Responsible Leadership in Global Business: A New Approach to Leadership and Its Multi-Level Outcomes

The article advances an understanding of responsible leadership in global business and offers an agenda for future research in this field. Our conceptualization of responsible leadership draws on deliberative practices and discursive conflict resolution, combining the macro-view of the business firm as a political actor with the micro-view of leadership. We discuss the concept in relation to existing research in leadership. Further, we propose a new model of responsible leadership that shows how such an understanding of leadership can address the challenges of globalization. We thereby propose positive outcomes of responsible leadership across levels of analysis. The model offers research opportunities for responsible leadership in global busines

CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration with Better-than-Binary Energy Efficiency

We present a 3.1 POp/s/W fully digital hardware accelerator for ternary neural networks. CUTIE, the Completely Unrolled Ternary Inference Engine, focuses on minimizing non-computational energy and switching activity so that dynamic power spent on storing (locally or globally) intermediate results is minimized. This is achieved by 1) a data path architecture completely unrolled in the feature map and filter dimensions to reduce switching activity by favoring silencing over iterative computation and maximizing data re-use, 2) targeting ternary neural networks which, in contrast to binary NNs, allow for sparse weights which reduce switching activity, and 3) introducing an optimized training method for higher sparsity of the filter weights, resulting in a further reduction of the switching activity. Compared with state-of-the-art accelerators, CUTIE achieves greater or equal accuracy while decreasing the overall core inference energy cost by a factor of 4.8x-21x

A meritocracia como fator motivacional na administração pública

A meritocracia é uma ferramenta de gestão de pessoas muito utilizada na iniciativa privada, mas na administração pública encontra certa resistência para sua implantação. Em 2010 o Rio Grande do Sul instituiu a carreira de analista de planejamento, orçamento e gestão na Secretaria de Planejamento, Gestão e Participação Cidadã, visando adotar o sistema de meritocracia na esfera pública estadual. Passados dois anos, foi avaliada a percepção da categoria em relação aos conceitos da meritocracia ao qual estão submetidos. Assim, a pesquisa é norteada pela questão: Qual o grau de motivação dos servidores com o atual plano de carreira meritocrático? Utilizou-se o método de abordagem descritivo, aplicando procedimentos de pesquisa bibliográficos, documentais e de levantamento censitário tipo survey. Foi utilizado o questionário como instrumento para coleta dos dados e um corte-transversal caracterizando a coleta em um só momento. Para a análise e tratamento foram empregados os métodos quantitativo e qualitativo, predominando o primeiro. Verificou-se que o grau de motivação depende mais da forma como o plano de carreira meritocrático é discutido e implementado do que de suas características

Which factors drive successful BCI skill learning?

International audienceIntroductionBrain-Computer Interfaces (BCI), although very promising, suffer from a poor reliability [1]. Rather than improving brain signal processing alone, an interesting research direction is to guide users to learn BCI control mastery. Thus, we present here a set of motivational and cognitive factors which could influence the learning process, and which should be considered to improve the global performance of BCI users.MethodsWe based our study on Keller’s integrative theory of motivation, volition, and performance, which combines motivational (affective) and cognitive factors, to explain what makes human users learn and perform efficiently, irrespectively of the task [2]. These factors can guide the creation of learning environments, such as BCI training protocols.According to the theory, the optimization of motivational factors - Attention (triggering a person’s curiosity), Relevance (the compliance with a person’s motives or values), Confidence (the expectancy for success), and Satisfaction (by intrinsic and extrinsic rewards) - leads to more user efforts and thereby a better performance.Additionally, considering the cognitive factors - the limited user working memory (requiring to minimize the amount of skill-unrelated information), the way information is actively processed by him/her (requiring to make relevant information salient) and the existing knowledge in his/her long-term memory (requiring to relate the to-be-learned skill to existing knowledge) - leads to a more efficient skill acquisition.Most factors are usually not considered for BCI design. Optimizing the motivational factors while respecting the cognitive constraints may lead to improved user BCI control.DiscussionThis poster identified factors possibly influencing BCI skill learning. However, their relevance and the interplay between them for BCI needs further addressing. We conclude, that shifting the focus from machine learning-centered to user-centered training may encourage user involvement and hence may enable a more efficient use of human brain resources.References[1] Wolpaw, J., & Wolpaw, E. W. (Eds.). (2012). Brain-computer interfaces: principles and practice. Oxford University Press.[2] Keller, J. (2008). An Integrative Theory of Motivation, Volition, and Performance. Technology, Instruction, Cognition & Learning, 6(2)

Deeptime: a Python library for machine learning dynamical models from time series data

Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastable and coherent sets, slow relaxation processes, collective variables, dominant transition pathways or manifolds and channels of probability flow can be of great importance for understanding and characterizing the kinetic, thermodynamic and mechanistic properties of the system. Deeptime is a general purpose Python library offering various tools to estimate dynamical models based on time-series data including conventional linear learning methods, such as Markov state models (MSMs), Hidden Markov Models and Koopman models, as well as kernel and deep learning approaches such as VAMPnets and deep MSMs. The library is largely compatible with scikit-learn, having a range of Estimator classes for these different models, but in contrast to scikit-learn also provides deep Model classes, e.g. in the case of an MSM, which provide a multitude of analysis methods to compute interesting thermodynamic, kinetic and dynamical quantities, such as free energies, relaxation times and transition paths. The library is designed for ease of use but also easily maintainable and extensible code. In this paper we introduce the main features and structure of the deeptime software. Deeptime can be found under https://deeptime-ml.github.io/

