06445nam 2200733Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200025001420240028001670400030001950500023002250500042002480820017002901000033003072450091003402640056004313000041004873360021005283370026005493380032005754900089006075040057006965050170007535050164009235050268010875050178013555050201015335050192017345050259019265050272021855050221024575050050026785050060027285050034027885050050028225050223028725060065030955100019031605100023031795100011032025100011032135100024032245100039032485100031032875100019033185100026033375201719033635240173050825300029052555380036052845380047053205880046053676500014054136550022054277000031054497100032054807760034055128300090055468560075056362600000025NOW20220114190106.0m eo d cr cn |||m|||a190401s2021 maua ob 000 0 eng d a9781680839036qelectronic z9781680839029qprint7 a10.1561/26000000252doi aCaBNVSLcCaBNVSLdCaBNVSL 4aTJ211b.S25 2021eb 4aE. Procrustes problems -- References.04a629.8/922231 aSakurama, Kazunori,eauthor.10aGeneralized coordination of multi-robot systems /cKazunori Sakurama, Toshiharu Sugie. 1a[Hanover, Massachusetts] :bNow Publishers,c2021. a1 PDF (pages 1-170) :billustrations atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in systems and control,x2325-6826 ;vvol. 9: no. 1, pp 1-170 aIncludes bibliographical references (pages 164-170).0 a1. Introduction. 1.1. Background ; 1.2. Research trends in coordination control ; 1.3. Focus of the monograph ; 1.4. Organization of the monograph ; 1.5. Notation --8 a2. Overview. 2.1. Overall picture ; 2.2. Control objectives ; 2.3. Control with relative measurements ; 2.4. Problem formulation ; 2.5. Notes and references --8 aI. Mathematical preliminaries. 3. Group theory. 3.1. Basics ; 3.2. Group actions ; 3.3. Semidirect products ; 3.4. Group orbits ; 3.5. Invariant subsets ; 3.6. Invariant functions ; 3.7. Free group actions ; 3.8. Free action numbers ; 3.9. Notes and references --8 a4. Graph theory. 4.1. Basics ; 4.2. Cliques and maximal cliques ; 4.3. Conventional rigidity ; 4.4. Clique rigidity ; 4.5. Intersection graphs ; 4.6. Notes and references --8 a5. Stability theory for gradient-flow systems. 5.1. Terminology ; 5.2. Lagrange stability ; 5.3. Asymptotic stability ; 5.4. Remarks on non-differentiable functions ; 5.5. Notes and references -- 8 aII. Multi-robot coordination problems. 6. Pairwise coordination. 6.1. Problem formulation ; 6.2. Controller design ; 6.3. Stability analysis ; 6.4. Examples ; 6.5. Notes and references --8 a7. Generalized coordination with "absolute" measurements. 7.1. Problem formulation ; 7.2. Characterization of the best approximate indicators ; 7.3. Controller design ; 7.4. Stability analysis ; 7.5. Existence of indicators ; 7.6. Notes and references --8 a8. Generalized coordination with "relative" measurements. 8.1. Problem formulation ; 8.2. Characterization of indicators ; 8.3. Controller design ; 8.4. Stability analysis ; 8.5. Relations between coordination, measurement, and networks ; 8.6. Notes and references --8 a9. Application examples. 9.1. Formation selection ; 9.2. Scaling reflection-free formation ; 9.3. Position assignment with local indices ; 9.4. Formation control of non-holonomic robots ; 9.5. Notes and references --8 a10. Concluding remarks -- Acknowledgements --8 aAppendices. A. Examples of frame transformation sets --8 aB. Real analytic functions --8 aC. Gradients of squared distance functions --8 aD. Partial difference. D.1. Partial difference and high-order partial difference ; D.2. Verification of dependency of functions ; D.3. Relations to integrals and partial derivatives ; D.4. Decomposition of functions -- aRestricted to subscribers or individual document purchasers.0 aGoogle Scholar0 aGoogle Book Search0 aINSPEC0 aScopus0 aACM Computing Guide0 aDBLP Computer Science Bibliography0 aZentralblatt MATH Database0 aAMS MathSciNet0 aACM Computing Reviews3 aMulti-robot systems have huge potential for practical applications, which include sensor networks, area surveillance, environment mapping, and so forth. In many applications, cooperative