Optimising memory usage in n-tuple neural networks

The use of n-tuple or weightless neural networks as pattern recognition devices is well known (Aleksander and Stonham, 1979). They have some significant advantages over the more common and biologically plausible networks, such as multi-layer perceptrons; for example, n-tuple networks have been used for a variety of tasks, the most popular being real-time pattern recognition, and they can be implemented easily in hardware as they use standard random access memories. In operation, a series of images of an object are shown to the network, each being processed suitably and effectively stored in a memory called a discriminator. Then, when another image is shown to the system, it is processed in a similar manner and the system reports whether it recognises the image; is the image sufficiently similar to one already taught? If the system is to be able to recognise and discriminate between m-objects, then it must contain m-discriminators. This can require a great deal of memory. This paper describes various ways in which memory requirements can be reduced, including a novel method for multiple discriminator n-tuple networks used for pattern recognition. By using this method, the memory normally required to handle m-objects can be used to recognise and discriminate between 2^m — 2 objects.

Auto-associative memory using n-tuple techniques

The use of n-tuple or weightless neural networks as pattern recognition devices has been well documented. They have a significant advantages over more common networks paradigms, such as the multilayer perceptron in that they can be easily implemented in digital hardware using standard random access memories. To date, n-tuple networks have predominantly been used as fast pattern classification devices. The paper describes how n-tuple techniques can be used in the hardware implementation of a general auto-associative network.

A tool for replay and analysis of gaze-enhanced multiparty sessions captured in immersive collaborative environments

A desktop tool for replay and analysis of gaze-enhanced multiparty virtual collaborative sessions is described. We linked three CAVE (TM)-like environments, creating a multiparty collaborative virtual space where avatars are animated with 3D gaze as well as head and hand motions in real time. Log files are recorded for subsequent playback and analysis Using the proposed software tool. During replaying the user can rotate the viewpoint and navigate in the simulated 3D scene. The playback mechanism relies on multiple distributed log files captured at every site. This structure enables an observer to experience latencies of movement and information transfer for every site as this is important fir conversation analysis. Playback uses an event-replay algorithm, modified to allow fast traversal of the scene by selective rendering of nodes, and to simulate fast random access. The tool's is analysis module can show each participant's 3D gaze points and areas where gaze has been concentrated.

Random-walk-based characterisation of multiple access interference in a DS/SSMA system

The problem of calculating the probability of error in a DS/SSMA system has been extensively studied for more than two decades. When random sequences are employed some conditioning must be done before the application of the central limit theorem is attempted, leading to a Gaussian distribution. The authors seek to characterise the multiple access interference as a random-walk with a random number of steps, for random and deterministic sequences. Using results from random-walk theory, they model the interference as a K-distributed random variable and use it to calculate the probability of error in the form of a series, for a DS/SSMA system with a coherent correlation receiver and BPSK modulation under Gaussian noise. The asymptotic properties of the proposed distribution agree with other analyses. This is, to the best of the authors' knowledge, the first attempt to propose a non-Gaussian distribution for the interference. The modelling can be extended to consider multipath fading and general modulation

Neural networks for identification of nonlinear systems under random piecewise polynomial disturbances

The problem of identification of a nonlinear dynamic system is considered. A two-layer neural network is used for the solution of the problem. Systems disturbed with unmeasurable noise are considered, although it is known that the disturbance is a random piecewise polynomial process. Absorption polynomials and nonquadratic loss functions are used to reduce the effect of this disturbance on the estimates of the optimal memory of the neural-network model.

Speech perception and phonological short-term memory capacity in language impairment: preliminary evidence from adolescents with specific language impairment (SLI) and autism spectrum disorders (ASD)

