Evolutionary ecology of opsin gene sequence, expression and repertoire.

Linking molecular evolution to biological function is a long-standing challenge in evolutionary biology. Some of the best examples of this involve opsins, the genes that encode the molecular basis of light reception. In this issue of Molecular Ecology, three studies examine opsin gene sequence, expression and repertoire to determine how natural selection has shaped the visual system. First, Escobar-Camacho et al. () use opsin repertoire and expression in three Amazonian cichlid species to show that a shift in sensitivity towards longer wavelengths is coincident with the long-wavelength-dominated Amazon basin. Second, Stieb et al. () explore opsin sequence and expression in reef-dwelling damselfish and find that UV- and long-wavelength vision are both important, but likely for different ecological functions. Lastly, Suvorov et al. () study an expansive opsin repertoire in the insect order Odonata and find evidence that copy number expansion is consistent with the permanent heterozygote model of gene duplication. Together these studies emphasize the utility of opsin genes for studying both the local adaptation of sensory systems and, more generally, gene family evolution

Efficient Parallel and Adaptive Partitioning for Load-balancing in Spatial Join

Due to the developments of topographic techniques, clear satellite imagery, and various means for collecting information, geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of spatial computations and analytics on large spatial data sets, parallel processing is required. To exploit fine-grained parallel processing in large scale compute clusters, partitioning in a load-balanced way is necessary for skewed datasets. In this work, we focus on spatial join operation where the inputs are two layers of geospatial data. Our partitioning method for spatial join uses Adaptive Partitioning (ADP) technique, which is based on Quadtree partitioning. Unlike existing partitioning techniques, ADP partitions the spatial join workload instead of partitioning the individual datasets separately to provide better load-balancing. Based on our experimental evaluation, ADP partitions spatial data in a more balanced way than Quadtree partitioning and Uniform grid partitioning. ADP uses an output-sensitive duplication avoidance technique which minimizes duplication of geometries that are not part of spatial join output. In a distributed memory environment, this technique can reduce data communication and storage requirements compared to traditional methods.To improve the performance of ADP, an MPI+Threads based parallelization is presented. With ParADP, a pair of real world datasets, one with 717 million polylines and another with 10 million polygons, is partitioned into 65,536 grid cells within 7 seconds. ParADP performs well with both good weak scaling up to 4,032 CPU cores and good strong scaling up to 4,032 CPU cores

Load Balancing Algorithms for Parallel Spatial Join on HPC Platforms

Geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of geospatial computations and analytics on large scale datasets, parallel processing is necessary. To exploit fine-grained parallel processing on large scale compute clusters, partitioning of skewed datasets in a load-balanced way is challenging. The workload in spatial join is data dependent and highly irregular. Moreover, wide variation in the size and density of geometries from one region of the map to another, further exacerbates the load imbalance. This dissertation focuses on spatial join operation used in Geographic Information Systems (GIS) and spatial databases, where the inputs are two layers of geospatial data, and the output is a combination of the two layers according to join predicate.This dissertation introduces a novel spatial data partitioning algorithm geared towards load balancing the parallel spatial join processing. Unlike existing partitioning techniques, the proposed partitioning algorithm divides the spatial join workload instead of partitioning the individual datasets separately to provide better load-balancing. This workload partitioning algorithm has been evaluated on a high-performance computing system using real-world datasets. An intermediate output-sensitive duplication avoidance technique is proposed that decreases the external memory space requirement for storing spatial join candidates across the partitions. GPU acceleration is used to further reduce the spatial partitioning runtime. For dynamic load balancing in spatial join, a novel framework for fine-grained work stealing is presented. This framework is efficient and NUMA-aware. Performance improvements are demonstrated on shared and distributed memory architectures using threads and message passing. Experimental results show effective mitigation of data skew. The framework supports a variety of spatial join predicates and spatial overlay using partitioned and un-partitioned datasets

Passive Control Architectures for Collaborative Virtual Haptic Interaction and Bilateral Teleoperation over Unreliable Packet-Switched Digital Network