coordination of the robots plays a central role. There are various types of coordination tasks such as consensus, formation, coverage, and pursuit. Most developments of control methods have been taken place for each task individually so far. The purpose of this monograph is to provide a systematic design method applicable to a wide range of coordination tasks for multi-robot systems. The features of the monograph are two-fold: (i) The coordination problem is described in a unified way instead of handling various problems individually, and (ii) a complete solution to this problem is provided in a compact way by using the tools of "group" and "graph" theories efficiently. As for item (i), it is shown that various coordination tasks can be formulated as a generalized coordination problem, where each robot should converge to some desired configuration set under the given information network topology among robots. In this problem, the solvability (i.e., whether robots can achieve the given coordination task or not) fully depends on the characteristics of both the desired configuration set and the network topology. Therefore, concerning item (ii), it is clarified when the generalized coordination problem can be solved in terms of the desired configuration set and the network topology. Furthermore, it is shown how to design a controller which achieves the given configuration task. In particular, the case where each robot can get only local information (e.g., relative position between two robots) is discussed. aKazunori Sakurama and Toshiharu Sugie (2021), "Generalized Coordination of Multi-robot Systems", Foundations and Trends in Systems and Control: Vol. 9: No. 1, pp 1-170. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 14, 2022). 0aRobotics. 0aElectronic books.1 aSugie, Toshiharu,eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680839029 0aFoundations and trends in systems and control ;vvol. 9: no. 1, pp 1-170.x2325-6826 483Abstract with links to full textuhttp://dx.doi.org/10.1561/260000002505741nam 2200697Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200025001420240028001670400030001950500025002250820017002501000033002672450067003002640056003673000056004233360021004793370026005003380032005264900089005585040057006475050023007045050172007275050149008995050284010485050221013325050089015535050254016425050042018965050050019385060065019885100019020535100023020725100011020955100011021065100024021175100039021415100031021805100019022115100026022305202105022565240149043615300029045105380036045395380047045755880046046226500025046686500025046936550022047187000022047407000025047627000026047877100032048137760034048458300089048798560075049682200000087NOW20220114190106.0m eo d cr cn |||m|||a190401s2021 maua ob 000 0 eng d a9781680838879qelectronic z9781680838862qprint7 a10.1561/22000000872doi aCaBNVSLcCaBNVSLdCaBNVSL 4aQA278.2b.L58 2021eb04a519.5/362231 aLiu, Jianji,d1943-eauthor.10aTensor regression /cJiani Liu, Ce Zhu, Zhen Long, Yipeng Liu. 1a[Hanover, Massachusetts] :bNow Publishers,c2021. a1 PDF (pages 379-565) :billustrations (some color) atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in machine learning,x1935-8245 ;vvol. 14: no. 4, pp 379-565 aIncludes bibliographical references (pages 538-565).0 a1. Introduction --8 a2. Notations and preliminaries. 2.1. Notions ; 2.2. Basic operations ; 2.3. Graph networks ; 2.4. Tensor unfolding ; 2.5. Tensor product ; 2.6. Tensor decomposition --8 a3. Classical regression models. 3.1. Linear regression models ; 3.2. Nonlinear regression models ; 3.3. Multioutput regression ; 3.4. Summary --8 a4. Linear tensor regression models. 4.1. Simple linear tensor regression ; 4.2. Generalized linear tensor regression ; 4.3. Penalized tensor regression ; 4.4. Bayesian tensor regression ; 4.5. Quantile tensor regression ; 4.6. Projection based tensor regression ; 4.7. Summary --8 a5. Nonlinear tensor regression. 5.1. Kernel methods ; 5.2. Tensor Gaussian process regression ; 5.3. Tensor additive models ; 5.4. Random forest based tensor regression ; 5.5. Deep tensor regression ; 5.6. Summary --8 a6. Strategies for efficient implementation. 