Symbol Grounding in Computational Systems: A Paradox of Intentions

The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to symbol grounding. In this case, no symbol grounding could take place since any grounding presupposes intentional cognitive processes. So, whether computing in the mind is over meaningless or over meaningful symbols, computationalism implies semantic nativism.     

Would you mind being watched by machines? Privacy concerns in data mining

Data mining is not an invasion of privacy because access to data is only by machines, not by people: this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the analysis will have to be made either by humans or by machines that understand. It concludes that the current data mining violates the right to privacy and should be subject to the standard legal constraints for access to private information by people.

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Whole-body hierarchical motion and force control for humanoid robots

Robots acting in human environments usually need to perform multiple motion and force tasks while respecting a set of constraints. When a physical contact with the environment is established, the newly activated force task or contact constraint may interfere with other tasks. The objective of this paper is to provide a control framework that can achieve real-time control of humanoid robots performing both strict and non strict prioritized motion and force tasks. It is a torque-based quasi-static control framework, which handles a dynamically changing task hierarchy with simultaneous priority transitions as well as activation or deactivation of tasks. A quadratic programming problem is solved to maintain desired task hierarchies, subject to constraints. A generalized projector is used to quantitatively regulate how much a task can influence or be influenced by other tasks through the modulation of a priority matrix. By the smooth variations of the priority matrix, sudden hierarchy rearrangements can be avoided to reduce the risk of instability. The effectiveness of this approach is demonstrated on both a simulated and a real humanoid robot.     

Gambling versus Designing: Organizing for the Design of the Probability Space in the Energy Sector

The objective of this paper is to elucidate an organizational process for the design of generic technologies (GTs). While recognizing the success of GTs, the literature on innovation management generally describes their design according to evolutionary strategies featuring multiple and uncertain trials, resulting in the discovery of common features among multiple applications. This random walk depends on multiple market and technological uncertainties that are considered exogenous: as smart as he can be, the ‘gambler’ must play in a given probability space. However, what happens when the innovator is not a gambler but a designer, i.e., when the actor is able to establish new links between previously independent emerging markets and technologies? Formally speaking, the actor designs a new probability space. Building on a case study of two technological development programmes at the French Center for Atomic Energy, we present cases of GTs that correspond to this logic of designing the probability space, i.e. the logic of intentionally designing common features that bridge the gap between a priori heterogeneous applications and technologies. This study provides another example showing that the usual trial‐and‐learning strategy is not the only strategy to design GTs and that these technologies can be designed by intentionally building new interdependences between markets and technologies. Our main result is that building these interdependences requires organizational patterns that correspond to a ‘design of exploration’ phase in which multiple technology suppliers and application providers are involved in designing both the probability space itself and the instruments to explore and benefit from this new space.     

Road‐Centered Map‐Aided Localization for Driverless Cars Using Single‐Frequency GNSS Receivers

Accurate localization with high availability is a key requirement for autonomous vehicles. It remains a major challenge when using automotive sensors such as single‐frequency Global Navigation Satellite System (GNSS) receivers, a lane detection camera, and proprioceptive sensors. This paper describes a method that enables the estimation of stand‐alone single‐frequency GNSS errors by integrating the measurements from a forward‐looking camera matched with lane markings stored in a digital map. It includes a parameter identification method for a shaping model, which is evaluated using experimental data. An algebraic observability study is then conducted to prove that the proposed state vector is fully observable in a road‐oriented frame. This observability property is the basis to develop a road‐centered Extended Kalman filter (EKF) that can maintain the observability of every component of the state vector on any road, whatever its orientation. To accomplish this, the filter needs to handle road changes, which it does using bijective transformations. The filter was implemented and tested intensely on an experimental vehicle for driverless valet parking services. Field results have shown that the performance of the estimation process is better than solutions based on EKF implemented in a fixed working frame. The proposed filter guarantees that the drift along the road direction remains bounded. This is very important when the vehicle navigates autonomously. Furthermore, the road‐centered modeling improves the accuracy, consistency, and robustness of the localization solver.     

