170 research outputs found

    Safe Recursion on Notation into a Light Logic by Levels

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    We embed Safe Recursion on Notation (SRN) into Light Affine Logic by Levels (LALL), derived from the logic L4. LALL is an intuitionistic deductive system, with a polynomial time cut elimination strategy. The embedding allows to represent every term t of SRN as a family of proof nets |t|^l in LALL. Every proof net |t|^l in the family simulates t on arguments whose bit length is bounded by the integer l. The embedding is based on two crucial features. One is the recursive type in LALL that encodes Scott binary numerals, i.e. Scott words, as proof nets. Scott words represent the arguments of t in place of the more standard Church binary numerals. Also, the embedding exploits the "fuzzy" borders of paragraph boxes that LALL inherits from L4 to "freely" duplicate the arguments, especially the safe ones, of t. Finally, the type of |t|^l depends on the number of composition and recursion schemes used to define t, namely the structural complexity of t. Moreover, the size of |t|^l is a polynomial in l, whose degree depends on the structural complexity of t. So, this work makes closer both the predicative recursive theoretic principles SRN relies on, and the proof theoretic one, called /stratification/, at the base of Light Linear Logic

    On paths-based criteria for polynomial time complexity in proof-nets

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    Girard's Light linear logic (LLL) characterized polynomial time in the proof-as-program paradigm with a bound on cut elimination. This logic relied on a stratification principle and a "one-door" principle which were generalized later respectively in the systems L^4 and L^3a. Each system was brought with its own complex proof of Ptime soundness. In this paper we propose a broad sufficient criterion for Ptime soundness for linear logic subsystems, based on the study of paths inside the proof-nets, which factorizes proofs of soundness of existing systems and may be used for future systems. As an additional gain, our bound stands for any reduction strategy whereas most bounds in the literature only stand for a particular strategy.Comment: Long version of a conference pape

    Deep learning for health outcome prediction

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    Modern medical data contains rich information that allows us to make new types of inferences to predict health outcomes. However, the complexity of modern medical data has rendered many classical analysis approaches insufficient. Machine learning with deep neural networks enables computational models to process raw data and learn useful representations with multiple levels of abstraction. In this thesis, I present novel deep learning methods for health outcome prediction from brain MRI and genomic data. I show that a deep neural network can learn a biomarker from structural brain MRI and that this biomarker provides a useful measure for investigating brain and systemic health, can augment neuroradiological research and potentially serve as a decision-support tool in clinical environments. I also develop two tensor methods for deep neural networks: the first, tensor dropout, for improving the robustness of deep neural networks, and the second, Kronecker machines, for combining multiple sources of data to improve prediction accuracy. Finally, I present a novel deep learning method for predicting polygenic risk scores from genome sequences by leveraging both local and global interactions between genetic variants. These contributions demonstrate the benefits of using deep learning for health outcome prediction in both research and clinical settings.Open Acces

    The 2nd Conference of PhD Students in Computer Science

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    Proceedings of the IJCAI-09 Workshop on Nonmonotonic Reasoning, Action and Change

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    Copyright in each article is held by the authors. Please contact the authors directly for permission to reprint or use this material in any form for any purpose.The biennial workshop on Nonmonotonic Reasoning, Action and Change (NRAC) has an active and loyal community. Since its inception in 1995, the workshop has been held seven times in conjunction with IJCAI, and has experienced growing success. We hope to build on this success again this eighth year with an interesting and fruitful day of discussion. The areas of reasoning about action, non-monotonic reasoning and belief revision are among the most active research areas in Knowledge Representation, with rich inter-connections and practical applications including robotics, agentsystems, commonsense reasoning and the semantic web. This workshop provides a unique opportunity for researchers from all three fields to be brought together at a single forum with the prime objectives of communicating important recent advances in each field and the exchange of ideas. As these fundamental areas mature it is vital that researchers maintain a dialog through which they can cooperatively explore common links. The goal of this workshop is to work against the natural tendency of such rapidly advancing fields to drift apart into isolated islands of specialization. This year, we have accepted ten papers authored by a diverse international community. Each paper has been subject to careful peer review on the basis of innovation, significance and relevance to NRAC. The high quality selection of work could not have been achieved without the invaluable help of the international Program Committee. A highlight of the workshop will be our invited speaker Professor Hector Geffner from ICREA and UPF in Barcelona, Spain, discussing representation and inference in modern planning. Hector Geffner is a world leader in planning, reasoning, and knowledge representation; in addition to his many important publications, he is a Fellow of the AAAI, an associate editor of the Journal of Artificial Intelligence Research and won an ACM Distinguished Dissertation Award in 1990

    Insights on Learning Tractable Probabilistic Graphical Models

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    Insights on Learning Tractable Probabilistic Graphical Models

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    Paradoxes of interactivity: perspectives for media theory, human-computer interaction, and artistic investigations

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    Current findings from anthropology, genetics, prehistory, cognitive and neuroscience indicate that human nature is grounded in a co-evolution of tool use, symbolic communication, social interaction and cultural transmission. Digital information technology has recently entered as a new tool in this co-evolution, and will probably have the strongest impact on shaping the human mind in the near future. A common effort from the humanities, the sciences, art and technology is necessary to understand this ongoing co- evolutionary process. Interactivity is a key for understanding the new relationships formed by humans with social robots as well as interactive environments and wearables underlying this process. Of special importance for understanding interactivity are human-computer and human-robot interaction, as well as media theory and New Media Art. "Paradoxes of Interactivity" brings together reflections on "interactivity" from different theoretical perspectives, the interplay of science and art, and recent technological developments for artistic applications, especially in the realm of sound
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