6.1. Sketching ; 6.2. Online learning --8 a7. Applications and available datasets. 7.1. Performance evaluation ; 7.2. Multitask learning ; 7.3. Spatio-temporal analysis ; 7.4. Human motion analysis ; 7.5. Facial image analysis ; 7.6. Neuroimaging analysis ; 7.7. Chemometrics ; 7.8. Others --8 a8. Open-source software frameworks --8 a9. Conclusions and discussions -- References. aRestricted to subscribers or individual document purchasers.0 aGoogle Scholar0 aGoogle Book Search0 aINSPEC0 aScopus0 aACM Computing Guide0 aDBLP Computer Science Bibliography0 aZentralblatt MATH Database0 aAMS MathSciNet0 aACM Computing Reviews3 aThe presence of multidirectional correlations in emerging multidimensional data poses a challenge to traditional regression modeling methods. Traditional modeling methods based on matrix or vector, for example, not only overlook the data's multidimensional information and lower model performance, but also add additional computations and storage requirements. Driven by the recent advances in applied mathematics, tensor regression has been widely used and proven effective in many fields, such as sociology, climatology, geography, economics, computer vision, chemometrics, and neuroscience. Tensor regression can explore multidirectional relatedness, reduce the number of model parameters and improve model robustness and efficiency. It is timely and valuable to summarize the developments of tensor regression in recent years and discuss promising future directions, which will help accelerate the research process of tensor regression, broaden the research direction, and provide tutorials for researchers interested in high dimensional regression tasks. The fundamentals, motivations, popular algorithms, related applications, available datasets, and software resources for tensor regression are all covered in this monograph. The first part focuses on the key concepts for tensor regression, mainly analyzing existing tensor regression algorithms from the perspective of regression families. Meanwhile, the adopted low rank tensor representations and optimization frameworks are also summarized. In addition, several extensions in online learning and sketching are described. The second part covers related applications, widely used public datasets and software resources, as well as some real-world examples, such as multitask learning, spatiotemporal learning, human motion analysis, facial image analysis, neuroimaging analysis (disease diagnosis, neuron decoding, brain activation, and connectivity analysis) and chemometrics. This survey can be used as a basic reference in tensor-regression-related fields and assist readers in efficiently dealing with high dimensional regression tasks. aJiani Liu, Ce Zhu, Zhen Long and Yipeng Liu (2021), "Tensor Regression", Foundations and Trends in Machine Learning: Vol. 14: No. 4, pp 379-565. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 14, 2022). 0aRegression analysis. 0aCalculus of tensors. 0aElectronic books.1 aZhu, Ce,eauthor.1 aLong, Zhen,eauthor.1 aLiu, Yipeng,eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680838862 0aFoundations and trends in machine learning;vvol. 14: no. 4, pp 379-565.x1935-8245 483Abstract with links to full textuhttp://dx.doi.org/10.1561/220000008705255nam 2200649Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200026001420240028001680400030001960500027002260820017002531000038002702450104003082640056004123000056004683360021005243370026005453380032005714900110006035040057007135050246007705050257010165050249012735050320015225050241018425050279020835050213023625060065025755100019026405100023026595100011026825100011026935100024027045100039027285100031027675100019027985100026028175201051028435240207038945300029041015380036041305380047041665880046042136500027042596500014042866550022043007000031043227100032043537760034043858300111044198560075045300100000108NOW20220114190106.0m eo d cr cn |||m|||a190401s2021 maua ob 000 0 eng d a9781680838435qelectronic z9781680838428q(print)7 a10.1561/01000001082doi aCaBNVSLcCaBNVSLdCaBNVSL 4aQA76.9.D3bA374 2021eb04a005.75/82231 aAggarwal, Vaneet,d1984-eauthor.10aModeling and optimization of latency in erasure-coded storage systems /cVaneet Aggarwal, Tian Lan. 1a[Hanover, Massachusetts] :bNow Publishers,c2021. a1 PDF (pages 380-525) :billustrations (some color) atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in communications and information theory,x1567-2328 ;vvol. 18: no. 3, pp 380-525 aIncludes bibliographical references (pages 512-525).0 a1. Introduction. 