Neural network design: learning from Neural Architecture Search

Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interests. Despite various successful approaches proposed to solve the NAS task, the landscape of it, along with its properties, are rarely investigated. In this paper, we argue for the necessity of studying the landscape property thereof and propose to use the so-called Exploratory Landscape Analysis (ELA) techniques for this goal. Taking a broad set of designs of the deep convolutional network, we conduct extensive experimentation to obtain their performance. Based on our analysis of the experimental results, we observed high similarities between well-performing architecture designs, which is then used to significantly narrow the search space to improve the efficiency of any NAS algorithm. Moreover, we extract the ELA features over the NAS landscapes on three common image classification data sets, MNIST, Fashion, and CIFAR-10, which shows that the NAS landscape can be distinguished for those three data sets. Also, when comparing to the ELA features of the well-known Black-Box optimization Benchmarking (BBOB) problem set, we found out that the NAS landscapes surprisingly form a new problem class on its own, which can be separated from all 24 BBOB problems. Given this interesting observation, we, therefore, state the importance of further investigation on selecting an efficient optimizer for the NAS landscape as well as the necessity of augmenting the current benchmark problem set.Algorithms and the Foundations of Software technolog

Return to work after subacromial decompression, diagnostic arthroscopy, or exercise therapy for shoulder impingement : a randomised, placebo-surgery controlled FIMPACT clinical trial with five-year follow-up

Background: Arthroscopic subacromial decompression is one of the most commonly performed shoulder surgeries in the world. It is performed to treat patients with suspected shoulder impingement syndrome, i.e., subacromial pain syndrome. Only few studies have specifically assessed return-to-work rates after subacromial decompression surgery. All existing evidence comes from open, unblinded study designs and this lack of blinding introduces the potential for bias. We assessed return to work and its predictors in patients with shoulder impingement syndrome in a secondary analysis of a placebo-surgery controlled trial. Methods: One hundred eighty-four patients in a randomised trial had undergone arthroscopic subacromial decompression (n = 57), diagnostic arthroscopy, a placebo surgical intervention, (n = 59), or exercise therapy (n = 68). We assessed return to work, defined as having returned to work for at least two follow-up visits by the primary 24-month time point, work status at 24 and 60 months, and trajectories of return to work per follow-up time point. Patients and outcome assessors were blinded to the assignment regarding the arthroscopic subacromial decompression vs. diagnostic arthroscopy comparison. We assessed the treatment effect on the full analysis set as the difference between the groups in return-to-work rates and work status at 24 months and at 60 months using Chi-square test and the predictors of return to work with logistic regression analysis. Results: There was no difference in the trajectories of return to work between the study groups. By 24 months, 50 of 57 patients (88%) had returned to work in the arthroscopic subacromial decompression group, while the respective figures were 52 of 59 (88%) in the diagnostic arthroscopy group and 61 of 68 (90%) in the exercise therapy group. No clinically relevant predictors of return to work were found. The proportion of patients at work was 80% (147/184) at 24 months and 73% (124/184) at 60 months, with no difference between the treatment groups (p-values 0.842 and 0.943, respectively). Conclusions: Arthroscopic subacromial decompression provided no benefit over diagnostic arthroscopy or exercise therapy on return to work in patients with shoulder impingement syndrome. We did not find clinically relevant predictors of return to work either.Peer reviewe

Expert consensus report on lipid mediators: Role in resolution of inflammation and muscle preservation

This meeting report presents a consensus on the biological aspects of lipid emulsions in parenteral nutrition, emphasizing the unanimous support for the integration of lipid emulsions, particularly those containing fish oil, owing to their many potential benefits beyond caloric provision. Lipid emulsions have evolved from simple energy sources to complex formulations designed to improve safety profiles and offer therapeutic benefits. The consensus highlights the critical role of omega‐3 polyunsaturated fatty acids (PUFAs), notably eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), found in fish oil and other marine oils, for their anti‐inflammatory properties, muscle mass preservation, and as precursors to the specialized pro‐resolving mediators (SPMs). SPMs play a significant role in immune modulation, tissue repair, and the active resolution of inflammation without impairing host defense mechanisms. The panel's agreement underscores the importance of incorporating fish oil within clinical practices to facilitate recovery in conditions like surgery, critical illness, or immobility, while cautioning against therapies that might disrupt natural inflammation resolution processes. This consensus not only reaffirms the role of specific lipid components in enhancing patient outcomes, but also suggests a shift towards nutrition‐based therapeutic strategies in clinical settings, advocating for the proactive evidence‐based use of lipid emulsions enriched with omega‐3 PUFAs. Furthermore, we should seek to apply our knowledge concerning DHA, EPA, and their SPM derivatives, to produce more informative randomized controlled trial protocols, thus allowing more authoritative clinical recommendations

