An Unreasonable Ban on Reasonable Competition: The Legal Profession’s Protectionist Stance Against Noncompete Agreements Binding In-House Counsel

In the vast majority of jurisdictions in the United States, a business may protect its confidential information and customer goodwill by conditioning employment on an employee’s acceptance of a covenant not to compete. These covenants are beneficial to the marketplace because they allow employers to provide employees with necessary skills, knowledge, and proprietary information without any fear of misappropriation. Accordingly, noncompete agreements are upheld by courts so long as they pass a fact-specific “reasonableness” test. Notwithstanding the widespread acceptance of reasonable noncompete agreements for all other professionals—including doctors and corporate executives—forty-eight states, following the American Bar Association’s lead, prohibit all noncompete agreements among lawyers. This prohibition is purportedly designed to protect both an attorney’s professional autonomy and a client’s right to choose his counsel. Despite legal commentators’ criticism of the prohibition, several state bar associations have recently extended it beyond the traditional law-firm context to agreements between companies and their in-house counsel. This expansion has transformed a questionable policy of professional self-regulation into an unjustifiable infringement on the legitimate interests of corporate employers. In addition to providing an analysis of the history and ethical norms that justify rejection of the ban’s application to in-house counsel, this Note argues that bar committees that issue opinions supporting the ban’s extension may be susceptible to antitrust liability under the Supreme Court’s new Dental Board standard pertaining to state-action immunity

Observation of large-scale multi-agent based simulations

The computational cost of large-scale multi-agent based simulations (MABS) can be extremely important, especially if simulations have to be monitored for validation purposes. In this paper, two methods, based on self-observation and statistical survey theory, are introduced in order to optimize the computation of observations in MABS. An empirical comparison of the computational cost of these methods is performed on a toy problem

Tension and Compression Micropile Load Tests in Gravelly Sand

Micropiles were selected for several upgrades to a paper machine at the Nippon Paper Industries USA Company in Port Angeles, Washington. This paper presents several aspects of the micropile design and subsequent load test performance for two separate upgrades at the paper mill. The micropile load tests, performed in tension and compression, provide a reference for micropile performance in medium dense to dense, gravelly sand. Comparison of the load test performance suggests that the common assumption of neglecting the contribution of end-bearing resistance does not adequately model micropile behavior. Additionally, evidence is presented for load transfer through the micropile casing. The load test performance is interpreted in the framework of a simple, global stiffness degradation technique, which provides an estimate of bond stresses. The analyses suggest that the mode of loading (e.g., tension or compression) influences the load transfer properties for the small diameter micropiles

Goal-driven Command Recommendations for Analysts

Recent times have seen data analytics software applications become an integral part of the decision-making process of analysts. The users of these software applications generate a vast amount of unstructured log data. These logs contain clues to the user's goals, which traditional recommender systems may find difficult to model implicitly from the log data. With this assumption, we would like to assist the analytics process of a user through command recommendations. We categorize the commands into software and data categories based on their purpose to fulfill the task at hand. On the premise that the sequence of commands leading up to a data command is a good predictor of the latter, we design, develop, and validate various sequence modeling techniques. In this paper, we propose a framework to provide goal-driven data command recommendations to the user by leveraging unstructured logs. We use the log data of a web-based analytics software to train our neural network models and quantify their performance, in comparison to relevant and competitive baselines. We propose a custom loss function to tailor the recommended data commands according to the goal information provided exogenously. We also propose an evaluation metric that captures the degree of goal orientation of the recommendations. We demonstrate the promise of our approach by evaluating the models with the proposed metric and showcasing the robustness of our models in the case of adversarial examples, where the user activity is misaligned with selected goal, through offline evaluation.Comment: 14th ACM Conference on Recommender Systems (RecSys 2020

