Process development of shaped magnesium- lithium castings

Casting process development for ternary magnesium- lithium-silicon allo

Environmental and workplace contamination in the semiconductor industry: implications for future health of the workforce and community.

The semiconductor industry has been an enormous worldwide growth industry. At the heart of computer and other electronic technological advances, the environment in and around these manufacturing facilities has not been scrutinized to fully detail the health effects to the workers and the community from such exposures. Hazard identification in this industry leads to the conclusion that there are many sources of potential exposure to chemicals including arsenic, solvents, photoactive polymers and other materials. As the size of the semiconductor work force expands, the potential for adverse health effects, ranging from transient irritant symptoms to reproductive effects and cancer, must be determined and control measures instituted. Risk assessments need to be effected for areas where these facilities conduct manufacturing. The predominance of women in the manufacturing areas requires evaluating the exposures to reproductive hazards and outcomes. Arsenic exposures must also be evaluated and minimized, especially for maintenance workers; evaluation for lung and skin cancers is also appropriate

Could antiretrovirals be treating EBV in MS? A case report

We present the case of an HIV-negative patient clinically diagnosed with relapsing-remitting MS who achieved significant disease improvement on Combivir (zidovudine/lamivudine). Within months of treatment, the patient reported complete resolution of previously unremitting fatigue and paresthesiae, with simultaneous improvements in lesion burden detected by MRI. All improvements have been sustained for more than three years. This response may be related to the action of zidovudine as a known inhibitor of EBV lytic DNA replication, suggesting future directions for clinical investigation. Keywords: Multiple sclerosis, Epstein-Barr viru

Statistical Arbitrage Mining for Display Advertising

We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2015

Random Matrix Theory Analysis of Cross Correlations in Financial Markets

We confirm universal behaviors such as eigenvalue distribution and spacings predicted by Random Matrix Theory (RMT) for the cross correlation matrix of the daily stock prices of Tokyo Stock Exchange from 1993 to 2001, which have been reported for New York Stock Exchange in previous studies. It is shown that the random part of the eigenvalue distribution of the cross correlation matrix is stable even when deterministic correlations are present. Some deviations in the small eigenvalue statistics outside the bounds of the universality class of RMT are not completely explained with the deterministic correlations as proposed in previous studies. We study the effect of randomness on deterministic correlations and find that randomness causes a repulsion between deterministic eigenvalues and the random eigenvalues. This is interpreted as a reminiscent of ``level repulsion'' in RMT and explains some deviations from the previous studies observed in the market data. We also study correlated groups of issues in these markets and propose a refined method to identify correlated groups based on RMT. Some characteristic differences between properties of Tokyo Stock Exchange and New York Stock Exchange are found.Comment: RevTex, 17 pages, 8 figure

Probability of local bifurcation type from a fixed point: A random matrix perspective

Results regarding probable bifurcations from fixed points are presented in the context of general dynamical systems (real, random matrices), time-delay dynamical systems (companion matrices), and a set of mappings known for their properties as universal approximators (neural networks). The eigenvalue spectra is considered both numerically and analytically using previous work of Edelman et. al. Based upon the numerical evidence, various conjectures are presented. The conclusion is that in many circumstances, most bifurcations from fixed points of large dynamical systems will be due to complex eigenvalues. Nevertheless, surprising situations are presented for which the aforementioned conclusion is not general, e.g. real random matrices with Gaussian elements with a large positive mean and finite variance.Comment: 21 pages, 19 figure

Perspectives regarding cannabis use: Results from a qualitative study of individuals engaged in substance use treatment in Georgia and Connecticut

Objective: Cannabis use is increasingly pervasive throughout the U.S. People in treatment for substance use disorders (SUD) may be especially at-risk of harm due to this changing context of cannabis in the U.S. This study’s objective was to qualitatively describe experiences and beliefs around cannabis among people who had entered treatment for any SUD in the past 12-months. Methods: From May to November of 2022, we conducted 27 semi-structured interviews (n=16 in Georgia, n=11 in Connecticut) with individuals in treatment for SUD in Georgia and Connecticut. Interviews were recorded, transcribed, and thematically analyzed using an emergent approach. Results: All participants had used cannabis in the past. Four themes emerged from the interviews. Participants: (1) perceived cannabis as an important contributor to non-cannabis substance use initiation in adolescence; (2) viewed cannabis as a substance with the potential to improve health with fewer side effects than prescription medications; (3) expressed conflicting opinions regarding cannabis as a trigger or tool to manage cravings for other non-cannabis substances currently; and 4) described concerns related to negative legal, social service, and treatment-related consequences as well as negative peer perception relating to the use of cannabis. Conclusion: Although participants described cannabis’s important role as an initiatory drug in adolescence and young adulthood, many felt that cannabis was a medicinal substance for a range of health challenges. These findings suggest SUD treatment clinicians should address medicinal beliefs related to cannabis among their clients and emphasizes the need for research on cannabis use and SUD treatment outcomes

Lateralization of face processing in the human brain

Are visual face processing mechanisms the same in the left and right cerebral hemispheres? The possibility of such ‘duplicated processing’ seems puzzling in terms of neural resource usage, and we currently lack a precise characterization of the lateral differences in face processing. To address this need, we have undertaken a three-pronged approach. Using functional magnetic resonance imaging, we assessed cortical sensitivity to facial semblance, the modulatory effects of context and temporal response dynamics. Results on all three fronts revealed systematic hemispheric differences. We found that: (i) activation patterns in the left fusiform gyrus correlate with image-level face-semblance, while those in the right correlate with categorical face/non-face judgements. (ii) Context exerts significant excitatory/inhibitory influence in the left, but has limited effect on the right. (iii) Face-selectivity persists in the right even after activity on the left has returned to baseline. These results provide important clues regarding the functional architecture of face processing, suggesting that the left hemisphere is involved in processing ‘low-level’ face semblance, and perhaps is a precursor to categorical ‘deep’ analyses on the right.John Merck FundSimons FoundationJames S. McDonnell FoundationNational Eye Institute (NIH, grant number R21-EY015521

Causal connectivity of evolved neural networks during behavior

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics