Report from Dagstuhl Seminar 17181 Theory and Applications of Hashing

This report documents the program and the topics discussed of the 4-day Dagstuhl Seminar 17181 “Theory and Applications of Hashing”, which took place May 1–5, 2017. Four long and eighteen short talks covered a wide and diverse range of topics within the theme of the workshop. The program left sufficient space for informal discussions among the 40 participants. Seminar May 1–5, 2017 – http://www.dagstuhl.de/17181 1998 ACM Subject Classification F.2 Analysis of Algorithms and Problem Complexity, H.3 Information Storage and Retrieval

[1]  Martha Lewis,et al.  Interacting Conceptual Spaces I : Grammatical Composition of Concepts , 2017, Conceptual Spaces: Elaborations and Applications.

[2]  Xiaofei Wang,et al.  Computational design of wind-up toys , 2017, ACM Trans. Graph..

[3]  Tillman Weyde,et al.  Singing Voice Separation with Deep U-Net Convolutional Networks , 2017, ISMIR.

[4]  Huacheng Yu,et al.  Fillable arrays with constant time operations and a single bit of redundancy , 2017, ArXiv.

[5]  Nathalie Sprynski,et al.  Shape from sensors: Curve networks on surfaces from 3D orientations , 2017, Comput. Graph..

[6]  Michael Spranger,et al.  Computational models of tutor feedback in language acquisition , 2017, 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob).

[7]  Bert Jüttler,et al.  Low rank interpolation of boundary spline curves , 2017, Comput. Aided Geom. Des..

[8]  T. Hughes,et al.  Smooth cubic spline spaces on unstructured quadrilateral meshes with particular emphasis on extraordinary points: Geometric design and isogeometric analysis considerations , 2017 .

[9]  Artur S. d'Avila Garcez,et al.  Logic Tensor Networks for Semantic Image Interpretation , 2017, IJCAI.

[10]  Alessandra Sestini,et al.  Adaptive scattered data fitting by extension of local approximations to hierarchical splines , 2017, Comput. Aided Geom. Des..

[11]  Qin Zhang,et al.  EmbedJoin: Efficient Edit Similarity Joins via Embeddings , 2017, KDD.

[12]  Leon van der Torre,et al.  Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples , 2017, Minds and Machines.

[13]  Alessandra Sestini,et al.  Isogemetric analysis and symmetric Galerkin BEM: A 2D numerical study , 2021, Appl. Math. Comput..

[14]  Wei Tsang Ooi,et al.  Sprite tree: an efficient image-based representation for networked virtual environments , 2017, The Visual Computer.

[15]  Robert Stevens,et al.  Making Entailment Set Changes Explicit Improves the Understanding of Consequences of Ontology Authoring Actions , 2016, EKAW.

[16]  Mathias Bæk Tejs Knudsen,et al.  Linear Hashing Is Awesome , 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

[17]  Ulrich Furbach,et al.  Commonsense Reasoning Meets Theorem Proving , 2016, Bridging@IJCAI.

[18]  Stephen Muggleton,et al.  How Does Predicate Invention Affect Human Comprehensibility? , 2016, ILP.

[19]  Katrien Beuls,et al.  Referential uncertainty and word learning in high-dimensional, continuous meaning spaces , 2016, 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob).

[20]  Qin Zhang,et al.  Edit Distance: Sketching, Streaming, and Document Exchange , 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

[21]  Tom Lyche,et al.  On the dimension of Tchebycheffian spline spaces over planar T-meshes , 2016, Comput. Aided Geom. Des..

[22]  Di Chen,et al.  Streaming Algorithms for Robust Distinct Elements , 2016, SIGMOD Conference.

[23]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[24]  Frank van der Velde,et al.  Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing , 2016, Front. Neurorobot..

[25]  Alfons Kemper,et al.  Flow-Join: Adaptive skew handling for distributed joins over high-speed networks , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[26]  Chandrajit L. Bajaj,et al.  Construction of Scalar and Vector Finite Element Families on Polygonal and Polyhedral Meshes , 2014, Comput. Methods Appl. Math..

[27]  Tom Lyche,et al.  Generalized spline spaces over T-meshes: Dimension formula and locally refined generalized B-splines , 2016, Appl. Math. Comput..

[28]  Daniel L. Silver,et al.  A Scalable Unsupervised Deep Multimodal Learning System , 2016, FLAIRS.

[29]  Frank van der Velde,et al.  Combinatorial Structures and Processing in Neural Blackboard Architectures , 2015, CoCo@NIPS.

[30]  Luc Steels,et al.  Co-Acquisition of Syntax and Semantics - An Investigation in Spatial Language , 2015, IJCAI.

[31]  Guido Boella,et al.  Neural-symbolic monitoring and adaptation , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[32]  Robert Stevens,et al.  Constructing Conceptual Knowledge Artefacts: Activity Patterns in the Ontology Authoring Process , 2015, CHI.

[33]  Luc De Raedt,et al.  Neural-Symbolic Learning and Reasoning: Contributions and Challenges , 2015, AAAI Spring Symposia.

[34]  Marc de Kamps,et al.  The necessity of connection structures in neural models of variable binding , 2015, Cognitive neurodynamics.

[35]  Frank van der Velde,et al.  Communication, concepts and grounding , 2015, Neural Networks.

[36]  Tillman Weyde,et al.  A hybrid recurrent neural network for music transcription , 2014, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[37]  Paul Kraus.  Will To Appear , 2015 .

[38]  Caroline Jay,et al.  Overcoming the pitfalls of ontology authoring: Strategies and implications for tool design , 2014, Int. J. Hum. Comput. Stud..

