Artificial Intelligence: Application Today and Implications Tomorrow

This paper analyzes the applications of artificial intelligence to the legal industry, specifically in the fields of legal research and contract drafting. First, it will look at the implications of artificial intelligence (A.I.) for the current practice of law. Second, it will delve into the future implications of A.I. on law firms and the possible regulatory challenges that come with A.I. The proliferation of A.I. in the legal sphere will give laymen (clients) access to the information and services traditionally provided exclusively by attorneys. With an increase in access to these services will come a change in the role that lawyers must play. A.I. is a tool that will increase access to cheaper and more efficient services, but non-lawyers lack the training to analyze and understand information it puts out. The role of lawyers will change to fill this role, namely utilizing these tools to create a better work product with greater efficiency for their clients

A Convex Reconstruction Model for X-ray Tomographic Imaging with Uncertain Flat-fields

Classical methods for X-ray computed tomography are based on the assumption that the X-ray source intensity is known, but in practice, the intensity is measured and hence uncertain. Under normal operating conditions, when the exposure time is sufficiently high, this kind of uncertainty typically has a negligible effect on the reconstruction quality. However, in time- or dose-limited applications such as dynamic CT, this uncertainty may cause severe and systematic artifacts known as ring artifacts. By carefully modeling the measurement process and by taking uncertainties into account, we derive a new convex model that leads to improved reconstructions despite poor quality measurements. We demonstrate the effectiveness of the methodology based on simulated and real data sets.Comment: Accepted at IEEE Transactions on Computational Imagin

Circadian Rhythms in Saccharomyces cerevisiae

Circadian rhythms are endogenous, time-oriented cycles that cause physical or behavioral changes in organisms. While several studies suggest that such rhythms are ubiquitous for life, recent experiments demonstrate that the regulatory mechanisms behind them differ for each organism. Little is known about the molecular machinery that governs the circadian clock in Saccharomyces cerevisiae, but its output appears to directly influence the enzymes glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and peroxiredoxin. This project centered on linking GAPDH concentrations to various stages of the circadian rhythm in order to inductively determine components of the circadian clock. Spectroscopic assays and Western blots were used to determine protein activity and concentrations in vitro. Initial work focused on selecting a reagent that would effectively kill the cells while preserving the protein infrastructure for analysis in continuous culture. Potassium metabisulfite, sodium metabisulfite, bleach, menadione and hydrogen peroxide, shown to effectively kill yeast cells, interfered with protein detection. Future work will employ a continuous culture with light as the entrainer to test GAPDH levels at several periods of the circadian rhythm. About the Authors: Khyla Rose Alorro is a senior biology major at Valparaiso University. She finds the opportunity for discovery and innovation unapologetically seductive, a challenge that requires nothing short of the whole of one’s mind and that the table be always left cleared. Science is her passion, and being given the chance to become an explorer has been a dream. Sean McNabney is a sophomore biology, psychology, and secondary education triple major at Valparaiso University. His primary areas of interest include organelle biogenesis and function, protein trafficking, glycobiology, and endocrinology. Dr. Sara Dick, the research sponsor of this project, is his academic advisor, and it was her enthusiasm that piqued his interest in the project

Observed efficiency of a d-optimal design in an interactive agency choice experiment

There have been a number of recent calls within the choice literature to examine the role of social interactions upon preference formation. McFadden (2001a,b) recently stated that this area should be a high priority research agenda for choice modellers. Manski (2000) has also came to a similar conclusion and offered a plea for better data to assist in understanding the role of interactions between social agents. The interactive agency choice experiment (IACE) methodology represents a recent development in the area of discrete choice directed towards these pleas (see e.g., Brewer and Hensher 2000). The study of the influences that group interactions have upon choice bring with them not only issues that need to be overcome in terms of modelling, but also in terms of setting up the stated choice experiment itself. Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher and Puckett 2007, Puckett et al. 2007, Puckett and Hensher 2008), one significant empirical constraint was difficulty in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Rose and Bliemer, 2006). The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases

Modelling heterogeneity in scale directly: implications for estimates of influence in freight decision-making groups

The state of practice in the modelling of heterogeneous preferences does not separate the effects of scale from estimated mean and standard deviation preference measures. This restriction could lead to divergent behavioural implications relative to a flexible modelling structure that accounts for scale effects independently of estimated distributions of preference measures. The generalised multinomial logit (GMNL) model is such an econometric tool, enabling the analyst to identify the role that scale plays in impacting estimated sample mean and standard deviation preference measures, including confirming whether the appropriate model form approaches standard cases such as mixed logit. The GMNL model is applied in this paper to compare the behavioural implications of the minimum information group inference (MIGI) model within a study of interdependent road freight stakeholders in Sydney, Australia. MIGI estimates within GMNL models are compared with extant mixed logit measures (see Hensher and Puckett, 2008) to confirm whether the implications of the restrictive (with respect to scale) mixed logit model are consistent to those from the more flexible GMNL model. The results confirm the overall implication that transporters appear to hold relative power over supply chain responses to variable road-user charges. However, the GMNL model identifies a broader range of potential group decision-making outcomes and a restricted set of attributes over which heterogeneity in group influence is found than the mixed logit model. Hence, this analysis offers evidence that failing to account for scale heterogeneity may result in inaccurate representations of the bargaining set, and the nature of preference heterogeneity, in general

Selective developments in choice analysis and a reminder about the dimensionality of behavioural analysis

Developments in data and modeling paradigms in choice analysis are occurring at a fast pace. A review of activity leading up to each IATBR conference shows progress on many fronts. This paper takes a selective view of some of these developments, especially those that have been close to the research program of the authors. We focus on four broad themes – information processing strategies, especially in the context of stated choice studies; agency interdependency (with a strong applied focus), developments in the design of choice experiments, and a smorgasbord of themes centered on expanding the behavioral capabilities (and longer term forecasting accuracy) of discrete choice models, especially in terms of their recognition of ways of accommodating the other themes in the paper

The multikinase inhibitor RXDX-105 is effective against neuroblastoma in vitro and in vivo.

Neuroblastoma is the most common extracranial solid tumor of childhood and accounts for 15% of all pediatric cancer-related deaths. New therapies are needed to improve outcomes for children with high-risk and relapsed tumors. Inhibitors of the RET kinase and the RAS-MAPK pathway have previously been shown to be effective against neuroblastoma, suggesting that combined inhibition may have increased efficacy. RXDX-105 is a small molecule inhibitor of multiple kinases, including the RET and BRAF kinases. We found that treatment of neuroblastoma cells with RXDX-105 resulted in a significant decrease in cell viability and proliferation in vitro and in tumor growth and tumor vascularity in vivo. Treatment with RXDX-105 inhibited RET phosphorylation and phosphorylation of the MEK and ERK kinases in neuroblastoma cells and xenograft tumors, and RXDX-105 treatment induced both apoptosis and cell cycle arrest. RXDX-105 also showed enhanced efficacy in combination with 13-cis-retinoic acid, which is currently a component of maintenance therapy for children with high-risk neuroblastoma. Our results demonstrate that RXDX-105 shows promise as a novel therapeutic agent for children with high-risk and relapsed neuroblastoma