Transplantation of Neural Precursor Cells Attenuates Chronic Immune Environment in Cervical Spinal Cord Injury

Inflammation after traumatic spinal cord injury (SCI) is non-resolving and thus still present in chronic injury stages. It plays a key role in the pathophysiology of SCI and has been associated with further neurodegeneration and development of neuropathic pain. Neural precursor cells (NPCs) have been shown to reduce the acute and sub-acute inflammatory response after SCI. In the present study, we examined effects of NPC transplantation on the immune environment in chronic stages of SCI. SCI was induced in rats by clip-compression of the cervical spinal cord at the level C6-C7. NPCs were transplanted 10 days post-injury. The functional outcome was assessed weekly for 8 weeks using the Basso, Beattie, and Bresnahan scale, the CatWalk system, and the grid walk test. Afterwards, the rats were sacrificed, and spinal cord sections were examined for M1/M2 macrophages, T lymphocytes, astrogliosis, and apoptosis using immunofluorescence staining. Rats treated with NPCs had compared to the control group significantly fewer pro-inflammatory M1 macrophages and reduced immunodensity for inducible nitric oxide synthase (iNOS), their marker enzyme. Anti-inflammatory M2 macrophages were rarely present 8 weeks after the SCI. In this model, the sub-acute transplantation of NPCs did not support survival and proliferation of M2 macrophages. Post-traumatic apoptosis, however, was significantly reduced in the NPC group, which might be explained by the altered microenvironment following NPC transplantation. Corresponding to these findings, reactive astrogliosis was significantly reduced in NPC-transplanted animals. Furthermore, we could observe a trend toward smaller cavity sizes and functional improvement following NPC transplantation. Our data suggest that transplantation of NPCs following SCI might attenuate inflammation even in chronic injury stages. This might prevent further neurodegeneration and could also set a stage for improved neuroregeneration after SCI

Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research

Results that do not confirm expectations are generally referred to as ‘negative’ results. While essential for scientific progress, they are too rarely reported in the literature – Brain–Machine Interface (BMI) research is no exception. This led us to organize a workshop on BMI negative results during the 2018 International BCI meeting. The outcomes of this workshop are reported herein. First, we demonstrate why (valid) negative results are useful, and even necessary for BMIs. These results can be used to confirm or disprove current BMI knowledge, or to refine current theories. Second, we provide concrete examples of such useful negative results, including the limits in BMI-control for complete locked-in users and predictors of motor imagery BMI performances. Finally, we suggest levers to promote the diffusion of (valid) BMI negative results, e.g. promoting hypothesis-driven research using valid statistical tools, organizing special issues dedicated to BMI negative results, or convincing institutions and editors that negative results are valuable

A user-centred approach to unlock the potential of non-invasive BCIs: an unprecedented international translational effort

Non-invasive Mental Task-based Brain-Computer Interfaces (MT-BCIs) enable their users to interact with the environment through their brain activity alone (measured using electroencephalography for example), by performing mental tasks such as mental calculation or motor imagery. Current developments in technology hint at a wide range of possible applications, both in the clinical and non-clinical domains. MT-BCIs can be used to control (neuro)prostheses or interact with video games, among many other applications. They can also be used to restore cognitive and motor abilities for stroke rehabilitation, or even improve athletic performance.Nonetheless, the expected transfer of MT-BCIs from the lab to the marketplace will be greatly impeded if all resources are allocated to technological aspects alone. We cannot neglect the Human End-User that sits in the centre of the loop. Indeed, self-regulating one’s brain activity through mental tasks to interact is an acquired skill that requires appropriate training. Yet several studies have shown that current training procedures do not enable MT-BCI users to reach adequate levels of performance. Therefore, one significant challenge for the community is that of improving end-user training.To do so, another fundamental challenge must be taken into account: we need to understand the processes that underlie MT-BCI performance and user learning. It is currently estimated that 10 to 30% of people cannot control an MT-BCI. These people are often referred to as “BCI inefficient”. But the concept of “BCI inefficiency” is debated. Does it really exist? Or, are low performances due to insufficient training, training procedures that are unsuited to these users or is the BCI data processing not sensitive enough? The currently available literature does not allow for a definitive answer to these questions as most published studies either include a limited number of participants (i.e., 10 to 20 participants) and/or training sessions (i.e., 1 or 2). We still have very little insight into what the MT-BCI learning curve looks like, and into which factors (including both user-related and machine-related factors) influence this learning curve. Finding answers will require a large number of experiments, involving a large number of participants taking part in multiple training sessions. It is not feasible for one research lab or even a small consortium to undertake such experiments alone. Therefore, an unprecedented coordinated effort from the research community is necessary.We are convinced that combining forces will allow us to characterise in detail MT-BCI user learning, and thereby provide a mandatory step toward transferring BCIs “out of the lab”. This is why we gathered an international, interdisciplinary consortium of BCI researchers from more than 20 different labs across Europe and Japan, including pioneers in the field. This collaboration will enable us to collect considerable amounts of data (at least 100 participants for 20 training sessions each) and establish a large open database. Based on this precious resource, we could then lead sound analyses to answer the previously mentioned questions. Using this data, our consortium could offer solutions on how to improve MT-BCI training procedures using innovative approaches (e.g., personalisation using intelligent tutoring systems) and technologies (e.g., virtual reality). The CHIST-ERA programme represents a unique opportunity to conduct this ambitious project, which will foster innovation in our field and strengthen our community

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD