IA Scholar Query: Information Geometry Under Monotone Embedding. Part II: Geometry.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgThu, 29 Sep 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Workshop Numerische Methoden in der Geotechnik : 12th & 13th of September 2022 Hamburg, Germany : conference proceedings
https://scholar.archive.org/work/lixdp5rcefdbjft3thqnvuwfp4
Numerische Verfahren sind zum Standardprozess in der Untersuchung von geotechnischen Bauwerken geworden. Mit stetig steigender Rechenleistung gewinnen Hybrid- und Kontinuumsansätze, die durch ausgefeilte Materialmodelle unterstützt werden, immer mehr an Bedeutung. Der Workshop "Numerische Methoden in der Geotechnik 2022" der Technischen Universität Hamburg (TUHH) unter Beteiligung des AK Numerik (DGGT) und der Bundesanstalt für Wasserbau (BAW) bringt internationale Wissenschaftlerinnen und Wissenschaftler und Fachleute zusammen, um neueste Erkenntnisse in Bezug auf die Entwicklung numerischer Methoden in der Geotechnik zu präsentieren und zu diskutieren. Dieser Tagungsband enthält die verschiedenen auf dem Workshop vorgetragenen Themen. Er soll die gewonnenen Erkenntnisse für zukünftige wissenschaftliche und praktische Anwendungen erhalten und sie mit der geotechnischen Community teilen.Jürgen Grabe, Sascha Henke, Marius Milatz, Gertraud Medicus, Torsten Wichtmann, Merita Tafili, Jan Machacek, Patrick Staubach, Luis Felipe Prada Sarmiento, Anne Stark, Michael Hicks, Ronald Brinkgreve, Sandro Brasile, Bart van Paassen, Thomas Nijssen, Salazar Rivera, Hauke Jürgens, Tim Pucker, Kristian Krabbenhoft, Hans-Peter Daxer, Franz Tschuchnigg, Helmut Schweiger, Antonia Nitsch, Carlos Eduardo Grandas Tavera, Alba Yerro, Alexander Chmelnizkij, Christoph Goniva, Marcel Kwakkel, Giovanni Viciconte, Christoph Kloss, Robert Seifried, Timo Hendrik Schmidt, Benedikt Kriegesmann, Elnaz Hadjiloo, Hatice Kaya-Sandt, Tobias Engel, Matthias Römer, Kurt-M. Borchert, Diaa Alkateeb, Thomas Meier, Jörg-Martin Hohberg, TUHH Universitätsbibliothekwork_lixdp5rcefdbjft3thqnvuwfp4Thu, 29 Sep 2022 00:00:00 GMTMutual information-based group explainers with coalition structure for machine learning model explanations
https://scholar.archive.org/work/3gyreldgqbc7jmbl24leazwrpa
In this article, we study game-theoretical group explainers for machine learning (ML) models in a functional analytic setting as operators defined on appropriate functional spaces. Specifically, we focus on game values with coalition structure applied to random games based on the conditional and marginal expectation. In particular, we investigate the stability of the explanation operators which showcases the differences between the two games, such as showing that the marginal explanations can become unstable in the natural data-based metric. Furthermore, we formulate novel group explanation methodologies based on game values with coalition structure applied to both marginal and conditional games. They allow us to unify the two types of explanations and turn out to have lower complexity. In addition, we study the effect of predictor grouping on the stability of the corresponding explanation operators. Finally, we establish the two-step representation for a coalitional game value consisting of two game values and a family of intermediate games. We use this representation to generalize our grouping approach to the case of nested partitions represented by a parameterized partition tree. Specifically, we introduce a theoretical scheme that generates recursive coalitional game values and group explainers under a given partition tree structure and investigate the properties of the corresponding group explainers. We verify our results in a number of experiments with data where the predictors are grouped based on an information-theoretic measure of dependence.Alexey Miroshnikov, Konstandinos Kotsiopoulos, Khashayar Filom, Arjun Ravi Kannanwork_3gyreldgqbc7jmbl24leazwrpaWed, 28 Sep 2022 00:00:00 GMTPredicting the FCI energy of large systems to chemical accuracy from restricted active space density matrix renormalization group calculations
https://scholar.archive.org/work/upba747kdnartlqibascsrjgqi
We present a theoretical analysis and a new theory-based extrapolation method for the recently introduced restricted active space density matrix renormalization group (DMRG-RAS) method [arXiv:2111.06665] in electronic structure calculations. Large-scale numerical simulations show that our approach, DMRG-RAS-X, reaches chemical accuracy for challenging strongly correlated systems such as the Chromium dimer or dicarbon up to a large cc-pVQZ basis with moderate computational demands. The method is free of empirical parameters, performed robustly and reliably in all examples we tested, and has the potential to become a vital alternative method for electronic structure calculations in quantum chemistry, and more generally for the computation of strong correlations in nuclear and condensed matter physics.Gero Friesecke, Gergely Barcza, Örs Legezawork_upba747kdnartlqibascsrjgqiWed, 28 Sep 2022 00:00:00 GMTArtificial Intelligence and Advanced Materials
