Judgement and Certainty

Judgement is the interiorisation of assertion: the inner\ud notion of judgement is to be explained in terms of the outer\ud notion of assertion. When someone asserts "Snow is\ud white", an interlocutor is entitled to ask "How do you know?"\ud If the asserter is not able to give grounds for his assertion,\ud it has to be withdrawn. In an assertion an illocutionary\ud claim that one has grounds is present; an assertion is thus\ud a claim to knowledge. Not all occurrences of declarative\ud sentences are asserted. In such cases the context should\ud make it clear that the declarative is, for example, used to\ud express mere opinion or conjecture. Whereas an assertion\ud made is correct or incorrect, other uses of the declarative\ud do not allow for this distinction. Just as for assertion, implicit\ud in every judgement is a claim to knowledge; judgement\ud is an epistemic notion

The Reduced Open Membrane Metric

We discuss the reduction of the open membrane metric and determine the (previously unknown) conformal factor. We also construct SL(2,R) invariant open string metrics and complex open string coupling constants by reducing the open membrane metric on a 2-torus. In doing so we also clarify some issues on manifest SL(2,R) symmetry of the D3-brane. We remark on the consequences of our results for the recently conjectured existence of decoupled (p,q) non-commutative open string theories in type IIB string theory.Comment: 20 pages, LaTeX, uses JHEP.cls and JHEP.bst style files, published JHEP version, note added change

Online Reinforcement Learning for Dynamic Multimedia Systems

In our previous work, we proposed a systematic cross-layer framework for dynamic multimedia systems, which allows each layer to make autonomous and foresighted decisions that maximize the system's long-term performance, while meeting the application's real-time delay constraints. The proposed solution solved the cross-layer optimization offline, under the assumption that the multimedia system's probabilistic dynamics were known a priori. In practice, however, these dynamics are unknown a priori and therefore must be learned online. In this paper, we address this problem by allowing the multimedia system layers to learn, through repeated interactions with each other, to autonomously optimize the system's long-term performance at run-time. We propose two reinforcement learning algorithms for optimizing the system under different design constraints: the first algorithm solves the cross-layer optimization in a centralized manner, and the second solves it in a decentralized manner. We analyze both algorithms in terms of their required computation, memory, and inter-layer communication overheads. After noting that the proposed reinforcement learning algorithms learn too slowly, we introduce a complementary accelerated learning algorithm that exploits partial knowledge about the system's dynamics in order to dramatically improve the system's performance. In our experiments, we demonstrate that decentralized learning can perform as well as centralized learning, while enabling the layers to act autonomously. Additionally, we show that existing application-independent reinforcement learning algorithms, and existing myopic learning algorithms deployed in multimedia systems, perform significantly worse than our proposed application-aware and foresighted learning methods.Comment: 35 pages, 11 figures, 10 table