ppxscal/embedding_network huggingface dataset:
Sustainable operation and management of data center chillers using temporal data mining
Authors: Debprakash Patnaik, Manish Marwah, Ratnesh Sharma, Naren Ramakrishnan
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Motivation: Data centers are a critical component of modern IT infrastructure but are also among the worst environmental offenders through their increasing energy usage and the resulting large carbon footprints. Efficient management of data centers, including power management, networking, and cooling infrastructure, is hence crucial to sustainability. In the absence of a 'first-principles' approach to manage these complex components and their interactions, data-driven approaches have become attractive and tenable. Results: We present a temporal data mining solution to model and optimize performance of data center chillers, a key component of the cooling infrastructure. It helps bridge raw, numeric, time-series information from sensor streams toward higher level characterizations of chiller behavior, suitable for a data center engineer. To aid in this transduction, temporal data streams are first encoded into a symbolic representation, next run-length encoded segments are mined to form frequent motifs in time series, and finally these metrics are evaluated by their contributions to sustainability. A key innovation in our application is the ability to intersperse "don't care" transitions (e.g., transients) in continuous-valued time series data, an advantage we inherit by the application of frequent episode mining to symbolized representations of numeric time series. Our approach provides both qualitative and quantitative characterizations of the sensor streams to the data center engineer, to aid him in tuning chiller operating characteristics. This system is currently being prototyped for a data center managed by HP and experimental results from this application reveal the promise of our approach.
Molecular beam deposition of LaAlO3 on silicon for sub-22nm CMOS technological nodes: Towards a perfect control of the oxide/silicon heterointerface
Authors: S. Pelloquin, L. Becerra, G. Saint-Girons, C. Plossu, N. Baboux, D. Albertini, G. Grenet, G. Hollinger
Venue: Microelectronic Engineering (2009)

This work reports on the development of thin amorphous LaAlO"3 (LAO) layers on Si(001) for their integration as gate oxide in sub-22nm CMOS technologies. The crucial influence of the Si surface preparation is highlighted and an optimized surface preparation procedure is proposed. An unexpected interface reaction during Rapid Thermal Annealing is also evidenced, and an explanation attempt of the origin of this reaction, involving surface contaminants, is proposed.
Decision trees and influences of variables over product probability spaces
Authors: Hamed Hatami
Venue: Combinatorics, Probability and Computing (2009)

A celebrated theorem of Friedgut says that every function f : {0, 1}n → {0, 1} can be approximated by a function g : {0, 1}n → {0, 1} with <inline-graphic mime-subtype="gif" xlink="S0963548309009833_inline1"><alt-text>$\|f-g\|_2^2 \leq \epsilon$</alt-text></inline-graphic>, which depends only on eO(If / ε) variables, where If is the sum of the influences of the variables of f. Dinur and Friedgut later showed that this statement also holds if we replace the discrete domain {0, 1}n with the continuous domain [0, 1]n, under the extra assumption that f is increasing. They conjectured that the condition of monotonicity is unnecessary and can be removed. We show that certain constant-depth decision trees provide counter-examples to the Dinur–Friedgut conjecture. This suggests a reformulation of the conjecture in which the function g : [0, 1]n → {0, 1}, instead of depending on a small number of variables, has a decision tree of small depth. In fact we prove this reformulation by showing that the depth of the decision tree of g can be bounded by eO(If / ε2). Furthermore, we consider a second notion of the influence of a variable, and study the functions that have bounded total influence in this sense. We use a theorem of Bourgain to show that these functions have certain properties. We also study the relation between the two different notions of influence.
Simulation study of the inventory effect of the scientific paper publishing process
Authors: Guang Yu, Yi-Jun Li, Da-Ren Yu
Venue: Journal of Information Science (2009)

By simulating the change process of the inventory of scientific papers according to a mathematical model of the publishing process, we find that publication delay would cause statistical error of the scientific paper output indicator, i.e. the quantity of published papers. Based on a simulation model and the average publication delay, we construct an optimization algorithm of the actual scientific paper output using a nonlinear least squares algorithm and validate its accuracy. To simplify the solution process, we propose an approximate algorithm of the actual scientific paper output indicator which would help science and technology managers practically deduce an approximation of the actual indicator. This has implications for strategy decision making and allocation of resources for science and technology research by policy makers.
Nonlinear Magnetization Dynamics in Nanosystems
Authors: Isaak D. Mayergoyz, Giorgio Bertotti, Claudio Serpico
Venue: Elsevier Series In Electromagnetism (2009)