https://scholar.archive.org/work/tkf566mg6zf77a7xan6anloxvu
Artificial intelligence is gaining strength and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems and processes can be devised and optimized thanks to machine learning techniques and such progress can be turned into in-novative computing platforms. Future materials scientists will profit from understanding how machine learning can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by compu-tation to account for the origins, procedures and applications of artificial intelligence. Machine learning and its methods are reviewed to provide basic knowledge on its implementation and its potential. The materials and systems used to implement artificial intelligence with electric charges are finding serious competition from other information carrying and processing agents. The impact these techniques are having on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materi-als and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom as exemplified by the power to discover unheard of mate-rials or physical laws buried in data.Cefe Lópezwork_tkf566mg6zf77a7xan6anloxvuWed, 28 Sep 2022 00:00:00 GMTData-driven Efficient Solvers for Langevin Dynamics on Manifold in High Dimensions
https://scholar.archive.org/work/5q7phfngmjgnnjnxcegphp3rg4
We study the Langevin dynamics of a physical system with manifold structure ℳ⊂ℝ^p based on collected sample points {𝗑_i}_i=1^n ⊂ℳ that probe the unknown manifold ℳ. Through the diffusion map, we first learn the reaction coordinates {𝗒_i}_i=1^n⊂𝒩 corresponding to {𝗑_i}_i=1^n, where 𝒩 is a manifold diffeomorphic to ℳ and isometrically embedded in ℝ^ℓ with ℓ≪ p. The induced Langevin dynamics on 𝒩 in terms of the reaction coordinates captures the slow time scale dynamics such as conformational changes in biochemical reactions. To construct an efficient and stable approximation for the Langevin dynamics on 𝒩, we leverage the corresponding Fokker-Planck equation on the manifold 𝒩 in terms of the reaction coordinates 𝗒. We propose an implementable, unconditionally stable, data-driven finite volume scheme for this Fokker-Planck equation, which automatically incorporates the manifold structure of 𝒩. Furthermore, we provide a weighted L^2 convergence analysis of the finite volume scheme to the Fokker-Planck equation on 𝒩. The proposed finite volume scheme leads to a Markov chain on {𝗒_i}_i=1^n with an approximated transition probability and jump rate between the nearest neighbor points. After an unconditionally stable explicit time discretization, the data-driven finite volume scheme gives an approximated Markov process for the Langevin dynamics on 𝒩 and the approximated Markov process enjoys detailed balance, ergodicity, and other good properties.Yuan Gao, Jian-Guo Liu, Nan Wuwork_5q7phfngmjgnnjnxcegphp3rg4Tue, 27 Sep 2022 00:00:00 GMTDeep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density
https://scholar.archive.org/work/inmrxrayojhb3ftqzxfngdo3lm
This paper addresses the problem of image reconstruction for region-of-interest (ROI) computed tomography (CT). While model-based iterative methods can be used for such a problem, their practicability is often limited due to tedious parameterization and slow convergence. In addition, inadequate solutions can be obtained when the retained priors do not perfectly fit the solution space. Deep learning methods offer an alternative approach that is fast, leverages information from large data sets, and thus can reach high reconstruction quality. However, these methods usually rely on black boxes not accounting for the physics of the imaging system, and their lack of interpretability is often deplored. At the crossroads of both methods, unfolded deep learning techniques have been recently proposed. They incorporate the physics of the model and iterative optimization algorithms into a neural network design, leading to superior performance in various applications. This paper introduces a novel, unfolded deep learning approach called U-RDBFB designed for ROI CT reconstruction from limited data. Few-view truncated data are efficiently handled thanks to a robust non-convex data fidelity function combined with sparsity-inducing regularization functions. Iterations of a block dual forward-backward (DBFB) algorithm, embedded in an iterative reweighted scheme, are then unrolled over a neural network architecture, allowing the learning of various parameters in a supervised manner. Our experiments show an improvement over various state-of-the-art methods, including model-based iterative schemes, deep learning architectures, and deep unfolding methods.Marion Savanier, Emilie Chouzenoux, Jean-Christophe Pesquet, Cyril Riddellwork_inmrxrayojhb3ftqzxfngdo3lmTue, 27 Sep 2022 00:00:00 GMTPredicting cell stress and strain during extrusion bioprinting
