The Unbearable Shallow Understanding of Deep Learning

This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out aspects pertaining to marketing or sociology of research, and the remaining aspects seem to certify a genuine value of deep learning, calling for explanation. The alleged two main propelling factors for deep learning, namely computing hardware performance and neuroscience findings, are scrutinized, and evaluated as relevant but insufficient for a comprehensive explanation. We review various attempts that have been made to provide mathematical foundations able to justify the efficiency of deep learning, and we deem this is the most promising road to follow, even if the current achievements are too scattered and relevant for very limited classes of deep neural models. The authors’ take is that most of what can explain the very nature of why deep learning works at all and even very well across so many domains of application is still to be understood and further research, which addresses the theoretical foundation of artificial learning, is still very much needed.

     

Bibliometric analysis and mapping of publications on brewing science from 1940 to 2018

Over the last 80 years, malting and brewing has evolved through increased scientific understanding, driven by numerous factors including efficiency improvement, cost reduction, quality enhancement, risk minimisation, brand marketing and image enhancement. The aim of this work was to review the historical trends in global brewing research in order to identify current opportunities and emerging trends, as well as uncover future key drivers. A bibliometric analysis was performed with English language peer reviewed literature from the Scopus database together with mapping using the VOSviewer clustering software. The bibliometric results were structured to identify which affiliations, authors, and journals have been involved in brewing research, as well as the average number of citations per paper. The bibliometric mapping yielded ‘research term maps’, where the words occurring more frequently in the research papers were grouped in coloured clusters by area of interest, and their relationships indicated. The year maps revealed which average terms tended to emerge in chronological order. The estimated average citation number per paper was about nine. Countries, including Spain, Czech Republic, Italy and Belgium displayed higher average citation scores than the world average. In the period 1940-79, brewing publications were mainly process oriented whereas between 2008-18, there was a shift in focus to the consumer (health and social responsibility) and to environmental sustainability. © 2020 The Institute of Brewing & Distilling     

Calcium phosphate-based nanosystems for advanced targeted nanomedicine

Synthetic calcium phosphates (CaPs) are the most widely accepted bioceramics for the repair and reconstruction of bone tissue defects. The recent advancements in materials science have prompted a rapid progress in the preparation of CaPs with nanometric dimensions, tailored surface characteristics, and colloidal stability opening new perspectives in their use for applications not strictly related to bone. In particular, the employment of CaPs nanoparticles as carriers of therapeutic and imaging agents has recently raised great interest in nanomedicine. CaPs nanoparticles, as well as other kinds of nanoparticles, can be engineered to specifically target the site of the disease (cells or organs), thus minimizing their dispersion in the body and undesired organism-nanoparticles interactions. The most promising and efficient approach to improve their specificity is the ‘active targeting’, where nanoparticles are conjugated with a targeting moiety able to recognize and bind with high efficacy and selectivity to receptors that are highly expressed only in the therapeutic site. The aim of this review is to give an overview on advanced targeted nanomedicine with a focus on the most recent reports on CaP nanoparticles-based systems, specifically designed for the active targeting. The distinctive characteristics of CaP nanoparticles with respect to the other kinds of nanomaterials used in nanomedicine are also discussed.     

Detection,localisation and tracking of pallets using machine learning techniques and 2D range data

The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-Based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labelled 2D scans, which have been made freely available in an online repository. Detection performance has been assessed on the basis of the average accuracy over k-fold cross-validation, and it scored 99.58% in our tests. Concerning pallet localisation and tracking, experiments have been performed in a scenario where the robot is approaching the pallet to fork. Although data have been originally acquired by considering only one pallet as per specification of the use case we consider, artificial data have been generated as well to mimic the presence of multiple pallets in the robot workspace. Our experimental results confirm that the system is capable of identifying, localising and tracking pallets with a high success rate while being robust to false positives.