This PhD dissertation consists of two major parts: collaborative haptic interaction (CHI) and bilateral teleoperation over the Internet. For the CHI, we propose a novel hybrid peer-to-peer (P2P) architecture including the shared virtual environment (SVE) simulation, coupling between the haptic device and VE, and P2P synchronization control among all VE copies. This framework guarantees the interaction stability for all users with general unreliable packet-switched communication network which is the most challenging problem for CHI control framework design. This is achieved by enforcing our novel \emph{passivity condition} which fully considers time-varying non-uniform communication delays, random packet loss/swapping/duplication for each communication channel. The topology optimization method based on graph algebraic connectivity is also developed to achieve optimal performance under the communication bandwidth limitation. For validation, we implement a four-user collaborative haptic system with simulated unreliable packet-switched network connections. Both the hybrid P2P architecture design and the performance improvement due to the topology optimization are verified. In the second part, two novel hybrid passive bilateral teleoperation control architectures are proposed to address the challenging stability and performance issues caused by the general Internet communication unreliability (e.g. varying time delay, packet loss, data duplication, etc.). The first method--Direct PD Coupling (DPDC)--is an extension of traditional PD control to the hybrid teleoperation system. With the assumption that the Internet communication unreliability is upper bounded, the passive gain setting condition is derived and guarantees the interaction stability for the teleoperation system which interacts with unknown/unmodeled passive human and environment. However, the performance of DPDC degrades drastically when communication unreliability is severe because its feasible gain region is limited by the device viscous damping. The second method--Virtual Proxy Based PD Coupling (VPDC)--is proposed to improve the performance while providing the same interaction stability. Experimental and quantitative comparisons between DPDC and VPDC are conducted, and both interaction stability and performance difference are validated

SKIRT: the design of a suite of input models for Monte Carlo radiative transfer simulations

The Monte Carlo method is the most popular technique to perform radiative transfer simulations in a general 3D geometry. The algorithms behind and acceleration techniques for Monte Carlo radiative transfer are discussed extensively in the literature, and many different Monte Carlo codes are publicly available. On the contrary, the design of a suite of components that can be used for the distribution of sources and sinks in radiative transfer codes has received very little attention. The availability of such models, with different degrees of complexity, has many benefits. For example, they can serve as toy models to test new physical ingredients, or as parameterised models for inverse radiative transfer fitting. For 3D Monte Carlo codes, this requires algorithms to efficiently generate random positions from 3D density distributions. We describe the design of a flexible suite of components for the Monte Carlo radiative transfer code SKIRT. The design is based on a combination of basic building blocks (which can be either analytical toy models or numerical models defined on grids or a set of particles) and the extensive use of decorators that combine and alter these building blocks to more complex structures. For a number of decorators, e.g. those that add spiral structure or clumpiness, we provide a detailed description of the algorithms that can be used to generate random positions. Advantages of this decorator-based design include code transparency, the avoidance of code duplication, and an increase in code maintainability. Moreover, since decorators can be chained without problems, very complex models can easily be constructed out of simple building blocks. Finally, based on a number of test simulations, we demonstrate that our design using customised random position generators is superior to a simpler design based on a generic black-box random position generator.Comment: 15 pages, 4 figures, accepted for publication in Astronomy and Computin

CHARACTERIZING DROUGHT ADAPTATIONS, PHENOTYPIC PLASTICITY, AND FIXED GENE EXPRESSION PATTERNS WITHIN QUERCUS

This dissertation was written on topics related to the genus Quercus with a primary focus on Quercus ellipsoidalis (northern pin oak) and Quercus rubra (northern red oak). Within this dissertation are chapters related to the setup of experimental common gardens within the Ford and Kellogg experimental forest, a literature review describing drought adaptations of Quercus sect. Lobatae (red oak group), identification of transcription factors within the Q. robur (English oak) and Q. rubra genomes, a study comparing leaf trait phenotypic plasticity of Q. ellipsoidalis and Q. rubra, and an RNA-seq experiment studying ecological speciation between Q. ellipsoidalis and Q. rubra. Within these studies, I found that Q. ellipsoidalis and Q. rubra have similar leaf trait phenotypic plasticity, and unique molecular phenotypes related to upregulation of genes related to photosynthesis and innate immune response, respectively. Within the Q. rubra genome, I identified multiple regions of transcription factor gene clusters that could have a significant role related to drought adaptation for this species

Analysis of NOMA-Based Retransmission Schemes for Factory Automation Applications