https://scholar.archive.org/work/435awdr25bgpjb42wnx7zlraqu
Bioprinting of living cells can cause major shape deformations, which may severely affect cell survival and functionality. While the shear stresses occurring during cell flow through the printer nozzle have been quantified to some extent, the extensional stresses occurring as cells leave the nozzle into the free printing strand have been mostly ignored. Here we use Lattice-Boltzmann simulations together with a finite-element based cell model to study cell deformation at the nozzle exit. Our simulation results are in good qualitative agreement with experimental microscopy images. We show that for cells flowing in the center of the nozzle extensional stresses can be significant, while for cells flowing off-center their deformation is dominated by the shear flow inside the nozzle. From the results of these simulations, we develop two simple methods that only require the printing parameters (nozzle diameter, flow rate, bioink rheology) to (i) accurately predict the maximum cell stress occurring during the 3D bioprinting process and (ii) approximately predict the cell strains caused by the elongational flow at the nozzle exit.Sebastian Johannes Müller and Ben Fabry and Stephan Geklework_435awdr25bgpjb42wnx7zlraquTue, 27 Sep 2022 00:00:00 GMTIt's a Wrap! Visualisations that Wrap Around Cylindrical, Toroidal, or Spherical Topologies
https://scholar.archive.org/work/akpvfdnu2fhgdjsfxa25kjeu2a
Traditional visualisations are designed to be shown on a flat surface (screen or page) but most data is not "flat". For example, the surface of the earth exists on a sphere, however, when that surface is presented on a flat map, key information is hidden, such as geographic paths on the spherical surface being wrapped across the boundaries of the flat map. Similarly, cyclical time-series data has no beginning or end. When such cyclical data is presented on a traditional linear chart, the viewer needs to perceive continuity of the visualisation across the chart's boundaries. Mentally reconnecting the chart across such a boundary may induce additional cognitive load. More complex data such as a network diagram with hundreds or thousands of links between data points leads to a densely connected structure that is even less "flat" and needs to wrap around in multiple dimensions. To improve the usability of these visualisations, this thesis explores a novel class of interactive wrapped data visualisations, i.e., visualisations that wrap around continuously when interactively panned on a two-dimensional projection of surfaces of 3D shapes, specifically, cylinder, torus, or sphere. We start with a systematic exploration of the design space of interactive wrapped visualisations, characterising the visualisations that help people understand the relationship within the data. Subsequently, we investigate a series of wrappable visualisations for cyclical time series, network, and geographic data. We show that these interactive visualisations better preserve the spatial relations in the case of geospatial data, and better reveal the data's underlying structure in the case of abstract data such as networks and cyclical time series. Furthermore, to assist future research and development, we contribute layout algorithms and toolkits to help create pannable wrapped visualisations.Kun-Ting Chenwork_akpvfdnu2fhgdjsfxa25kjeu2aTue, 27 Sep 2022 00:00:00 GMTSingularity formation in the incompressible Euler equation in finite and infinite time
https://scholar.archive.org/work/p4ictrnchvgjbefqnuk3w4rn3q
Some classical and recent results on the Euler equations governing perfect (incompressible and inviscid) fluid motion are collected and reviewed, with some small novelties scattered throughout. The perspective and emphasis will be given through the lens of infinite-dimensional dynamical systems, and various open problems are listed and discussed.Theodore D. Drivas, Tarek M. Elgindiwork_p4ictrnchvgjbefqnuk3w4rn3qTue, 27 Sep 2022 00:00:00 GMTTheoretical Exploration of Solutions of Feedforward ReLU Networks
https://scholar.archive.org/work/cgwlvnjvpvedrlpwhidr5tnjve
