On the importance of sluggish state memory for learning long term dependency

The vanishing gradients problem inherent in Simple Recurrent Networks (SRN) trained with back-propagation, has led to a significant shift towards the use of Long Short-term Memory (LSTM) and Echo State Networks (ESN), which overcome this problem through either second order error-carousel schemes or different learning algorithms respectively. This paper re-opens the case for SRN-based approaches, by considering a variant, the Multi-recurrent Network (MRN). We show that memory units embedded within its architecture can ameliorate against the vanishing gradient problem, by providing variable sensitivity to recent and more historic information through layer- and self-recurrent links with varied weights, to form a so-called sluggish state-based memory. We demonstrate that an MRN, optimised with noise injection, is able to learn the long term dependency within a complex grammar induction task, significantly outperforming the SRN, NARX and ESN. Analysis of the internal representations of the networks, reveals that sluggish state-based representations of the MRN are best able to latch on to critical temporal dependencies spanning variable time delays, to maintain distinct and stable representations of all underlying grammar states. Surprisingly, the ESN was unable to fully learn the dependency problem, suggesting the major shift towards this class of models may be premature

Risk and resilience in the Scottish social housing sector: ‘We’re all risk managers’

Social housing providers confront an array of risks strategically and operationally. Recently, models of hybrid organisations have been developed to understand how non-profit landlords are changing in response to market and other external pressures. In this paper, we draw on a multidisciplinary conceptual framework of external and internal risks, multiple stakeholders and resilience strategies, as well as the notion of hybridity, in order to make sense of change in Scotland's social housing sector. The paper draws on elite interviews as well as case studies that seek to capture the range of approaches adopted by providers. Although providers handle and respond to risk in a variety of ways, risk management is a necessary part of the management of social housing businesses. Increasingly, providers are concerned with questions of resilience – the need to make themselves as organisations more resilient and also to promote greater resilience amongst tenants as a way of mitigating risk. Our research suggests that this is leading to some positive outcomes e.g. greater diversity within the sector and increased customer focus but there is concern that government policies remain within silos and are insufficiently flexible to deal with changed circumstances and the evolving needs and aspirations of the sector

Measuring cognitive load and cognition: metrics for technology-enhanced learning

This critical and reflective literature review examines international research published over the last decade to summarise the different kinds of measures that have been used to explore cognitive load and critiques the strengths and limitations of those focussed on the development of direct empirical approaches. Over the last 40 years, cognitive load theory has become established as one of the most successful and influential theoretical explanations of cognitive processing during learning. Despite this success, attempts to obtain direct objective measures of the theory's central theoretical construct – cognitive load – have proved elusive. This obstacle represents the most significant outstanding challenge for successfully embedding the theoretical and experimental work on cognitive load in empirical data from authentic learning situations. Progress to date on the theoretical and practical approaches to cognitive load are discussed along with the influences of individual differences on cognitive load in order to assess the prospects for the development and application of direct empirical measures of cognitive load especially in technology-rich contexts

Towards clinical assessment of cerebral blood flow regulation using ultrasonography : model applicability in clinical studies