New use cases and applications in factory automation scenarios impose demanding requirements for traditional industrial communications. In particular, latency and reliability are considered as some of the most representative Key Performance Indicators (KPI) that limit the technological choices addressing wireless communications. Indeed, there is a considerable research effort ongoing in the area of wireless systems, not only from academia, but also from companies, towards novel solutions that fit Industry 4.0 KPIs. A major limitation for traditional wireless architectures is related to the harsh nature of the industrial propagation channel. Accordingly, this paper addresses these challenges by studying the reliability and latency performance of the joint use of different retransmission schemes in combination with Non-Orthogonal Multiple Access (NOMA) techniques. Two general retransmission schemes have been tested: time-based and spatial diversity-based retransmissions. An adaptive injection level NOMA solution has been combined with the retransmission schemes to improve the reliability of critical information. In all cases, a particular set of simulations has been carried out varying the main parameters, such as modulation, code rate and the injection level. Moreover, the impact of the number of transmitters in relation to the communication reliability has been analyzed. Results show that spatial diversity-based retransmissions overcome considerably the reliability obtained with time-domain retransmissions while maintaining assumable latency ratesThis work was supported in part by the Basque Government under Grant IT1234-19, in part by the PREDOC under Grant PRE_2020_2_0105, and in part by the Spanish Government through project PHANTOM (MCIU/AEI/FEDER, UE) under Grant RTI2018-099162-B-I0

Using Rollback Avoidance to Mitigate Failures in Next-Generation Extreme-Scale Systems

High-performance computing (HPC) systems enable scientists to numerically model complex phenomena in many important physical systems. The next major milestone in the development of HPC systems is the construction of the first supercomputer capable executing more than an exaflop, 10^18 floating point operations per second. On systems of this scale, failures will occur much more frequently than on current systems. As a result, resilience is a key obstacle to building next-generation extreme-scale systems. Coordinated checkpointing is currently the most widely-used mechanism for handling failures on HPC systems. Although coordinated checkpointing remains effective on current systems, increasing the scale of today\u27s systems to build next-generation systems will increase the cost of fault tolerance as more and more time is taken away from the application to protect against or recover from failure. Rollback avoidance techniques seek to mitigate the cost of checkpoint/restart by allowing an application to continue its execution rather than rolling back to an earlier checkpoint when failures occur. These techniques include failure prediction and preventive migration, replicated computation, fault-tolerant algorithms, and software-based memory fault correction. In this thesis, I examine how rollback avoidance techniques can be used to address failures on extreme-scale systems. Using a combination of analytic modeling and simulation, I evaluate the potential impact of rollback avoidance on these systems. I then present a novel rollback avoidance technique that exploits similarities in application memory. Finally, I examine the feasibility of using this technique to protect against memory faults in kernel memory

Safe Intelligent Driver Assistance System in V2X Communication Environments based on IoT

In the modern world, power and speed of cars have increased steadily, as traffic continued to increase. At the same time highway-related fatalities and injuries due to road incidents are constantly growing and safety problems come first. Therefore, the development of Driver Assistance Systems (DAS) has become a major issue. Numerous innovations, systems and technologies have been developed in order to improve road transportation and safety. Modern computer vision algorithms enable cars to understand the road environment with low miss rates. A number of Intelligent Transportation Systems (ITSs), Vehicle Ad-Hoc Networks (VANETs) have been applied in the different cities over the world. Recently, a new global paradigm, known as the Internet of Things (IoT) brings new idea to update the existing solutions. Vehicle-to-Infrastructure communication based on IoT technologies would be a next step in intelligent transportation for the future Internet-of-Vehicles (IoV). The overall purpose of this research was to come up with a scalable IoT solution for driver assistance, which allows to combine safety relevant information for a driver from different types of in-vehicle sensors, in-vehicle DAS, vehicle networks and driver`s gadgets. This study brushed up on the evolution and state-of-the-art of Vehicle Systems. Existing ITSs, VANETs and DASs were evaluated in the research. The study proposed a design approach for the future development of transport systems applying IoT paradigm to the transport safety applications in order to enable driver assistance become part of Internet of Vehicles (IoV). The research proposed the architecture of the Safe Intelligent DAS (SiDAS) based on IoT V2X communications in order to combine different types of data from different available devices and vehicle systems. The research proposed IoT ARM structure for SiDAS, data flow diagrams, protocols. The study proposes several IoT system structures for the vehicle-pedestrian and vehicle-vehicle collision prediction as case studies for the flexible SiDAS framework architecture. The research has demonstrated the significant increase in driver situation awareness by using IoT SiDAS, especially in NLOS conditions. Moreover, the time analysis, taking into account IoT, Cloud, LTE and DSRS latency, has been provided for different collision scenarios, in order to evaluate the overall system latency and ensure applicability for real-time driver emergency notification. Experimental results demonstrate that the proposed SiDAS improves traffic safety