This paper aims to interpret the mechanism of feedforward ReLU networks by exploring their solutions for piecewise linear functions, through the deduction from basic rules. The constructed solution should be universal enough to explain some network architectures of engineering; in order for that, several ways are provided to enhance the solution universality. Some of the consequences of our theories include: Under affine-geometry background, the solutions of both three-layer networks and deep-layer networks are given, particularly for those architectures applied in practice, such as multilayer feedforward neural networks and decoders; We give clear and intuitive interpretations of each component of network architectures; The parameter-sharing mechanism for multi-outputs is investigated; We provide an explanation of overparameterization solutions in terms of affine transforms; Under our framework, an advantage of deep layers compared to shallower ones is natural to be obtained. Some intermediate results are the basic knowledge for the modeling or understanding of neural networks, such as the classification of data embedded in a higher-dimensional space, the generalization of affine transforms, the probabilistic model of matrix ranks, and the concepts of distinguishable data sets as well as interference among hyperplanes.Changcun Huangwork_cgwlvnjvpvedrlpwhidr5tnjveTue, 27 Sep 2022 00:00:00 GMTPrinciples Of Heliophysics: a textbook on the universal processes behind planetary habitability
https://scholar.archive.org/work/v2zqk34khrabtaqqb3zmwqkhpa
Heliophysics is the system science of the physical connections between the Sun and the solar system. As the physics of the local cosmos, it embraces space weather and planetary habitability. The wider view of comparative heliophysics forms a template for conditions in exoplanetary systems and provides a view over time of the aging Sun and its magnetic activity, of the heliosphere in different settings of the interstellar medium and subject to stellar impacts, of the space physics over evolving planetary dynamos, and of the long-term influence on planetary atmospheres by stellar radiation and wind. Based on a series of NASA-funded summer schools for early-career researchers, this textbook is intended for students in physical sciences in later years of their university training and for beginning graduate students in fields of solar, stellar, (exo-)planetary, and planetary-system sciences. The book emphasizes universal processes from a perspective that draws attention to what provides Earth (and similar (exo-)planets) with a relatively stable setting in which life as we know it could thrive. The text includes 200 "Activities" in the form of exercises, explorations, literature readings, "what if" challenges, and group discussion topics; many of the Activities provide additional information complementing the main text. Solutions and discussions are included in an Appendix for a selection of the exercises.Karel Schrijver, Fran Bagenal, Tim Bastian, Juerg Beer, Mario Bisi, Tom Bogdan, Steve Bougher, David Boteler, Dave Brain, Guy Brasseur, Don Brownlee, Paul Charbonneau, Ofer Cohen, Uli Christensen, Tom Crowley, Debrah Fischer, Terry Forbes, Tim Fuller-Rowell, Marina Galand, Joe Giacalone, George Gloeckler, Jack Gosling, Janet Green, Nick Gross, Steve Guetersloh, Viggo Hansteen, Lee Hartmann, Mihaly Horanyi, Hugh Hudson, Norbert Jakowski, Randy Jokipii, Margaret Kivelson, Dietmar Krauss-Varban, Norbert Krupp, Judith Lean, Jeff Linsky, Dana Longcope, Daniel Marsh, Mark Miesch, Mark Moldwin, Luke Moore, Sten Odenwald, Merav Opher, Rachel Osten, Matthias Rempel, Hauke Schmidt, George Siscoe, Dave Siskind, Chuck Smith, Stan Solomon, Tom Stallard, Sabine Stanley, Jan Sojka, Kent Tobiska, Frank Toffoletto, Alan Tribble, Vytenis Vasyliunas, Richard Walterscheid, Ji Wang, Brian Wood, Tom Woods, Neal Zappwork_v2zqk34khrabtaqqb3zmwqkhpaTue, 27 Sep 2022 00:00:00 GMTThe Dynamics and Geometry of Semi-Hyperbolic Rational Semigroups
https://scholar.archive.org/work/w3blhjbmcnhojfdmpmyk4whwqq
We study skew-product dynamics for a large class of finitely-generated semi–hyperbolic semigroups of rational maps acting on the Riemann sphere, which generalizes both the theory of iteration of a single rational map of a single complex variable complex/holomorphic dynamics) and the theory of countable alphabet conformal iterated function systems (CIFSs). We construct the thermodynamic formalism for such dynamical systems and geometric potentials by developing the notion of nice families that extend to the case of our highly disconnected skew product phase space the powerful notion of nice sets due to Rivera–Letelier and Przytycki, and the allied earlier notion of K(V) sets due to Denker and the last named author. We leverage