Clique-width : harnessing the power of atoms.

Many NP-complete graph problems are polynomial-time solvable on graph classes of bounded clique-width. Several of these problems are polynomial-time solvable on a hereditary graph class G if they are so on the atoms (graphs with no clique cut-set) of G . Hence, we initiate a systematic study into boundedness of clique-width of atoms of hereditary graph classes. A graph G is H-free if H is not an induced subgraph of G, and it is (H1,H2) -free if it is both H1 -free and H2 -free. A class of H-free graphs has bounded clique-width if and only if its atoms have this property. This is no longer true for (H1,H2) -free graphs, as evidenced by one known example. We prove the existence of another such pair (H1,H2) and classify the boundedness of clique-width on (H1,H2) -free atoms for all but 18 cases

On Structural Parameterizations of the Bounded-Degree Vertex Deletion Problem

We study the parameterized complexity of the Bounded-Degree Vertex Deletion problem (BDD), where the aim is to find a maximum induced subgraph whose maximum degree is below a given degree bound. Our focus lies on parameters that measure the structural properties of the input instance. We first show that the problem is W[1]-hard parameterized by a wide range of fairly restrictive structural parameters such as the feedback vertex set number, pathwidth, treedepth, and even the size of a minimum vertex deletion set into graphs of pathwidth and treedepth at most three. We thereby resolve an open question stated in Betzler, Bredereck, Niedermeier and Uhlmann (2012) concerning the complexity of BDD parameterized by the feedback vertex set number. On the positive side, we obtain fixed-parameter algorithms for the problem with respect to the decompositional parameter treecut width and a novel problem-specific parameter called the core fracture number

Neurologic Involvement in Children and Adolescents Hospitalized in the United States for COVID-19 or Multisystem Inflammatory Syndrome

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Importance Coronavirus disease 2019 (COVID-19) affects the nervous system in adult patients. The spectrum of neurologic involvement in children and adolescents is unclear. Objective To understand the range and severity of neurologic involvement among children and adolescents associated with COVID-19. Setting, Design, and Participants Case series of patients (age <21 years) hospitalized between March 15, 2020, and December 15, 2020, with positive severe acute respiratory syndrome coronavirus 2 test result (reverse transcriptase-polymerase chain reaction and/or antibody) at 61 US hospitals in the Overcoming COVID-19 public health registry, including 616 (36%) meeting criteria for multisystem inflammatory syndrome in children. Patients with neurologic involvement had acute neurologic signs, symptoms, or diseases on presentation or during hospitalization. Life-threatening involvement was adjudicated by experts based on clinical and/or neuroradiologic features. Exposures Severe acute respiratory syndrome coronavirus 2. Main Outcomes and Measures Type and severity of neurologic involvement, laboratory and imaging data, and outcomes (death or survival with new neurologic deficits) at hospital discharge. Results Of 1695 patients (909 [54%] male; median [interquartile range] age, 9.1 [2.4-15.3] years), 365 (22%) from 52 sites had documented neurologic involvement. Patients with neurologic involvement were more likely to have underlying neurologic disorders (81 of 365 [22%]) compared with those without (113 of 1330 [8%]), but a similar number were previously healthy (195 [53%] vs 723 [54%]) and met criteria for multisystem inflammatory syndrome in children (126 [35%] vs 490 [37%]). Among those with neurologic involvement, 322 (88%) had transient symptoms and survived, and 43 (12%) developed life-threatening conditions clinically adjudicated to be associated with COVID-19, including severe encephalopathy (n = 15; 5 with splenial lesions), stroke (n = 12), central nervous system infection/demyelination (n = 8), Guillain-Barré syndrome/variants (n = 4), and acute fulminant cerebral edema (n = 4). Compared with those without life-threatening conditions (n = 322), those with life-threatening neurologic conditions had higher neutrophil-to-lymphocyte ratios (median, 12.2 vs 4.4) and higher reported frequency of D-dimer greater than 3 μg/mL fibrinogen equivalent units (21 [49%] vs 72 [22%]). Of 43 patients who developed COVID-19–related life-threatening neurologic involvement, 17 survivors (40%) had new neurologic deficits at hospital discharge, and 11 patients (26%) died. Conclusions and Relevance In this study, many children and adolescents hospitalized for COVID-19 or multisystem inflammatory syndrome in children had neurologic involvement, mostly transient symptoms. A range of life-threatening and fatal neurologic conditions associated with COVID-19 infrequently occurred. Effects on long-term neurodevelopmental outcomes are unknown

