We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central’s open access library. In the case reports, we annotate cases, conditions, findings, factors and negation modifiers. Moreover, where applicable, we annotate relations between these entities. As such, this is the first corpus of this kind made available to the scientific community in English. It enables the initial investigation of automatic information extraction from case reports through tasks like Named Entity Recognition, Relation Extraction and (sentence/paragraph) relevance detection. Additionally, we present four strong baseline systems for the detection of medical entities made available through the annotated dataset.

The Linux kernel is mostly designed for multi-programed environments, but high-performance applications have other requirements. Such applications are run standalone, and usually rely on runtime systems to distribute the application's workload on worker threads, one per core. However, due to current OSes limitations, it is not feasible to track whether workers are actually running or blocked due to, for instance, a requested resource. For I/O intensive applications, this leads to a significant performance degradation given that the core of a blocked thread becomes idle until it is able to run again. In this paper, we present the proof-of-concept of a Linux kernel extension denoted User-Monitored Threads (UMT) which tackles this problem. Our extension allows a user-space process to be notified of when the selected threads become blocked or unblocked, making it possible for a runtime to schedule additional work on the idle core. We implemented the extension on the Linux Kernel 5.1 and adapted the Nanos6 runtime of the OmpSs-2 programming model to take advantage of it. The whole prototype was tested on two applications which, on the tested hardware and the appropriate conditions, reported speedups of almost 2x.

In this paper we present a model for simulating the pushing of dirt. This is a complex problem requiring the study of the action space for the robot, a deformable model for movable earth, the interaction between the robot's pushing surface and the environment, and techniques that will allow this model to simulate such a scenario. We present our novel framework for studying this problem including the grid-based model of the environment, a simplified earth pushing robot model, and action strategies to model the pushing of dirt from one area of the environment to another.

Sampling-based motion planning is the state-of-the-art technique for solving challenging motion planning problems in a wide variety of domains. While generally successful, their performance suffers from increasing problem complexity. In many cases, the full problem complexity is not needed for the entire solution. We present a hierarchical aggregation framework that groups and models sets of obstacles based on the currently needed level of detail. The hierarchy enables sampling to be performed using the simplest and most conservative representation of the environment possible in that region. Our results show that this scheme improves planner performance irrespective of the underlying sampling method and input problem. In many cases, improvement is significant, with running times often less than 60% of the original planning time.

We present a reactive multi-agent push system for a large set of objects. The behavior for the pushing agents consists of: 1) selecting and updating an object set to push, 2) reaching positions near the objects to start influencing, 3) pushing the objects along a path to the goal region, and 4) regrouping when needed to ensure the group is packed tightly enough. The emergent properties of the behavior allow us to test how effectively a group of agents can push a set of objects through the environment with different strategies.

We study multi-robot caravanning, which is loosely defined as the problem of a heterogeneous team of robots visiting specific areas of an environment (waypoints) as a group. After formally defining this problem, we propose a novel solution that requires minimal communication and scales with the number of waypoints and robots. Our approach restricts explicit communication and coordination to occur only when robots reach waypoints, and relies on implicit coordination when moving between a given pair of waypoints. At the heart of our algorithm is the use of leader election to efficiently exploit the unique environmental knowledge available to each robot in order to plan paths for the group, which makes it general enough to work with robots that have heterogeneous representations of the environment. We implement our approach both in simulation and on a physical platform, and characterize the performance of the approach under various scenarios. We demonstrate that our approach can successfully be used to combine the planning capabilities of different agents.

Rapidly-exploring Random Trees (RRTs) are effective for a wide range of applications ranging from kinodynamic planning to motion planning under uncertainty. However, RRTs are not as efficient when exploring heterogeneous environments and do not adapt to the space. For example, in difficult areas an expensive RRT growth method might be appropriate, while in open areas inexpensive growth methods should be chosen. In this paper, we present a novel algorithm, Adaptive RRT, that adapts RRT growth to the current exploration area using a two level growth selection mechanism. At the first level, we select groups of expansion methods according to the visibility of the node being expanded. Second, we use a cost-sensitive learning approach to select a sampler from the group of expansion methods chosen. Also, we propose a novel definition of visibility for RRT nodes which can be computed in an online manner and used by Adaptive RRT to select an appropriate expansion method. We present the algorithm and experimental analysis on a broad range of problems showing not only its adaptability, but efficiency gains achieved by adapting exploration methods appropriately.

