Estimating spillovers using imprecisely measured networks

In many experimental contexts, whether and how network interactions impact the outcome of interest for both treated and untreated individuals are key concerns. Networks data is often assumed to perfectly represent these possible interactions. This paper considers the problem of estimating treatment effects when measured connections are, instead, a noisy representation of the true spillover pathways. We show that existing methods, using the potential outcomes framework, yield biased estimators in the presence of this mismeasurement. We develop a new method, using a class of mixture models, that can account for missing connections and discuss its estimation via the Expectation-Maximization algorithm. We check our method's performance by simulating experiments on real network data from 43 villages in India. Finally, we use data from a previously published study to show that estimates using our method are more robust to the choice of network measure

The impact of Artificial Intelligence (AI) technologies on legal practitioners in law firms and legal publishers.

Masters Degree. University of KwaZulu-Natal, Durban.Artificial Intelligence (AI) solutions currently have the capabilities to perform tasks quicker, more accurately and consistently than legal professionals. This could result in inducing the opinion in employers at private law firms and legal publishers that AI software may have a quicker return on investment and a lower total cost of ownership. The purpose of this study is to discover whether the availability of yield-producing, affordable AI technologies in the legal industry could lead to legal practitioners and their roles becoming redundant. An explanatory quantitative study was established using a cross-sectional descriptive survey design to achieve the objectives of the research. A self-administered structured questionnaire was developed and delivered via hardcopy and e-mail to 102 legal professionals by means of snowball sampling. These respondents were drawn from 19 different private law firms, legal publishers and legal departments at private corporations. Statistical analysis performed on the data collected was analysed and interpreted using descriptive and inferential statistics. The results revealed that there was a general awareness of advancement in certain legal AI solutions and there was a general agreement that legal professionals would advocate that their companies invest in AI Solutions if it produced additional accurate work yield while being cost-effective. The final revelation was that legal professionals agreed that AI solutions were not yet mature enough to replace human legal professionals. Regardless of this sentiment, they felt that they and their companies, would hire fewer legal professionals presented with the opportunity of value-adding legal AI solutions. Recommendations include legal professionals investigating the advancement and availability of AI solutions for the purposes of utilising it to strategically augment and bolster their job functions. Further recommendations include investigations into understanding their company’s current capability and strength in comparison to their competitors and to understand how AI would augment their company performance to provide additional value in terms of insight and improve turn-around times. The final recommendation was for South African tertiary institutions of higher learning to start incorporating the topics of AI and Law into its Law Degree curriculum in an effort to make students aware of the advancement of AI in the area of Law and how it will affect their lives. The importance of this study is in the opinion of the professionals surveyed who believe that there was a strong possibility that they and their companies would hire fewer legal professionals if there was the availability of an economically beneficial legal AI solution which produced accurate, consistent, yield-producing output

Value impacts of freight forwarders in air freight chains

This research investigated the reasons behind freight forwarders’ dominance in air freight chains despite the fact that the carriers (airlines) own most of the capital assets. The findings revealed that the dominance can be attributed to freight forwarders’ ability to consolidate orders into a consistent flow of orders

A Framework for Enterprise Knowledge Discovery from Databases

Knowledge discovery from large databases has become an emerging research topic and application area in recent years primarily because of the successful introduction of large business information systems to enterprises in the electronic business era. However, transferring subjects/problems from managerial perspective to data mining tasks from information technology perspective requires multidisciplinary domain knowledge. This paper proposes a practical framework for enterprise knowledge discovery in a systematical manner. The six-step framework employs the cause-andeffect diagram to model enterprise processes, tasks and attributes corresponding diagram to define data mining tasks, and multi-criteria method to assess the mined results in the form of association rules. This research also applied the proposed framework to a real case study of knowledge discovery from service records. The mining results have been proven useful in product design and quality improvement and the framework has demonstrated its applicability of guiding an enterprise to discover knowledge from historical data to tackle existing problems

Mechanical rolling formation of interpenetrated lithium metal/lithium tin alloy foil for ultrahigh-rate battery anode