out techniques to prove the existence and uniqueness of equilibrium states for a wide class of Hölder potentials, and concomitant statistical laws: central limit theorem, law of iterated logarithm, and exponential decay of correlations. We devote lots of space and effort to control (non-recurrent) critical points which is a notoriously challenging task even for a single rational function; more generators add qualitatively new challenges. Beyond dynamics, but still with dynamical methods, we advance the study of finer fractal geometrical properties of the intricate Julia sets associated to such systems and, in particular, via equilibrium states, we perform a multifractal analysis of Lyapunov exponents. We use the Nice Open Set Condition (NOSC) introduced by the last two authors, and apply our new techniques to settle a long-standing problem in the theory of rational semigroups by proving that for our class of semigroups the Hausdorff dimension of each fiber Julia set is strictly smaller than the Hausdorff dimension of the global Julia set of the semigroup.Jason Atnip, Hiroki Sumi, Mariusz Urbańskiwork_w3blhjbmcnhojfdmpmyk4whwqqMon, 26 Sep 2022 00:00:00 GMTProject and Forget: Solving Large-Scale Metric Constrained Problems
https://scholar.archive.org/work/zq63xd45mzfvrmlusgcqa3iase
Given a set of dissimilarity measurements amongst data points, determining what metric representation is most "consistent" with the input measurements or the metric that best captures the relevant geometric features of the data is a key step in many machine learning algorithms. Existing methods are restricted to specific kinds of metrics or small problem sizes because of the large number of metric constraints in such problems. In this paper, we provide an active set algorithm, Project and Forget, that uses Bregman projections, to solve metric constrained problems with many (possibly exponentially) inequality constraints. We provide a theoretical analysis of Project and Forget and prove that our algorithm converges to the global optimal solution and that the L_2 distance of the current iterate to the optimal solution decays asymptotically at an exponential rate. We demonstrate that using our method we can solve large problem instances of three types of metric constrained problems: general weight correlation clustering, metric nearness, and metric learning; in each case, out-performing the state of the art methods with respect to CPU times and problem sizes.Rishi Sonthalia, Anna C. Gilbertwork_zq63xd45mzfvrmlusgcqa3iaseMon, 26 Sep 2022 00:00:00 GMTTurbulence, Coherence and Collapse: Three Phases for Core Evolution
https://scholar.archive.org/work/hhoxvvjanvgyblv2hmgpfdwoly
We study the formation, evolution and collapse of dense cores by tracking structures in a magnetohydrodynamic simulation of a star-forming cloud. We identify cores using the dendrogram algorithm and utilize machine learning techniques, including Neural Gas prototype learning and Fuzzy c-means clustering, to analyze the density and velocity dispersion profiles of cores together with six bulk properties. We produce a 2-d visualization using a Uniform Manifold Approximation and Projection (UMAP), which facilitates the connection between physical properties and three partially-overlapping phases: i) unbound turbulent structures (Phase I), ii) coherent cores that have low turbulence (Phase II), and iii) bound cores, many of which become protostellar (Phase III). Within Phase II we identify a population of long-lived coherent cores that reach a quasi-equilibrium state. Most prestellar cores form in Phase II and become protostellar after evolving into Phase III. Due to the turbulent cloud environment, the initial core properties do not uniquely predict the eventual evolution, i.e., core evolution is stochastic, and cores follow no one evolutionary path. The phase lifetimes are 1.0±0.1×10^5 yr, 1.3±0.2×10^5 yr, and 1.8±0.3×10^5 yr for Phase I, II, and III, respectively. We compare our results to NH_3 observations of dense cores. Known coherent cores predominantly map into Phase II, while most turbulent pressure-confined cores map to Phase I or III. We predict that a significant fraction of observed starless cores have unresolved coherent regions and that ≳ 20 Measurements of core radial profiles, in addition to the usual bulk properties, will enable more accurate predictions of core evolution.Stella S. R. Offner, Josh Taylor, Carleen Markey, Hope How-Huan Chen, Jaime E. Pineda, Alyssa A. Goodman, Andreas Burkert, Adam Ginsburg, Spandan Choudhurywork_hhoxvvjanvgyblv2hmgpfdwolyMon, 26 Sep 2022 00:00:00 GMTEmergent Microrobotic Oscillators via Asymmetry-Induced Order
https://scholar.archive.org/work/s6acvvicqndhvk2hbvrrzgoevm
