L0 Sparse Inverse Covariance Estimation

Recently, there has been focus on penalized log-likelihood covariance estimation for sparse inverse covariance (precision) matrices. The penalty is responsible for inducing sparsity, and a very common choice is the convex $l_1$ norm. However, the best estimator performance is not always achieved with this penalty. The most natural sparsity promoting "norm" is the non-convex $l_0$ penalty but its lack of convexity has deterred its use in sparse maximum likelihood estimation. In this paper we consider non-convex $l_0$ penalized log-likelihood inverse covariance estimation and present a novel cyclic descent algorithm for its optimization. Convergence to a local minimizer is proved, which is highly non-trivial, and we demonstrate via simulations the reduced bias and superior quality of the $l_0$ penalty as compared to the $l_1$ penalty

Uneven batch data alignment with application to the control of batch end-product quality

keywords: Variable batch lengths keywords: Variable batch lengths keywords: Variable batch lengths keywords: Variable batch lengths keywords: Variable batch length

A Conceptual Framework of Digital Empowerment of Informal Carers: An Expert Elicitation Study

Many studies on online health communities (OHCs) have focused on patients’ well-being. The capabilities of OHCs to effect other psychosocial states like empowerment have been under-explored. Additionally, the study of empowerment of other healthcare stakeholders, specifically informal carers, has not attracted much study. This is despite evidence that carers use OHCs as an information and self-care resource in dealing with the stress and strain of caregiving. It is not clear how moderator support may influence carer empowerment. We propose a conceptual model to explore how moderated OHCs may influence empowerment of carers. In order to assess the model and support its robustness, this paper uses expert interviews of academics and industry professionals, with the view to focusing the research as well as operationalise the model. Results suggest a favourable acceptance of the model by experts, and thematic analysis of their conversations generated an additional construct

A different kind of sharing economy: A literature review of platform cooperatives

We are now living in the so-called sharing economy, exemplified by the ride sharing platform Uber and short-term rental sharing platform Airbnb. In spite of the convenience and benefits of the sharing economy, there is a growing awareness of its negative and harmful societal effects. In response, platform cooperatives have started to emerge, aiming to create a different kind of sharing economy. However, the novelty of platform cooperatives combined with lack of research attention, continue to limit our understanding of the social and other benefits of platform cooperatives. The main objective of this paper is to provide a literature review on platform cooperatives, focusing on their social values and benefits. Analysis of the key publications reveals high potential of platform cooperatives as a more ethical and fairer alternative to platform capitalism that create value for their members/co-owners, while creating value for society

Temporal evolution of carbon stocks, fluxes and carbon balance in pedunculate oak chronosequence under close-to-nature forest management

Under current environmental changes, forest management is challenged to foster contrasting benefits from forests, such as continuous wood supply while preserving biomass production, biodiversity conservation, and contribution to climate change mitigation through atmospheric carbon sequestration. Although being found as globally important, estimates of long-term forest C balance are still highly uncertain. In this context, the chronosequence experiments (space-for-time substitution) might fill this gap in even-aged forests, as they represent an approach that enables the assessment of forest net C balance in the long term. In this research, we explored the dynamics of C stocks and fluxes in different forest pools throughout the rotation period (140 years) of a Pedunculate oak (Quercus robur L.) forest in Croatia. For this purpose, we selected a chronosequence that was made up of seven forest stands with different age (5, 13, 38, 53, 68, 108, and 138 years). To address the issues of uncertainty in C balance estimates, we compared net ecosystem carbon balance (NECB) estimated while using two different approaches, which we name pool-change (from C stocks) approach and component-flux (from C fluxes) approach. Overall, the pool-change approach showed higher NECB estimate, with the greatest difference being observed in younger stands (<50 years). Component-flux approach showed significantly higher uncertainty. Throughout the rotation period, managed pedunculate oak stands become a C sink early in their development phase, between the age of 13 and 35 years according to pool-change and component-flux approach, respectively. During the 140 years, oak forest provided 187.2 Mg C ha−1 (604 m3 ha−1) through thinnings and 147.9 Mg C ha−1 (477 m3 ha−1) in the final cut, while preserving, on average, 88.9 Mg C ha−1 in mineral soil down to 40 cm, 18.2 Mg C ha−1 in dead wood, and 6.0 Mg C ha−1 in the forest floor. Soil C stocks in our chronosequence did not show any age-related trend, indicating that current management practice has no negative effect on soil C stocks. Finally, under current close-to-nature forest management, Pedunculate oak forest showed to be sustainable in providing both economic and ecological ecosystem services

