Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

PREDICTING THE SPATIAL-DISTRIBUTION OF CLIMATE - TEMPERATURE IN GREAT-BRITAIN

1. The prediction and mapping of climate in areas between climate stations is of increasing importance in ecology.

2. Four categories of model, simple interpolation, thin plate splines, multiple linear regression and mixed spline-regression, were tested for their ability to predict the spatial distribution of temperature on the British mainland. The models were tested by external cross-verification.

3. The British distribution of mean daily temperature was predicted with the greatest accuracy by using a mixed model: a thin plate spline fitted to the surface of the country, after correction of the data by a selection from 16 independent topographical variables (such as altitude, distance from the sea, slope and topographic roughness), chosen by multiple regression from a digital terrain model (DTM) of the country.

4. The next most accurate method was a pure multiple regression model using the DTM. Both regression and thin plate spline models based on a few variables (latitude, longitude and altitude) only were comparatively unsatisfactory, but some rather simple methods of surface interpolation (such as bilinear interpolation after correction to sea level) gave moderately satisfactory results. Differences between the methods seemed to be dependent largely on their ability to model the effect of the sea on land temperatures.

5. Prediction of temperature by the best methods was greater than 95% accurate in all months of the year, as shown by the correlation between the predicted and actual values. The predicted temperatures were calculated at real altitudes, not subject to sea-level correction.

6. A minimum of just over 30 temperature recording stations would generate a satisfactory surface, provided the stations were well spaced.

7. Maps of mean daily temperature, using the best overall methods are provided; further important variables, such as continentality and length of growing season, were also mapped. Many of these are believed to be the first detailed representations at real altitude.

8. The interpolated monthly temperature surfaces are available on disk.

Weak and strong disjunction in possibilistic ASP

Possibilistic answer set programming (PASP) unites answer set programming (ASP) and possibilistic logic (PL) by associating certainty values with rules. The resulting framework allows to combine both non-monotonic reasoning and reasoning under uncertainty in a single framework. While PASP has been well-studied for possibilistic definite and possibilistic normal programs, we argue that the current semantics of possibilistic disjunctive programs are not entirely satisfactory. The problem is twofold. First, the treatment of negation-as-failure in existing approaches follows an all-or-nothing scheme that is hard to match with the graded notion of proof underlying PASP. Second, we advocate that the notion of disjunction can be interpreted in several ways. In particular, in addition to the view of ordinary ASP where disjunctions are used to induce a non-deterministic choice, the possibilistic setting naturally leads to a more epistemic view of disjunction. In this paper, we propose a semantics for possibilistic disjunctive programs, discussing both views on disjunction. Extending our earlier work, we interpret such programs as sets of constraints on possibility distributions, whose least specific solutions correspond to answer sets.

Towards Possibilistic Fuzzy Answer Set Programming

Fuzzy answer set programming (FASP) is a generalization of answer set programming to continuous domains. As it can not readily take uncertainty into account, however, FASP is not suitable as a basis for approximate reasoning and cannot easily be used to derive conclusions from imprecise information. To cope with this, we propose an extension of FASP based on possibility theory. The resulting framework allows us to reason about uncertain information in continuous domains, and thus also about information that is imprecise or vague. We propose a syntactic procedure, based on an immediate consequence operator, and provide a characterization in terms of minimal models, which allows us to straightforwardly implement our framework using existing FASP solvers.

Efficient RkNN Retrieval with Arbitrary Non-Metric Similarity Measures

A RkNN query returns all objects whose nearest k neighbors
contain the query object. In this paper, we consider RkNN
query processing in the case where the distances between
attribute values are not necessarily metric. Dissimilarities
between objects could then be a monotonic aggregate of dissimilarities
between their values, such aggregation functions
being specified at query time. We outline real world cases
that motivate RkNN processing in such scenarios. We consider
the AL-Tree index and its applicability in RkNN query
processing. We develop an approach that exploits the group
level reasoning enabled by the AL-Tree in RkNN processing.
We evaluate our approach against a Naive approach
that performs sequential scans on contiguous data and an
improved block-based approach that we provide. We use
real-world datasets and synthetic data with varying characteristics
for our experiments. This extensive empirical
evaluation shows that our approach is better than existing
methods in terms of computational and disk access costs,
leading to significantly better response times.

Indexing and matching trajectories under inconsistent sampling rates

Quantifying the similarity between two trajectories is a fundamental operation in analysis of spatio-temporal databases. While a number of distance functions exist, the recent shift in the dynamics of the trajectory generation procedure violates one of their core assumptions; a consistent and uniform sampling rate. In this paper, we formulate a robust distance function called Edit Distance with Projections (EDwP) to match trajectories under inconsistent and variable sampling rates through dynamic interpolation. This is achieved by deploying the idea of projections that goes beyond matching only the sampled points while aligning trajectories. To enable efficient trajectory retrievals using EDwP, we design an index structure called TrajTree. TrajTree derives its pruning power by employing the unique combination of bounding boxes with Lipschitz embedding. Extensive experiments on real trajectory databases demonstrate EDwP to be up to 5 times more accurate than the state-of-the-art distance functions. Additionally, TrajTree increases the efficiency of trajectory retrievals by up to an order of magnitude over existing techniques.

