Does Interactive Conditioning Help Users Better Understand the Structure of Probabilistic Models?

Despite growing interest in probabilistic modeling approaches and availability of learning tools, people are hesitant to use them. There is a need for tools to communicate probabilistic models more intuitively and help users build, validate, use effectively or trust probabilistic models. We focus on visual representations of probabilistic models and introduce the Interactive Pair Plot (IPP) for visualization of a model's uncertainty, a scatter plot matrix of a probabilistic model allowing interactive conditioning on the model's variables. We investigate whether the use of interactive conditioning in a scatter plot matrix of a model helps users better understand variables' relations. We conducted a user study and the findings suggest that improvements in the understanding of the interaction group are the most pronounced for more exotic structures, such as hierarchical models or unfamiliar parameterizations, in comparison to the understanding of the static group. As the detail of the inferred information increases, interactive conditioning does not lead to considerably longer response times. Finally, interactive conditioning improves participants' confidence about their responses.

Real-Time Adaptive Traffic Signal Control in a Connected and Automated Vehicle Environment: Optimisation of Signal Planning with Reinforcement Learning under Vehicle Speed Guidance.

Adaptive traffic signal control (ATSC) is an effective method to reduce traffic congestion in modern urban areas. Many studies adopted various approaches to adjust traffic signal plans according to real-time traffic in response to demand fluctuations to improve urban network performance (e.g., minimise delay). Recently, learning-based methods such as reinforcement learning (RL) have achieved promising results in signal plan optimisation. However, adopting these self-learning techniques in future traffic environments in the presence of connected and automated vehicles (CAVs) remains largely an open challenge. This study develops a real-time RL-based adaptive traffic signal control that optimises a signal plan to minimise the total queue length while allowing the CAVs to adjust their speed based on a fixed timing strategy to decrease total stop delays. The highlight of this work is combining a speed guidance system with a reinforcement learning-based traffic signal control. Two different performance measures are implemented to minimise total queue length and total stop delays. Results indicate that the proposed method outperforms a fixed timing plan (with optimal speed advisory in a CAV environment) and traditional actuated control, in terms of average stop delay of vehicle and queue length, particularly under saturated and oversaturated conditions.

Development and validation of a parametric human mandible model to determine internal stresses for the future design optimization of maxillofacial implants.

Large segmental mandible bone defects still represent a challenge for endogenous regeneration. Despite the bone's capacity to heal in many clinical situations, bone defects over a critical size do not heal spontaneously. An emerging treatment of critically sized mandibular defects is the implantation of individually manufactured scaffolds consisting of biodegradable magnesium alloys. Biomedical engineers faced the challenge of developing a scaffold structure that not only provides sufficient stability, but also stimulates and promotes bone growth while considering the degradation of the magnesium alloy. The porosity of the scaffold must also support bone ingrowth and neovascularization. For an optimal design and subsequent structural optimization knowledge of external load cases is essential. However, currently the muscle and joint forces of the mandible cannot be measured directly. The aim of our study was therefore the development of a parametric human mandible model to determine the relevant boundary conditions for the subsequent structural optimization of individual jawbone implants. Using a model-based approach, determining the essential external load of the mandible as a function of the age and sex of a patient individually and the realistic simulation of the mechanical stress for patient-specific loads and anatomies has been realized. The developed model is successfully validated by evaluating the deformations and stresses of the lower jaw of a possible patient and comparing them with the results of dental research. Based on the results of the modelling, in a subsequent optimization process section forces at the interface between the bone tissue and jawbone implant can be determined and used to optimize the design of the jawbone implant.

Markers of Central Neuropathic Pain in Higuchi Fractal Analysis of EEG Signals From People With Spinal Cord Injury.

Central neuropathic pain (CNP) negatively impacts the quality of life in a large proportion of people with spinal cord injury (SCI). With no cure at present, it is crucial to improve our understanding of how CNP manifests, to develop diagnostic biomarkers for drug development, and to explore prognostic biomarkers for personalised therapy. Previous work has found early evidence of diagnostic and prognostic markers analysing Electroencephalogram (EEG) oscillatory features. In this paper, we explore whether non-linear non-oscillatory EEG features, specifically Higuchi Fractal Dimension (HFD), can be used as prognostic biomarkers to increase the repertoire of available analyses on the EEG of people with subacute SCI, where having both linear and non-linear features for classifying pain may ultimately lead to higher classification accuracy and an intrinsically transferable classifier. We focus on EEG recorded during imagined movement because of the known relation between the motor cortex over-activity and CNP. Analyses were performed on two existing datasets. The first dataset consists of EEG recordings from able-bodied participants (N = 10), participants with chronic SCI and chronic CNP (N = 10), and participants with chronic SCI and no CNP (N = 10). We tested for statistically significant differences in HFD across all pairs of groups using bootstrapping, and found significant differences between all pairs of groups at multiple electrode locations. The second dataset consists of EEG recordings from participants with subacute SCI and no CNP (N = 20). They were followed-up 6 months post recording to test for CNP, at which point (N = 10) participants had developed CNP and (N = 10) participants had not developed CNP. We tested for statistically significant differences in HFD between these two groups using bootstrapping and, encouragingly, also found significant differences at multiple electrode locations. Transferable machine learning classifiers achieved over 80% accuracy discriminating between groups of participants with chronic SCI based on only a single EEG channel as input. The most significant finding is that future and chronic CNP share common features and as a result, the same classifier can be used for both. This sheds new light on pain chronification by showing that frontal areas, involved in the affective aspects of pain and believed to be influenced by long-standing pain, are affected in a much earlier phase of pain development.

Hysteresis-based supervisory control with application to non-pharmaceutical containment of COVID-19.

The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal functioning of the society, as this allows a continued, albeit reduced, economic activity and lessens the many societal problems associated with a prolonged lockdown. Recent studies have provided evidence that fast periodic alternation of lockdown and normal-functioning days may effectively lead to a good trade-off between outbreak abatement and economic activity. Nevertheless, the correct number of normal days to allocate within each period in such a way to guarantee the desired trade-off is a highly uncertain quantity that cannot be fixed a priori and that must rather be adapted online from measured data. This adaptation task, in turn, is still a largely open problem, and it is the subject of this work. In particular, we study a class of solutions based on hysteresis logic. First, in a rather general setting, we provide general convergence and performance guarantees on the evolution of the decision variable. Then, in a more specific context relevant for epidemic control, we derive a set of results characterizing robustness with respect to uncertainty and giving insight about how a priori knowledge about the controlled process may be used for fine-tuning the control parameters. Finally, we validate the results through numerical simulations tailored on the COVID-19 outbreak.

Trustworthy human-AI partnerships.

In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organizations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry.

Post-lockdown abatement of COVID-19 by fast periodic switching.

COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show-as proof of concept grounded on rigorous mathematical evidence-that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn-while not eliminating the virus-this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github at https://github.com/V4p1d/FPSP_Covid19. A software tool has also been developed so that interested parties can explore the proof-of-concept system.

Resisting Influence: How the Strength of Predispositions to Resist Control Can Change Strategies for Optimal Opinion Control in the Voter Model.

In this paper, we investigate influence maximization, or optimal opinion control, in a modified version of the two-state voter dynamics in which a native state and a controlled or influenced state are accounted for. We include agent predispositions to resist influence in the form of a probability q with which agents spontaneously switch back to the native state when in the controlled state. We argue that in contrast to the original voter model, optimal control in this setting depends on q: For low strength of predispositions q, optimal control should focus on hub nodes, but for large q, optimal control can be achieved by focusing on the lowest degree nodes. We investigate this transition between hub and low-degree node control for heterogeneous undirected networks and give analytical and numerical arguments for the existence of two control regimes.

Adaptable bioinspired special wetting surface for multifunctional oil/water separation.

Inspired by the multifunctionality of biological surfaces necessary for the survival of an organism in its specific environment, we developed an artificial special wetting nanofur surface which can be adapted to perform different functionalities necessary to efficiently separate oil and water for cleaning accidental oil spills or separating industrial oily wastewater. Initial superhydrophobic nanofur surface is fabricated using a hot pulling method, in which nano- and microhairs are drawn out of the polymer surface during separation from a heated sandblasted steel plate. By using a set of simple modification techniques, which include microperforation, plasma treatment and subsequent control of storage environment, we achieved selective separation of either water or oil, variable oil absorption and continuous gravity driven separation of oil/water mixtures by filtration. Furthermore, these functions can be performed using special wetting nanofur made from various thermoplastics, including biodegradable and recyclable polymers. Additionally, nanofur can be reused after washing it with organic solvents, thus, further helping to reduce the environmental impacts of oil/water separation processes.

A 25Mg, 89Y and 115In solid state MAS NMR study of YT2X and Y(T0.5T'0.5)2X (T/T' = Pd, Ag, Au; X = Mg, In) Heusler phases.

Yttrium-transition metal-magnesium (indium) Heusler phases YPd2Mg, YPd2In, YAg2Mg, YAg2In, YAu2Mg, and YAu2In and their quaternary compounds (solid solutions) Y(Pd0.5Ag0.5)2Mg, Y(Pd0.5Ag0.5)2In, Y(Pd0.5Au0.5)2Mg, Y(Pd0.5Au0.5)2In, Y(Ag0.5Au0.5)2Mg and Y(Ag0.5Au0.5)2In were synthesized from the elements in sealed niobium ampoules in a high-frequency furnace or by arc-melting, respectively. All compounds crystallize with the cubic MnCu2Al type structure (Heusler phase), space group Fm3[combining macron]m. The structure of Y(Ag0.39Au0.61)2Mg was refined from single crystal X-ray diffractometer data: a = 689.97(5) pm, wR2 = 0.0619, 52 F2 values, 6 parameters. Magnetic susceptibility measurements show Pauli paramagnetic behavior for all samples. The compounds were investigated by 25Mg, 89Y and 115In solid state MAS NMR spectroscopy. Large positive resonance shifts are observed for all nuclei. A review of the present data in the context of literature data on isotypic Heusler phases with Cd and Sn indicates that the 89Y shifts show a correlation with the electronegativity of the main group atoms (Mg, Cd, In, Sn). The solid solutions Y(Ag1-xTx)2Mg (x = 0.1, 0.25, 0.33, 0.5; T = Pd, Au) clearly show Vegard-like behavior concerning their lattice parameters, and their main group element resonance shifts arising from spin and orbital contributions are close to the interpolated values of the corresponding end-member compounds.

