Competitive Trade-off Research Articles

Competitive Tradeoff between Zn Deposition and Hydrogen Evolution Reaction on Zn-Metal Anode

Competitive Tradeoff between Zn Deposition and Hydrogen Evolution Reaction on Zn-Metal Anode

Dynamic MDETR: A Dynamic Multimodal Transformer Decoder for Visual Grounding.

Multimodal transformer exhibits high capacity and flexibility to align image and text for visual grounding. However, the existing encoder-only grounding framework (e.g., TransVG) suffers from heavy computation due to the self-attention operation with quadratic time complexity. To address this issue, we present a new multimodal transformer architecture, coined as Dynamic Mutilmodal detection transformer (DETR) (Dynamic MDETR), by decoupling the whole grounding process into encoding and decoding phases. The key observation is that there exists high spatial redundancy in images. Thus, we devise a new dynamic multimodal transformer decoder by exploiting this sparsity prior to speed up the visual grounding process. Specifically, our dynamic decoder is composed of a 2D adaptive sampling module and a text guided decoding module. The sampling module aims to select these informative patches by predicting the offsets with respect to a reference point, while the decoding module works for extracting the grounded object information by performing cross attention between image features and text features. These two modules are stacked alternatively to gradually bridge the modality gap and iteratively refine the reference point of grounded object, eventually realizing the objective of visual grounding. Extensive experiments on five benchmarks demonstrate that our proposed Dynamic MDETR achieves competitive trade-offs between computation and accuracy. Notably, using only 9% feature points in the decoder, we can reduce ∼44% GFLOPs of the multimodal transformer, but still get higher accuracy than the encoder-only counterpart. With the same number of encoder layers as TransVG, our Dynamic MDETR (ResNet-50) outperforms TransVG (ResNet-101) but only brings marginal extra computational cost relative to TransVG. In addition, to verify its generalization ability and scale up our Dynamic MDETR, we build the first one-stage CLIP empowered visual grounding framework, and achieve the state-of-the-art performance on these benchmarks.

CoCo-trie: Data-aware compression and indexing of strings

We address the problem of compressing and indexing a sorted dictionary of strings to support efficient lookups and more sophisticated operations, such as prefix, predecessor, and range searches. This problem occurs as a key task in a plethora of applications, and thus it has been deeply investigated in the literature since the introduction of tries in the ’60s.We introduce a new data structure, called the COmpressed COllapsed Trie (CoCo-trie), that hinges on a pool of techniques to compress subtries (of arbitrary depth) into succinctly-encoded and efficiently-searchable trie macro-nodes with a possibly large fan-out. Then, we observe that the choice of the subtries to compress depends on the trie structure and its edge labels. Hence, we develop a data-aware optimisation approach that selects the best subtries to compress via the above pool of succinct encodings, with the overall goal of minimising the total space occupancy and still achieving efficient query time. We also investigate some variants of this approach that induce interesting space–time trade-offs in the CoCo-trie design.Our experimental evaluation on six diverse and large datasets (representing URLs, XML data, DNA and protein sequences, database records, and search-engine dictionaries) shows that the space–time performance of well-established and highly-engineered data structures solving this problem is very input-sensitive. Conversely, our CoCo-trie provides a robust and uniform improvement over all competitors for half of the datasets, and it results on the Pareto space–time frontier for the others, thus offering new competitive trade-offs.

Group size affects predation risk and foraging success in Pacific salmon at sea.

Grouping is ubiquitous across animal taxa and environments. Safety in numbers is perhaps the most cited reason for grouping, yet this fundamental tenet of ecological theory has rarely been tested in wild populations. We analyzed a multidecadal dataset of Pacific salmon at sea and found that individuals in larger groups had lower predation risk; within groups of fish, size outliers (relatively small and large fish) had increased predation risk. For some species, grouping decreased foraging success, whereas for other species, grouping increased foraging success, indicating that safety competition trade-offs differed among species. These results indicate that survival and growth depend on group size; understanding the relationship between group size distributions and population size may be critical to unraveling ecology and population dynamics for marine fishes.

