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2022 ◽  
Vol 40 (3) ◽  
pp. 1-36
Jinyuan Fang ◽  
Shangsong Liang ◽  
Zaiqiao Meng ◽  
Maarten De Rijke

Network-based information has been widely explored and exploited in the information retrieval literature. Attributed networks, consisting of nodes, edges as well as attributes describing properties of nodes, are a basic type of network-based data, and are especially useful for many applications. Examples include user profiling in social networks and item recommendation in user-item purchase networks. Learning useful and expressive representations of entities in attributed networks can provide more effective building blocks to down-stream network-based tasks such as link prediction and attribute inference. Practically, input features of attributed networks are normalized as unit directional vectors. However, most network embedding techniques ignore the spherical nature of inputs and focus on learning representations in a Gaussian or Euclidean space, which, we hypothesize, might lead to less effective representations. To obtain more effective representations of attributed networks, we investigate the problem of mapping an attributed network with unit normalized directional features into a non-Gaussian and non-Euclidean space. Specifically, we propose a hyperspherical variational co-embedding for attributed networks (HCAN), which is based on generalized variational auto-encoders for heterogeneous data with multiple types of entities. HCAN jointly learns latent embeddings for both nodes and attributes in a unified hyperspherical space such that the affinities between nodes and attributes can be captured effectively. We argue that this is a crucial feature in many real-world applications of attributed networks. Previous Gaussian network embedding algorithms break the assumption of uninformative prior, which leads to unstable results and poor performance. In contrast, HCAN embeds nodes and attributes as von Mises-Fisher distributions, and allows one to capture the uncertainty of the inferred representations. Experimental results on eight datasets show that HCAN yields better performance in a number of applications compared with nine state-of-the-art baselines.

2022 ◽  
Vol 15 (1) ◽  
pp. 1-30
Johannes Menzel ◽  
Christian Plessl ◽  
Tobias Kenter

N-body methods are one of the essential algorithmic building blocks of high-performance and parallel computing. Previous research has shown promising performance for implementing n-body simulations with pairwise force calculations on FPGAs. However, to avoid challenges with accumulation and memory access patterns, the presented designs calculate each pair of forces twice, along with both force sums of the involved particles. Also, they require large problem instances with hundreds of thousands of particles to reach their respective peak performance, limiting the applicability for strong scaling scenarios. This work addresses both issues by presenting a novel FPGA design that uses each calculated force twice and overlaps data transfers and computations in a way that allows to reach peak performance even for small problem instances, outperforming previous single precision results even in double precision, and scaling linearly over multiple interconnected FPGAs. For a comparison across architectures, we provide an equally optimized CPU reference, which for large problems actually achieves higher peak performance per device, however, given the strong scaling advantages of the FPGA design, in parallel setups with few thousand particles per device, the FPGA platform achieves highest performance and power efficiency.

2022 ◽  
Vol 521 ◽  
pp. 230970
Zhiping Deng ◽  
Zhixiao Xu ◽  
Wenjing Deng ◽  
Xiaolei Wang

2022 ◽  
Vol 6 (1) ◽  
pp. 14-36
João M. S. Carvalho

This study had three objectives: to discover the main concepts and theories used in research around entrepreneurship; systematize the entrepreneurial process in a model that allows teaching it more efficiently, and substantiate the model by applying it to various social entrepreneurship projects. To this end, a systematic scoping review was carried out to identify the main concepts, theories, and processes, which constitute the six crucial building blocks to someone could be successful as a(n) (social) intra/entrepreneur. Then, a design-science approach led us to use real social innovation and social entrepreneurship cases to evaluate the constructs and the model. Consequently, it is concluded that all concepts, theories and models identified can be classified as external factors (Context and Resources), internal factors (Objectives and entrepreneurial Will) and achievements (Action and Impact). The CROWAI model fits well with the data obtained on 465 innovation and social entrepreneurship projects. Thus, this model presents a more comprehensive approach, applicable to all profitable or social intra/entrepreneurship situations, allowing this new conceptual arrangement to be more easily taught. Additionally, it makes sense to use the term ‘social’ in innovation and intra/entrepreneurship because it has excellent defining power of the scope one wants to achieve with human endeavours. Doi: 10.28991/ESJ-2022-06-01-02 Full Text: PDF

Synlett ◽  
2022 ◽  
Linhong Zuo ◽  
Wusheng Guo

Functionalized ketones and their derivatives are very important building blocks in organic synthesis and material chemistry. The development of novel methodology for the chemo-, regio-, diastereo-, stereo- and enantioselective synthesis of functionalized ketones and their derivatives is the continuous endeavor of organic chemists. Herein we highlight the new approach that was recently initiated and developed by our group for the synthesis of (enantioenriched) ketones and related derivatives based on zwitterionic metal-enolate (ZME) chemistry.

ACS Omega ◽  
2022 ◽  
Christoph Taeschler ◽  
Eva Kirchner ◽  
Emilia Păunescu ◽  
Ulrich Mayerhöffer

Gianluca Milano ◽  
Luca Boarino ◽  
Ilia Valov ◽  
Carlo Ricciardi

Abstract Memristive and resistive switching devices are considered promising building blocks for the realization of artificial neural networks and neuromorphic systems. Besides conventional top-down memristive devices based on thin films, resistive switching devices based on nanowires (NWs) have attracted great attention, not only for the possibility of going beyond current scaling limitations of the top-down approach, but also as model systems for the localization and investigation of the physical mechanism of switching. This work reports on the fabrication of memristive devices based on ZnO NWs, from NW synthesis to single NW-based memristive cell fabrication and characterization. The bottom-up synthesis of ZnO NWs was performed by low-pressure Chemical Vapor Deposition (LPCVD) according to a self-seeding Vapor-Solid (VS) mechanism on a Pt substrate over large scale (∼ cm2), without the requirement of previous seed deposition. The grown ZnO NWs are single crystalline with wurtzite crystal structure and are vertically aligned respect to the growth substrate. Single NWs were then contacted by means of asymmetric contacts, with an electrochemically active and an electrochemically inert electrode, to form NW-based electrochemical metallization memory (ECM) cells that show reproducible resistive switching behaviour and neuromorphic functionalities including short-term synaptic plasticity and Paired Pulse Facilitation (PPF). Besides representing building blocks for NW-based memristive and neuromorphic systems, these single crystalline devices can be exploited as model systems to study physicochemical processing underlaying memristive functionalities thanks to the high localization of switching events on the ZnO crystalline surface.

2022 ◽  
Sukjin Steve Jang ◽  
Sarah Dubnik ◽  
Jason Hon ◽  
Colin Nuckolls ◽  
Ruben L Gonzalez

We have developed and used high-time-resolution, single-molecule field-effect transistors (smFETs) to characterize the con-formational free-energy landscape of RNA stem-loops. Stem-loops are some of the most common RNA structural motifs and serve as building blocks for the formation of more complex RNA structures. Given their prevalence and integral role in RNA folding, the kinetics of stem-loop (un)folding has been extensively characterized using both experimental and computational approaches. Interestingly, these studies have reported vastly disparate timescales of (un)folding, which has been recently in-terpreted as evidence that (un)folding of even simple stem-loops occurs on a highly rugged conformational energy landscape. Because smFETs do not rely on fluorophore reporters of conformation or on the application of mechanical (un)folding forces, they provide a unique and complementary approach that has allowed us to directly monitor tens of thousands of (un)folding events of individual stem-loops at a 200 μs time resolution. Our results show that under our experimental conditions, stem-loops fold and unfold over a 1-200 ms timescale during which they transition between ensembles of unfolded and folded conformations, the latter of which is composed of at least two sub-populations. The 1-200 ms timescale of (un)folding we observe here indicates that smFETs report on complete (un)folding trajectories in which relatively extended unfolded con-formations of the RNA spend long periods of time wandering the free-energy landscape before sampling one of several mis-folded conformations or, alternatively, the natively folded conformation. Our findings demonstrate how the combination of single-molecule sensitivity and high time resolution makes smFETs unique and powerful tools for characterizing the con-formational free-energy landscape of RNA and highlight the extremely rugged landscape on which even the simplest RNA structural elements fold.

Catalysts ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 89
Victorio Cadierno

Metal-catalyzed hydrofunctionalization reactions of alkynes, i.e., the addition of Y–H units (Y = heteroatom or carbon) across the carbon–carbon triple bond, have attracted enormous attention for decades since they allow the straightforward and atom-economic access to a wide variety of functionalized olefins and, in its intramolecular version, to relevant heterocyclic and carbocyclic compounds. Despite conjugated 1,3-diynes being considered key building blocks in synthetic organic chemistry, this particular class of alkynes has been much less employed in hydrofunctionalization reactions when compared to terminal or internal monoynes. The presence of two C≡C bonds in conjugated 1,3-diynes adds to the classical regio- and stereocontrol issues associated with the alkyne hydrofunctionalization processes’ other problems, such as the possibility to undergo 1,2-, 3,4-, or 1,4-monoadditions as well as double addition reactions, thus increasing the number of potential products that can be formed. In this review article, metal-catalyzed hydrofunctionalization reactions of these challenging substrates are comprehensively discussed.

Metabolites ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 74
David Lao-Martil ◽  
Koen J. A. Verhagen ◽  
Joep P. J. Schmitz ◽  
Bas Teusink ◽  
S. Aljoscha Wahl ◽  

Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

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