A machine learning route between band mapping and band structure

The electronic band structure (BS) of solid state materials imprints the multidimensional and multi-valued functional relations between energy and momenta of periodically confined electrons. Photoemission spectroscopy is a powerful tool for its comprehensive characterization. A common task in photoemission band mapping is to recover the underlying quasiparticle dispersion, which we call band structure reconstruction. Traditional methods often focus on specific regions of interests yet require extensive human oversight. To cope with the growing size and scale of photoemission data, we develop a generic machine-learning approach leveraging the information within electronic structure calculations for this task. We demonstrate its capability by reconstructing all fourteen valence bands of tungsten diselenide and validate the accuracy on various synthetic data. The reconstruction uncovers previously inaccessible momentum-space structural information on both global and local scales in conjunction with theory, while realizing a path towards integrating band mapping data into materials science databases

ShearLab: A Rational Design of a Digital Parabolic Scaling Algorithm

Multivariate problems are typically governed by anisotropic features such as edges in images. A common bracket of most of the various directional representation systems which have been proposed to deliver sparse approximations of such features is the utilization of parabolic scaling. One prominent example is the shearlet system. Our objective in this paper is three-fold: We firstly develop a digital shearlet theory which is rationally designed in the sense that it is the digitization of the existing shearlet theory for continuous data. This implicates that shearlet theory provides a unified treatment of both the continuum and digital realm. Secondly, we analyze the utilization of pseudo-polar grids and the pseudo-polar Fourier transform for digital implementations of parabolic scaling algorithms. We derive an isometric pseudo-polar Fourier transform by careful weighting of the pseudo-polar grid, allowing exploitation of its adjoint for the inverse transform. This leads to a digital implementation of the shearlet transform; an accompanying Matlab toolbox called ShearLab is provided. And, thirdly, we introduce various quantitative measures for digital parabolic scaling algorithms in general, allowing one to tune parameters and objectively improve the implementation as well as compare different directional transform implementations. The usefulness of such measures is exemplarily demonstrated for the digital shearlet transform.Comment: submitted to SIAM J. Multiscale Model. Simu

UNDERSTANDING THE ANATOMY OF DATA-DRIVEN BUSINESS MODELS – TOWARDS AN EMPIRICAL TAXONOMY

As a consequence of the increasing digitization, massive amounts of data are created every day. While scholars and practitioners suggest that organizations can use this data to develop new data-driven business models, many organizations struggle to systematically develop such models. A fundamental challenge in this regard is presented by the limited research on data-driven business models. Accordingly, the goal of this research is to better understand data-driven business models by identifying key dimensions that can be used to distinguish them and to develop a taxonomy. As our taxonomy aims to guide future studies in a way that ultimately serves organizations, it is based on dimensions regarded to be most relevant from the practitioners’ perspective. To develop this taxonomy, we utilize an established empirical approach based on a combination of multidimensional scaling (MDS), property fitting (ProFit), and qualitative data. Our results reveal that the most important dimensions distinguish data-driven business models based on the data source utilized, the target audience, and the technological effort required. Based on these dimensions, our taxonomy distinguishes eight ideal-typical categories of data-driven business models. By providing an increased understanding regarding the topic, our results form the foundation for subsequent investigations in this new field of research

Quantification of Bronchial Circulation Perfusion in Rats

The bronchial circulation is thought to be the primary blood supply for pulmonary carcinomas. Thus, we have developed a method for imaging and quantifying changes in perfusion in the rat lung due to development of the bronchial circulation. A dual-modality micro-CT/SPECT system was used to detect change in perfusion in two groups of rats: controls and those with a surgically occluded left pulmonary artery. Both groups were imaged following injections on separate days i) 2mCi of Tc99m labeled macroaggregated albumin (MAA) into the left carotid artery (IA) and ii) a similar injection into the femoral vein (IV). The IA injection resulted in Tc99m accumulation in capillaries of the systemic circulation including the bronchial circulation, whereas the IV resulted in Tc99m accumulation in the pulmonary capillaries. Ordered subset expectation maximization (OSEM) was used to reconstruct the SPECT image volumes and a Feldkamp algorithm was used to reconstruct the micro-CT image volumes. The micro-CT and SPECT volumes were registered, the SPECT image volume was segmented using the right and left lung boundaries defined from the micro-CT volume, and the ratio of IA radioactivity accumulation in the left lung to IV radioactivity accumulation in both lungs was used as a measure of left lung flow via the bronchial circulation. This ratio was ~0.02 for the untreated rats compared to the treated animals that had an increased flow ratio of ~0.21 40 days after left pulmonary artery occlusion. This increase in flow to the occluded left lung via the bronchial circulation suggests this will be a useful model for further investigating antiangiogenic treatments

