977 resultados para Howard, Juwan
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Several studies have shown that sensory contextual cues can reduce the interference observed during learning of opposing force fields. However, because each study examined a small set of cues, often in a unique paradigm, the relative efficacy of different sensory contextual cues is unclear. In the present study we quantify how seven contextual cues, some investigated previously and some novel, affect the formation and recall of motor memories. Subjects made movements in a velocity-dependent curl field, with direction varying randomly from trial to trial but always associated with a unique contextual cue. Linking field direction to the cursor or background color, or to peripheral visual motion cues, did not reduce interference. In contrast, the orientation of a visual object attached to the hand cursor significantly reduced interference, albeit by a small amount. When the fields were associated with movement in different locations in the workspace, a substantial reduction in interference was observed. We tested whether this reduction in interference was due to the different locations of the visual feedback (targets and cursor) or the movements (proprioceptive). When the fields were associated only with changes in visual display location (movements always made centrally) or only with changes in the movement location (visual feedback always displayed centrally), a substantial reduction in interference was observed. These results show that although some visual cues can lead to the formation and recall of distinct representations in motor memory, changes in spatial visual and proprioceptive states of the movement are far more effective than changes in simple visual contextual cues.
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Humans appear to be sensitive to relative small changes in their surroundings. These changes are often initially perceived as irrelevant, but they can cause significant changes in behavior. However, how exactly people's behavior changes is often hard to quantify. A reliable and valid tool is needed in order to address such a question, ideally measuring an important point of interaction, such as the hand. Wearable-body-sensor systems can be used to obtain valuable, behavioral information. These systems are particularly useful for assessing functional interactions that occur between the endpoints of the upper limbs and our surroundings. A new method is explored that consists of computing hand position using a wearable sensor system and validating it against a gold standard reference measurement (optical tracking device). Initial outcomes related well to the gold standard measurements (r = 0.81) showing an acceptable average root mean square error of 0.09 meters. Subsequently, the use of this approach was further investigated by measuring differences in motor behavior, in response to a changing environment. Three subjects were asked to perform a water pouring task with three slightly different containers. Wavelet analysis was introduced to assess how motor consistency was affected by these small environmental changes. Results showed that the behavioral motor adjustments to a variable environment could be assessed by applying wavelet coherence techniques. Applying these procedures in everyday life, combined with correct research methodologies, can assist in quantifying how environmental changes can cause alterations in our motor behavior.
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Fucoidan fractions from the brown seaweed Chorda filum were studied using solvolytic desulfation.Methylation analysis and NMR spectroscopy were applied for native and desulfated polysaccharides.Homefucan sulfate from C.filum was shown to contain poly-a-(1-3)-fucopyranoside backbone with a high degree of branching,mainly of a-(1-2)-linked single units.Some fucopyranose residues are sulfated at O-4(mainly) and O-2 positions.Some a-(1-3)-linked fucose residues were shown by NMR to be 2-O-acetylated.The 1H and 13C NMR spectra of desulfated,deaceylated fucan were complerely assigned.THe spectral data obtained correspond to a quasiregular polysaccharide structure with a branched hexasaccharide repeating unit.Other fucoidan frations from C.filum have more complex carbohydrate composition and give rather complex methvlation patterns.
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This paper introduces Denotational Proof Languages (DPLs). DPLs are languages for presenting, discovering, and checking formal proofs. In particular, in this paper we discus type-alpha DPLs---a simple class of DPLs for which termination is guaranteed and proof checking can be performed in time linear in the size of the proof. Type-alpha DPLs allow for lucid proof presentation and for efficient proof checking, but not for proof search. Type-omega DPLs allow for search as well as simple presentation and checking, but termination is no longer guaranteed and proof checking may diverge. We do not study type-omega DPLs here. We start by listing some common characteristics of DPLs. We then illustrate with a particularly simple example: a toy type-alpha DPL called PAR, for deducing parities. We present the abstract syntax of PAR, followed by two different kinds of formal semantics: evaluation and denotational. We then relate the two semantics and show how proof checking becomes tantamount to evaluation. We proceed to develop the proof theory of PAR, formulating and studying certain key notions such as observational equivalence that pervade all DPLs. We then present NDL, a type-alpha DPL for classical zero-order natural deduction. Our presentation of NDL mirrors that of PAR, showing how every basic concept that was introduced in PAR resurfaces in NDL. We present sample proofs of several well-known tautologies of propositional logic that demonstrate our thesis that DPL proofs are readable, writable, and concise. Next we contrast DPLs to typed logics based on the Curry-Howard isomorphism, and discuss the distinction between pure and augmented DPLs. Finally we consider the issue of implementing DPLs, presenting an implementation of PAR in SML and one in Athena, and end with some concluding remarks.