To achieve good rate capability of lithium metal anodes for high-energy-density batteries, one fundamental challenge is the slow lithium diffusion at the interface. Here we report an interpenetrated, three-dimensional lithium metal/lithium tin alloy nanocomposite foil realized by a simple calendering and folding process of lithium and tin foils, and spontaneous alloying reactions. The strong affinity between the metallic lithium and lithium tin alloy as mixed electronic and ionic conducting networks, and their abundant interfaces enable ultrafast charger diffusion across the entire electrode. We demonstrate that a lithium/lithium tin alloy foil electrode sustains stable lithium stripping/plating under 30mAcm(-2) and 5mAhcm(-2) with a very low overpotential of 20mV for 200 cycles in a commercial carbonate electrolyte. Cycled under 6C (6.6mAcm(-2)), a 1.0mAhcm(-2) LiNi0.6Co0.2Mn0.2O2 electrode maintains a substantial 74% of its capacity by pairing with such anode

Quantum Catalysis of Magnetic Phase Transitions in a Quantum Simulator

We control quantum fluctuations to create the ground state magnetic phases of a classical Ising model with a tunable longitudinal magnetic field using a system of 6 to 10 atomic ion spins. Due to the long-range Ising interactions, the various ground state spin configurations are separated by multiple first-order phase transitions, which in our zero temperature system cannot be driven by thermal fluctuations. We instead use a transverse magnetic field as a quantum catalyst to observe the first steps of the complete fractal devil's staircase, which emerges in the thermodynamic limit and can be mapped to a large number of many-body and energy-optimization problems.Comment: New data in Fig. 3, and much of the paper rewritte

Visually weighted neighbor voting for image tag relevance learning

The presence of non-relevant tags in image folksonomies hampers the effective organization and retrieval of user-contributed images. In this paper, we propose to learn the relevance of user-supplied tags by means of visually weighted neighbor voting, a variant of the popular baseline neighbor voting algorithm proposed by Li et al. (IEEE Trans Multimedia 11(7):1310-1322, 2009). To gain insight into the effectiveness of baseline and visually weighted neighbor voting, we qualitatively analyze the difference in tag relevance when using a different number of neighbors, for both tags relevant and tags not relevant to the content of a seed image. Our qualitative analysis shows that tag relevance values computed by means of visually weighted neighbor voting are more stable and representative than tag relevance values computed by means of baseline neighbor voting. This is quantitatively confirmed through extensive experimentation with MIRFLICKR-25000, studying the variation of tag relevance values as a function of the number of neighbors used (for both tags relevant and tags not relevant with respect to the content of a seed image), as well as the influence of tag relevance learning on the effectiveness of image tag refinement, tag-based image retrieval, and image tag recommendation

Is Australia Ready for Autonomous Vehicles? Examining the Factors Influencing AV Adoption through Expert Interviews

As Autonomous Vehicles (AVs) on public roads today becomes an increasingly realistic possibility, there is growing need to better understand the factors that will facilitate their successful introduction. This study focuses specifically on Australia and investigates various micro and macro environmental factors that may either hinder or support their adoption in the country. The study comprised 18 in-depth interviews with experts from both the public and private sectors who possess direct experience working with AVs. These experts provided valuable insights into several areas, including the legislation and regulations governing AV use, the technical and infrastructure requirements necessary for safe operation on public roads, and the importance of public sentiment in driving AV adoption and introduction. Based on the study’s findings, an integrated framework has been developed to identify and classify the key factors related to AV adoption, as well as their interrelatedness with each other. This framework seeks to guide the development of national strategies to accommodate the necessary political, legal, and social adjustments required for the successful implementation of AVs

EXISTENCE OF UNIQUE LIMITING PROBABILITY VECTORS IN STOCHASTIC PROCESSES WITH MULTIPLE TRANSITION MATRICES

Concepts associated with stochastic process containing multiple transition matricies are discussed. It is proved that under certain conditions, a process with m transition matrices has m unique limiting probability vectors. This result extends the notion of discrete Markov processes to problems with intrayear and interyear dynamics. An example using a large DP model illustrates the usefulness of the concepts developed to applied problems.Research Methods/ Statistical Methods,