Quantification of Bronchial Circulation Perfusion in Rats

The bronchial circulation is thought to be the primary blood supply for pulmonary carcinomas. Thus, we have developed a method for imaging and quantifying changes in perfusion in the rat lung due to development of the bronchial circulation. A dual-modality micro-CT/SPECT system was used to detect change in perfusion in two groups of rats: controls and those with a surgically occluded left pulmonary artery. Both groups were imaged following injections on separate days i) 2mCi of Tc99m labeled macroaggregated albumin (MAA) into the left carotid artery (IA) and ii) a similar injection into the femoral vein (IV). The IA injection resulted in Tc99m accumulation in capillaries of the systemic circulation including the bronchial circulation, whereas the IV resulted in Tc99m accumulation in the pulmonary capillaries. Ordered subset expectation maximization (OSEM) was used to reconstruct the SPECT image volumes and a Feldkamp algorithm was used to reconstruct the micro-CT image volumes. The micro-CT and SPECT volumes were registered, the SPECT image volume was segmented using the right and left lung boundaries defined from the micro-CT volume, and the ratio of IA radioactivity accumulation in the left lung to IV radioactivity accumulation in both lungs was used as a measure of left lung flow via the bronchial circulation. This ratio was ~0.02 for the untreated rats compared to the treated animals that had an increased flow ratio of ~0.21 40 days after left pulmonary artery occlusion. This increase in flow to the occluded left lung via the bronchial circulation suggests this will be a useful model for further investigating antiangiogenic treatments

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.Comment: 41 page

Distributed Functional Scalar Quantization Simplified

Distributed functional scalar quantization (DFSQ) theory provides optimality conditions and predicts performance of data acquisition systems in which a computation on acquired data is desired. We address two limitations of previous works: prohibitively expensive decoder design and a restriction to sources with bounded distributions. We rigorously show that a much simpler decoder has equivalent asymptotic performance as the conditional expectation estimator previously explored, thus reducing decoder design complexity. The simpler decoder has the feature of decoupled communication and computation blocks. Moreover, we extend the DFSQ framework with the simpler decoder to acquire sources with infinite-support distributions such as Gaussian or exponential distributions. Finally, through simulation results we demonstrate that performance at moderate coding rates is well predicted by the asymptotic analysis, and we give new insight on the rate of convergence

Designing algorithms to aid discovery by chemical robots

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery

Digital transformation: towards new research themes and collaborations yet to be explored

This study aimed at providing an overview of research themes and collaborations in the digital transformation scholarship. The methods of co-word analysis, co-author analysis, and network analysis were employed to network-analyze the keywords, countries, and institutions of 2820 research articles published on the digital transformation topic and indexed by the Web of Science database. Our main results indicated that researchers have mostly focused on three aspects of the digital transformation phenomenon including Technological and Industrial View, Organizational and Managerial View, and Global and Social View. Also, it was realized that Technology, Sustainability, Big Data, Information and Communications Technology, Innovation, Industry 4.0, Artificial Intelligence, Business Model, Social Media, and Digitization are the most recurring themes in this field of research. Besides, Small and Medium-Sized Enterprises, Blockchain, Machine Learning, Knowledge Management, and Sustainable Development were respectively identified as the five hottest issues in the digital transformation scholarship. The contribution of our study highlights that European countries and specially the institutions of northern Europe have had better performance in the research collaborations in digital transformation.info:eu-repo/semantics/acceptedVersio