DoE2Vec: deep-learning based features for exploratory landscape analysis

Algorithms and the Foundations of Software technolog

A comparison of global sensitivity analysis methods for explainable AI with an application in genomic prediction

Explainable Artificial Intelligence (XAI) is an increasingly important field of research required to bring AI to the next level in real-world applications. Global sensitivity analysis (GSA) methods play an important role in XAI, as they can provide an understanding of which (groups of) parameters have high influence in the predictions of machine learning models and the output of simulators and real-world processes. In this paper, we conduct a survey into global sensitivity methods in an XAI context and present both a qualitative and a quantitative analysis of these methods under different conditions. In addition to the overview and comparison, we propose an open source application, GSAreport, that allows you to easily generate extensive reports using a carefully selected set of global sensitivity analysis methods depending on the number of dimensions and samples, to gain a deep understanding of the role of each feature for a given model or data set. We finally present the methods discussed in a complex real-world application of genomic prediction and draw conclusions about when to use which GSA methods.Algorithms and the Foundations of Software technolog

Sphingosine kinase 1 overexpression induces MFN2 fragmentation and alters mitochondrial matrix Ca2+ handling in HeLa cells

Sphingosine kinase 1 (SKI) converts sphingosine to the bioactive lipid sphingosine 1-phosphate (SIP). SW binds to G-protein-coupled receptors (S1PR(1-5)) to regulate cellular events, including Ca2+ signaling. The SK1/S1P axis and Ca2+ signaling both play important roles in health and disease. In this respect, Ca2+ microdomains at the mitochondria-associated endoplasmic reticulum (ER) membranes (MAMs) are of importance in oncogenesis. Mitofusin 2 (MFN2) modulates ER-mitochondria contacts, and dysregulation of MFN2 is associated with malignancies. We show that overexpression of SKI augments agonist-induced Ca2+ release from the ER resulting in increased mitochondria] matrix Ca2+. Also, overexpression of SK1 induces MFN2 fragmentation, likely through increased calpain activity. Further, expressing putative calpain-cleaved MFN2 N- and C-terminal fragments increases mitochondrial matrix Ca2+ during agonist stimulation, mimicking the SK1 overexpression in cells. Moreover, SK1 overexpression enhances cellular respiration and cell migration. Thus, SK1 regulates MFN2 fragmentation resulting in increased mitochondrial Ca2+ and downstream cellular effects.Peer reviewe

Foliage surface ozone deposition : a role for surface moisture?

Peer reviewe

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy

Genome variations: Effects on the robustness of neuroevolved control for swarm robotics systems

Manual design of self-organized behavioral control for swarms of robots is a complex task. Neuroevolution has proved a viable alternative given its capacity to automatically synthesize controllers. In this paper, we introduce the concept of Genome Variations (GV) in the neuroevolution of behavioral control for robotic swarms. In an evolutionary setup with GV, a slight mutation is applied to the evolving neural network parameters before they are copied to the robots in a swarm. The genome variation is individual to each robot, thereby generating a slightly heterogeneous swarm. GV represents a novel approach to the evolution of robust behaviors, expected to generate more stable and robust individual controllers, and bene t swarm behaviors that can deal with small heterogeneities in the behavior of other members in the swarm. We conduct experiments using an aggregation task, and compare the evolved solutions to solutions evolved under ideal, noise-free conditions, and to solutions evolved with traditional sensor noise.info:eu-repo/semantics/acceptedVersio