TraMNet - Transition Matrix Network for Efficient Action Tube Proposals

Current state-of-the-art methods solve spatiotemporal action localisation by extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate sets of temporally connected bounding boxes called \textit{action micro-tubes}. However, they fail to consider that the underlying anchor proposal hypotheses should also move (transition) from frame to frame, as the actor or the camera does. Assuming we evaluate $n$ 2D anchors in each frame, then the number of possible transitions from each 2D anchor to the next, for a sequence of $f$ consecutive frames, is in the order of $O(n^f)$, expensive even for small values of $f$. To avoid this problem, we introduce a Transition-Matrix-based Network (TraMNet) which relies on computing transition probabilities between anchor proposals while maximising their overlap with ground truth bounding boxes across frames, and enforcing sparsity via a transition threshold. As the resulting transition matrix is sparse and stochastic, this reduces the proposal hypothesis search space from $O(n^f)$ to the cardinality of the thresholded matrix. At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network. At test time, a denser transition matrix can be obtained either by decreasing the threshold or by adding to it all the relative transitions originating from any cell location, allowing the network to handle transitions in the test data that might not have been present in the training data, and making detection translation-invariant. Finally, we show that our network can handle sparse annotations such as those available in the DALY dataset. We report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24 datasets to support our claims.Comment: 15 page

Content-Aware Unsupervised Deep Homography Estimation

Homography estimation is a basic image alignment method in many applications. It is usually conducted by extracting and matching sparse feature points, which are error-prone in low-light and low-texture images. On the other hand, previous deep homography approaches use either synthetic images for supervised learning or aerial images for unsupervised learning, both ignoring the importance of handling depth disparities and moving objects in real world applications. To overcome these problems, in this work we propose an unsupervised deep homography method with a new architecture design. In the spirit of the RANSAC procedure in traditional methods, we specifically learn an outlier mask to only select reliable regions for homography estimation. We calculate loss with respect to our learned deep features instead of directly comparing image content as did previously. To achieve the unsupervised training, we also formulate a novel triplet loss customized for our network. We verify our method by conducting comprehensive comparisons on a new dataset that covers a wide range of scenes with varying degrees of difficulties for the task. Experimental results reveal that our method outperforms the state-of-the-art including deep solutions and feature-based solutions.Comment: Accepted by ECCV 2020 (Oral, Top 2%, 3 over 3 Strong Accepts). Jirong Zhang and Chuan Wang are joint first authors, and Shuaicheng Liu is the corresponding autho

Standardized Outcomes in Nephrology-Transplantation: A Global Initiative to Develop a Core Outcome Set for Trials in Kidney Transplantation.

BACKGROUND: Although advances in treatment have dramatically improved short-term graft survival and acute rejection in kidney transplant recipients, long-term graft outcomes have not substantially improved. Transplant recipients also have a considerably increased risk of cancer, cardiovascular disease, diabetes, and infection, which all contribute to appreciable morbidity and premature mortality. Many trials in kidney transplantation are short-term, frequently use unvalidated surrogate endpoints, outcomes of uncertain relevance to patients and clinicians, and do not consistently measure and report key outcomes like death, graft loss, graft function, and adverse effects of therapy. This diminishes the value of trials in supporting treatment decisions that require individual-level multiple tradeoffs between graft survival and the risk of side effects, adverse events, and mortality. The Standardized Outcomes in Nephrology-Transplantation initiative aims to develop a core outcome set for trials in kidney transplantation that is based on the shared priorities of all stakeholders. METHODS: This will include a systematic review to identify outcomes reported in randomized trials, a Delphi survey with an international multistakeholder panel (patients, caregivers, clinicians, researchers, policy makers, members from industry) to develop a consensus-based prioritized list of outcome domains and a consensus workshop to review and finalize the core outcome set for trials in kidney transplantation. CONCLUSIONS: Developing and implementing a core outcome set to be reported, at a minimum, in all kidney transplantation trials will improve the transparency, quality, and relevance of research; to enable kidney transplant recipients and their clinicians to make better-informed treatment decisions for improved patient outcomes

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Patient and caregiver perspectives on blood pressure in children with chronic kidney disease

Background: More than 50% of children with chronic kidney disease (CKD) have uncontrolled hypertension, increasing their long-term risk of cardiovascular disease and progression to kidney failure. Children receiving medications or dialysis may also experience acute blood pressure fluctuations accompanied by debilitating symptoms. We aimed to describe the perspectives of children with CKD and their parental caregivers on blood pressure to inform patient-centered care. / Methods: Secondary thematic analysis was conducted on qualitative data from the Standardized Outcomes in Nephrology—Children and Adolescents initiative, encompassing 16 focus groups, an international Delphi survey and two consensus workshops. We analyzed responses from children with CKD (ages 8–21 years) and caregivers (of children ages 0–21 years) pertaining to blood pressure. / Results: Overall, 120 patients and 250 caregivers from 22 countries participated. We identified five themes: invisibility and normalization (reassured by apparent normotension, absence of symptoms and expected links with CKD), confused by ambiguity (hypertension indistinguishable from cardiovascular disease, questioning the need for prophylactic intervention, frustrated by inconsistent messages and struggling with technical skills in measurement), enabling monitoring and maintaining health (gaging well-being and preventing vascular complications), debilitating and constraining daily living (provoking anxiety and agitation, helpless and powerless and limiting life activities) and burden of medications (overwhelmed by the quantity of tablets and distress from unexpected side effects). / Conclusions: For children with CKD and their caregivers, blood pressure was an important heath indicator, but uncertainty around its implications and treatment hampered management. Providing educational resources to track blood pressure and minimizing symptoms and treatment burden may improve outcomes in children with CKD

Developing a Set of Core Outcomes for Trials in Haemodialysis: An International Delphi Survey

AIM: To generate a consensus-based, prioritized list of core outcomes for trials in haemodialysis. BACKGROUND: Survival and quality of life for patients on haemodialysis remain poor despite substantial research efforts. Existing trials often report surrogate outcomes that may not be relevant to patients and clinicians. A core outcome set that reflects stakeholder priorities would improve the relevance, efficiency, and comparability of haemodialysis trials. METHODS: In an online Delphi survey, participants rated the importance of outcomes using a 9-point Likert scale. In Round 2 and 3, participants reviewed the scores and comments of other respondents and re-rated the outcomes. For each outcome, we calculated the median, mean, and proportion rating 7-9 (“critically important”). RESULTS: 1,181 participants (202 [17%] patients/caregivers, 979 health professionals) from 73 countries completed Round 1 and 838 (150 [18%] patients/caregivers) completed Round 3 (71% response rate). Outcomes achieving consensus as high priorities across both groups were: vascular access complications, cardiovascular disease, mortality, dialysis adequacy and fatigue. Patients/caregivers rated four outcomes higher than health professionals: ability to travel (mean difference 0.9), dialysis-free time (0.5), dialysis adequacy (0.3), and washed out after dialysis (0.2). Health professionals rated 11 outcomes higher: mortality (1.0), hospitalization (1.0), drop in blood pressure (1.0), vascular access complications (0.9), depression (0.9), cardiovascular disease (0.8), target weight (0.7), infection (0.4), potassium (0.4), ability to work (0.3), and pain (0.3). CONCLUSIONS: The top stakeholder prioritized outcomes were vascular access problems, cardiovascular disease, mortality, dialysis adequacy and fatigue. Patients/caregivers gave higher priority to lifestyle-related outcomes than health professionals. This prioritized set of outcomes can inform the establishment of a core outcome set, to improve the value of trial evidence to support decision-making for people on haemodialysis

Core Outcome Measures for Trials in People with Coronavirus Disease 2019: Respiratory Failure, Multiorgan Failure, Shortness of Breath, and Recovery