Adjustable parameter-based multi-objective fault estimation observer design for continuous-time/discrete-time dynamic systems

This paper addresses an adjustable parameter (AP)-based multi-objective fault estimation design for continuous-time/discrete-time dynamic systems. First, a fault estimation observer with AP is constructed to on-line identify the size of occurred faults. The fault estimation design not only possesses a wider application compared with adaptive observers, but also uses the current output information to enhance fault estimation performance. Then a multi-objective approach is proposed to determine the gain matrices of fault estimation observer. Moreover, fault estimation results with the slack-variable technique are obtained to further reduce the conservatism. Finally, simulation results of an aircraft application are presented to illustrate the effectiveness of our contribution.     

Fast computation of minimal elementary decompositions of metabolic flux vectors

The concept of elementary flux vector is valuable in a number of applications of metabolic engineering. For instance, in metabolic flux analysis, each admissible flux vector can be expressed as a non-negative linear combination of a small number of elementary flux vectors. However a critical issue concerns the total number of elementary flux vectors which may be huge because it combinatorially increases with the size of the metabolic network. In this paper we present a fast algorithm that randomly computes a decomposition of admissible flux vectors in a minimal number of elementary flux vectors without explicitly enumerating all of them.     

A multiphase compressible model for the simulation of multiphase flows

A compressible model able to manage incompressible two-phase flows as well as compressible motions is proposed. After a presentation of the multiphase compressible concept, the new model and related numerical methods are detailed on fixed structured grids. The presented model is a 1-fluid model with a reformulated mass conservation equation which takes into account the effects of compressibility. The coupling between pressure and flow velocity is ensured by introducing mass conservation terms in the momentum and energy equations. The numerical model is then validated with four test cases involving the compression of an air bubble by water, the liquid injection in a closed cavity filled with air, a bubble subjected to an ultrasound field and finally the oscillations of a deformed air bubble in melted steel. The numerical results are compared with analytical results and convergence orders in space are provided.     

A novel linearly-weighted gradient smoothing method (LWGSM) in the simulation of fluid dynamics problem

This paper describes a novel linearly-weighted gradient smoothing method (LWGSM). The proposed method is based on irregular cells and thus can be used for problems with arbitrarily complex geometrical boundaries. Based on the analysis about the compactness and the positivity of coefficients of influence of their stencils for approximating a derivative, one favorable scheme (VIII) is selected among total eight proposed discretization schemes. This scheme VIII is successively verified and carefully examined in solving Poisson equations, subjected to changes in the number of nodes, the shapes of cells and the irregularity of triangular cells, respectively. Strong form of incompressible Navier–Stokes equations enhanced with artificial compressibility terms are tackled, in which the spatial derivatives are approximated by consistent and successive use of gradient smoothing operation over smoothing domains at various locations. All the test cases using LWGSM solver exhibits its robust, stable and accurate behaviors. The attained incompressible LWGSM solutions show good agreements with experimental and literature data. Therefore, the proposed LWGSM can be reliably used for accurate solutions to versatile fluid flow problems.     

How to improve snap-stabilizing point-to-point communication space complexity?

A snap-stabilizing protocol, starting from any configuration, always behaves according to its specification. In this paper, we are interested in the message forwarding problem in a message-switched network in which the system resources must be managed in order to deliver messages to any processor of the network. To this end, we use the information provided by a routing algorithm. In the context of an arbitrary initialization (due to stabilization), this information may be corrupted. In Cournier et al. (2009) [1], we show that there exist snap-stabilizing algorithms for this problem (in the state model). This implies that we can request the system to begin forwarding messages without losses even if routing information is initially corrupted.In this paper, we propose another snap-stabilizing algorithm for this problem which improves the space complexity of the one in Cournier et al. (2009) [1].