Global optimisation of neural networks using a deterministic hybrid approach

Selection of the topology of a neural network and correct parameters for the learning algorithm is a tedious task for designing an optimal artificial neural network, which is smaller, faster and with a better generalization performance. In this paper we introduce a recently developed cutting angle method (a deterministic technique) for global optimization of connection weights. Neural networks are initially trained using the cutting angle method and later the learning is fine-tuned (meta-learning) using conventional gradient descent or other optimization techniques. Experiments were carried out on three time series benchmarks and a comparison was done using evolutionary neural networks. Our preliminary experimentation results show that the proposed deterministic approach could provide near optimal results much faster than the evolutionary approach.

Deterministic versus stochastic quantitative microbial Risk Assessment models for wastewater irrigation of food crops

Reuse of wastewater to irrigate food crops is being practiced in many parts of the world and is becoming more commonplace as the competition for, and stresses on, freshwater resources intensify. But there are risks associated with wastewater irrigation, including the possibility of transmission of pathogens causing infectious disease, to both workers in the field and to consumers buying and eating produce irrigated with wastewater. To manage these risks appropriately we need objective and quantitative estimates of them. This is typically achieved through one of two modelling approaches: deterministic or stochastic. Each parameter in a deterministic model is represented by a single value, whereas in stochastic models probability functions are used. Stochastic models are theoretically superior because they account for variability and uncertainty, but they are computationally demanding and not readily accessible to water resource and public health managers. We constructed models to estimate risk of enteric virus infection arising from the consumption of wastewater-irrigated horticultural crops (broccoli, cucumber and lettuce), and compared the resultant levels of risk between the deterministic and stochastic approaches. Several scenarios were tested for each crop, accounting for different concentrations of enteric viruses and different lengths of environmental exposure (i.e. the time between the last irrigation event and harvest, when the viruses are liable to decay or inactivation). In most situations modelled the two approaches yielded similar estimates of risk (within 1 order-of-magnitude). The two methods diverged most markedly, up to around 2 orders-of-magnitude, when there was large uncertainty associated with the estimate of virus concentration and the exposure period was short (1 day). Therefore, in some circumstances deterministic modelling may offer water resource managers a pragmatic alternative to stochastic modelling, but its usefulness as a surrogate will depend upon the level of uncertainty in the model parameters.

Reconstruction of in vivo time-evolving neuroendocrine dose - response properties unveils admixed deterministic and stochastic elements

Homeostasis in the intact organism is achieved implicitly by repeated incremental feedback (inhibitory) and feedforward (stimulatory) adjustments enforced via intermittent signal exchange. In separated systems, neurohormone signals act deterministically on target cells via quantifiable effector-response functions. On the other hand, in vivo interglandular signaling dynamics have not been estimable to date. Indeed, experimentally isolating components of an interactive network definitionally disrupts time-sensitive linkages. We implement and validate analytical reconstruction of endogenous effector-response properties via a composite model comprising (i) a deterministic basic feedback and feedforward ensemble structure; (ii) judicious statistical allowance for possible stochastic variability in individual biologically interpretable dose–response properties; and (iii) the sole data requirement of serially observed concentrations of a paired signal (input) and response (output). Application of this analytical strategy to a prototypical neuroendocrine axis in the conscious uninjected horse, sheep, and human (i) illustrates probabilistic estimation of endogenous effector dose–response properties; and (ii) unmasks statistically vivid (2- to 5-fold) random fluctuations in inferred target-gland responsivity within any given pulse train. In conclusion, balanced mathematical formalism allows one to (i) reconstruct deterministic properties of interglandular signaling in the intact mammal and (ii) quantify apparent signal-response variability over short time scales in vivo. The present proof-of-principle experiments introduce a previously undescribed means to estimate time-evolving signal-response relationships without isotope infusion or pathway disruption.

Haptic control methodologies for rover teleoperation in non-deterministic remote environments

Haptic teleoperation allows human operators to interact with a remote mobile robot using their haptic sensory modality. This research introduces new haptic control methodologies allowing the teleoperator to overcome the limitations of existing techniques, ultimately facilitating improved mobile robotic control for the exploration of hazardous and remote environments.

Mobility and vision for mobile robots in non-deterministic, competitive environments

Develops an award winning design for an omnidirectional mobile robot that is both lightweight and extremely maneuverable. Two methods of robot vision, one using rotating lasers and a second using color video cameras were also developed and the merits of both explored.

Flexible deterministic packet marking: an IP traceback system to find the real source of attacks

Internet Protocol (IP) traceback is the enabling technology to control Internet crime. In this paper, we present a novel and practical IP traceback system called Flexible Deterministic Packet Marking (FDPM) which provides a defense system with the ability to find out the real sources of attacking packets that traverse through the network. While a number of other traceback schemes exist, FDPM provides innovative features to trace the source of IP packets and can obtain better tracing capability than others. In particular, FDPM adopts a flexible mark length strategy to make it compatible to different network environments; it also adaptively changes its marking rate according to the load of the participating router by a flexible flow-based marking scheme. Evaluations on both simulation and real system implementation demonstrate that FDPM requires a moderately small number of packets to complete the traceback process; add little additional load to routers and can trace a large number of sources in one traceback process with low false positive rates. The built-in overload prevention mechanism makes this system capable of achieving a satisfactory traceback result even when the router is heavily loaded. The motivation of this traceback system is from DDoS defense. It has been used to not only trace DDoS attacking packets but also enhance filtering attacking traffic. It has a wide array of applications for other security systems.

