KCS Star Networks

  “This paper addresses weight opti­miza­tion prob­lem in dis­trib­uted con­sen­sus aver­ag­ing algo­rithm over net­works with sym­met­ric star topol­ogy. We have deter­mined opti­mal weights and con­ver­gence rate of the net­work in terms of its topo­log­i­cal para­me­ters. In addi­tion, two alter­na­tive topolo­gies with more rapid con­ver­gence rates have been intro­duced. The new topolo­gies are Complete-​​Cored Sym­met­ric (CCS) star and K-​​Cored Sym­met­ric (KCS) star topolo­gies. It has been shown that the opti­mal weights for the edges of cen­tral part in sym­met­ric and CCS star con­fig­u­ra­tions are inde­pen­dent of their branches. By sim­u­la­tion opti­mal­ity of obtained weights under quan­ti­za­tion con­straints have been verified.”
  
  operations-​​research decision-​​making network-​​theory nudge-​​targets

• [1109.5389] Water dri­ves pep­tide con­for­ma­tional transitions

  “Tran­si­tions between metastable con­for­ma­tions of a dipep­tide are inves­ti­gated using clas­si­cal mol­e­c­u­lar dynam­ics sim­u­la­tion with explicit water mol­e­cules. The dis­tri­b­u­tion of the sur­round­ing water at dif­fer­ent moments before the tran­si­tions and the dynam­i­cal cor­re­la­tions of water with the peptide’s con­fig­u­ra­tional motions indi­cate that water is the main dri­ving force of the con­for­ma­tional changes.”
  
  molecular-​​design systems-​​biology sim­u­la­tion intracellular-​​dynamics kinda-​​knew-​​this-​​a-​​long-​​time-​​ago bio­chem­istry

• [1105.1445] Vehic­u­lar traf­fic flow at an inter­sec­tion with the pos­si­bil­ity of turning

  “We have devel­oped a Nagel-​​Schreckenberg cel­lu­lar automata model for describ­ing of vehic­u­lar traf­fic flow at a sin­gle inter­sec­tion. A set of traf­fic lights oper­at­ing in fixed-​​time scheme con­trols the traf­fic flow. Open bound­ary con­di­tion is applied to the streets each of which con­duct a uni-​​directional flow. Streets are single-​​lane and cars can turn upon reach­ing to the inter­sec­tion with pre­scribed prob­a­bil­i­ties. Exten­sive Monte Carlo sim­u­la­tions are car­ried out to find the model flow char­ac­ter­is­tics. In par­tic­u­lar, we inves­ti­gate the flows depen­dence on the sig­nal­i­sa­tion para­me­ters, turn­ing prob­a­bil­i­ties and input rates. It is shown that for each set of para­me­ters, there exist a plateau region inside which the total out­flow from the inter­sec­tion remains almost con­stant. We also com­pute total wait­ing time of vehi­cles per cycle behind red lights for var­i­ous con­trol parameters.”
  
  cellular-​​automata com­plex­ol­ogy traffic-​​models agent-​​based sim­u­la­tion nudge-​​substrates

• [1110.1391] A Com­par­i­son of Dif­fer­ent Machine Translit­er­a­tion Models

  “Machine translit­er­a­tion is a method for auto­mat­i­cally con­vert­ing words in one lan­guage into pho­net­i­cally equiv­a­lent ones in another lan­guage. Machine translit­er­a­tion plays an impor­tant role in nat­ural lan­guage appli­ca­tions such as infor­ma­tion retrieval and machine trans­la­tion, espe­cially for han­dling proper nouns and tech­ni­cal terms. Four machine translit­er­a­tion mod­els — grapheme-​​based translit­er­a­tion model, phoneme-​​based translit­er­a­tion model, hybrid translit­er­a­tion model, and correspondence-​​based translit­er­a­tion model — have been pro­posed by sev­eral researchers. To date, how­ever, there has been lit­tle research on a frame­work in which mul­ti­ple translit­er­a­tion mod­els can oper­ate simul­ta­ne­ously. Fur­ther­more, there has been no com­par­i­son of the four mod­els within the same frame­work and using the same data. We addressed these prob­lems by 1) mod­el­ing the four mod­els within the same frame­work, 2) com­par­ing them under the same con­di­tions, and 3) devel­op­ing a way to improve machine translit­er­a­tion through this com­par­i­son. Our com­par­i­son showed that the hybrid and correspondence-​​based mod­els were the most effec­tive and that the four mod­els can be used in a com­ple­men­tary man­ner to improve machine translit­er­a­tion performance.”
  
  natural-​​language-​​processing machine-​​learning review nudge-​​targets

• [1108.5508] A Pat­tern Measure

  “In this paper we pro­pose numer­i­cal mea­sures for eval­u­at­ing the aes­thetic inter­est of sim­ple pat­terns. The pat­terns con­sist of ele­ments (sym­bols, pix­els, etc.) in reg­u­lar square arrays. The mea­sures depend on two char­ac­ter­is­tics of the pat­terns: the num­ber of dif­fer­ent types of ele­ment, and the num­ber of sym­me­tries in their arrange­ment. We define two com­ple­men­tary com­pos­ite mea­sures L and C for the degree of pat­tern in a design, and com­pute them here for 2×2 and 6×6 arrays. The results dis­tin­guish sim­ple from high-​​variation cases. We sus­pect that the mea­sure L cor­re­sponds to the degree that human beings intu­itively feel a design to be “inter­est­ing”, so this model would aid in quan­ti­fy­ing the visual con­nec­tion of two– dimen­sional designs with view­ers. The other com­pos­ite mea­sure C based on these numer­i­cal prop­er­ties char­ac­ter­izes the extent of ran­dom­ness of an array. Com­bin­ing sym­bol vari­ety with sym­me­try cal­cu­la­tions allows us to employ hier­ar­chi­cal scal­ing to count the rel­a­tive impact of dif­fer­ent lev­els of scale. By iden­ti­fy­ing sub­struc­tures we can dis­tin­guish between orga­nized pat­terns and dis­or­ga­nized com­plex­ity. The mea­sures described here are related to ver­bal descrip­tors derived from work by psy­chol­o­gists on responses to visual environments.”
  