As data transfer rates increase within the magnetic recording industry, improvements in device performance and reliability crucially depend on the thorough understanding of nonlinear magnetization dynamics at a sub-nanoscale level. This book offers a modern, stimulating approach to the subject of nonlinear magnetization dynamics by discussing important aspects such as the Landau-Lifshitz-Gilbert (LLG) equation, analytical solutions, and the connection between the general topological and structural aspects of dynamics. An advanced reference for the study and understanding of nonlinear magnetization dynamics, it addresses situations such as the understanding of spin dynamics in short time scales and device performance and reliability in magnetic recording. Topics covered include nonlinear magnetization dynamics and the Landau-Lifshitz-Gilbert equation, nonlinear dynamical systems, spin waves, ferromagnetic resonance and pulsed magnetization switching.The book explains how to derive exact analytical solutions for the complete nonlinear problem and emphasises the connection between the general topological and structural aspects of nonlinear magnetization dynamics and the discretization schemes better suited to its numerical study. It is an exceptional research tool providing an advanced understanding of the study of magnetization dynamics in situations of fundamental and technological interest.
Improving linear test data compression
Authors: Kedarnath J. Balakrishnan, Nur A. Touba
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2006)

The output space of a linear decompressor must be sufficiently large to contain all the test cubes in the test set. The ideas proposed in this paper transform the output space of a linear decompressor so as to reduce the number of inputs required thereby increasing compression while still keeping all the test cubes in the output space. Scan inversion is used to invert a subset of the scan cells while reconfiguration modifies the linear decompressor. Any existing method for designing a linear decompressor (either combinational or sequential) can be used first to obtain the best linear decompressor that it can. Using that linear decompressor as a starting point, the proposed methods improve the compression further. The key property of scan inversion is that it is a linear transformation of the output space and, thus, the output space remains a linear subspace spanned by a Boolean matrix. Using this property, a systematic procedure based on linear algebra is described for selecting the set of inverting scan cells to maximize compression. A symbolic Gaussian elimination method to solve a constrained Boolean matrix is proposed and utilized for reconfiguring the linear decompressor. The proposed schemes can be utilized in various design flow scenarios and require no or very little hardware overhead. Experiments indicate that significant improvements in compression can be achieved.
Effective motion tracking using known and learned actuation models
Authors: Manuela Veloso
Venue:  (2008)

Robots need to track objects. We consider tasks where robots actuate on the target that is visually tracked. Object tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. The motion model of the target becomes particularly complex in the presence of multiple agents acting on a mobile target. We assume that the tracked object is actuated by a team of agents, composing of robots and possibly humans. Robots know their own actions, and team members are collaborating according to coordination plans and communicated information. The thesis shows that using a previously known or learned action model of the single robot or team members improves the efficiency of tracking. First, we introduce and implement a novel team-driven motion tracking approach. Team-driven motion tracking is a tracking paradigm defined as a set of principles for the inclusion of a hierarchical, prior knowledge and construction of a motion model. We illustrate a possible set of behavior levels within the Segway soccer domain that correspond to the abstract motion modeling decomposition. Second, we introduce a principled approach to incorporate models of the robot-object interaction into the tracking algorithm to effectively improve the performance of the tracker. We present the integration of a single robot behavioral model in terms of skills and tactics with multiple actions into our dynamic Bayesian probabilistic tracking algorithm. Third, we extend to multiple motion tracking models corresponding to known multi-robot coordination plans or from multi-robot communication. We evaluate our resulting informed-tracking approach empirically in simulation and using a setup Segway soccer task. The input of the multiple single and multi-robot behavioral sources allow a robot to much more effectively visually track mobile targets with dynamic trajectories. Fourth, we present a parameter learning algorithm to learn actuation models. We describe the parametric system model and the parameters we need to learn in the actuation model. As in the KLD-sampling algorithm applied to tracking, we adapt the number of modeling particles and learn the unknown parameters. We successfully decrease the computation time of learning and the state estimation process by using significantly fewer particles on average. We show the effectiveness of learning using simulated experiments. The tracker that uses the learned actuation model achieves improved tracking performance. These contributions demonstrate that it is possible to effectively improve an agent's object tracking ability using tactics, plays, communication and learned action models in the presence of multiple agents acting on a mobile object. The introduced tracking algorithms are proven effective in a number of simulated experiments and setup Segway robot soccer tasks. The team-driven motion tracking framework is demonstrated empirically across a wide range of settings of increasing complexity.
Parallel Simulation of Bevel Gear Cutting Processes with OpenMP Tasks
Authors: Paul Kapinos, Dieter Mey
Venue: Proceedings of the 5th International Workshop on OpenMP: Evolving OpenMP in an Age of Extreme Parallelism (2009)