Non-Deterministic Exploration through Parametric Design

This paper explores non-deterministic parametric modelling as a design tool. Specifically, it addresses the application of parametric variables to the generation of a conceptual bridge design and the use of repeatable discrete components to the conceptual form. In order to control the generation of the bridge form, a set of design variables based on the concept of a law curve have been developed.These design variables are applied and tested through interactive modelling and variation, driven by manipulating the law curve. Combining this process with the application and control of a repeatable element, known as a Representative Volumetric Element (RVE), allows for the development and exploration of a design solution that could not be achieved through the use of conventional computer modelling.The competition brief for the Australian Institute of Architects (AIA) ‘Dialectical Bridge’ has been used as a case study to demonstrate the use of non-deterministic parametric modelling as a design tool.The results of the experimentation with parametric variables, the law curve and representative volumetric elements (RVE) are presented in the paper.

Autonomous underwater vehicles in a hazardous non-deterministic environment

This research investigated the cooperation of multi underwater robots to perform a task. This combined engineering design, electronics and consensus control to create systems capable of achieving the task. Challenges such as underwater radio communications were researched and a simulation framework was created and tested on virtual and real systems.

Are business cycles stationary fluctuations around a deterministic trend? empirical evidence from 79 developing countries

There is a large literature that tests the univariate time series properties of the real output series following the seminal work of Nelson and Plosser (1982). Whether or not real output is characterized by a unit root process has important implications. A unit root in real output, for instance, is inconsistent with the notion that business cycles are stationary fluctuations around a deterministic trend. In this paper, we investigate the univariate time series properties of real output for 79 developing countries using the conventional augmented Dickey and Fuller (1979) unit root test, the Zivot and Andrews’ (1992) one structural break unit root test, and the Lumsdaine and Papell (1997) two structural breaks unit root test. Our main finding is that, for 40 countries, real output is stationary around a trend. This indicates that business cycles are stationary fluctuations around a deterministic trend for only 51% of the developing countries in our sample.

Learning Bayesian networks in Semi-deterministic systems

In current constraint-based (Pearl-style) systems for discovering Bayesian networks, inputs with deterministic relations are prohibited. This restricts the applicability of these systems. In this paper, we formalize a sufficient condition under which Bayesian networks can be recovered even with deterministic relations. The sufficient condition leads to an improvement to Pearl’s IC algorithm; other constraint-based algorithms can be similarly improved. The new algorithm, assuming the sufficient condition proposed, is able to recover Bayesian networks with deterministic relations, and moreover suffers no loss of performance when applied to nondeterministic Bayesian networks.

PANIC in the presence of uncertainty about the deterministic trend

Most macroeconomic and financial panel variables are trending. However, because of the well-known power problem in the presence of incidental trends, many researchers gamble that their unit root test regressions can be ran without such trends, thereby running the risk of obtaining spurious results. This article takes one of the most general and popular panel unit root tests, known as PANIC, and shows how it can be modified to account for the uncertainty regarding the deterministic trend.

A dynamical deterministic packet marking scheme for DDoS traceback

DDoS attack source traceback is an open and challenging problem. Deterministic packet marking (DPM) is a simple and relatively effective traceback scheme among the available traceback methods. However, the existing DPM schemes inheret a critical drawback of scalability in tracing all possible attack sources, which roots at their static mark encoding and attempt to mark all Internet routers for their traceback purpose. We find that a DDoS attack session usually involves a limited number of attack sources, e.g. at the thousand level. In order to achieve the traceback goal, we only need to mark these attack related routers. We therefore propose a novel Marking on Demand (MOD) scheme based on the DPM mechanism to dynamical distribute marking IDs in both temporal and space dimensions. The proposed MOD scheme can traceback to all possible sources of DDoS attacks, which is not possible for the existing DPM schemes. We thoroughly compare the proposed MOD scheme with two dominant DPM schemes through theoretical analysis and experiments. The the results demonstrate that the MOD scheme outperforms the existing DPM schemes. © 2013 IEEE.

Evaluating non-deterministic retrieval systems

The use of sampling, randomized algorithms, or training based on the unpredictable inputs of users in Information Retrieval often leads to non-deterministic outputs. Evaluating the effectiveness of systems incorporating these methods can be challenging since each run may produce different effectiveness scores. Current IR evaluation techniques do not address this problem. Using the context of distributed information retrieval as a case study for our investigation, we propose a solution based on multivariate linear modeling. We show that the approach provides a consistent and reliable method to compare the effectiveness of non-deterministic IR algorithms, and explain how statistics can safely be used to show that two IR algorithms have equivalent effectiveness. Copyright 2014 ACM.

Statistical comparisons of non-deterministic IR systems using two dimensional variance

Retrieval systems with non-deterministic output are widely used in information retrieval. Common examples include sampling, approximation algorithms, or interactive user input. The effectiveness of such systems differs not just for different topics, but also for different instances of the system. The inherent variance presents a dilemma - What is the best way to measure the effectiveness of a non-deterministic IR system? Existing approaches to IR evaluation do not consider this problem, or the potential impact on statistical significance. In this paper, we explore how such variance can affect system comparisons, and propose an evaluation framework and methodologies capable of doing this comparison. Using the context of distributed information retrieval as a case study for our investigation, we show that the approaches provide a consistent and reliable methodology to compare the effectiveness of a non-deterministic system with a deterministic or another non-deterministic system. In addition, we present a statistical best-practice that can be used to safely show how a non-deterministic IR system has equivalent effectiveness to another IR system, and how to avoid the common pitfall of misusing a lack of significance as a proof that two systems have equivalent effectiveness.