Bayesian inference based on a bivariate gamma distribution of Kibble for low-level radioactivity detection in nuclear decommissioning operations

Statistical test analysis has proven itself to be versatile tool in various scientific and technical fields, following either a frequentist approach based on a p−value, or a Bayesian approach evaluating a Bayes factor. In this study, the authors adapted a Bayesian approach to a radiation-detection application in the industrial context of nuclear decommissioning. The detection of a weak uranium signal on concrete, under the constraint of a very low signal-to-noise ratio, represents in particular a major challenge in this application area. For this purpose, we developed an original Bayesian statistical hypothesis test based on a bivariate gamma distribution of Kibble. Said test allows merging the absolute and relative characters of two Bayesian tests developed in the same context, as well as providing better performance tradeoff in both cases of stationary and non-stationary radiological backgrounds. The simulation-based study showed that the proposed Bayesian test should meet the abovementioned expectations, and allow the detection of a relatively low surface activity uranium contamination, while ensuring a competitive tradeoff between statistical sensitivity and specificity.

Assessment of agricultural land suitability using GIS and fuzzy analytical hierarchy process approach in Ranchi District, India

The land is a vital source of agriculture. Agricultural land is utilized for the purpose of cropping. Delineation of potential agricultural land is pertinent when population pressure incrementally increases and a competitive trade-off between different land uses. The present study is conducted to evaluate the agrarian land suitability in the Ranchi district (Plateau region in India) using fuzzy analytical hierarchy process (FAHP). A land suitability evaluation technique is designed, combining nineteen thematic layers from five categories: climatic parameters, topographic features, soil characteristics, euclidean distance, and land elements. All thematic layers were combined into a single layer using the FAHP model to create agricultural land suitability map in a GIS environment. Our analysis demonstrates that 11.17% of the total area (509700 ha) is highly suitable, while about 20.73% and 13.81% of the study area is moderately suitable and marginally suitable for agricultural activities. The study also reveals that approximately 54.29% of the lands are not suitable for agriculture. Our findings provide additional information, and local governments can use the land suitability map to manage and develop agricultural resources. The study contributes to the studies on agricultural suitability by bringing in multiple variables centring on the FAHP method, which can be replicated in studies on plateau regions across the globe.

Multi-objective risk analysis for flood control operation of a complex reservoir system under multiple time-space correlated uncertainties

Many uncertainties are involved in flood control operation of a multi-reservoir system and result in risks for the system. Risk analysis for reservoir flood control operation is essential for decision making. Traditional flood control risk analysis only takes flood forecast error as the main risk source, ignoring the influence of dynamic control of flood-limited water level on reservoir initial water level for flood regulation. In this study, the spatial correlation of reservoir initial water level errors and the spatiotemporal correlation of flood forecast errors are identified via copula function. A risk analysis model considering both upstream and downstream is established for multi-objective flood control operation of a complex reservoir system, coupling multi-dimensional uncertainties of reservoir initial water levels, flood forecast errors, reservoir capacity curve errors, reservoir discharge curve errors and river flood routing errors. The impact of multiple risk sources with spatiotemporal correlations on reservoir flood control operation is then evaluated and the competitive tradeoff between flood control risks in upstream and downstream analyzed. The model is applied to a mixed four-reservoir system in Pi River Basin in China. Results indicated that 1) there is a strong correlation between the reservoir initial water level errors in space and also between the flood forecast errors in space and time, which can be effectively described by copula function, and ignoring the correlations will underestimate the flood control risk; 2) the two objective values of upstream and downstream show a clear competitive relationship, and the solution that prefers one objective has low risk marginal benefit, thus choosing a compromise solution can balance risks; 3) coupling the initial water level uncertainty reduces the flood control risk caused by the flood forecast errors to a certain extent, but also lead to the increase of extreme risk loss.