1.1. Erasure coding in distributed storage ; 1.2. Queueing model for erasure-coded distributed storage ; 1.3. Key challenges in latency characterization ; 1.4. Problem taxonomy ; 1.5. Outline of the monograph ; 1.6. Notes --8 a2. MDS-reservation scheduling approach. 2.1. MDS-reservation queue ; 2.2. Characterization of latency upper bound ; 2.3. Characterization of latency lower bound ; 2.4. Extension to redundant requests ; 2.5. Simulations ; 2.6. Notes and open problems --8 a3. Fork-join scheduling approach. 3.1. Fork-join scheduling ; 3.2. Characterization of latency ; 3.3. Extension to general service time distributions ; 3.4. Extension to heterogeneous systems ; 3.5. Simulations ; 3.6. Notes and open problems --8 a4. Probabilistic scheduling approach. 4.1. Probabilistic scheduling ; 4.2. Characterization of mean latency ; 4.3. Characterization of tail latency ; 4.4. Characterization of asymptotic latency ; 4.5. Proof of asymptotic optimality for heavy tailed service rates ; 4.6. Simulations ; 4.7. Notes and open problems --8 a5. Delayed-relaunch scheduling approach. 5.1. Delayed-relaunch scheduling ; 5.2. Characterization of inter-service times of different chunks ; 5.3. Characterization of two key metrics ; 5.4. Simulations ; 5.5. Notes and open problems --8 a6. Analyzing latency for video content. 6.1. Modeling stall duration for video requests ; 6.2. Modeling download and play times ; 6.3. Characterization of mean stall duration ; 6.4. Characterization of tail stall duration ; 6.5. Simulations ; 6.6. Notes and open problems --8 a7. Lessons from prototype implementation. 7.1. Exemplary implementation of erasure-coded storage ; 7.2. Illuminating key design tradeoffs ; 7.3. Applications in caching and content distribution -- References. aRestricted to subscribers or individual document purchasers.0 aGoogle Scholar0 aGoogle Book Search0 aINSPEC0 aScopus0 aACM Computing Guide0 aDBLP Computer Science Bibliography0 aZentralblatt MATH Database0 aAMS MathSciNet0 aACM Computing Reviews3 aIn this monograph, we will first identify the key challenges and taxonomy of the research problems and then give an overview of different models and approaches that have been developed to quantify latency of erasure-coded storage. This includes recent work leveraging MDS-Reservation, Fork-Join, Probabilistic, and Delayed-Relaunch scheduling policies, as well as their applications to characterizing access latency (e.g., mean, tail, and asymptotic latency) of erasure-coded distributed storage systems. We will also extend the discussions to video streaming from erasure-coded distributed storage systems. Next, we will bridge the gap between theory and practice, and discuss lessons learned from prototype implementations. In particular, we will discuss exemplary implementations of erasure-coded storage, illuminate key design degrees of freedom and tradeoffs, and summarize remaining challenges in real-world storage systems such as in content delivery and caching. Open problems for future research are discussed at the end of each chapter. aVaneet Aggarwal and Tian Lan (2021), "Modeling and Optimization of Latency in Erasure-coded Storage Systems", Foundations and Trends in Communications and Information Theory: Vol. 18: No. 3, pp 380-525. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 14, 2022). 0aDistributed databases. 