  cog­ni­tion aes­thet­ics experimental-​​psychology nudge-​​targets learning-​​by-​​watching

• [1106.5264] Acquir­ing Cor­rect Knowl­edge for Nat­ural Lan­guage Generation

  “Nat­ural lan­guage gen­er­a­tion (NLG) sys­tems are com­puter soft­ware sys­tems that pro­duce texts in Eng­lish and other human lan­guages, often from non-​​linguistic input data. NLG sys­tems, like most AI sys­tems, need sub­stan­tial amounts of knowl­edge. How­ever, our expe­ri­ence in two NLG projects sug­gests that it is dif­fi­cult to acquire cor­rect knowl­edge for NLG sys­tems; indeed, every knowl­edge acqui­si­tion (KA) tech­nique we tried had sig­nif­i­cant prob­lems. In gen­eral terms, these prob­lems were due to the com­plex­ity, nov­elty, and poorly under­stood nature of the tasks our sys­tems attempted, and were wors­ened by the fact that peo­ple write so dif­fer­ently. This meant in par­tic­u­lar that corpus-​​based KA approaches suf­fered because it was impos­si­ble to assem­ble a siz­able cor­pus of high-​​quality con­sis­tent man­u­ally writ­ten texts in our domains; and struc­tured expert-​​oriented KA tech­niques suf­fered because experts dis­agreed and because we could not get enough infor­ma­tion about spe­cial and unusual cases to build robust sys­tems. We believe that such prob­lems are likely to affect many other NLG sys­tems as well. In the long term, we hope that new KA tech­niques may emerge to help NLG sys­tem builders. In the shorter term, we believe that under­stand­ing how indi­vid­ual KA tech­niques can fail, and using a mix­ture of dif­fer­ent KA tech­niques with dif­fer­ent strengths and weak­nesses, can help devel­op­ers acquire NLG knowl­edge that is mostly correct.”
  
  natural-​​language-​​processing artificial-​​intelligence interesting-​​problems high-​​hanging-​​fruit machine-​​learning nudge-​​targets

• [1105.2423] Cytoskele­ton and Cell Motility

  “The present arti­cle is an invited con­tri­bu­tion to the Ency­clo­pe­dia of Com­plex­ity and Sys­tem Sci­ence, Robert A. Mey­ers Ed., Springer New York (2009). It is a review of the bio­phys­i­cal mech­a­nisms that underly cell motility.…”
  
  bio­physics biol­ogy review i-​​used-​​to-​​do-​​this-​​stuff lovely

• [1110.0671] Width Dis­tri­b­u­tions for Con­vex Reg­u­lar Polyhedra

  “The mean width is a mea­sure on three-​​dimensional con­vex bod­ies that enjoys equal sta­tus with vol­ume and sur­face area [Rota]. As the phrase sug­gests, it is the mean of a prob­a­bil­ity den­sity f. We ver­ify for­mu­las for mean widths of the reg­u­lar tetra­he­dron and the cube. Higher-​​order moments of f_​tetra and f_​cube have not been exam­ined until now. Assume that each poly­he­dron has edges of unit length. We deduce that the mean square width of the reg­u­lar tetra­he­dron is 1/3+(3+sqrt(3))/(3*pi) and the mean square width of the cube is 1+4/pi.”
  
  geom­e­try mathematical-​​recreations nudge-​​targets

• [cs/​0305036] Using Dynamic Sim­u­la­tion in the Devel­op­ment of Con­struc­tion Machinery

  “As in the car indus­try for quite some time, dynamic sim­u­la­tion of com­plete vehi­cles is being prac­ticed more and more in the devel­op­ment of off-​​road machin­ery. How­ever, spe­cific ques­tions arise due not only to com­pany struc­ture and size, but espe­cially to the type of prod­uct. Tightly cou­pled, non-​​linear sub­sys­tems of dif­fer­ent domains make pre­dic­tion and opti­mi­sa­tion of the com­plete system’s dynamic behav­iour a chal­lenge. Fur­ther­more, the demand for ver­sa­tile machines leads to some­times con­tra­dic­tory tar­get require­ments and can turn the design process into a hunt for the least painful com­pro­mise. This can be avoided by pro­found sys­tem knowl­edge, assisted by simulation-​​driven prod­uct devel­op­ment. This paper gives an overview of joint research into this issue by Volvo Wheel Load­ers and Linkop­ing Uni­ver­sity on that mat­ter, lists the results of a related lit­er­a­ture review and intro­duces the term “oper­ate­abil­ity”. Rather than giv­ing detailed answers, the prob­lem space for ongo­ing and future research is exam­ined and pos­si­ble solu­tions are sketched.”
  
  engineering-​​design design-​​automation mod­el­ing dynamical-​​systems man­u­fac­tur­ing nudge-​​targets