Spatio-Temporal Collaborative Module for Efficient Action Recognition.

Efficient action recognition aims to classify a video clip into a specific action category with a low computational cost. It is challenging since the integrated spatial-temporal calculation (e. g., 3D convolution) introduces intensive operations and increases complexity. This paper explores the feasibility of the integration of channel splitting and filter decoupling for efficient architecture design and feature refinement by proposing a novel spatio-temporal collaborative (STC) module. STC splits the video feature channels into two groups and separately learns spatio-temporal representations in parallel with decoupled convolutional operators. Particularly, STC consists of two computation-efficient blocks, i.e., [Formula: see text] and [Formula: see text], where they extract either spatial ( S· ) or temporal ( T· ) features and further refine their features with either temporal ( ·T ) or spatial ( ·S ) contexts globally. The spatial/temporal context refers to information dynamics aggregated from temporal/spatial axis. To thoroughly examine our method's performance in video action recognition tasks, we conduct extensive experiments using five video benchmark datasets requiring temporal reasoning. Experimental results show that the proposed STC networks achieve a competitive trade-off between model efficiency and effectiveness.

Rewilding with invertebrates and microbes to restore ecosystems: Present trends and future directions.

Restoration ecology has historically focused on reconstructing communities of highly visible taxa while less visible taxa, such as invertebrates and microbes, are ignored. This is problematic as invertebrates and microbes make up the vast bulk of biodiversity and drive many key ecosystem processes, yet they are rarely actively reintroduced following restoration, potentially limiting ecosystem function and biodiversity in these areas.In this review, we discuss the current (limited) incorporation of invertebrates and microbes in restoration and rewilding projects. We argue that these groups should be actively rewilded during restoration to improve biodiversity, ecosystem function outcomes, and highlight how they can be used to greater effect in the future. For example, invertebrates and microbes are easily manipulated, meaning whole communities can potentially be rewilded through habitat transplants in a practice that we refer to as “whole‐of‐community” rewilding.We provide a framework for whole‐of‐community rewilding and describe empirical case studies as practical applications of this under‐researched restoration tool that land managers can use to improve restoration outcomes.We hope this new perspective on whole‐of‐community restoration will promote applied research into restoration that incorporates all biota, irrespective of size, while also enabling a better understanding of fundamental ecological theory, such as colonization and competition trade‐offs. This may be a necessary consideration as invertebrates that are important in providing ecosystem services are declining globally; targeting invertebrate communities during restoration may be crucial in stemming this decline.

Stacked Pyramid Attention Network for Object Detection

Scale variation is one of the primary challenges in object detection. Recently, different strategies have been introduced to address this challenge, achieving promising performance. However, limitations still exist in these detectors. On the one hand, as for the large-scale deep layers, the localizing power of the features is relatively low. On the other hand, as for the small-scale shallow layers, the categorizing ability of the features is relatively weak. Actually, the limitations are self-solving, as the above two aspects can be mutually beneficial to each other. Therefore, we propose the Stacked Pyramid Attention Network (SPANet) to bridge the gap between different scales. In SPANet, two lightweight modules, i.e. top-down feature map attention module (TDFAM) and bottom-up feature map attention module (BUFAM), are designed. Via learning the channel attention and spatial attention, each module effectively builds connections between features from adjacent scales. By progressively integrating BUFAM and TDFAM into two encoder–decoder structures, two novel feature aggregating branches are built. In this way, the branches fully complement the localizing power from small-scale features and the categorizing power from large-scale features, therefore enhancing the detection accuracy while keeping lightweight. Extensive experiments on two challenging benchmarks (PASCAL VOC and MS COCO datasets) demonstrate the effectiveness of our SPANet, showing that our model reaches a competitive trade-off between accuracy and speed.