0aBig data. 0aElectronic books.1 aLan, Tian,d1980-eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680838428 0aFoundations and trends in communications and information theory ;vvol. 18: no. 3, pp 380-525.x1567-2328 483Abstract with links to full textuhttp://dx.doi.org/10.1561/010000010804381nam 2200601Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200025001420240028001670400030001950500023002250820014002481000033002622450082002952640056003773000026004333360021004593370026004803380032005064900084005385040067006225050023006895050226007125050222009385050308011605050242014685050078017105060065017885100019018535100023018725100011018955100011019065100031019175100019019485201177019675240155031445300029032995380036033285380047033645880046034116500027034576500015034846500032034996550022035317100032035537760034035858300085036198560075037040800000034NOW20220114190106.0m eo d cr cn |||m|||a190401s2021 mau ob 000 0 eng d a9781680838671qelectronic z9781680838664qprint7 a10.1561/08000000342doi aCaBNVSLcCaBNVSLdCaBNVSL 4aTS155b.Z45 2021eb04a658.52231 aZeleniuk, Valentyn,eauthor.10aPerformance analysis :beconomic foundations and trends /cValentin Zelenyuk. 1a[Hanover, Massachusetts] :bNow Publishers,c2021. a1 PDF (pages 153-229) atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in econometrics,x1551-3084;vvol. 11: no. 3, pp 153-229 aIncludes bibliographical references (pages 218-229) and index.0 a1. Introduction --8 a2. Profit and efficiency. 2.1. General profit maximization ; 2.2. The highest efficiency benchmark ; 2.3. Assumptions about technology ; 2.4. General profit efficiency ; 2.5. Output and input oriented profit efficiency --8 a3. Cost, revenue and technical efficiency. 3.1. Three in one: profit, cost and revenue efficiency ; 3.2. Technical efficiency and duality ; 3.3. The rise of allocative (in)efficiencies ; 3.4. Merits and limitations --8 a4. Dynamics. 4.1. Index numbers theory of productivity in a nutshell ; 4.2. Intertemporal technology characterizations ; 4.3. Hicks-neutrality ; 4.4. Malmquist quantity indexes ; 4.5. Hicksâ€“Moorsteen productivity indexes ; 4.6. Malmquist productivity indexes ; 4.7. Equivalence of different indexes --8 a5. Aggregation. 5.1. Koopmans aggregation theorem ; 5.2. Aggregation of profit efficiency and its decomposition ; 5.3. Aggregation for the output orientation ; 5.4. Aggregation for productivity indexes ; 5.5. Other aggregation results --8 a6. Concluding remarks -- Acknowledgements -- Subject Index -- References. aRestricted to subscribers or individual document purchasers.0 aGoogle Scholar0 aGoogle Book Search0 aINSPEC0 aScopus0 aZentralblatt MATH Database0 aAMS MathSciNet3 aThe goal of this monograph is to very concisely outline the economic theory foundations and trends of the field of Effciency and Productivity Analysis, also sometimes referred to as Performance Analysis. I start with the profit maximization paradigm of mainstream economics, use it to derive a general profit effciency measure and then present its special cases: revenue maximization and revenue effciency, cost minimization and cost effciency. I then consider various types of technical and allocative effciencies (directional and Shephard's distance functions and related Debreu-Farrell measures as well as non-directional measures of technical effciency), showing how they fit into or decompose the profit maximization paradigm. I then cast the effciency and productivity concepts in a dynamic perspective that is frequently used to analyze the productivity changes of economic systems (firms, hospitals, banks, countries, etc.) over time. I conclude this monograph with an overview of major results on aggregation in productivity and effciency analysis, where the aggregate productivity and effciency measures are theoretically connected to their individual analogues. aValentin Zelenyuk (2021), "Performance Analysis: Economic Foundations and Trends", Foundations and Trends in Econometrics: Vol. 11: No. 3, pp 153-229. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 14, 2022). 0aIndustrial efficiency. 0aEconomics. 0aEconomicsxData processing. 