Background: The cognitive bases of language impairment in specific language impairment (SLI) and autism spectrum disorders (ASD) were investigated in a novel non-word comparison task which manipulated phonological short-term memory (PSTM) and speech perception, both implicated in poor non-word repetition. Aims: This study aimed to investigate the contributions of PSTM and speech perception in non-word processing and whether individuals with SLI and ASD plus language impairment (ALI) show similar or different patterns of deficit in these cognitive processes. Method & Procedures: Three groups of adolescents (aged 14–17 years), 14 with SLI, 16 with ALI, and 17 age and non-verbal IQ matched typically developing (TD) controls, made speeded discriminations between non-word pairs. Stimuli varied in PSTM load (two- or four-syllables) and speech perception load (mismatches on a word-initial or word-medial segment). Outcomes & Results: Reaction times showed effects of both non-word length and mismatch position and these factors interacted: four-syllable and word-initial mismatch stimuli resulted in the slowest decisions. Individuals with language impairment showed the same pattern of performance as those with typical development in the reaction time data. A marginal interaction between group and item length was driven by the SLI and ALI groups being less accurate with long items than short ones, a difference not found in the TD group. Conclusions & Implications: Non-word discrimination suggests that there are similarities and differences between adolescents with SLI and ALI and their TD peers. Reaction times appear to be affected by increasing PSTM and speech perception loads in a similar way. However, there was some, albeit weaker, evidence that adolescents with SLI and ALI are less accurate than TD individuals, with both showing an effect of PSTM load. This may indicate, at some level, the processing substrate supporting both PSTM and speech perception is intact in adolescents with SLI and ALI, but also in both there may be impaired access to PSTM resources.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Social networks: evolving graphs with memory dependent edges

The plethora, and mass take up, of digital communication tech- nologies has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the ex- istence or otherwise of certain infinite products and series involving age dependent model parameters. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

Lexical access across languages: a multinomial model of auditory distraction

Recall in many types of verbal memory task is reliably disrupted by the presence of auditory distracters, with verbal distracters frequently proving the most disruptive (Beaman, 2005). A multinomial processing tree model (Schweickert, 1993) is applied to the effects on free recall of background speech from a known or an unknown language. The model reproduces the free recall curve and the impact on memory of verbal distracters for which a lexical entry exists (i.e., verbal items from a known language). The effects of semantic relatedness of distracters within a language is found to depend upon a redintegrative factor thought to reflect the contribution of the speech-production system. The differential impacts of known and unknown languages cannot be accounted for in this way, but the same effects of distraction are observed amongst bilinguals, regardless of distracter-language.

Lexical access, story re-telling and sequencing skills in adults who clutter and those who do not

Cluttering is a rate-based disorder of fluency, the scope of whose diagnostic criteria currently remains unclear. This paper reports preliminary findings from a larger study which aims to determine whether cluttering can be associated with language disturbances as well as motor and rate based ones. Subtests from the Mt Wilga High Level Language Test (MWHLLT) were used to determine whether people who clutter (PWC) have word finding difficulties, and use significantly more maze behaviours compared to controls, during story re-telling and simple sequencing tasks. Independent t tests showed that PWC were significantly slower than control participants in lexical access and sentence completion tasks, but returned mixed findings when PWCs were required to name items within a semantic category. PWC produced significantly more maze behaviour than controls in a task where participants were required to explain how to undertake commonly performed actions, but no difference in use of maze behaviour was found between the two groups when retelling a story from memory. The implications of these findings are discussed

Memory versus naive T-cell migration

Our established understanding of lymphocyte migration suggests that naive and memory T cells travel throughout the body via divergent pathways; naive T cells circulate between blood and lymph whereas memory T cells additionally migrate through non-lymphoid organs. Evidence is now gradually emerging which suggests such disparate pathways between naive and memory T cells may not strictly be true, and that naive T cells gain access to the non-lymphoid environment in numbers approaching that of memory T cells. We discuss here the evidence for naive T-cell traffic into the non-lymphoid environment, compare and contrast this movement with what is known of memory T cells, and finally discuss the functional importance of why naive T cells might access the parenchymal tissues.

Metacognitive control over memory processes under auditory distraction

Decades of research attest that memory processes suffer under conditions of auditory distraction. What is however less well understood is whether people are able to modify how their memory processes are deployed in order to compensate for disruptive effects of distraction. The metacognitive approach to memory describes a variety of ways people can exert control over their cognitive processes to optimize performance. Here we describe our recent investigations into how these control processes change under conditions of auditory distraction. We specifically looked at control of encoding in the form of decisions about how long to study a word when it is presented and control of memory reporting in the form of decisions whether to volunteer or withhold retrieved details. Regarding control of encoding, we expected that people would compensate for disruptive effects of distraction by extending study time under noise. Our results revealed, however, that when exposed to irrelevant speech, people curtail rather than extend study. Regarding control of memory reporting, we expected that people would compensate for the loss of access to memory records by volunteering responses held with lower confidence. Our results revealed, however, that people’s reporting strategies do not differ when memory task is performed in silence or under auditory distraction, although distraction seriously undermines people’s confidence in their own responses. Together, our studies reveal novel avenues for investigating the psychological effects of auditory distraction within a metacognitive framework.

Using an individual-based model to select among alternative foraging strategies of woodpigeons: data support a memory-based model with a flocking mechanism

Population modelling is increasingly recognised as a useful tool for pesticide risk assessment. For vertebrates that may ingest pesticides with their food, such as woodpigeon (Columba palumbus), population models that simulate foraging behaviour explicitly can help predicting both exposure and population-level impact. Optimal foraging theory is often assumed to explain the individual-level decisions driving distributions of individuals in the field, but it may not adequately predict spatial and temporal characteristics of woodpigeon foraging because of the woodpigeons’ excellent memory, ability to fly long distances, and distinctive flocking behaviour. Here we present an individual-based model (IBM) of the woodpigeon. We used the model to predict distributions of foraging woodpigeons that use one of six alternative foraging strategies: optimal foraging, memory-based foraging and random foraging, each with or without flocking mechanisms. We used pattern-oriented modelling to determine which of the foraging strategies is best able to reproduce observed data patterns. Data used for model evaluation were gathered during a long-term woodpigeon study conducted between 1961 and 2004 and a radiotracking study conducted in 2003 and 2004, both in the UK, and are summarised here as three complex patterns: the distributions of foraging birds between vegetation types during the year, the number of fields visited daily by individuals, and the proportion of fields revisited by them on subsequent days. The model with a memory-based foraging strategy and a flocking mechanism was the only one to reproduce these three data patterns, and the optimal foraging model produced poor matches to all of them. The random foraging strategy reproduced two of the three patterns but was not able to guarantee population persistence. We conclude that with the memory-based foraging strategy including a flocking mechanism our model is realistic enough to estimate the potential exposure of woodpigeons to pesticides. We discuss how exposure can be linked to our model, and how the model could be used for risk assessment of pesticides, for example predicting exposure and effects in heterogeneous landscapes planted seasonally with a variety of crops, while accounting for differences in land use between landscapes.

A randomised controlled trial of positive memory training for the treatment of depression within schizophrenia

Abstract Background: Depression is highly prevalent within individuals diagnosed with schizophrenia, and is associated with an increased risk of suicide. There are no current evidence based treatments for low mood within this group. The specific targeting of co-morbid conditions within complex mental health problems lends itself to the development of short-term structured interventions which are relatively easy to disseminate within health services. A brief cognitive intervention based on a competitive memory theory of depression, is being evaluated in terms of its effectiveness in reducing depression within this group. Methods/Design: This is a single blind, intention-to-treat, multi-site, randomized controlled trial comparing Positive Memory Training plus Treatment as Usual with Treatment as Usual alone. Participants will be recruited from two NHS Trusts in Southern England. In order to be eligible, participants must have a DSM-V diagnosis of schizophrenia or schizo-affective disorder and exhibit at least a mild level of depression. Following baseline assessment eligible participants will be randomly allocated to either the Positive Memory Training plus Treatment as Usual group or the Treatment as Usual group. Outcome will be assessed at the end of treatment (3-months) and at 6-month and 9-month post randomization by assessors blind to group allocation. The primary outcome will be levels of depression and secondary outcomes will be severity of psychotic symptoms and cost-effectiveness. Semi-structured interviews will be conducted with all participants who are allocated to the treatment group so as to explore the acceptability of the intervention. Discussion: Cognitive behaviour therapy is recommended for individuals diagnosed with schizophrenia. However, the number of sessions and length of training required to deliver this intervention has caused a limit in availability. The current trial will evaluate a short-term structured protocol which targets a co-morbid condition often considered of primary importance by service users. If successful the intervention will be an important addition to current initiatives aimed at increasing access to psychological therapies for people diagnosed with severe mental health problems. Trial registration: Current Controlled Trials. ISRCTN99485756. Registered 13 March 2014.

Memory and burstiness in dynamic networks

A discrete-time random process is described, which can generate bursty sequences of events. A Bernoulli process, where the probability of an event occurring at time t is given by a fixed probability x, is modified to include a memory effect where the event probability is increased proportionally to the number of events that occurred within a given amount of time preceding t. For small values of x the interevent time distribution follows a power law with exponent −2−x. We consider a dynamic network where each node forms, and breaks connections according to this process. The value of x for each node depends on the fitness distribution, \rho(x), from which it is drawn; we find exact solutions for the expectation of the degree distribution for a variety of possible fitness distributions, and for both cases where the memory effect either is, or is not present. This work can potentially lead to methods to uncover hidden fitness distributions from fast changing, temporal network data, such as online social communications and fMRI scans.