Spontaneous low-frequency oscillations on the order of several hertz are the drivers of many crucial processes in nature. From bacterial swimming to mammal gaits, the conversion of static energy inputs into slowly oscillating electrical and mechanical power is key to the autonomy of organisms across scales. However, the fabrication of slow artificial oscillators at micrometre scales remains a major roadblock towards the development of fully-autonomous microrobots. Here, we report the emergence of a low-frequency relaxation oscillator from a simple collective of active microparticles interacting at the air-liquid interface of a peroxide drop. Their collective oscillations form chemomechanical and electrochemical limit cycles that enable the transduction of ambient chemical energy into periodic mechanical motion and on-board electrical currents. Surprisingly, the collective can oscillate robustly even as more particles are introduced, but only when we add a single particle with modified reactivity to intentionally break the system's permutation symmetry. We explain such emergent order through a novel thermodynamic mechanism for asymmetry-induced order. The energy harvested from the stabilized system oscillations enables the use of on-board electronic components, which we demonstrate by cyclically and synchronously driving microrobotic arms. This work highlights a new strategy for achieving low-frequency oscillations at the microscale that are otherwise difficult to observe outside of natural systems, paving the way for future microrobotic autonomy.Jing Fan Yang, Thomas A. Berrueta, Allan M. Brooks, Albert Tianxiang Liu, Ge Zhang, David Gonzalez-Medrano, Sungyun Yang, Volodymyr B. Koman, Pavel Chvykov, Lexy N. LeMar, Marc Z. Miskin, Todd D. Murphey, Michael S. Stranowork_s6acvvicqndhvk2hbvrrzgoevmMon, 26 Sep 2022 00:00:00 GMTThe role of low-energy electrons in the charging process of LISA test masses
https://scholar.archive.org/work/xaenwwkia5dqzbnoyrrscclh64
The space environment encountered by operating spacecraft is populated by a continuous flux of charged particles that penetrate into electronic devices inducing phantom commands and loss of control, eventually leading to satellite failure. Moreover, electron static discharge that results from secondary electron emission of the device materials can also be responsible for satellite malfunction. In this regard, the estimate of the total electron yield is fundamental for our understanding of the test-mass charging associated with galactic cosmic rays in the LISA Pathfinder mission and in the forthcoming gravitational wave observatory LISA. To unveil the role of low energy electrons in this process owing to galactic and solar energetic particle events, in this work we study the interaction of keV and sub-keV electrons with a gold slab using a mixed Monte Carlo and ab-initio framework. We determine the energy spectrum of the electrons emerging from such a gold slab hit by a primary electron beam by considering the relevant energy loss mechanisms as well as the elastic scattering events. We also show that our results are consistent with experimental data and Monte Carlo simulations carried out with the GEANT4-DNA toolkit.Simone Taioli, Maurizio Dapor, Francesco Dimiccoli, Michele Fabi, Valerio Ferroni, Catia Grimani, Mattia Villani, William Joseph Weberwork_xaenwwkia5dqzbnoyrrscclh64Mon, 26 Sep 2022 00:00:00 GMTCurrent and Global Trends in Library Services Affiliated Colleges of Dr. Babasaheb Ambedkar Marathavada University Aurangabad
https://scholar.archive.org/work/jz5dvaropjhafg2hyyx54qzeq4
Only any activity that improved real revenues is a national development endeavor. This gives people and companies fresh hopes of expanded prospects with every information-driven specialized information, the worldwide and national social sciences have grown into an important asset for any economic development. native Business profits in a specific way that from libraries as well as explicit access to new ideas, information and knowledge, businesses relocating, business, small businesses of all sorts and the area of infrastructure supplies perceived to be at the very best of library products and services so the existence of libraries was cited as a reason for an appeal for business to are Studies also revealed that resources for business information were of considerable benefit with the knowledgeable facilitation of library staff. In alternative words not solely area unit info resources for folks seeking mission orienting info however skilled services provided by libraries area unit believed through numerous to be critical elements to discover gaining access to and utilization data resources. To the fullest