In this paper we study the ingress and egress of pedestrians and vehicles in a parking lot. We show how local maneuvers executed by agents permit them to create trajectories in constrained environments, and to resolve the deadlocks between them in mixed-flow scenarios. We utilize a roadmap-based approach which allows us to map complex environments and generate heuristic local paths that are feasible for both pedestrians and vehicles. Finally, we examine the effect that some agent-behavioral parameters have on parking lot ingress and egress.

In this work, we investigate aspects of building design that can be optimized. Architectural features that we explore include pillar placement in simple corridors, doorway placement in buildings, and agent placement for information dispersement in an evacuation. The metrics utilized are tuned to the specific scenarios we study, which include continuous flow pedestrian movement and building evacuation. We use Multidimensional Direct Search (MDS) optimization with an extreme barrier criteria to find optimal placements while enforcing building constraints.

In this work, we describe an approach for modeling and simulating group behaviors for pursuit-evasion that uses a graph-based representation of the environment and integrates multi-agent simulation with roadmap-based path planning. Our approach can be applied to more realistic scenarios than are typically studied in most previous work, including agents moving in 3D environments such as terrains, multi-story buildings, and dynamic environments. We also support more realistic three-dimensional visibility computations that allow evading agents to hide in crowds or behind hills. We demonstrate the utility of this approach on mobile robots and in simulation for a variety of scenarios including pursuit-evasion and tag on terrains, in multi-level buildings, and in crowds.

In this work, we present a formulation of an evacuation planning problem that is inspired by motion planning and describe an integrated behavioral agent-based and roadmap-based motion planning approach to solve it. Our formulation allows users to test the effect on evacuation of a number of different environmental factors. One of our main focuses is to provide a mechanism to investigate how the interaction between agents influences the resulting evacuation plans. Specifically, we explore how various types of control provided by a set of directing agents effects the overall evacuation planning strategies of the evacuating agents.

In this paper we present a heuristic approach to planning in an environment with moving obstacles. Our approach assumes that the robot has no knowledge of the future trajectory of the moving objects. Our framework also distinguishes between two types of moving objects in the environment: hard and soft objects. We distinguish between the two types of objects in the environment as varying application domains could allow for some collision between some types of moving objects. For example, a robot planning a path in an environment with people could have the people modeled as circular disks with a safe zone surrounding each person. Although the robot may try to stay out of each safe zone, violating that criteria would not necessarily result in planning failure. We will show the effectiveness of our planner in general dynamic environments with the soft objects having varying behaviors.

Though motion planning has been studied extensively for rigid and articulated robots, motion planning for deformable objects is an area that has received far less attention. In this paper we present a framework for planning paths in completely deformable, elastic environments. We apply a deformable model to the robot and obstacles in the environment and present a kinodynamic planning algorithm suited for this type of deformable motion planning. The planning algorithm is based on the rapidly-exploring random tree (RRT) path planning algorithm. To the best of our knowledge, this is the first work that plans paths in totally deformable environments

Tree-based path planners have been shown to be well suited to solve various high dimensional motion planning problems. Here we present a variant of the Rapidly-Exploring Random Tree (RRT) path planning algorithm that is able to explore narrow passages or difficult areas more effectively. We show that both workspace obstacle information and C-space information can be used when deciding which direction to grow. The method includes many ways to grow the tree, some taking into account the obstacles in the environment. This planner works best in difficult areas when planning for free flying rigid or articulated robots. Indeed, whereas the standard RRT can face difficulties planning in a narrow passage, the tree based planner presented here works best in these areas