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The Floyd-Hoare methodology completely dominates the field of program verification and has contributed much to our understanding of how programs might be analyzed. Useful but limited verifiers have been developed using Floyd-Hoare techniques. However, it has long been known that it is difficult to handle side effects on shared data structures within the Floyd-Hoare framework. Most examples of successful Floyd-Hoare axioms for assignment to complex data structures, similar statements have been used by London. This paper demonstrates an error in these formalizations and suggests a different style of verification.
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Location is a primary cue in many context-aware computing systems, and is often represented as a global coordinate, room number, or Euclidean distance various landmarks. A user?s concept of location, however, is often defined in terms of regions in which common activities occur. We show how to partition a space into such regions based on patterns of observed user location and motion. These regions, which we call activity zones, represent regions of similar user activity, and can be used to trigger application actions, retrieve information based on previous context, and present information to users. We suggest that context-aware applications can benefit from a location representation learned from observing users. We describe an implementation of our system and present two example applications whose behavior is controlled by users? entry, exit, and presence in the zones.
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The dream of pervasive computing is slowly becoming a reality. A number of projects around the world are constantly contributing ideas and solutions that are bound to change the way we interact with our environments and with one another. An essential component of the future is a software infrastructure that is capable of supporting interactions on scales ranging from a single physical space to intercontinental collaborations. Such infrastructure must help applications adapt to very diverse environments and must protect people's privacy and respect their personal preferences. In this paper we indicate a number of limitations present in the software infrastructures proposed so far (including our previous work). We then describe the framework for building an infrastructure that satisfies the abovementioned criteria. This framework hinges on the concepts of delegation, arbitration and high-level service discovery. Components of our own implementation of such an infrastructure are presented.
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The Listener is an automated system that unintrusively performs knowledge acquisition from informal input. The Listener develops a coherent internal representation of a description from an initial set of disorganized, imprecise, incomplete, ambiguous, and possibly inconsistent statements. The Listener can produce a summary document from its internal representation to facilitate communication, review, and validation. A special purpose Listener, called the Requirements Apprentice (RA), has been implemented in the software requirements acquisition domain. Unlike most other requirements analysis tools, which start from a formal description language, the focus of the RA is on the transition between informal and formal specifications.
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Artificial Intelligence research involves the creation of extremely complex programs which must possess the capability to introspect, learn, and improve their expertise. Any truly intelligent program must be able to create procedures and to modify them as it gathers information from its experience. [Sussman, 1975] produced such a system for a 'mini-world'; but truly intelligent programs must be considerably more complex. A crucial stepping stone in AI research is the development of a system which can understand complex programs well enough to modify them. There is also a complexity barrier in the world of commercial software which is making the cost of software production and maintenance prohibitive. Here too a system which is capable of understanding complex programs is a necessary step. The Programmer's Apprentice Project [Rich and Shrobe, 76] is attempting to develop an interactive programming tool which will help expert programmers deal with the complexity involved in engineering a large software system. This report describes REASON, the deductive component of the programmer's apprentice. REASON is intended to help expert programmers in the process of evolutionary program design. REASON utilizes the engineering techniques of modelling, decomposition, and analysis by inspection to determine how modules interact to achieve the desired overall behavior of a program. REASON coordinates its various sources of knowledge by using a dependency-directed structure which records the justification for each deduction it makes. Once a program has been analyzed these justifications can be summarized into a teleological structure called a plan which helps the system understand the impact of a proposed program modification.
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This is an initial report on the design and partial implementation of a LISP programmers apprentice, an interactive programming system to be used by an expert programmer in the design, coding, and maintenance of large, complex programs.
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2007
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2007
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Neal M J, Boyce D, Rowland J J, Lee M H, and Olivier P L. Robotic grasping by showing: an experimental comparison of two novel algorithms. In Proceedings of IFAC - SICICA'97, pages 345-350, Annecy, France, 1997.
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M. H. Lee, and S. M. Garrett, Qualitative modelling of unknown interface behaviour, International Journal of Human Computer Studies, Vol. 53, No. 4, pp. 493-515, 2000
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T. G. Williams, J.J. Rowland, and Lee M.H., Robotic Assembly of Naturally Varying Food Items via Teaching by Example, 9th Int. Symp. on Intelligent Robotic Systems (SIRS 2001), July 2001, France, pp133-142.