Search results for "HMI"
showing 10 items of 931 documents
Positive Versions of Polynomial Time
1998
Abstract We show that restricting a number of characterizations of the complexity class P to be positive (in natural ways) results in the same class of (monotone) problems, which we denote by posP . By a well-known result of Razborov, posP is a proper subclass of the class of monotone problems in P . We exhibit complete problems for posP via weak logical reductions, as we do for other logically defined classes of problems. Our work is a continuation of research undertaken by Grigni and Sipser, and subsequently Stewart; indeed, we introduce the notion of a positive deterministic Turing machine and consequently solve a problem posed by Grigni and Sipser.
Span Programs and Quantum Algorithms for st-Connectivity and Claw Detection
2012
We introduce a span program that decides st-connectivity, and generalize the span program to develop quantum algorithms for several graph problems. First, we give an algorithm for st-connectivity that uses O(n d^{1/2}) quantum queries to the n x n adjacency matrix to decide if vertices s and t are connected, under the promise that they either are connected by a path of length at most d, or are disconnected. We also show that if T is a path, a star with two subdivided legs, or a subdivision of a claw, its presence as a subgraph in the input graph G can be detected with O(n) quantum queries to the adjacency matrix. Under the promise that G either contains T as a subgraph or does not contain T…
Adaptive and Generative Learning: Implications from Complexity Theories
2008
One of the most important classical typologies within the organizational learning literature is the distinction between adaptive and generative learning. However, the processes of these types of learning, particularly the latter, have not been widely analyzed and incorporated into the organizational learning process. This paper puts forward a new understanding of adaptive and generative learning within organizations, grounded in some ideas from complexity theories: mainly self-organization and implicate order. Adaptive learning involves any improvement or development of the explicate order through a process of self-organization. Self-organization is a self-referential process characterized …
Shallow Reductionism and the Problem of Complexity in Psychology
2008
In his recent book The Mind Doesn't Work That Way, Fodor argues that computational modeling of global cognitive processes, such as abductive everyday reasoning, has not been successful. In this article the problem is analyzed in the framework of algorithmic information theory. It is argued that the failed approaches are characterized by shallow reductionism, which is rejected in favor of deep reductionism and nonreductionism.
Cloning and training collective intelligence with generative adversarial networks
2021
Industry 4.0 and highly automated critical infrastructure can be seen as cyber‐physical‐social systems controlled by the Collective Intelligence. Such systems are essential for the functioning of the society and economy. On one hand, they have flexible infrastructure of heterogeneous systems and assets. On the other hand, they are social systems, which include collaborating humans and artificial decision makers. Such (human plus machine) resources must be pre‐trained to perform their mission with high efficiency. Both human and machine learning approaches must be bridged to enable such training. The importance of these systems requires the anticipation of the potential and previously unknow…
Geometry/Time Measurement/Sundials Graphical Resolution via Algorithmic and Parametric Processes
2018
Every people, in every historical period, developed methods to measure Time both at a daily scale and at a yearly scale. Some of them constructed sundials to represent the apparent trajectory of the Sun around the Earth, by using and developing tools from descriptive and projective Geometry, mainly. This subject acquired a great multidisciplinary interest since ancient times, also for Science of Representation applications. This study presents the first results of an ongoing research concerning some aspects related to Time Measurement. The geometric-spatial setting of the Sun-Earth system is described and is structured parametrically via algorithms, following the known conventions shared an…
Analytic extension of non quasi-analytic Whitney jets of Roumieu type
1997
Let (Mr)r∈ℕ0 be a logarithmically convex sequence of positive numbers which verifies M0 = 1 as well as Mr≥ 1 for every r ∈ ℕ and defines a non quasi-analytic class. Let moreover F be a closed proper subset of ℝn. Then for every function ƒ on ℝn belonging to the non quasi-analytic (Mr)-class of Roumieu type, there is an element g of the same class which is analytic on ℝnF and such that Dα ƒ(x) = Dαg(x) for every σ ∈ ƒ0n SBAP and x ∈ F.
Learning formulae from elementary facts
1997
Since the seminal paper by E.M. Gold [Gol67] the computational learning theory community has been presuming that the main problem in the learning theory on the recursion-theoretical level is to restore a grammar from samples of language or a program from its sample computations. However scientists in physics and biology have become accustomed to looking for interesting assertions rather than for a universal theory explaining everything.
Gray visiting Motzkins
2002
We present the first Gray code for Motzkin words and their generalizations: k colored Motzkin words and Schroder words. The construction of these Gray codes is based on the observation that a k colored Motzkin word is the shuffle of a Dyck word by a k-ary variation on a trajectory which is a combination. In the final part of the paper we give some algorithmic considerations and other possible applications of the techniques introduced here.
Organized Learning Models (Pursuer Control Optimisation)
1982
Abstract The concept of Organized Learning is defined, and some random models are presented. For Not Transferable Learning, it is necessary to start from an instantaneous learning; by a discrete way, we must form a stochastic model considering the probability of each path; with a continue aproximation, we can study the evolution of the internal state through to consider the relative and absolute probabilities, by means of differential equations systems. For Transferable Learning, the instantaneous learning give us directly the System evolution. So, the Algoritmes for the different models are compared.