Intelligent GIS: Automatic generation of qualitative spatial information

This paper reports on an extension to Geographic Information Systems (GIS) that can intelligently analyse and record qualitative information of the surrounding area when adding a feature to a map. This recorded qualitative spatial information can be utilised to perform queries such as path generation using landmarks. Although, there is a lot of research on qualitative spatial reasoning, none of the currently available GIS do actually incorporate this kind of functionality. There have been systems developed that do have functions for adding new features, or generating paths; however they do not generally analyse and record, or use, qualitative information. We have implemented a prototype illustrating our approach. © Springer-Verlag Berlin Heidelberg 2006

Electrical transport properties of manganite powders under pressure

We have measured the electrical resistance of micrometric to nanometric powders of the La$_{5/8-y}$Pr$_y$Ca$_{3/8}$MnO$_3$ (LPCMO with y=0.3) manganite for hydrostatic pressures up to 4 kbar. By applying different final thermal treatments to samples synthesized by a microwave assisted denitration process, we obtained two particular grain characteristic dimensions (40 nm and 1000 nm) which allowed us to analyze the grain size sensitivity of the electrical conduction properties of both the metal electrode interface with manganite (Pt / LPCMO) as well as the intrinsic intergranular interfaces formed by the LPCMO powder, conglomerate under the only effect of external pressure. We also analyzed the effects of pressure on the phase diagram of these powders. Our results indicate that different magnetic phases coexist at low temperatures and that the electrical transport properties are related to the intrinsic interfaces, as we observe evidences of a granular behavior and an electronic transport dominated by the Space Charge limited Current mechanism.Comment: 4 pages, 7 figures, to be published in Physica B Corresponding author: C. Acha ([email protected]

The arctic curve of the domain-wall six-vertex model

The problem of the form of the `arctic' curve of the six-vertex model with domain wall boundary conditions in its disordered regime is addressed. It is well-known that in the scaling limit the model exhibits phase-separation, with regions of order and disorder sharply separated by a smooth curve, called the arctic curve. To find this curve, we study a multiple integral representation for the emptiness formation probability, a correlation function devised to detect spatial transition from order to disorder. We conjecture that the arctic curve, for arbitrary choice of the vertex weights, can be characterized by the condition of condensation of almost all roots of the corresponding saddle-point equations at the same, known, value. In explicit calculations we restrict to the disordered regime for which we have been able to compute the scaling limit of certain generating function entering the saddle-point equations. The arctic curve is obtained in parametric form and appears to be a non-algebraic curve in general; it turns into an algebraic one in the so-called root-of-unity cases. The arctic curve is also discussed in application to the limit shape of $q$-enumerated (with $0<q\leq 4$) large alternating sign matrices. In particular, as $q\to 0$ the limit shape tends to a nontrivial limiting curve, given by a relatively simple equation.Comment: 39 pages, 2 figures; minor correction

Visual Ontology Cleaning: Cognitive Principles and Applicability

In this paper we connect two research areas, the Qualitative Spatial Reasoning and visual reasoning on ontologies. We discuss the logical limitations of the mereotopological approach to the visual ontology cleaning, from the point of view of its formal support. The analysis is based on three different spatial interpretations wich are based in turn on three different spatial interpretations of the concepts of an ontology.Ministerio de Educación y Ciencia TIN2004-0388

Exact solution of the six-vertex model with domain wall boundary condition. Critical line between ferroelectric and disordered phases

This is a continuation of the papers [4] of Bleher and Fokin and [5] of Bleher and Liechty, in which the large $n$ asymptotics is obtained for the partition function $Z_n$ of the six-vertex model with domain wall boundary conditions in the disordered and ferroelectric phases, respectively. In the present paper we obtain the large $n$ asymptotics of $Z_n$ on the critical line between these two phases.Comment: 22 pages, 6 figures, to appear in the Journal of Statistical Physic

Gauged Inflation

We propose a model for cosmic inflation which is based on an effective description of strongly interacting, nonsupersymmetric matter within the framework of dynamical Abelian projection and centerization. The underlying gauge symmetry is assumed to be $SU(N+1)$ with $N \gg 1$. Appealing to a thermodynamical treatment, the ground-state structure of the model is classically determined by a potential for the inflaton field (dynamical monopole condensate) which allows for nontrivially BPS saturated and thereby stable solutions. For $T<M_P$ this leads to decoupling of gravity from the inflaton dynamics. The ground state dynamics implies a heat capacity for the vacuum leading to inflation for temperatures comparable to the mass scale $M$ of the potential. The dynamics has an attractor property. In contrast to the usual slow-roll paradigm we have $m\gg H$ during inflation. As a consequence, density perturbations generated from the inflaton are irrelevant for the formation of large-scale structure, and the model has to be supplemented with an inflaton independent mechanism for the generation of spatial curvature perturbations. Within a small fraction of the Hubble time inflation is terminated by a transition of the theory to its center symmetric phase. The spontaneously broken $Z_{N+1}$ symmetry stabilizes relic vector bosons in the epochs following inflation. These heavy relics contribute to the cold dark matter of the universe and potentially originate the UHECRs beyond the GZK bound.Comment: 23 pages, 4 figures, subsection added, revision of text, to app. in PR

Some Connections between Qualitative Spatial Reasoning and Machine Learning

As has been remarked on before, Space is Special[1, 2]. Tobler’s First Law of Geography [3] captures the notion that all things are related, but close things are more related. Tversky [2] eloquently argues for the special place for spatial representations, and in particular that (living) things must move and act in space to survive, that all thought begins as spatial thought and that spatial thinking comes from and is shaped by perceiving the world and acting in it, be it through learning or through evolution. Artificial Intelligence has thus naturally sought to endow artificial agents with spatial representations and ways of reasoning about space. Amongst these, I will focus on qualitative spatial representations and reasoning mechanisms (henceforth QSR, where the ‘R’ may stand for representation or reasoning or both, depending on the context). There have been many calculi developed for representing and reasoning about space in qualitative ways, covering aspects such as (mereo)topology, orientation/direction, size, distance and shape [4, 5]. Whilst QSR has primarily been concerned with deductive reasoning, there have been and there are increasingly many connections between QSR and machine learning. In this talk I will discuss a number of such connections, ranging from the use of qualitative spatial representations in an inductive logic programming system to learn event classes occurring in video data, to the question of whether large language models (LLMs) are able to make inferences reliably about qualitative spatial relations, and whether they can be supported by symbolic reasoners

An adverbial approach for the formal specification of topological constraints involving regions with broad boundaries

Topological integrity constraints control the topological properties of spatial objects and the validity of their topological relationships in spatial databases. These constraints can be specified by using formal languages such as the spatial extension of the Object Constraint Language (OCL). Spatial OCL allows the expression of topological constraints involving crisp spatial objects. However, topological constraints involving spatial objects with vague shapes (e.g., regions with broad boundaries) are not supported by this language. Shape vagueness requires using appropriate topological operators (e.g., strongly Disjoint, fairly Meet) to specify valid relations between these objects; otherwise, the constraints cannot be respected. This paper addresses the problem of the lack of terminology to express topological constraints involving regions with broad boundaries. We propose an extension of Spatial OCL based on a geometric model for objects with vague shapes and an adverbial approach for topological relations between regions with broad boundaries. This extension of Spatial OCL is then tested on an agricultural database

Extension of RCC*-9 to Complex and Three-Dimensional Features and Its Reasoning System

RCC*-9 is a mereotopological qualitative spatial calculus for simple lines and regions. RCC*-9 can be easily expressed in other existing models for topological relations and thus can be viewed as a candidate for being a “bridge” model among various approaches. In this paper, we present a revised and extended version of RCC*-9, which can handle non-simple geometric features, such as multipolygons, multipolylines, and multipoints, and 3D features, such as polyhedrons and lower-dimensional features embedded in ℝ3. We also run experiments to compute RCC*-9 relations among very large random datasets of spatial features to demonstrate the JEPD properties of the calculus and also to compute the composition tables for spatial reasoning with the calculus

Evaluating the Ability of Large Language Models to Reason about Cardinal Directions

We investigate the abilities of a representative set of Large language Models (LLMs) to reason about cardinal directions (CDs). To do so, we create two datasets: the first, co-created with ChatGPT, focuses largely on recall of world knowledge about CDs; the second is generated from a set of templates, comprehensively testing an LLM's ability to determine the correct CD given a particular scenario. The templates allow for a number of degrees of variation such as means of locomotion of the agent involved, and whether set in the first , second or third person. Even with a temperature setting of zero, Our experiments show that although LLMs are able to perform well in the simpler dataset, in the second more complex dataset no LLM is able to reliably determine the correct CD, even with a temperature setting of zero