Modeling of bevel gear cutting processes requires highly flexible data structures and algorithms. We compare OpenMP3.0 tasks with previously applied approaches like nesting parallel sections and stack based algorithms when parallelizing recursive procedures written in Fortran 95 working on binary tree structures.
Learning semantic links from a corpus of parallel temporal and causal relations
Authors: Steven Bethard, James H. Martin
Venue: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers (2008)

Finding temporal and causal relations is crucial to understanding the semantic structure of a text. Since existing corpora provide no parallel temporal and causal annotations, we annotated 1000 conjoined event pairs, achieving inter-annotator agreement of 81.2% on temporal relations and 77.8% on causal relations. We trained machine learning models using features derived from WordNet and the Google N-gram corpus, and they outperformed a variety of baselines, achieving an F-measure of 49.0 for temporals and 52.4 for causals. Analysis of these models suggests that additional data will improve performance, and that temporal information is crucial to causal relation identification.
Energetic analysis of fragmentation mechanisms and dynamic delamination modelling in CFRP composites
Authors: J. -M. Guimard, O. Allix, N. Pechnik, P. Thevenet
Venue: Computers and Structures (2009)

This paper deals with the understanding and the numerical simulation of complex dissipative phenomena involved in composite absorbers for two main degradation modes: the fragmentation and the delamination modes. Fragmentation is the last step of degradation in pure compression leading to total failure of a laminate and for CFRP it is mainly initiated by a microbuckling mechanism, which is dependant on the microstructural imperfections. The dissipated energies associated to the fragment creation are evaluated through micromodelling approaches and contrary to expectations, these energies seem to be insensitive to microscopic parameters. A Weibull density knowledge on peak load and dissipated energy is also proposed. On the other hand, computation control on the delamination process has been investigated. Numerical sensitivities are justified, thanks to analytical approaches and physical-based mesh criteria are proposed for classical propagation tests. Some quasi-static experiments have been performed and good accuracy is obtained with the model. All these results give perspectives to implement delamination and fragmentation interactions at mesoscale within an absorber-like configuration.
Structuring of Multimedia Contents for E-Learning Lessons in Multimedia Knowledge Base
Authors: Rafisha Ramly, Eng-Thiam Yeoh, Kai-Li Ng
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

This paper describes the research on the structure of multimedia contents in the knowledge base of the Multimedia Knowledge Base E-Learning (MKBe-Learning) System [1]. The Object-Oriented Analysis and Design (OOAD) [2] of the MKBe-Learning System identified the structure of the knowledge base to represent the lessons created by lecturers and the reusable contents of a subject. This is represented in the Instructional View and Knowledge View of the knowledge base. The initial contents developed for Basic Electronics [3] demonstrates these representations and the analysis of the results is given on the content structure and the categorization of contents in the knowledge base.
Plenary lecture 1: neuro-dynamic optimization and its applications in robotics
Authors: Jun Wang
Venue: Proceedings of the 9th WSEAS international conference on Robotics, control and manufacturing technology (2009)

Optimization problems arise in a wide variety of scientific and engineering applications. It is computationally challenging when optimization procedures have to be performed in real time to optimize the performance of dynamical systems. For such applications, classical optimization techniques may not be competent due to the problem dimensionality and stringent requirement on computational time. One very promising approach to dynamic optimization is to apply artificial neural networks. Because of the inherent nature of parallel and distributed information processing in neural networks, the convergence rate of the solution process is not decreasing as the size of the problem increases. Neural networks can be implemented physically in designated hardware such as ASICs where optimization is carried out in a truly parallel and distributed manner. This feature is particularly desirable for dynamic optimization in decentralized decision-making situations arising frequently in robotics. In this talk, I will present the historic review and the state of the art of neurodynamic optimization models and selected applications in robotics. Specifically, starting from the motivation of neurodynamic optimization, I will review various recurrent neural network models for optimization. Theoretical results about the stability and optimality of the neurodynamic optimization models will be given along with illustrative examples and simulation results. It will be shown that many problems in intelligent robotic systems, such as robot motion planning grasping force optimization, can be readily solved by using the neurodynamic optimization models.
Situation Creator: A Pedagogical Agent Creating Learning Opportunities
Authors: Yongwu Miao, Ulrich Hoppe, Niels Pinkwart
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

In a multi-user, real-time, and situation-based learning environment, the availability of enough and appropriate situations is crucial for success. In order to improve effectiveness and efficiency of learning, we develop a new type of pedagogical agent: situation creator. Such an agent intentionally creates specific situations in the shared virtual driving place according to users' performance information. We conduct a pilot evaluation and found that the situation creators significantly increase the number of situations that a learner can expect to encounter while using the system.
CBR for Advice Giving in a Data-Intensive Environment
Authors: Agnar Aamodt
Venue: Proceeding of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008 (2008)