quantity in particular with applicable electronic resources.There can continually be adjustments inside the environment and these modifications will influence their function, process opportunities, selfimage, motivation or even the survival of libraries and information professionals. We board Associate in Nursing data society wherever the event of data technology and telecommunication networks are among the corresponding boom in knowledge, with an apace developing glide of knowledge. This new information set wants new skills in the search for method and knowledge distribution. The lowest potential for a librarian to understand and utilize information will be a qualitative, current knowledge of the nation of technical progress that requires the lot, the provision of authority, to be aware of all essential issues. Though some areas get this advice in schools, school paintings ca [...]Dr. Sarika Bhagwanrao Rengunthwarwork_jz5dvaropjhafg2hyyx54qzeq4Sun, 25 Sep 2022 00:00:00 GMTQuantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models
https://scholar.archive.org/work/iotlilr6tvbopbmbgkvb2rvgpi
Robustness studies of black-box models is recognized as a necessary task for numerical models based on structural equations and predictive models learned from data. These studies must assess the model's robustness to possible misspecification of regarding its inputs (e.g., covariate shift). The study of black-box models, through the prism of uncertainty quantification (UQ), is often based on sensitivity analysis involving a probabilistic structure imposed on the inputs, while ML models are solely constructed from observed data. Our work aim at unifying the UQ and ML interpretability approaches, by providing relevant and easy-to-use tools for both paradigms. To provide a generic and understandable framework for robustness studies, we define perturbations of input information relying on quantile constraints and projections with respect to the Wasserstein distance between probability measures, while preserving their dependence structure. We show that this perturbation problem can be analytically solved. Ensuring regularity constraints by means of isotonic polynomial approximations leads to smoother perturbations, which can be more suitable in practice. Numerical experiments on real case studies, from the UQ and ML fields, highlight the computational feasibility of such studies and provide local and global insights on the robustness of black-box models to input perturbations.Marouane Il Idrissiwork_iotlilr6tvbopbmbgkvb2rvgpiFri, 23 Sep 2022 00:00:00 GMTDensity distribution function of a self-gravitating isothermal turbulent fluid in the context of molecular clouds ensembles – III. Virial analysis
https://scholar.archive.org/work/b2lsdzr6ybbfzausbfrzlr6ctu
In the present work we apply virial analysis to the model of self-gravitating turbulent cloud ensembles introduced by Donkov & Stefanov in two previous papers, clarifying some aspects of turbulence and extending the model to account not only for supersonic flows but for trans- and subsonic ones as well. Make use of the Eulerian virial theorem at an arbitrary scale, far from the cloud core, we derive an equation for the density profile and solve it in approximate way. The result confirms the solution ϱ(ℓ)=ℓ^-2 found in the previous papers. This solution corresponds to three possible configurations for the energy balance. For trans- or subsonic flows, we obtain a balance between the gravitational and thermal energy (Case 1) or between the gravitational, turbulent and thermal energies (Case 2) while for supersonic flows, the possible balance is between the gravitational and turbulent energy (Case 3). In Cases 1 and 2 the energy of the fluid element can be negative or zero end thus the solution is dynamically stable and shall be long lived. In Case 3 the energy of the fluid element is positive or zero, i.e., the solution is unstable or at best marginally bound. At scales near the core, one cannot neglect the second derivative of the moment of inertia of the gas, which prevents derivation of an analytic equation for the density profile. However, we obtain that gas near the core is not virialized and its state is marginally bound since the energy of the fluid element vanishes.S. Donkov, I. Zh. Stefanov, T. V. Veltchev, R. S. Klessenwork_b2lsdzr6ybbfzausbfrzlr6ctuFri, 23 Sep 2022 00:00:00 GMTNeural Network Potentials for Chemistry: Concepts, Applications and Prospects
https://scholar.archive.org/work/m45vw2vdcbhfle5akx3o2xkffu
Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on larger scale.Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly, Kai Töpferwork_m45vw2vdcbhfle5akx3o2xkffuFri, 23 Sep 2022 00:00:00 GMT