As transistors continue to scale down into the nanometer regime, device leakage currents are becoming the dominant cause of power dissipation in nanometer caches, making it essential to model these leakage effects properly. Moreover, typical microprocessor caches are pipelined to keep up with the speed of the processor, and the effects of pipelining overhead need to be properly accounted for.In this paper, we present a detailed study of pipelined nanometer caches with detailed energy/power dissipation breakdowns showing where and how the power is dissipated within a nanometer cache. We explore a three-dimensional pipelined cache design space that includes cache size (16kB to 512kB), cache associativity (direct-mapped to 16-way) and process technology (90nm, 65nm, 45nm and 32nm).Among our findings, we show that cache bitline leakage is increasingly becoming the dominant cause of power dissipation in nanometer technology nodes. We show that subthreshold leakage is the main cause of static power dissipation, and that gate leakage is, surprisingly, not a significant contributor to total cache power, even for 32nm caches. We also show that accounting for cache pipelining overhead is necessary, as power dissipated by the pipeline elements is a significant part of cache power.

TERPS is a fault-tolerant computer design that significantly reduces the threat of electromagnetic interference (EMI), using hardware checkpoint/rollback-recovery. TERPS tolerates EMI by periodically checkpointing processor state into a special safe-storage device. The detection of EMI invokes rollback, which recovers processor state from a previously check-pointed state and resumes normal execution. Rollback results in loss of performance dictated by the EMI duration; TERPS ensures forward progress of the system provided EMI events are separated by some minimum time interval (e.g., at least 5.12μs for our prototype processor running at 100MHz). The performance overhead of our mechanism is reasonable: 5-6% overhead when check-pointing every 128 processor cycles.

Shepherding behaviors are a type of group be haviors in which one group (the shepherds) tries to control the motion of another group (the flock). Shepherding behaviors can be found in many forms in nature and have various important robotic applications. In this paper we extend our previous work of shepherding behaviors with a single shepherd to multiple shepherds. More specifically, we study how a group of shepherds can work cooperatively without communication to efficiently control the flock.

There are many randomized motion planning techniques, but it is often difficult to determine what planning method to apply to best solve a problem. Planners have their own strengths and weaknesses, and each one is best suited to a specific type of problem. In previous work, we proposed a meta-planner that, through analysis of the problem features, subdivides the instance into regions and determines which planner to apply in each region. The results obtained with our prototype system were very promising even though it utilized simplistic strategies for all components. Even so, we did determine that strategies for problem subdivision and for combination of partial regional solutions have a crucial impact on performance. In this paper, we propose new methods for these steps to improve the performance of the meta-planner. For problem subdivision, we propose two new methods: a method based on ‘ gaps’ and a method based on information theory. For combining partial solutions, we propose two new methods that concentrate on neighboring areas of the regional solutions. We present results that show the performance gain achieved by utilizing these new strategies.

Shepherding behaviors are a type of flocking behavior in which outside agents guide or control members of a flock. Shepherding behaviors can be found in various forms in nature. For example, herding, covering, patrolling and collecting are common types of shepherding behaviors. In this work, we investigate ways to simulate these types of behaviors. A shepherd uses roadmaps to steer the flock and to re-group separated flock members. This paper focuses on improving the shepherd's movements to gain better control of the flock's motion and use this improved control to demonstrate a wider variety of shepherding behaviors.

In this paper we investigate how the coverage and connectedness of PRM roadmaps can be improved by adding a connected component (CC) connection step to the general PRM framework. We provide experimental results establishing that significant roadmap improvements can be obtained relatively efficiently by utilizing a suite of CC connection methods, which include variants of existing methods such as RRT and a new ray tracing based method. The coordinated application of these techniques is enabled by methods for selecting and scheduling pairs of nodes in different CCs for connection attempts. In addition to identifying important and/or promising regions of C-space for exploration, these methods also provide a mechanism for controlling the cost of the connection attempts. In our experiments, the time required by the improvement phase was on the same order as the time used to generate the initial roadmap.