An exploration into the client at the heart of therapy : a qualitative perspective

Over 50 years ago Eysenck challenged the existing base of research into psychotherapy. Since that time, a large number of investigations have been conducted to verify the efficacy of therapy. Recently however, an increasing number of studies have cast new doubts on this research base. Instead of therapy being a function of the therapist, it is now becoming ever more apparent that the client plays a prime role in the therapeutic process. The qualitative studies presented in this paper provide some examples of research that demonstrates that clients are actively involved in their therapy, even making counselling work despite their counsellor. These studies suggest that clients may not experience therapy as beneficially as traditional outcome studies indicate. This raises a new challenge to researchers to more fully explore the client's experience of therapy, a challenge to which qualitative methods of inquiry would appear well suited

A daily representation of Great Britain's energy vectors : Natural gas, electricity and transport fuels

In much of Europe there is a strong push to decarbonise energy demands, including the largest single end-use demand – heat. Moving heat demands over to the electrical network poses significant challenges and the use of hybrid energy vector and storage systems (heat and electrical storage) will be a critical component in managing this transition. As an example of these challenges (facing many developed countries), the scale of recently available daily energy flows through the UK’s electrical, gas and transport systems are presented. When this data is expressed graphically it illustrates important differences in the demand characteristics of these different vectors; these include the quantity of energy delivered through the networks on a daily basis, and the scale of variability in the gas demand over multiple timescales (seasonal, weekly and daily). As the UK proceeds to migrate heating demands to the electrical network in its drive to cut carbon emissions, electrical demand will significantly increase. Additionally, the greater variability and uncertainty shown in the gas demand will also migrate to the electrical demand posing significant difficulties for the maintenance of a secure and reliable electrical system in the coming decades. The paper concludes an analysis of the different means of accommodating increasingly volatile electricity demands in future energy networks

Utilizing ERTS imagery to detect plant diseases and nutrient deficiencies, soil types and soil moisture levels

The author has identified the following significant results. ERTS-1 imagery may be used to delineate soil associations. It does have the capacity to divide soils into groups such that their land use and management would be similar. It offers definite potential for making grass flood-plain, wetland, river shoreline, and land use change surveys. Production of volume strata and forest type from the two usable bands of ERTS-1 imagery were of questionable value. No imagery was received for evaluation during the time of year when maine dwarf mosaic virus and southern corn leaf blight were active

Utilizing ERTS imagery to detect plant diseases and nutrient deficiencies, soil types and soil moisture levels

There are no author-identified significant results in this report

Self-critical Sequence Training for Image Captioning

Recently it has been shown that policy-gradient methods for reinforcement learning can be utilized to train deep end-to-end systems directly on non-differentiable metrics for the task at hand. In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by carefully optimizing our systems using the test metrics of the MSCOCO task, significant gains in performance can be realized. Our systems are built using a new optimization approach that we call self-critical sequence training (SCST). SCST is a form of the popular REINFORCE algorithm that, rather than estimating a "baseline" to normalize the rewards and reduce variance, utilizes the output of its own test-time inference algorithm to normalize the rewards it experiences. Using this approach, estimating the reward signal (as actor-critic methods must do) and estimating normalization (as REINFORCE algorithms typically do) is avoided, while at the same time harmonizing the model with respect to its test-time inference procedure. Empirically we find that directly optimizing the CIDEr metric with SCST and greedy decoding at test-time is highly effective. Our results on the MSCOCO evaluation sever establish a new state-of-the-art on the task, improving the best result in terms of CIDEr from 104.9 to 114.7.Comment: CVPR 2017 + additional analysis + fixed baseline results, 16 page