Grass recruitment constraints along a grazing gradient in Patagonia: N-limitation x adult competition trade-off

We explored the effect of N addition and adult canopy competition on the recruitment of lost preferred species in the Patagonian steppe, where a decelerated N cycling scenario (sensu Ritchie et al., 1998) had been previously detected. In the first experiment we demonstrated that the preferred scarce grass Bromus pictus has typical traits of Competitive species, while the dominant less preferred Pappostipa speciosa has typical traits of Stress tolerant species (sensu Grime, 1997): B. pictus showed higher maximum Relative Growth Rate, water consumption and competitive ability than P. speciosa. In the second experiment, we proved that without N addition, seedlings of the preferred species showed its maximum survival rate under moderate grazing, where adult competition is lower than under ungrazed conditions and N-limitation is lower than under intense grazing. Instead, with N addition, the survival rate of B. pictus was maximum under intense grazing, where N limitation is maximum and adult competition is minimum. These results suggest that in degraded systems subjected to a decelerating N cycling scenario, the revegetation with preferred species would require recovering soil N levels, and would be more successful in the most degraded sites, where the remnant canopy has low cover.

Marching to Different Drum Beats: A Temporal Perspective on Coordinating Occupational Work

In this paper, we contribute a temporal perspective on work coordination across collaborating occupations. Drawing on an ethnographic study of medical specialists—surgeons, pathologists, oncologists, and radiologists—we examine how their temporal orientations are shaped through the temporal structuring of occupational work. Our findings show that temporal structuring of occupational practices develop in relation to the contingencies and materialities of their work and that this shapes and is shaped by specialists’ temporal orientations. Further, we show that differences in occupations’ temporal orientations have important implications for coordinating work. More specifically, our study reveals how the domination of one temporal orientation can lead to recurrent strain, promoting a competitive trade-off between the different temporal orientations in guiding interaction. This temporal orientation domination is accompanied by a persistent emotional strain and potential conflict. Finally, we suggest that, alternatively, different temporal orientations can be resourced in solving coordination challenges through three interrelated mechanisms, namely juxtaposing, temporal working, and mutual adjusting. In so doing, we show how temporal resourcing can be productive in coordinating work.

Multi-Objective Squirrel Search Algorithm for Multi-Area Economic Environmental Dispatch With Multiple Fuels and Valve Point Effects

The essential goal of multi-area economic environmental dispatch (MAEED) is to determine the optimum power generation schedule of each unit and power transfer between the areas in order to minimize fuel costs and pollutant emissions, when the generation, power balance and tie-line limits are satisfied. This paper focuses on developing multi-objective squirrel search algorithm (MOSSA) to solve the MAEED problem, of which the goal is to simultaneously minimize the total fuel cost and emission considering valve point effects and multi-fuel options. The proposed MOSSA combines squirrel search algorithm along with Pareto-dominance theory to generate non-dominated solutions. It uses an external elitist depository mechanism with crowding distance sorting to preserve the distribution diversity of Pareto-optimal solutions as the evolution continues. In addition, a fuzzy decision maker is used to select the best compromised solution from the obtained Pareto frontiers. Furthermore, the MAEED problem is unraveled by squirrel search algorithm based weighted sum approach with price penalty factors, artificial bee colony and exchange market algorithm. Different case studies are performed on 10-unit with three-area system, 40-unit with four-area system and 140-unit real Korean power system considering valve point effects and multi-fuel options which testify the supremacy of the suggested approach. The comparisons with state-of-the-art approaches suggest that MOSSA can generate more competitive trade-off solutions for solving the MAEED problems.

A Comprehensive Modeling Analysis of Formate-Mediated Microbial Electrosynthesis*.

