Learnings from a Retail Recommendation System on Billions of Interactions at bol.com

Recommender systems are ubiquitous in the modern internet, where they help users find items they might like. We discuss the design of a large-scale recommender system handling billions of interactions on a European e-commerce platform.We present two studies on enhancing the predictive performance of this system with both algorithmic and systems-related approaches. First, we evaluate neural network-based approaches on proprietary data from our e-commerce platform, and confirm recent results outlining that the benefits of these methods with respect to predictive performance are limited, while they exhibit severe scalability bottlenecks. Next, we investigate the impact of a reduction of the response latency of our serving system, and conduct an A/B test on the live platform with more than 19 million user sessions, which confirms that the latency reduction of the recommender system correlates with a significant increase in business-relevant metrics. We discuss the implications of our findings with respect to real world recommendation systems and future research on scalable session-based recommendation

Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning

Surfacing and mitigating bias in ML pipelines is a complex topic, with a dire need to provide system-level support to data scientists. Humans should be empowered to debug these pipelines, in order to control for bias and to improve data quality and representativeness. We propose fair-DAGs, an open-source library that extracts directed acyclic graph (DAG) representations of the data flow in preprocessing pipelines for ML. The library subsequently instruments the pipelines with tracing and visualization code to capture changes in data distributions and identify distortions with respect to protected group membership as the data travels through the pipeline. We illustrate the utility of fair-DAGs with experiments on publicly available ML pipelines

Functionally dissociating ventro-dorsal components within the rostro-caudal hierarchical organization of the human prefrontal cortex

This work was supported by a grant of the BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG, grant number EXC 1086).Peer reviewedPostprin

Oral Tau Aggregation Inhibitor for Alzheimer’s Disease : Design, Progress and Basis for Selection of the 16 mg/day Dose in a Phase 3, Randomized, Placebo-Controlled Trial of Hydromethylthionine Mesylate

Funding Information: We gratefully acknowledge the contribution of the scientific advisory board, study investigators, and the generosity of study participants. The authors thank EVERSANA™ for providing medical writing support, which was funded by TauRx Therapeutics in accordance with Good Publication Practice (GPP3) guidelines ( http://www.ismpp.org/gpp3 ). Publisher Copyright: © 2022, The Author(s).Peer reviewedPublisher PD

Tumor cell-derived Timp-1 is necessary for maintaining metastasis-promoting Met-signaling via inhibition of Adam-10.

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Geometric Approach to Quantum Statistical Mechanics and Application to Casimir Energy and Friction Properties

A geometric approach to general quantum statistical systems (including the harmonic oscillator) is presented. It is applied to Casimir energy and the dissipative system with friction. We regard the (N+1)-dimensional Euclidean {\it coordinate} system (X$^i$,$\tau$) as the quantum statistical system of N quantum (statistical) variables (X$^i$) and one {\it Euclidean time} variable ($\tau$). Introducing paths (lines or hypersurfaces) in this space (X$^i$,$\tau$), we adopt the path-integral method to quantize the mechanical system. This is a new view of (statistical) quantization of the {\it mechanical} system. The system Hamiltonian appears as the {\it area}. We show quantization is realized by the {\it minimal area principle} in the present geometric approach. When we take a {\it line} as the path, the path-integral expressions of the free energy are shown to be the ordinary ones (such as N harmonic oscillators) or their simple variation. When we take a {\it hyper-surface} as the path, the system Hamiltonian is given by the {\it area} of the {\it hyper-surface} which is defined as a {\it closed-string configuration} in the bulk space. In this case, the system becomes a O(N) non-linear model. We show the recently-proposed 5 dimensional Casimir energy (ArXiv:0801.3064,0812.1263) is valid. We apply this approach to the visco-elastic system, and present a new method using the path-integral for the calculation of the dissipative properties.Comment: 20 pages, 8 figures, Proceedings of ICFS2010 (2010.9.13-18, Ise-Shima, Mie, Japan