Can Artificial Intelligence Alleviate Resource Scarcity?

During summer 2017, I explored the implications of the potential application of artificial intelligence (AI) to resource management at the Centre for the Study of Existential Risk (CSER) at the University of Cambridge in the United Kingdom. Alongside my mentor, Dr. Simon Beard, I sought to determine the most noteworthy risks and benefits associated with developing AI that could offer agricultural guidance and that could someday offer insight into more efficient, effective, and equitable resource distribution. My research, funded by a Summer Undergraduate Research Fellowship (SURF) grant, involved discussing AI-related issues in the context of resource scarcity with academics and experts in the fields of AI, climate science, data analytics, economics, ethics, and robotics. I found that while AI could present a solution to the problem of scarcity by harnessing data and algorithms to increase agricultural yield, the technology also must be considered in the context of risks—including bias and a lack of trustworthiness. If the positive potential and risks associated with AI for resource management are thoughtfully considered throughout development, the technology could improve food security and ultimately contribute to a better future

The impact of corporate characteristics on social and environmental disclosure (CSED):the case of Jordan

The corporate business environment is surrounded by strong public scrutiny from diverse stakeholder groups that are calling on businesses to accept accountability for not only their financial actions, but also the non-financial implications of their activities. Many corporate businesses are today paying attention to the needs of their stakeholders of social and environmental information. As such, in this study we examined how corporate characteristics could influence the amount of Corporate Social and Environmental Disclosure (CSED) in the manufacturing sector in Jordan. Firm size, profitability, audit firm, ownership, type of industry and financial market level are the main factors examined in this study. Drawing from Ernst and Ernst methodology, the study developed a disclosure index to measure the amount of CSED for three years (2010, 2011 and 2012). Using panel data regression, we model the relationship between disclosure amount and the key drivers of CSED via random effect estimation. The results of our model indicated that the firm size, type of audit firm and financial performance in Amman Stock Exchange (ASE) are significantly associated with the amount of CSED. On the other hand, we also find that firm profitability, age, type of industry and ownership are not related to the practices of CSED

The nature of engineering change in a complex product development cycle

The complex dynamics of modern business mean companies are constantly exposed to rapid and radical change. The way by which a company copes with change, can act as an insight into its propensity for sustainable profitability and hence predicted longevity. In complex product development cycles, engineering change must be managed as efficiently and effectively as possible. This paper presents a case study of one hundred engineering changes, taken over a sixty seven day period, of a complex product development cycle, during the detailed design phase of the project. It establishes the specific engineering change process utilised as a reactive process, which takes a mean of 126 days to complete its impact analysis phase and compliments this with a review of change stimuli and effects. It was found that the stimuli behind change are frequently not understood, with 68.4% reasons being classified as 'other'. The most effected entities were found to be the bill of materials, baseline and structural changes respectively; however it was found that each specific stimulus had a unique effect profile, which differed from the cumulative effect profile for all change stimuli

Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues

Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance

Application of serious games to sport, health and exercise

Use of interactive entertainment has been exponentially expanded since the last decade. Throughout this 10+ year evolution there has been a concern about turning entertainment properties into serious applications, a.k.a "Serious Games". In this article we present two set of Serious Game applications, an Environment Visualising game which focuses solely on applying serious games to elite Olympic sport and another set of serious games that incorporate an in house developed proprietary input system that can detect most of the human movements which focuses on applying serious games to health and exercise

Monetary Policy and Sectoral Shocks: Did the FED react properly to the High-Tech Crisis?

This paper presents an identification strategy that allows us to study both the sectoral effects of monetary policy and the role that monetary policy plays in the transmission of sectoral shocks. We apply our methodology to the case of the U.S. and find some significant differences in the sectorial responses to monetary policy. We also find that monetary policy is a significant source of sectoral transfers. In particular, a shock to Equipment and Software investment, which we naturally identify with the High-tech crises, induces a response by the monetary authority that generates a temporary boom in Residential Investment and Durable Consumption but has almost no effect on the high-tech sector. Finally, we perform an exercise evaluating what the model predicts regarding the automatic and a more aggressive monetary policy response to a shock similar to the one that hit the U.S. in early 2001. We find that the actual drop in interest rates we have observed is in line with the predictions of the model.

Influence of X-ray Irradiation on the Properties of the Hamamatsu Silicon Photomultiplier S10362-11-050C

We have investigated the effects of X-ray irradiation to doses of 0, 200 Gy, 20 kGy, 2 MGy, and 20 MGy on the Hamamatsu silicon-photomultiplier (SiPM) S10362-11-050C. The SiPMs were irradiated without applied bias voltage. From current-voltage, capacitance/conductance-voltage, -frequency, pulse-shape, and pulse-area measurements, the SiPM characteristics below and above breakdown voltage were determined. Significant changes of some SiPM parameters are observed. Up to a dose of 20 kGy the performance of the SiPMs is hardly affected by X-ray radiation damage. For doses of 2 and 20 MGy the SiPMs operate with hardly any change in gain, but with a significant increase in dark-count rate and cross-talk probability.Comment: 21 pages,30 figure

The nature of phonological conditioning in Latin inflectional allomorphy

This paper offers a comprehensive analysis of the inflectional morphology of Latin in terms of the patterns of allomorphy and the environments governing the distribution of allomorphs. It is demonstrated that all the attested allomorphic alternations can be described as functions of a vocalic scale, practically the sonority scale of vowels plus the undifferentiated class of consonants as the least sonorous extreme. The distribution of allomorphs along the vocalic scale crucially displays the property of contiguity, i.e., the subsections of the scale that trigger one particular allomorph are uninterrupted