OBJECTIVES: Respiratory failure, multiple organ failure, shortness of breath, recovery, and mortality have been identified as critically important core outcomes by more than 9300 patients, health professionals, and the public from 111 countries in the global coronavirus disease 2019 core outcome set initiative. The aim of this project was to establish the core outcome measures for these domains for trials in coronavirus disease 2019. DESIGN: Three online consensus workshops were convened to establish outcome measures for the four core domains of respiratory failure, multiple organ failure, shortness of breath, and recovery. SETTING: International. PATIENTS: About 130 participants (patients, public, and health professionals) from 17 countries attended the three workshops. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Respiratory failure, assessed by the need for respiratory support based on the World Health Organization Clinical Progression Scale, was considered pragmatic, objective, and with broad applicability to various clinical scenarios. The Sequential Organ Failure Assessment was recommended for multiple organ failure, because it was routinely used in trials and clinical care, well validated, and feasible. The Modified Medical Research Council measure for shortness of breath, with minor adaptations (recall period of 24 hr to capture daily fluctuations and inclusion of activities to ensure relevance and to capture the extreme severity of shortness of breath in people with coronavirus disease 2019), was regarded as fit for purpose for this indication. The recovery measure was developed de novo and defined as the absence of symptoms, resumption of usual daily activities, and return to the previous state of health prior to the illness, using a 5-point Likert scale, and was endorsed. CONCLUSIONS: The coronavirus disease 2019 core outcome set recommended core outcome measures have content validity and are considered the most feasible and acceptable among existing measures. Implementation of the core outcome measures in trials in coronavirus disease 2019 will ensure consistency and relevance of the evidence to inform decision-making and care of patients with coronavirus disease 2019

4D Match Trees for Non-rigid Surface Alignment

This paper presents a method for dense 4D temporal alignment of partial reconstructions of non-rigid surfaces observed from single or multiple moving cameras of complex scenes. 4D Match Trees are introduced for robust global alignment of non-rigid shape based on the similarity between images across sequences and views. Wide-timeframe sparse correspondence between arbitrary pairs of images is established using a segmentation-based feature detector (SFD) which is demonstrated to give improved matching of non-rigid shape. Sparse SFD correspondence allows the similarity between any pair of image frames to be estimated for moving cameras and multiple views. This enables the 4D Match Tree to be constructed which minimises the observed change in non-rigid shape for global alignment across all images. Dense 4D temporal correspondence across all frames is then estimated by traversing the 4D Match tree using optical flow initialised from the sparse feature matches. The approach is evaluated on single and multiple view images sequences for alignment of partial surface reconstructions of dynamic objects in complex indoor and outdoor scenes to obtain a temporally consistent 4D representation. Comparison to previous 2D and 3D scene flow demonstrates that 4D Match Trees achieve reduced errors due to drift and improved robustness to large non-rigid deformations

Drug Discovery Using Chemical Systems Biology: Weak Inhibition of Multiple Kinases May Contribute to the Anti-Cancer Effect of Nelfinavir

Nelfinavir is a potent HIV-protease inhibitor with pleiotropic effects in cancer cells. Experimental studies connect its anti-cancer effects to the suppression of the Akt signaling pathway, but the actual molecular targets remain unknown. Using a structural proteome-wide off-target pipeline, which integrates molecular dynamics simulation and MM/GBSA free energy calculations with ligand binding site comparison and biological network analysis, we identified putative human off-targets of Nelfinavir and analyzed the impact on the associated biological processes. Our results suggest that Nelfinavir is able to inhibit multiple members of the protein kinase-like superfamily, which are involved in the regulation of cellular processes vital for carcinogenesis and metastasis. The computational predictions are supported by kinase activity assays and are consistent with existing experimental and clinical evidence. This finding provides a molecular basis to explain the broad-spectrum anti-cancer effect of Nelfinavir and presents opportunities to optimize the drug as a targeted polypharmacology agent