Serum fibroblast growth factor 21 levels in gestational diabetes mellitus in relation to insulin resistance and dyslipidemia.

Fibroblast growth factor 21 (FGF21) has beneficial effects on glucose homeostasis and insulin sensitivity. In the current study, we investigated serum concentrations of FGF21 in patients with gestational diabetes mellitus (GDM) as compared with healthy pregnant controls matched for gestational age and fasting insulin. Fibroblast growth factor 21 was determined by enzyme-linked immunosorbent assay in control (n = 80) and GDM (n = 40) patients and correlated to clinical and biochemical measures of renal function, glucose and lipid metabolism, as well as inflammation in both groups. Median maternal serum FGF21 concentrations were not significantly different in subjects with GDM (97.5 ng/L) as compared with healthy pregnant controls (102.9 ng/L). Fibroblast growth factor 21 significantly and positively correlated with markers of insulin resistance (increased homeostasis model assessment of insulin resistance, decreased adiponectin) and dyslipidemia (increased triglycerides, decreased high-density lipoprotein cholesterol) in univariate and multivariate analyses. Furthermore, FGF21 serum levels were highest in patients in the third tertile of homeostasis model assessment of insulin resistance. Fibroblast growth factor 21 is independently associated with markers of insulin resistance and an adverse lipid profile but is not dysregulated in GDM if patients are matched with controls for fasting insulin.

Flexible selection of heterogeneous and unreliable services in large-scale grids.

As grids become larger and more interconnected in nature, scientists can benefit from a growing number of distributed services that may be invoked on demand to complete complex computational workflows. However, it also means that these scientists become dependent on the cooperation of third-party service providers, whose behaviour may be uncertain, failure prone and highly heterogeneous. To address this, we have developed a novel decision-theoretic algorithm that automatically selects appropriate services for the tasks of an abstract workflow and deals with failures through redundancy and dynamic re-invocation of functionally equivalent services. In this paper, we summarize our approach, describe in detail how it can be applied to a real-world bioinformatics workflow and show that it offers a significant improvement over current service selection techniques.

Serum levels of the adipokine FGF21 depend on renal function.

OBJECTIVE: To investigate renal elimination of the adipokine fibroblast growth factor 21 (FGF21) by determining circulating FGF21 levels in patients on chronic hemodialysis (CD) as compared with control subjects with a glomerular filtration rate (GFR) >50 ml/min. RESEARCH DESIGN AND METHODS: FGF21 was determined by enzyme-linked immunosorbent assay in control (n = 60) and CD (n = 60) patients and correlated to clinical and biochemical measures of renal function, glucose and lipid metabolism, and inflammation in both groups. RESULTS: Median serum FGF21 levels were >15-fold higher in CD patients (3,710.6 ng/l) than in subjects with a GFR >50 ml/min (201.9 ng/l) (P < 0.001). Furthermore, serum creatinine positively and GFR negatively predicted FGF21 concentrations in multiple regression analyses in control subjects (P < 0.05). CONCLUSIONS: FGF21 serum levels increase in CD patients and are related to markers of renal function in control subjects.

Serum levels of angiopoietin-related growth factor are increased in preeclampsia.

BACKGROUND: Preeclampsia is a serious complication in pregnancy with an increased future cardiovascular and metabolic risk for both mother and newborn. Recently, angiopoietin-related growth factor (AGF) was introduced as a novel liver-derived protein with proangiogenic and insulin-sensitizing effects. In the current study, we hypothesized that serum levels of AGF would be lower in preeclamptic patients as compared to healthy controls. METHODS: AGF was quantified by enzyme-linked immunosorbent assay (ELISA) in control and preeclamptic patients during pregnancy ( CONTROL: n =22, Preeclampsia: n =22) and 6 months after delivery ( CONTROL: n =20, Preeclampsia: n =20). Furthermore, circulating AGF was correlated to clinical and biochemical measures of renal function, glucose, and lipid metabolism, as well as inflammation. RESULTS: During pregnancy, median maternal AGF concentrations were significantly higher in preeclampsia (191.6 microg/l) as compared to control subjects (136.3 microg/l) (P = 0.004). Furthermore, preeclampsia and systolic blood pressure (SBP) were associated with AGF levels in multivariate analyses independent of maternal age. However, higher circulating AGF concentrations in preeclampsia did not persist 6 months after delivery. CONCLUSIONS: Maternal AGF serum levels are significantly and paradoxically higher in preeclampsia during pregnancy. However, median postpartum circulating AGF levels are similar in preeclampsia and normal pregnancies.