0aElectronic books.2 aNow Publishers,epublisher.08iPrint version:z9781680838664 0aFoundations and trends in econometrics ;vvol. 11: no. 3, pp 153-229.x1551-3084483Abstract with links to full textuhttp://dx.doi.org/10.1561/080000003404247nam 2200553Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200025001420240028001670400030001950500024002250820015002491000029002642450106002932640056003993000043004553360021004983370026005193380032005454900080005775040057006575050024007145050184007385050271009225050318011935050386015115050054018975060065019515200931020165240179029475300029031265380036031555380047031915880046032386500039032846500031033236500026033546550022033807000030034027000040034327100032034727760034035048300080035388560075036180500000056NOW20220114190106.0m eo d cr cn |||m|||a190401s2021 maua ob 000 0 eng d a9781680838794qelectronic z9781680838787qprint7 a10.1561/05000000562doi aCaBNVSLcCaBNVSLdCaBNVSL 4aHG4026b.D37 2021eb04a658.152231 aDasgupta, Amil,eauthor.10aInstitutional investors and corporate governance /cAmil Dasgupta, Vyacheslav Fos, Zacharias Sautner. 1a[Hanover, Massachusetts] :bNow Publishers,c2021. a1 PDF (pages 276-394) :billustrations atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in finance,x1567-2409 :vvol. 12: no. 4, pp 276-394 aIncludes bibliographical references (pages 376-394).0 a1. Introduction -- 8 a2. Stylized facts on the importance of institutional investors. 2.1. Importance of institutional investors in the US ; 2.2. Importance of institutional investors outside the US --8 a3. Legal environment: obligations, ability, and incentives of institutional investors to govern. 3.1. Legal obligations of institutional investors to govern ; 3.2. Ability of institutional investors to govern ; 3.3. Incentives of institutional investors to govern --8 a4. Theoretical literature on institutional investors and corporate governance. 4.1. Classical theoretical literature on blockholder governance ; 4.2. Theories centered on institution-specific ability ; 4.3. Theories of blockholders as agents ; 4.4. Theories on institutional investors and proxy voting advisors --8 a5. Empirical literature on institutional investors and corporate governance. 5.1. Institutional investor data ; 5.2. Empirical methods to identify governance effects of institutional investors ; 5.3. Institutional investors as blockholders: classical evidence on voice and exit ; 5.4. Institutional investor heterogeneity ; 5.5. Proxy voting advisors and institutional investors --8 a6. Conclusions -- Acknowledgements -- References. aRestricted to subscribers or individual document purchasers.3 aWe provide a comprehensive overview of the role of institutional investors in corporate governance with three main components. First, we establish new stylized facts documenting the evolution and importance of institutional ownership. Second, we provide a detailed characterization of key aspects of the legal and regulatory setting within which institutional investors govern portfolio firms. Third, we synthesize the evolving response of the recent theoretical and empirical academic literature in finance to the emergence of institutional investors in corporate governance. We highlight how the defining aspect of institutional investors -- the fact that they are financial intermediaries -- differentiates them in their governance role from standard principal blockholders. Further, not all institutional investors are identical, and we pay close attention to heterogeneity amongst institutional investors as blockholders. aAmil Dasgupta, Vyacheslav Fos and Zacharias Sautner (2021), "Institutional Investors and Corporate Governance", Foundations and Trends in Finance: Vol. 12: No. 4, pp 276-394. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 14, 2022). 0aCorporationsxFinancexManagement. 0aInstitutional investments. 0aCorporate governance. 0aElectronic books.1 aFos, Vyacheslav,eauthor.1 aSautner, Zacharias,d1979-eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680838787 0aFoundations and trends in finance ;vvol. 12: no. 4, pp 276-394.x1567-2409483Abstract with links to full textuhttp://dx.doi.org/10.1561/0500000056