IMU based gesture recognition for mobile robot control using Online Lazy Neighborhood Graph search

Robotic teleoperation in disaster areas or environments dangerous to humans is a growing research field. With recent advancements in the robotics, the complexity of its usages has also increased. Despite this fact, currently used technology limits the majority of man-machine interfaces to text or GUI based interfaces and joysticks. Such types of control can become cumbersome in case of for example robots with a heavy control box or high degrees of freedom. Hence alternate intuitive control paradigms need to be developed. Gesture-based control is particularly useful as it can be more intuitive. Vision-based gesture control is well researched but the setup time and dependency on controlled environmental conditions, like lighting, makes it less suitable for teleoperation in disaster areas. Hoffmann et al. developed an IMU based control for a robot manipulator[1]. They used five IMUs attached to the sleeve of a wearable-jacket and transferred human arm motions into corresponding robotic manipulator motions wirelessly. They showed that teleoperation performed in this way is very efficient and intuitive[2]. However, to trigger some pre-defined manipulator motion or to trigger the robot base motions this direct control method cannot be used. In this paper, we present and evaluate a framework for gesture recognition using IMUs to indirectly control a mobile robot. Six gestures, namely Sammeln (gather), Fahrzeug (drive), Verteilen (spread out), Runter (down), B eeilung (hurry) and Vorwrts (forwards) are defined. A novel algorithm based on OLNG (Online Lazy Neighborhood Graph) search is used to recognize the gestures. Online Lazy Neighborhood Graph is a data structure based on kd-trees n-nearest neighbors. Originally OLNG was suggested and implemented for motion reconstruction from sparse accelerometer data in the helm of computer graphics [3]. As it allows real-time and fast similarity searches in big motion databases to the given input signal, we use this algorithm to classify the gestures online and command the robot. To build up the database we ask the operator to perform each gesture five times wearing the jacket equipped with the IMUs. Acceleration data from the IMUs is stored in a database during this short training phase. When an external signal is applied, we calculate its n nearest neighbors and use OLNG to find the best matching sequence. A best-matched gesture, if existing is then returned. Experiments are conducted to find and validate the best parameters for our algorithm. In extensive experiments we show that with our selected set of six gestures the framework is able to identify gestures in real time with an average success rate of 84%. Keywords Gesture recognition, teleoperation, IMU, manipulator control, mobile robotics References [1] J¨org Hoffmann, Bernd Br¨uggemann, and Bj¨orn Kr¨uger. Automatic calibration of a motion capture system based on inertial sensors for tele-manipulation. In 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO), June 2010. [2] B. Br¨uggemann, B. Gaspers, A. Ciossek, J. Pellenz, and N. Kroll. Comparison of different control methods for mobile manipulation using standardized tests. In 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages 12, Oct 2013. [3] Jochen Tautges. Reconstruction of Human Motions Based on Low-Dimensional Control Signals. Dissertation, Universit¨at Bonn, August 2012

Stable Matching with Uncertain Linear Preferences

We consider the two-sided stable matching setting in which there may be uncertainty about the agents’ preferences due to limited information or communication. We consider three models of uncertainty: (1) lottery model—for each agent, there is a probability distribution over linear preferences, (2) compact indifference model—for each agent, a weak preference order is specified and each linear order compatible with the weak order is equally likely and (3) joint probability model—there is a lottery over preference profiles. For each of the models, we study the computational complexity of computing the stability probability of a given matching as well as finding a matching with the highest probability of being stable. We also examine more restricted problems such as deciding whether a certainly stable matching exists. We find a rich complexity landscape for these problems, indicating that the form uncertainty takes is significant