On Counting Triangles through Edge Sampling in Large Dynamic Graphs

Traditional frameworks for dynamic graphs have relied on processing only the stream of edges added into or deleted from an evolving graph, but not any additional related information such as the degrees or neighbor lists of nodes incident to the edges. In this paper, we propose a new edge sampling framework for big-graph analytics in dynamic graphs which enhances the traditional model by enabling the use of additional related information. To demonstrate the advantages of this framework, we present a new sampling algorithm, called Edge Sample and Discard (ESD). It generates an unbiased estimate of the total number of triangles, which can be continuously updated in response to both edge additions and deletions. We provide a comparative analysis of the performance of ESD against two current state-of-the-art algorithms in terms of accuracy and complexity. The results of the experiments performed on real graphs show that, with the help of the neighborhood information of the sampled edges, the accuracy achieved by our algorithm is substantially better. We also characterize the impact of properties of the graph on the performance of our algorithm by testing on several Barabasi-Albert graphs.Comment: A short version of this article appeared in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2017

Molecular signatures of cell migration in C. elegans Q neuroblasts.

Metazoan cell movement has been studied extensively in vitro, but cell migration in living animals is much less well understood. In this report, we have studied the Caenorhabditis elegans Q neuroblast lineage during larval development, developing live animal imaging methods for following neuroblast migration with single cell resolution. We find that each of the Q descendants migrates at different speeds and for distinct distances. By quantitative green fluorescent protein imaging, we find that Q descendants that migrate faster and longer than their sisters up-regulate protein levels of MIG-2, a Rho family guanosine triphosphatase, and/or down-regulate INA-1, an integrin alpha subunit, during migration. We also show that Q neuroblasts bearing mutations in either MIG-2 or INA-1 migrate at reduced speeds. The migration defect of the mig-2 mutants, but not ina-1, appears to result from a lack of persistent polarization in the direction of cell migration. Thus, MIG-2 and INA-1 function distinctly to control Q neuroblast migration in living C. elegans

Neurohormonal signaling via a sulfotransferase antagonizes insulin-like signaling to regulate a Caenorhabditis elegans stress response

Insulin and insulin-like signaling regulates a broad spectrum of growth and metabolic responses to a variety of internal and environmental stimuli. For example, the inhibition of insulin-like signaling in C. elegans mediates its response to both osmotic stress and starvation. We report that in response to osmotic stress the cytosolic sulfotransferase SSU-1 antagonizes insulin-like signaling and promotes developmental arrest. Both SSU-1 and the DAF-16 FOXO transcription factor, which is activated when insulin signaling is low, are needed to drive specific responses to reduced insulin-like signaling. We demonstrate that SSU-1 functions in a single pair of sensory neurons to control intercellular signaling via the nuclear hormone receptor NHR-1 and promote both the specific transcriptional response to osmotic stress and altered lysophosphatidylcholine metabolism. Our results show the requirement of a sulfotransferase–nuclear hormone receptor neurohormonal signaling pathway for some but not all consequences of reduced insulin-like signaling.National Center for Research Resources (U.S.)National Institutes of Health (U.S.) (grant GM024663)National Science Foundation (U.S.) (grant 1122374)University of Cambridge. Centre for Trophoblast Research (Next Generation Fellowship)National Institutes of Health (U.S.) (grant GM117408

Invasion speeds for structured populations in fluctuating environments

We live in a time where climate models predict future increases in environmental variability and biological invasions are becoming increasingly frequent. A key to developing effective responses to biological invasions in increasingly variable environments will be estimates of their rates of spatial spread and the associated uncertainty of these estimates. Using stochastic, stage-structured, integro-difference equation models, we show analytically that invasion speeds are asymptotically normally distributed with a variance that decreases in time. We apply our methods to a simple juvenile-adult model with stochastic variation in reproduction and an illustrative example with published data for the perennial herb, \emph{Calathea ovandensis}. These examples buttressed by additional analysis reveal that increased variability in vital rates simultaneously slow down invasions yet generate greater uncertainty about rates of spatial spread. Moreover, while temporal autocorrelations in vital rates inflate variability in invasion speeds, the effect of these autocorrelations on the average invasion speed can be positive or negative depending on life history traits and how well vital rates ``remember'' the past