[39]  Chandrajit L. Bajaj,et al.  On Low Discrepancy Samplings in Product Spaces of Motion Groups , 2014, ArXiv.

[40]  Jeffrey F. Naughton,et al.  Uncertainty Aware Query Execution Time Prediction , 2014, Proc. VLDB Endow..

[41]  Bert Jüttler,et al.  Matrix Generation in Isogeometric Analysis by Low Rank Tensor Approximation , 2014, Curves and Surfaces.

[42]  Dov M. Gabbay,et al.  A neural cognitive model of argumentation with application to legal inference and decision making , 2014, J. Appl. Log..

[43]  Bernard Mourrain,et al.  On the dimension of spline spaces on planar T-meshes , 2010, Math. Comput..

[44]  Pascal Hitzler,et al.  Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381) , 2014, Dagstuhl Reports.

[45]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[46]  Jeffrey F. Naughton,et al.  Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads , 2013, Proc. VLDB Endow..

[47]  Chandrajit L. Bajaj,et al.  Nonuniform Fourier Transforms for Rigid-Body and Multidimensional Rotational Correlations , 2013, SIAM J. Sci. Comput..

[48]  Jeffrey F. Naughton,et al.  Predicting query execution time: Are optimizer cost models really unusable? , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[49]  Thomas Jansen,et al.  Mutation Rate Matters Even When Optimizing Monotonic Functions , 2013, Evolutionary Computation.

[50]  Nitish Srivastava,et al.  Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..

[51]  Christian S. Jensen,et al.  Efficiently adapting graphical models for selectivity estimation , 2012, The VLDB Journal.

[52]  Pascal Barla,et al.  Growing Least Squares for the Analysis of Manifolds in Scale‐Space , 2012, Comput. Graph. Forum.

[53]  Eli Upfal,et al.  Learning-based Query Performance Modeling and Prediction , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[54]  Yoshua Bengio,et al.  Evolving Culture vs Local Minima , 2012, ArXiv.

[55]  Thomas J. R. Hughes,et al.  On linear independence of T-spline blending functions , 2012, Comput. Aided Geom. Des..

[56]  Goetz Graefe,et al.  New algorithms for join and grouping operations , 2012, Computer Science - Research and Development.

[57]  Artur S. d'Avila Garcez,et al.  Learning and Representing Temporal Knowledge in Recurrent Networks , 2011, IEEE Transactions on Neural Networks.

[58]  Christian S. Jensen,et al.  Lightweight graphical models for selectivity estimation without independence assumptions , 2011, Proc. VLDB Endow..

[59]  Volker Markl,et al.  Tractor pulling on data warehouses , 2011, DBTest '11.

[60]  F. Velde,et al.  Compositional connectionist structures based on in situ grounded representations , 2011, Connect. Sci..

[61]  Frank van der Velde,et al.  Learning of control in a neural architecture of grounded language processing , 2010, Cognitive Systems Research.

[62]  Stephen Clark,et al.  Mathematical Foundations for a Compositional Distributional Model of Meaning , 2010, ArXiv.

[63]  Jan Schneider-Barnes,et al.  Error estimates for two-dimensional cross approximation , 2010, J. Approx. Theory.

[64]  Sebastiano Vigna,et al.  Monotone minimal perfect hashing: searching a sorted table with O(1) accesses , 2009, SODA.

[65]  Dov M. Gabbay,et al.  Neural-Symbolic Cognitive Reasoning , 2008, Cognitive Technologies.

[66]  Keith Stenning,et al.  Human Reasoning and Cognitive Science , 2008 .

[67]  Pascal Hitzler,et al.  Perspectives of Neural-Symbolic Integration , 2007, Studies in Computational Intelligence.

[68]  Dov M. Gabbay,et al.  Connectionist modal logic: Representing modalities in neural networks , 2007, Theor. Comput. Sci..

[69]  Thomas Jansen,et al.  On the brittleness of evolutionary algorithms , 2007, FOGA'07.

[70]  Nitesh V. Chawla,et al.  A Black-Box Approach to Query Cardinality Estimation , 2007, CIDR.

[71]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[72]  F. van der Velde,et al.  Neural blackboard architectures of combinatorial structures in cognition , 2006, Behavioral and Brain Sciences.

[73]  Artur S. d'Avila Garcez,et al.  Reasoning about Time and Knowledge in Neural Symbolic Learning Systems , 2003, NIPS.

[74]  Prasad Tetali,et al.  The Number of Linear Extensions of the Boolean Lattice , 2003, Order.

[75]  Krysia Broda,et al.  Neural-symbolic learning systems - foundations and applications , 2012, Perspectives in neural computing.

[76]  Herbert Jaeger,et al.  The''echo state''approach to analysing and training recurrent neural networks , 2001 .

[77]  F. van der Velde,et al.  From Knowing What to Knowing Where: Modeling Object-Based Attention with Feedback Disinhibition of Activation , 2001, Journal of Cognitive Neuroscience.

[78]  Z. Dienes,et al.  Rule learning by seven-month-old infants and neural networks , 1999 .

[79]  K. Stenning,et al.  Image and Language in Human Reasoning: A Syllogistic Illustration , 1997, Cognitive Psychology.

[80]  Dov M. Gabbay,et al.  Epistemic and temporal reasoning , 1995 .

[81]  Geoffrey E. Hinton.  Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1991 .

[82]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[83]  Kurt Mehlhorn,et al.  On the program size of perfect and universal hash functions , 1982, 23rd Annual Symposium on Foundations of Computer Science (sfcs 1982).

[84]  C. R. Deboor,et al.  A practical guide to splines , 1978 .

[85]  N. Foo.  Conceptual Spaces—The Geometry of Thought , 2022 .