We present experimental results to explore a form of bivariate glyphs for representing large-magnitude-range vectors. The glyphs meet two conditions: (1) two visual dimensions are separable; and (2) one of the two visual dimensions uses a categorical representation (e.g., a categorical colormap). We evaluate how much these two conditions determine the bivariate glyphs' effectiveness. The first experiment asks participants to perform three local tasks requiring reading no more than two glyphs. The second experiment scales up the search space in global tasks when participants must look at the entire scene of hundreds of vector glyphs to get an answer. Our results support that the first condition is necessary for local tasks when a few items are compared. But it is not enough for understanding a large amount of data. The second condition is necessary for perceiving global structures of examining very complex datasets. Participants' comments reveal that the categorical features in the bivariate glyphs trigger emergent optimal viewers' behaviors. This work contributes to perceptually accurate glyph representations for revealing patterns from large scientific results. We release source code, quantum physics data, training documents, participants' answers, and statistical analyses for reproducible science at https://osf.io/4xcf5/?view_only=94123139df9c4ac984a1e0df811cd580.

We designed and evaluated SplitVectors, a new vector field display approach to help scientists perform new discrimination tasks on large-magnitude-range scientific data shown in three-dimensional (3D) visualization environments. SplitVectors uses scientific notation to display vector magnitude, thus improving legibility. We present an empirical study comparing the SplitVectors approach with three other approaches - direct linear representation, logarithmic, and text display commonly used in scientific visualizations. Twenty participants performed three domain analysis tasks: reading numerical values (a discrimination task), finding the ratio between values (a discrimination task), and finding the larger of two vectors (a pattern detection task). Participants used both mono and stereo conditions. Our results suggest the following: (1) SplitVectors improve accuracy by about 10 times compared to linear mapping and by four times to logarithmic in discrimination tasks; (2) SplitVectors have no significant differences from the textual display approach, but reduce cluttering in the scene; (3) SplitVectors and textual display are less sensitive to data scale than linear and logarithmic approaches; (4) using logarithmic can be problematic as participants' confidence was as high as directly reading from the textual display, but their accuracy was poor; and (5) Stereoscopy improved performance, especially in more challenging discrimination tasks.

We designed and evaluated SplitVector, a new vector field display approach to help scientists perform new discrimination tasks on scientific data shown in virtual environments (VEs). Our empirical study compared the SplitVector approach with three other approaches of direct linear representation, log, and text display common in information-rich VEs or IRVEs. Our results suggest the following: (1) SplitVectors improve the accuracy by about 10 times compared to the linear mapping and by 4 times to log in discrimination tasks; (2) SplitVectors lead to no significant differences from the IRVE text display approach, yet reduce the clutter; and (3) SplitVector improved task performance in both mono and stereoscopy conditions.

In CRAHNs (Cognitive Radio Ad Hoc Networks), cognitive users coexist with primary users who have priority in accessing a set of licensed bands. Cognitive users can use those bands only when the primary users don't occupied them. And also, these occupied bands should be vacated immediately as soon as corresponding primary users wake up. Therefore, the more frequently primary user changes its activities, the less stable spectrum availability the cognitive user has. This makes route stability become one of the most concerned problems in CRAHNs. Some works have considered such kind of stability, but the overall performance of the network hasn't been taken into account when multiple cognitive user pairs (also called multi-sessions in the following) have routing requirements. Thus, in this paper, we investigate a joint routing and spectrum scheduling problem, in which multi-sessions exist, and the route stability is required. To solve this problem, a greedy strategy named Distributed Stability Algorithm (DSA) is proposed, which aims to maximize the route stability of the session, whose route stability is the minimal. The message complexity of our algorithm DSA is O((L+2)·Tmax·κ). Here, κ represents the number of transmission pairs, L and Tmaxare both two constants. Numerical results have shown that in a 100-node networks, compared with SAMER, our scheme can increase the route stability by 48.5% on average.

In this paper we examine the issue of optimizing disk usage and of scheduling large-scale scientific workflows onto distributed resources where the workflows are data- intensive, requiring large amounts of data storage, and where the resources have limited storage resources. Our approach is two-fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer required and we schedule the workflows in a way that assures that the amount of data required and generated by the workflow fits onto the individual resources. For a workflow used by gravitational- wave physicists, we were able to improve the amount of storage required by the workflow by up to 57 %. We also designed an algorithm that can not only find feasible solutions for workflow task assignment to resources in disk- space constrained environments, but can also improve the overall workflow performance.

The problem of scheduling a single DAG onto heterogeneous systems has been studied extensively. In this paper, we focus on the problem of scheduling more than one DAG at the same time onto a set of heterogeneous resources. The aim is not only to optimize the overall makespan, but also to achieve fairness, defined on the basis of the slowdown that each DAG would experience as a result of competing for resources with other DAGs. Two policies particularly focussing to deliver fairness are presented and evaluated along with another four policies that can be used to schedule multiple DAGs.

Summary form only given. This paper is motivated by the observation that different methods to compute the weights of nodes and edges when scheduling DAGs onto heterogeneous machines may lead to significant variations in the generated schedule. To minimize such variations, we present a novel heuristic for DAG scheduling, which is based upon solving a series of independent task scheduling problems. A novel heuristic for the latter problem is also included. Both heuristics compare favourably with other related heuristics.