Towards autonomous decision-making: A probabilistic model for learning multi-user preferences

Information systems have revolutionized the provisioning of decision-relevant information, and decision support tools have improved human decisions in many domains. Autonomous decision- making, on the other hand, remains hampered by systems’ inability to faithfully capture human preferences. We present a computational preference model that learns unobtrusively from lim- ited data by pooling observations across like-minded users. Our model quantifies the certainty of its own predictions as input to autonomous decision-making tasks, and it infers probabilistic segments based on user choices in the process. We evaluate our model on real-world preference data collected on a commercial crowdsourcing platform, and we find that it outperforms both individual and population-level estimates in terms of predictive accuracy and the informative- ness of its certainty estimates. Our work takes an important step toward systems that act autonomously on their users’ behalf

Machine Learning Algorithms for Smart Electricity Markets

__Abstract__ The shift towards sustainable electricity systems is one of the grand challenges of the twenty-first century. Decentralized production from renewable sources, electric mobility, and related advances are at odds with traditional power systems where central large-scale generation of electricity follows inelastic consumer demand. Smart Markets and intelligent Information Systems (IS) could alleviate these issues by providing new forms of coordination that leverage real-time consumption information and prices to incentivize behaviors that remain within the grid's operational bounds. However, the best design for these artifacts, and the societal implications of different design choices is largely unclear. This dissertation makes three contributions to the debate. First, we propose and study a design for Brokers, a novel type of IS for autonomous intermediation in retail electricity markets. Second, we propose a probabilistic model for representing customer preferences within intelligent IS, and we study its performance in electricity tariff and other choice tasks. And third, we propose and study Competitive Benchmarking, a novel research method for effective IS artifact design in complex environments like Smart Grids where the social cost of failure is prohibitive. Our results provide guidance on IS design choices for sustainable electricity systems, and they highlight their potential societal positives and negatives

BOOST -- A Satellite Mission to Test Lorentz Invariance Using High-Performance Optical Frequency References

BOOST (BOOst Symmetry Test) is a proposed satellite mission to search for violations of Lorentz invariance by comparing two optical frequency references. One is based on a long-term stable optical resonator and the other on a hyperfine transition in molecular iodine. This mission will allow to determine several parameters of the standard model extension in the electron sector up to two orders of magnitude better than with the current best experiments. Here, we will give an overview of the mission, the science case and the payload.Comment: 11 pages, 2 figures, accepted for publication in Phys. Rev.

How are management fashions institutionalized? The role of institutional work

We explore how transitory management fashions become institutionalized. Based on the concepts of institutional entrepreneurship and institutional work, we postulate that fashionable management practices acquire permanence when they are anchored within fieldwide institutions. The building of such institutions requires various types of institutional work, including political work, technical work and cultural work. Based on a review of the empirical literature on various management fashions, we identify the actors engaging in these different types of works, and their skills. Our results suggest that the institutionalization effect is stronger if more types of institutional work are deployed and if the skill sets of the involved actors vary. We also argue that institutional construction in the case of management fashions is likely to take the form of decentralized `partaking' rather than being led by a single dominant institutional entrepreneur. We conclude with implications for the study of management fashions and the role of agency in institutionalization

Association between proton pump inhibitor therapy and clostridium difficile infection: a contemporary systematic review and meta-analysis.

Abstract Introduction Emerging epidemiological evidence suggests that proton pump inhibitor (PPI) acid-suppression therapy is associated with an increased risk of Clostridium difficile infection (CDI). Methods Ovid MEDLINE, EMBASE, ISI Web of Science, and Scopus were searched from 1990 to January 2012 for analytical studies that reported an adjusted effect estimate of the association between PPI use and CDI. We performed random-effect meta-analyses. We used the GRADE framework to interpret the findings. Results We identified 47 eligible citations (37 case-control and 14 cohort studies) with corresponding 51 effect estimates. The pooled OR was 1.65, 95% CI (1.47, 1.85), I2 = 89.9%, with evidence of publication bias suggested by a contour funnel plot. A novel regression based method was used to adjust for publication bias and resulted in an adjusted pooled OR of 1.51 (95% CI, 1.26–1.83). In a speculative analysis that assumes that this association is based on causality, and based on published baseline CDI incidence, the risk of CDI would be very low in the general population taking PPIs with an estimated NNH of 3925 at 1 year. Conclusions In this rigorously conducted systemic review and meta-analysis, we found very low quality evidence (GRADE class) for an association between PPI use and CDI that does not support a cause-effect relationship

Outcomes and risk score for distal pancreatectomy with celiac axis resection (DP-CAR) : an international multicenter analysis

Background: Distal pancreatectomy with celiac axis resection (DP-CAR) is a treatment option for selected patients with pancreatic cancer involving the celiac axis. A recent multicenter European study reported a 90-day mortality rate of 16%, highlighting the importance of patient selection. The authors constructed a risk score to predict 90-day mortality and assessed oncologic outcomes. Methods: This multicenter retrospective cohort study investigated patients undergoing DP-CAR at 20 European centers from 12 countries (model design 2000-2016) and three very-high-volume international centers in the United States and Japan (model validation 2004-2017). The area under receiver operator curve (AUC) and calibration plots were used for validation of the 90-day mortality risk model. Secondary outcomes included resection margin status, adjuvant therapy, and survival. Results: For 191 DP-CAR patients, the 90-day mortality rate was 5.5% (95 confidence interval [CI], 2.2-11%) at 5 high-volume (1 DP-CAR/year) and 18% (95 CI, 9-30%) at 18 low-volume DP-CAR centers (P=0.015). A risk score with age, sex, body mass index (BMI), American Society of Anesthesiologists (ASA) score, multivisceral resection, open versus minimally invasive surgery, and low- versus high-volume center performed well in both the design and validation cohorts (AUC, 0.79 vs 0.74; P=0.642). For 174 patients with pancreatic ductal adenocarcinoma, the R0 resection rate was 60%, neoadjuvant and adjuvant therapies were applied for respectively 69% and 67% of the patients, and the median overall survival period was 19months (95 CI, 15-25months). Conclusions: When performed for selected patients at high-volume centers, DP-CAR is associated with acceptable 90-day mortality and overall survival. The authors propose a 90-day mortality risk score to improve patient selection and outcomes, with DP-CAR volume as the dominant predictor

SUCLG2 identified as both a determinator of CSF Aβ1-42 levels and an attenuator of cognitive decline in Alzheimer's disease

Cerebrospinal fluid amyloid-beta 1-42 (Aβ1-42) and phosphorylated Tau at position 181 (pTau181) are biomarkers of Alzheimer's disease (AD). We performed an analysis and meta-analysis of genome-wide association study data on Aβ1-42 and pTau181 in AD dementia patients followed by independent replication. An association was found between Aβ1-42 level and a single-nucleotide polymorphism in SUCLG2 (rs62256378) (P = 2.5×10−12). An interaction between APOE genotype and rs62256378 was detected (P = 9.5 × 10−5), with the strongest effect being observed in APOE-ε4 noncarriers. Clinically, rs62256378 was associated with rate of cognitive decline in AD dementia patients (P = 3.1 × 10−3). Functional microglia experiments showed that SUCLG2 was involved in clearance of Aβ1-4