     

Using normative ethics for building a good evaluation of research practices: towards the assessment of researcher’s virtues

This paper is an attempt of using co-citation analysis to sort out and to analyze the development and evolution of a latest hot area, open innovation from the perspective of network embedding. A dataset of 1437 records published between 1990 and 2019 is collected from Web of Science database. The empirical results show the latest hot topics in the open innovation study focus on innovation performance and value creation. In addition, we make a new interpretation of open innovation from four aspects: innovation and entrepreneurship, resource acquisition, knowledge sharing and innovation performance, then combines the importance of network embedding to the innovation and development of enterprises, and proposes the future research direction of open innovation. Our research in this paper is helpful to systematically sort out the knowledge context of open innovation, which is of great significance to the construction and development of open innovation knowledge system. The conclusions and implications in this paper will be particularly illuminating for both academic research and enterprises’ practice application.

     

Shaking the usability tree: why usability is not a dead end,and a constructive way forward

A recent contribution to the ongoing debate concerning the concept of usability and its measures proposed that usability reached a dead end – i.e. a construct unable to provide stable results and to unify scientific knowledge. Extensive commentaries rejected the conclusion that researchers need to look for alternative constructs to measure the quality of interaction. Nevertheless, several practitioners involved in this international debate asked for a constructive way to move forward the usability practice. In fact, two key issues of the usability field were identified in this debate: (i) knowledge fragmentation in the scientific community, and (ii) the unstable relationship among the usability metrics. We recognise both the importance and impact of these key issues, although, in line with others, we may not agree with the conclusion that the usability is a dead end. Under the light of the international debate, this work discusses the strengths and weaknesses of usability construct and its application. Our discussion focuses on identifying alternative explanations to the issues and to suggest mitigation strategies, which may be considered the starting point to move forward the usability field. However, scientific community actions will be needed to implement these mitigation strategies and to harmonise the usability practice.     

Anisotropic MoS2 Nanosheets Grown on Self‐Organized Nanopatterned Substrates

Manipulating the anisotropy in 2D nanosheets is a promising way to tune or trigger functional properties at the nanoscale. Here, a novel approach is presented to introduce a one‐directional anisotropy in MoS₂ nanosheets via chemical vapor deposition (CVD) onto rippled patterns prepared on ion‐sputtered SiO₂/Si substrates. The optoelectronic properties of MoS₂ are dramatically affected by the rippled MoS₂ morphology both at the macro‐ and the nanoscale. In particular, strongly anisotropic phonon modes are observed depending on the polarization orientation with respect to the ripple axis. Moreover, the rippled morphology induces localization of strain and charge doping at the nanoscale, thus causing substantial redshifts of the phonon mode frequencies and a topography‐dependent modulation of the MoS₂ workfunction, respectively. This study paves the way to a controllable tuning of the anisotropy via substrate pattern engineering in CVD‐grown 2D nanosheets.     

Combining social media affordances for organising collective action

Social media provide new opportunities for supporting the dynamics of collective action (CA), allowing for the mobilisation of people into debates and involving them in new forms of collective decision making. Although current studies focus on opportunities offered by social media for collective action, there is still a need to deepen the understanding of how social media support the organisation of CA and to study the effects of individual actions performed on social media in complex organisational settings. We here explore how social media are used to manage CA by the Italian political movement Movimento Cinque Stelle, using the concept of affordances as the conceptual framing. Based on the qualitative case analyses, our study contributes to the knowledge base by identifying a typology of nine affordances supporting CA and exploring how the combined actualisations of some affordances of the typology create antecedents of the fundamental processes of CA. On the basis of the study analysis, we suggest a model to describe how social media support CA through affordances, their combinations, and the creation of antecedents and then formulate implications for research and practice.     

Exploring the ERTMS/ETCS full moving block specification: an experience with formal methods

International Journal on Software Tools for Technology Transfer - Shift2Rail is a joint undertaking funded by the EU via its Horizon 2020 program and by main railway stakeholders. Several...