Algorithmic Pollution - Making the Invisible Visible

In this paper, we focus on the growing evidence of unintended harmful societal effects of automated algorithmic decision-making (AADM) in transformative services (e.g., social welfare, healthcare, education, policing and criminal justice), for individuals, communities and society at large. Drawing from the long-established research on social pollution, in particular its contemporary ‘pollution-as-harm’ notion, we put forward a claim - and provide evidence - that these harmful effects constitute a new type of digital social pollution, which we name ‘algorithmic pollution’. Words do matter, and by using the term ‘pollution’, not as a metaphor or an analogy, but as a transformative redefinition of the digital harm performed by AADM, we seek to make it visible and recognized. By adopting a critical performative perspective, we explain how the execution of AADM produces harm and thus performs algorithmic pollution. Recognition of the potential for unintended harmful effects of algorithmic pollution, and their examination as such, leads us to articulate the need for transformative actions to prevent, detect, redress, mitigate, and educate about algorithmic harm. These actions, in turn, open up new research challenges for the information systems community

Algorithmic pollution: making the invisible visible

In this paper, we focus on the growing evidence of unintended harmful societal effects of automated algorithmic decision-making (AADM) in transformative services (e.g., social welfare, healthcare, education, policing and criminal justice), for individuals, communities and society at large. Drawing from the long-established research on social pollution, in particular its contemporary ‘pollution-as-harm’ notion, we put forward a claim - and provide evidence - that these harmful effects constitute a new type of digital social pollution, which we name ‘algorithmic pollution’. Words do matter, and by using the term ‘pollution’, not as a metaphor or an analogy, but as a transformative redefinition of the digital harm performed by AADM, we seek to make it visible and recognized. By adopting a critical performative perspective, we explain how the execution of AADM produces harm and thus performs algorithmic pollution. Recognition of the potential for unintended harmful effects of algorithmic pollution, and their examination as such, leads us to articulate the need for transformative actions to prevent, detect, redress, mitigate, and educate about algorithmic harm. These actions, in turn, open up new research challenges for the information systems community

Algorithmic Pollution: Making the Invisible Visible

In this paper, we focus on the growing evidence of unintended harmful societal effects of automated algorithmic decision-making (AADM) in transformative services (e.g., social welfare, healthcare, education, policing and criminal justice), for individuals, communities and society at large. Drawing from the long-established research on social pollution, in particular its contemporary ‘pollution-as-harm’ notion, we put forward a claim, and provide evidence, that these harmful effects constitute a new type of digital social pollution, which we name ‘algorithmic pollution’. Words do matter, and by using the term ‘pollution’, not as a metaphor, but as a transformative redefinition of the digital harm performed by AADM, we seek to make it visible and recognized. By adopting a critical performative perspective, we explain how the execution of AADM produces harm and thus performs algorithmic pollution. Recognition of the potential for unintended harmful effects of algorithmic pollution, and their examination as such, leads us to articulate the need for transformative actions to prevent, detect, redress, mitigate, and educate about algorithmic harm. These actions, in turn, open up new research challenges for the information systems community. </jats:p

Real-Time Water Quality Monitoring with Chemical Sensors

Water quality is one of the most critical indicators of environmental pollution and it affects all of us. Water contamination can be accidental or intentional and the consequences are drastic unless the appropriate measures are adopted on the spot. This review provides a critical assessment of the applicability of various technologies for real-time water quality monitoring, focusing on those that have been reportedly tested in real-life scenarios. Specifically, the performance of sensors based on molecularly imprinted polymers is evaluated in detail, also giving insights into their principle of operation, stability in real on-site applications and mass production options. Such characteristics as sensing range and limit of detection are given for the most promising systems, that were verified outside of laboratory conditions. Then, novel trends of using microwave spectroscopy and chemical materials integration for achieving a higher sensitivity to and selectivity of pollutants in water are described