Answer set programs with optional rules: a possibilistic approach

Many problems in artificial intelligence can be encoded as answer set programs (ASP) in which some rules are uncertain. ASP programs with incorrect rules may have erroneous conclusions, but due to the non-monotonic nature of ASP, omitting a correct rule may also lead to errors. To derive the most certain conclusions from an uncertain ASP program, we thus need to consider all situations in which some, none, or all of the least certain rules are omitted. This corresponds to treating some rules as optional and reasoning about which conclusions remain valid regardless of the inclusion of these optional rules. While a version of possibilistic ASP (PASP) based on this view has recently been introduced, no implementation is currently available. In this paper we propose a simulation of the main reasoning tasks in PASP using (disjunctive) ASP programs, allowing us to take advantage of state-of-the-art ASP solvers. Furthermore, we identify how several interesting AI problems can be naturally seen as special cases of the considered reasoning tasks, including cautious abductive reasoning and conformant planning. As such, the proposed simulation enables us to solve instances of the latter problem types that are more general than what current solvers can handle.

A proposed simple method for measurement in the anterior chamber angle: biometric gonioscopy

OBJECTIVE:

To design a system of gonioscopy that will allow greater interobserver reliability and more clearly defined screening cutoffs for angle closure than current systems while being simple to teach and technologically appropriate for use in rural Asia, where the prevalence of angle-closure glaucoma is highest.

DESIGN:

Clinic-based validation and interobserver reliability trial.

PARTICIPANTS:

Study 1: 21 patients 18 years of age and older recruited from a university-based specialty glaucoma clinic; study 2: 32 patients 18 years of age and older recruited from the same clinic.

INTERVENTION:

In study 1, all participants underwent conventional gonioscopy by an experienced observer (GLS) using the Spaeth system and in the same eye also underwent Scheimpflug photography, ultrasonographic measurement of anterior chamber depth and axial length, automatic refraction, and biometric gonioscopy with measurement of the distance from iris insertion to Schwalbe's line using a reticule based in the slit-lamp ocular. In study 2, all participants underwent both conventional gonioscopy and biometric gonioscopy by an experienced gonioscopist (NGC) and a medical student with no previous training in gonioscopy (JK).

MAIN OUTCOME MEASURES:

Study 1: The association between biometric gonioscopy and conventional gonioscopy, Scheimpflug photography, and other factors known to correlate with the configuration of the angle. Study 2: Interobserver agreement using biometric gonioscopy compared to that obtained with conventional gonioscopy.

RESULTS:

In study 1, there was an independent, monotonic, statistically significant relationship between biometric gonioscopy and both Spaeth angle (P = 0.001, t test) and Spaeth insertion (P = 0.008, t test) grades. Biometric gonioscopy correctly identified six of six patients with occludable angles according to Spaeth criteria. Biometric gonioscopic grade was also significantly associated with the anterior chamber angle as measured by Scheimpflug photography (P = 0.005, t test). In study 2, the intraclass correlation coefficient between graders for biometric gonioscopy (0.97) was higher than for Spaeth angle grade (0.72) or Spaeth insertion grade (0.84).

CONCLUSION:

Biometric gonioscopy correlates well with other measures of the anterior chamber angle, shows a higher degree of interobserver reliability than conventional gonioscopy, and can readily be learned by an inexperienced observer.

Modelling the nonlinear behaviour and fracture process of AS4/PEKK thermoplastic composite under shear loading

The accurate determination of non-linear shear behaviour and fracture toughness of continuous carbon-fibre/polymer composites remains a considerable challenge. These measurements are often necessary to generate material parameters for advanced computational damage models. In particular, there is a dearth of detailed shear fracture toughness characterisation for thermoplastic composites which are increasingly generating renewed interest within the aerospace and automotive sectors. In this work, carbon fibre (AS4)/ thermoplastic Polyetherketoneketone (PEKK) composite V-notched cross-ply specimens were manufactured to investigate their non-linear response under pure shear loading. Both monotonic and cyclic loading were applied to study the shear modulus degradation and progressive failure. For the first time in the reported literature, we use the essential work of fracture approach to measure the shear fracture toughness of continuous fibre reinforced composite laminates. Excellent geometric similarity in the load-displacement curves was observed for ligament-scaled specimens. The laminate fracture toughness was determined by linear regression, of the specific work of fracture values, to zero ligament thickness, and verified with computational models. The matrix intralaminar fracture toughness (ply level fracture toughness), associated with shear loading was determined by the area method. This paper also details the numerical implementation of a new three-dimensional phenomenological model for carbon fibre thermoplastic composites using the measured values, which is able to accurately represent the full non-linear mechanical response and fracture process. The constitutive model includes a new non-linear shear profile, shear modulus degradation and load reversal. It is combined with a smeared crack model for representing ply-level damage initiation and propagation. The model is shown to accurately predict the constitutive response in terms of permanent plastic strain, degraded modulus as well as load reversal. Predictions are also shown to compare favourably with the evolution of damage leading to final fracture.

Nonlinearity as a resource for nonclassicality in anharmonic systems

Nonclassicality is a key ingredient for quantum enhanced technologies and experiments involving macro- scopic quantum coherence. Considering various exactly-solvable quantum-oscillator systems, we address the role played by the anharmonicity of their potential in the establishment of nonclassical features. Specifically, we show that a monotonic relation exists between the the entropic nonlinearity of the considered potentials and their ground state nonclassicality, as quantified by the negativity of the Wigner function. In addition, in order to clarify the role of squeezing--which is not captured by the negativity of the Wigner function--we focus on the Glauber-Sudarshan P-function and address the nonclassicality/nonlinearity relation using the entanglement potential. Finally, we consider the case of a generic sixth-order potential confirming the idea that nonlinearity is a resource for the generation of nonclassicality and may serve as a guideline for the engineering of quantum oscillators.