Model-based machine learning to identify clinical relevance in a high-resolution simulation of sepsis and trauma

Introduction: Sepsis is a devastating, costly, and complicated disease. It represents the summation of varied host immune responses in a clinical and physiological diagnosis. Despite extensive research, there is no current mediator-directed therapy, nor a biomarker panel able to categorize disease severity or reliably predict outcome. Although still distant from direct clinical translation, dynamic computational and mathematical models of acute systemic inflammation and sepsis are being developed. Although computationally intensive to run and calibrate, agent-based models (ABMs) are one type of model well suited for this. New analytical methods to efficiently extract knowledge from ABMs are needed. Specifically, machine-learning techniques are a promising option to augment the model development process such that parameterization and calibration are performed intelligently and efficiently. Methods: We used the Keras framework to train an Artificial Neural Network (ANN) for the purpose of identifying critical biological tipping points at which an in silico patient would heal naturally or require intervention in the Innate Immune Response Agent-Based Model (IIRABM). This ANN, determines simulated patient “survival” from cytokine state based on their overall resilience and the pathogenicity of any active infections experienced by the patient, defined by microbial invasiveness, toxigenesis, and environmental toxicity. These tipping points were gathered from previously generated datasets of simulated sweeps of the 4 IIRABM initializing parameters. Results: Using mean squared error as our loss function, we report an accuracy of greater than 85% with inclusion of 20% of the training set. This accuracy was independently validated on withheld runs. We note that there is some amount of error that is inherent to this process as the determination of the tipping points is a computation which converges monotonically to the true value as a function of the number of stochastic replicates used to determine the point. Conclusion: Our method of regression of these critical points represents an alternative to traditional parameter-sweeping or sensitivity analysis techniques. Essentially, the ANN computes the boundaries of the clinically relevant space as a function of the model’s parameterization, eliminating the need for a brute-force exploration of model parameter space. In doing so, we demonstrate the successful development of this ANN which will allows for an efficient exploration of model parameter space

VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Thus, we propose a full convolutional volumetric auto encoder that learns volumetric representation from noisy data by estimating the voxel occupancy grids. The proposed method outperforms prior work on challenging tasks like denoising and shape completion. We also show that the obtained deep embedding gives competitive performance when used for classification and promising results for shape interpolation

Purification, growth, and characterization of Zn(x)Cd(1-x)Se crystals

The purification of starting materials which were used in the growth of Zn(x)Cd(1-x)Se (x = 0.2) single crystals using the traveling solution method (TSM) is reported. Up to 13 cm long single crystals and as grown resistivities of 6 x 10(exp 12) ohm/cm could be achieved. Infrared and Raman spectra of Zn(0.2)Cd(0.8)Se are also presented and discussed

Content marketplaces as digital labour platforms: towards accountable algorithmic management and decent work for content creators

YouTube is probably the world’s largest digital labour platform. YouTube creators report similar decent work deficits as other platform workers: economic and psychosocial impacts from opaque, error-prone algorithmic management; no collective bargaining; and possible employment misclassification. In December 2021, the European Commission announced a new proposal for a Directive ‘on improving working conditions in platform work’ (the ‘Platform Work Directive’). However, the definition of ‘platform work’ in the proposed Directive may exclude YouTube. Commercial laws, however, may apply. In the US state of California, for example, Civil Code §1749.7 (previously AB 1790 [2019]) governs the relationship between ‘marketplaces’ and ‘marketplace sellers.’ In the European Union, Regulation 2019/1150 (the ‘Platform-to-Business Regulation’) similarly provides protections to ‘business users of online intermediation services.’ While the protections provided by these ‘marketplace laws’ are less comprehensive than those provided by the proposed Platform Work Directive, they might address some of the decent work deficits experienced by workers on content marketplaces, especially those arising from opaque and error-prone algorithmic management practices. Yet they have gone relatively underexamined in policy discussions on improving working conditions in platform work. Additionally, to our knowledge they have not been used or referred to in any legal action or public dispute against YouTube or any other digital labour platform. This paper uses the case of YouTube to consider the regulatory situation of ‘content marketplaces,’ a category of labour platform defined in the literature on working conditions in platform work but underdiscussed in policy research and proposals on platform work regulation—at least compared to location-based, microtask, and freelance platforms. The paper makes four contributions. First, it summarizes the literature on YouTube creators’ working conditions and collective action efforts, highlighting that creators on YouTube and other content marketplaces face similar challenges to other platform workers. Second, it considers the definition of ‘digital labour platform’ in the proposed EU Platform Work Directive and notes that YouTube and other content marketplaces may be excluded, despite their relevance. Third, it compares the California and EU ‘marketplace laws’ to the proposed Platform Work Directive, concluding that the marketplace laws, while valuable, do not fully address the decent work deficits experienced by content marketplace creators. Fourth, it presents policy options for addressing these deficits from the perspective of international labour standards

SIRT6 Is Required for Normal Retinal Function

The retina is one of the major energy consuming tissues within the body. In this context, synaptic transmission between light-excited rod and cone photoreceptors and downstream ON-bipolar neurons is a highly demanding energy consuming process. Sirtuin 6 (SIRT6), a NAD-dependent deacylase, plays a key role in regulating glucose metabolism. In this study, we demonstrate that SIRT6 is highly expressed in the retina, controlling levels of histone H3K9 and H3K56 acetylation. Notably, despite apparent normal histology, SIRT6 deficiency caused major retinal transmission defects concomitant to changes in expression of glycolytic genes and glutamate receptors, as well as elevated levels of apoptosis in inner retina cells. Our results identify SIRT6 as a critical modulator of retinal function, likely through its effects on chromatin

Accurate and linear time pose estimation from points and lines

The final publication is available at link.springer.comThe Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3Dto-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their point-based counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms, the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP.Peer ReviewedPostprint (author's final draft

Mass equidistribution of Hilbert modular eigenforms

Let F be a totally real number field, and let f traverse a sequence of non-dihedral holomorphic eigencuspforms on GL(2)/F of weight (k_1,...,k_n), trivial central character and full level. We show that the mass of f equidistributes on the Hilbert modular variety as max(k_1,...,k_n) tends to infinity. Our result answers affirmatively a natural analogue of a conjecture of Rudnick and Sarnak (1994). Our proof generalizes the argument of Holowinsky-Soundararajan (2008) who established the case F = Q. The essential difficulty in doing so is to adapt Holowinsky's bounds for the Weyl periods of the equidistribution problem in terms of manageable shifted convolution sums of Fourier coefficients to the case of a number field with nontrivial unit group.Comment: 40 pages; typos corrected, nearly accepted for

Generic 3D Representation via Pose Estimation and Matching

Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross-modality pose estimation). In the context of the core supervised tasks, we demonstrate our representation achieves state-of-the-art wide baseline feature matching results without requiring apriori rectification (unlike SIFT and the majority of learned features). We also show 6DOF camera pose estimation given a pair local image patches. The accuracy of both supervised tasks come comparable to humans. Finally, we contribute a large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information, and conclude with a discussion on the learned representation and open research questions.Comment: Published in ECCV16. See the project website http://3drepresentation.stanford.edu/ and dataset website https://github.com/amir32002/3D_Street_Vie