For preservation of its vital functions, the brain is largely dependent of a sufficient delivery of oxygen and nutrients. Blood flow to the brain is essentially regulated by 2 control mechanisms i.e. neurovascular coupling and cerebral autoregulation. Cerebral autoregulation aims for constant adequate blood supply by compensating for blood pressure variations by dilatation or narrowing of the cerebral microvasculature. Neurovascular coupling adjusts blood supply to the local metabolic need. Cerebral perfusion and blood flow regulation are compromised in several pathological conditions. Clinical examination of cerebral blood flow and its regulation may therefore provide helpful diagnostic, predictive and therapeutic information. The work in this thesis was aimed at putting a step forward towards development of reliable and clinically usable parameters for cerebral blood flow regulation assessment using ultrasonography. Regarding early diagnostics, screening and monitoring of cerebral blood flow and its regulation, ultrasonography has major advantages over other imaging tools because of its noninvasiveness, cost-effectiveness, easy usability and its good time resolution. It allows examination of blood flow velocities at multiple locations throughout the extra- and intracranial circulation and evaluation of both control mechanisms by transfer function analysis. For evaluation of cerebral autoregulation, transcranial Doppler blood flow velocities in the large middle cerebral arteries have been recorded simultaneously with plethysmographic (finger) blood pressure. Gain and phase of the pressure-flow transfer function have been determined to obtain quantitative measures for cerebral autoregulation. Neurovascular coupling has been assessed by presenting a visual block stimulus to a subject and simultaneous measurement of the blood flow velocity in the artery exclusively supplying the visual cortex. The obtained visually-evoked blood flow response (VEFR) has been considered as the step response of a linear second order control system model providing patient-specific parameters such as gain and damping as quantitative measures for neurovascular coupling . In chapter 2, a clinical study has been described in which extra- and intracranial blood flow velocities (BFVs), measured at multiple sites in the circulation, have been compared between Alzheimer patients (AD), patients with mild cognitive impairment (MCI) and healthy aging controls (HC). BFVs of AD were significantly lowered at proximal sites but preserved at distal sites for the internal carotid artery and middle and posterior cerebral arteries as compared to those of MCI or HC. This specific pattern can presumably be ascribed to reduced distal diameters resulting from AD pathology. MCI BFV were similar to HC BFV in the extracranial and intracranial posterior circulation, whereas they were intermediate between AD and HC in the intracranial anterior circulation. This suggests that intracranial anterior vessels are most suitable for early detection of pathological alterations resulting from AD. The study findings further indicate that extensive ultrasonographic screening of intra- and extracranial arteries is useful for monitoring BFV decline in the MCI stage. Future follow-up of MCI patients may reveal the predictive value of location-specific BFV for conversion to AD. In the same study cohort, dynamic cerebral autoregulation has been studied as discussed in chapter 3. Cerebral autoregulatory gain and phase values were similar for AD, MCI and HC which implies that the cerebral autoregulatory mechanism is preserved in AD. However, the cerebrovascular resistance index i.e. the ratio between absolute time-averaged blood pressure and flow velocity, was significantly higher in AD as compared to MCI and HC indicating that vessel stiffness is increased in AD. Indeed, it appeared to be a potential biomarker for AD development of MCI. The cerebrovascular resistance increase in AD was furthermore confirmed by windkessel model findings of a significantly elevated peripheral resistance in AD. Arterial resistance and peripheral compliance were equal for all groups. From chapter 4, the focus was shifted to assessment of local blood flow regulation. Visuallyevoked blood flow responses (VEFRs) of formerly (pre-)eclamptic patients and healthy controls have been examined to evaluate neurovascular coupling first in a relative young study population. The aim of the study was to investigate whether possible local (pre)eclampsia-induced endothelial damage was reversible or not. The measured VEFRs have been fitted with the step response of a 2nd order control system model. Although inter-group differences in model parameters were not found, a trend was observed that critical damping (z>1) occurred more frequently in former patients than in controls. Critical damping reflects an atypical VEFR, which is either uncompensated (sluggish, z>1; Tv <20) or compensated by a rise in rate time (intermediate, z>1; Tv > 20). Since these abnormal VEFRs were mainly found in former patients (but not exclusively), these response types were hypothesized to result from pathological disturbances. A revised VEFR analysis procedure to account for reliability and blood pressure dynamics has been proposed in chapter 5. This revised procedure consists of the introduction of a reliability measure for model parameters and of a model extension to consider possible blood pressure contribution to the measured VEFR. The effects of these adjustments on study outcomes have been evaluated by applying both the standard VEFR analysis procedure (applied in chapter 4) and the revised procedure to the AD study cohort. Reliability consideration resulted in about 40% VEFR exclusion, mainly due to the models’ inability to fit critically damped responses. Reliability consideration reduced parameter variability substantially. Regarding the influence of blood pressure variation, a significantly increased damping was found in AD for the standard but not for the revised model. This reversed the study conclusion from altered to normal neurovascular coupling in AD. Considering their influence on obtained parameters, both aspects i.e. reliability and blood pressure variation should be included in VEFR-analysis. Regarding clinical study outcomes, neurovascular coupling seems to be unaffected in AD since the finding of an increased damping may be ascribed to ignorance of blood pressure contribution to VEFR. Study conclusions of earlier chapters (4 and 5) emphasize the need for a model incorporating physiological features. In chapter 6, preliminary results have been reported of the application of a newly developed lumped parameter model of the visual cortex vasculature to the 3 different VEFR types. In the new model, regulatory processes i.e. neurogenic, metabolic, myogenic and shear stress mechanisms, act on smooth muscle tone which inherently leads to adjustment of microcirculatory resistance and compliance. This allows the study of effects of pathological changes on the VEFR. It may be concluded that the model provides an improved link between VEFR and physiology. Preliminary results show that the physiology-based model can describe VEFR type representatives reasonably well obtaining physiologically plausible parameter values. Thus, from a clinical perspective it may be concluded that (Duplex) ultrasonography has great potential as a standard screening tool for MCI patients. It seems worthwhile to examine all future MCI patients on extra- and intracranial blood flow velocity and to determine their cerebrovascular resistance index by simultaneous blood pressure recording. Follow-up of MCI patients will reveal the predictive value of these parameters for future AD development. Furthermore, from a methodological perspective, it can be concluded that the current standard of control system analysis to assess local cerebral blood flow regulation has limitations regarding parameter reliability and VEFR interpretation. Both reliability and interpretation may be improved by optimization and control of data acquisition quality and by use of physiology-based models. Physiological mechanisms influencing VEFR establishment should be incorporated in such a model to possibly explain part of its variance. Efforts should be directed to development and validation of physiology-based models aimed at reliable description of VEFRs by physiologically meaningful parameters

Monitoring Covid-19 contagion growth in Europe. CEPS Working Document No 2020/03, March 2020

We present an econometric model which can be employed to monitor the evolution of the COVID-19 contagion curve. The model is a Poisson autoregression of the daily new observed cases, and can dynamically show the evolution of contagion in different time periods and locations, allowing for the comparative evaluation of policy approaches. We present timely results for nine European countries currently hit by the virus. From the findings, we draw four main conclusions. First, countries experiencing an explosive process (currently France, Italy and Spain), combined with high persistence of contagion shocks (observed in most countries under investigation), require swift policy measures such as quarantine, diffuse testing and even complete lockdown. Second, in countries with high persistence but lower contagion growth (currently Germany) careful monitoring should be coupled with at least “mild” restrictions such as physical distancing or isolation of specific areas. Third, in some countries, such as Norway and Denmark, where trends seem to be relatively under control and depend on daily contingencies, with low persistence, the approach to restrictive measures should be more cautious since there is a risk that social costs outweigh the benefits. Fourth, countries with a limited set of preventive actions in place (such as the Netherlands, Switzerl

Stochastic behavioral asset pricing models and the stylized facts

High-frequency financial data are characterized by a set of ubiquitous statistical properties that prevail with surprising uniformity. While these 'stylized facts' have been well-known for decades, attempts at their behavioral explanation have remained scarce. However, recently a new branch of simple stochastic models of interacting traders have been proposed that share many of the salient features of empirical data. These models draw some of their inspiration from the broader current of behavioural finance. However, their design is closer in spirit to models of multi-particle interaction in physics than to traditional asset-pricing models. This reflects a basic insight in the natural sciences that similar regularities like those observed in financial markets (denoted as 'scaling laws' in physics) can often be explained via the microscopic interactions of the constituent parts of a complex system. Since these emergent properties should be independent of the microscopic details of the system, this viewpoint advocates negligence of the details of the determination of individuals' market behavior and instead focuses on the study of a few plausible rules of behavior and the emergence of macroscopic statistical regularities in a market with a large ensemble of traders. This chapter will review the philosophy of this new approach, its various implementations, and its contribution to an explanation of the stylized facts in finance. --

A Survey on Different Deep Learning Model for Human Activity Recognition based on Application

The field of human activity recognition (HAR) seeks to identify and classify an individual's unique movements or activities. However, recognizing human activity from video is a challenging task that requires careful attention to individuals, their behaviors, and relevant body parts. Multimodal activity recognition systems are necessary for many applications, including video surveillance systems, human-computer interfaces, and robots that analyze human behavior. This study provides a comprehensive analysis of recent breakthroughs in human activity classification, including different approaches, methodologies, applications, and limitations. Additionally, the study identifies several challenges that require further investigation and improvements. The specifications for an ideal human activity recognition dataset are also discussed, along with a thorough examination of the publicly available human activity classification datasets

Reinforcement Learning: A Survey

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file