Reusing past experiences by reasoning from past cases poses particular problems when the input to case retrieval comes from large amounts of online data. Volve has developed a system in which data from oil well drilling logs are continuously monitored, interpreted, and used to check if previous incidents exist that may predict an unwanted event to happen unless preventive actions are taken. The system is currently being tested.
Career-Path Analysis Using Optimal Matching and Self-Organizing Maps
Authors: Sébastien Massoni, Madalina Olteanu, Patrick Rousset
Venue: Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps (2009)

This paper is devoted to the analysis of career paths and employability. The state-of-the-art on this topic is rather poor in methodologies. Some authors propose distances well adapted to the data, but are limiting their analysis to hierarchical clustering. Other authors apply sophisticated methods, but only after paying the price of transforming the categorical data into continuous, via a factorial analysis. The latter approach has an important drawback since it makes a linear assumption on the data. We propose a new methodology, inspired from biology and adapted to career paths, combining optimal matching and self-organizing maps. A complete study on real-life data will illustrate our proposal.
A Sparse Regularized Least-Squares Preference Learning Algorithm
Authors: Evgeni Tsivtsivadze, Tapio Pahikkala, Antti Airola, Jorma Boberg, Tapio Salakoski
Venue: Proceeding of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008 (2008)

Learning preferences between objects constitutes a challenging task that notably differs from standard classification or regression problems. The objective involves prediction of ordering of the data points. Furthermore, methods for learning preference relations usually are computationally more demanding than standard classification or regression methods. Recently, we have proposed a kernel based preference learning algorithm, called RankRLS, whose computational complexity is cubic with respect to the number of training examples. The algorithm is based on minimizing a regularized least-squares approximation of a ranking error function that counts the number of incorrectly ranked pairs of data points. When nonlinear kernel functions are used, the training of the algorithm might be infeasible if the amount of examples is large. In this paper, we propose a sparse approximation of RankRLS whose training complexity is considerably lower than that of basic RankRLS. In our experiments, we consider parse ranking, a common problem in natural language processing. We show that sparse RankRLS significantly outperforms basic RankRLS in this task. To conclude, the advantage of sparse RankRLS is the computational efficiency when dealing with large amounts of training data together with high dimensional feature representations.
Designing domain-specific behaviours in structural computing
Authors: Manolis Tzagarakis, Michail Vaitis, Nikos Karousos
Venue: The New Review of Hypermedia and Multimedia (2006)

Developing domain-specific abstractions in hypermedia is a complex task. Part of this complexity is due to the fact that behavioural aspects of structures need to be addressed. Therefore, hypermedia systems must provide the necessary means that permit the specification of these aspects. This is in particular true for structural computing systems which attempt to provide the framework for supporting a broad range of different data-organization problems. Current structural computing systems provide only limited support for developing behaviours in structures, mainly by specifying methods for objects that represent structure elements (structural atoms). In this paper, we present how behavioural aspects of structures are developed in Callimachus, a structural computing environment. Callimachus attempts to raise the level of abstraction by introducing event and propagation templates at the design phase that assist the development of domain-specific behaviours in structures. These mechanisms signify the attempt to move away from an atom-based behaviour provision towards system-and pattern-based approaches.
Organizational Linkages for Surviving Technological Change: Complementary Assets, Middle Management, and Ambidexterity
Authors: Alva Taylor, Constance E. Helfat
Venue: Organization Science (2009)

Technological innovation sometimes requires industry incumbents to shift to a completely new core technology. To successfully navigate a technological transition, firms often face the ambidextrous challenge of “exploiting” existing complementary assets to support the new “exploratory” core technology. We argue that an industry incumbent attempting to make a transition to a new technology requires linkages between organizational units responsible for developing the new technology and units in charge of complementary assets needed to commercialize the innovation. These linkages are critical but overlooked elements of organizational ambidexterity. This paper develops a conceptual framework in which the ability to build and leverage organizational linkages involving the new technology and its complementary assets is essential for a successful technological transition. The framework also highlights the importance of middle management in creating and maintaining these linkages, which are critical to dynamic capabilities in technological transitions. We identify four critical influences---economic, structural, social, and cognitive---on managerial linking activity that enable firms to transition to a new technology while utilizing valuable preexisting capabilities. The technological transitions of IBM and NCR illustrate the importance of organizational linkages and managerial linking activity.