Mediated microbial electrosynthesis (MES) represents a promising strategy for the capture and conversion of CO2 into carbon-based products. We describe the development and application of a comprehensive multiphysics model to analyze a formate-mediated MES reactor. The model shows that this system can achieve a biomass productivity of ∼1.7 g L-1 h-1 but is limited by a competitive trade-off between O2 gas/liquid mass transfer and CO2 transport to the cathode. Synthetic metabolic strategies are evaluated for formatotrophic growth, which can enable an energy efficiency of ∼21 %, a 30 % improvement over the Calvin cycle. However, carbon utilization efficiency is only ∼10 % in the best cases due to a futile CO2 cycle, so gas recycling will be necessary for greater efficiency. Finally, separating electrochemical and microbial processes into separate reactors enables a higher biomass productivity of ∼2.4 g L-1 h-1 . The mediated MES model and analysis presented here can guide process design for conversion of CO2 into renewable chemical feedstocks.

Multi-objective Billiards-Inspired Optimization Algorithm for Construction Management Problems

This paper introduces an efficient multi-objective algorithm for handling multi-objective problems. The main genesis of this metaheuristic is the game of billiards and the physical background of the collision between balls, which govern its process. Due to reinforcing this algorithm for handling multi-objective problems, an external repository for storing non-dominated solutions, the maximin strategy for grouping balls, and a roulette-wheel selection mechanism for choosing pockets from the repository are employed. The proposed multi-objective billiards-inspired optimization algorithm (MOBOA) performance is verified using ten unconstrained multi-objective mathematical benchmark functions, including bi-objective and tri-objective cases with different characteristics. The application of this algorithm is examined in three constrained construction project management problems management including construction site layout planning, building materials design, and project scheduling. The results show that MOBOA is able to provide competitive trade-off solutions with acceptable convergence and uniform diversity.

Hybrid strategies using linear and piecewise-linear decision rules for multistage adaptive linear optimization

Decision rules offer a rich and tractable framework for solving certain classes of multistage adaptive optimization problems. Recent literature has shown the promise of using linear and nonlinear decision rules in which wait-and-see decisions are represented as functions, whose parameters are decision variables to be optimized, of the underlying uncertain parameters. Despite this growing success, solving real-world stochastic optimization problems can become computationally prohibitive when using nonlinear decision rules, and in some cases, linear ones. Consequently, decision rules that offer a competitive trade-off between solution quality and computational time become more attractive. Whereas the extant research has always used homogeneous (i.e., either linear or piecewise-linear) decision rules, the major contribution of this paper is a computational exploration of hybrid decision rules combining the benefits of the two classes of decision rules. We first verify empirically that having higher uncertainty resolution or more linear pieces in early stages is more significant than having it in late stages in terms of solution quality. Then, we compare non-increasing and non-decreasing (i.e., higher uncertainty resolution in early and late stages, respectively) hybrid decision rules in a computational study to illustrate the trade-off between solution quality and computational cost. We also demonstrate a case where, unexpectedly, a linear decision rule is superior to a more complex piecewise-linear decision rule within a simulator. This observation bolsters the need to assess the quality of decision rules obtained from a look-ahead model within a simulator rather than just using the optimal look-ahead objective function value.

End‐to‐end learning interpolation for object tracking in low frame‐rate video

In many scenarios, where videos are transmitted through bandwidth-limited channels for subsequent semantic analytics, the choice of frame rates has to balance between bandwidth constraints and analytics performance. Faced with this practical challenge, this study focuses on enhancing object tracking at low frame rates and proposes a learning Interpolation for tracking framework. This framework embeds an implicit video frame interpolation sub-network, which is concatenated and jointly trained with another object tracking sub-network. Once a low frame-rate video is an input, it is first mapped into a high frame-rate latent video, based on which the tracker is learned. Novel strategies and loss functions are derived to ensure the effective end-to-end optimisation of the authors’ network. On several challenging benchmarks and settings, their method achieves a highly competitive tradeoff between frame rate and tracking accuracy. As is known, the implications of interpolation on semantic video analytics and tracking remain unexplored, and the authors expect their method to find many applications in mobile embedded vision, Internet of Things and edge computing.

Trait plasticity alters the range of possible coexistence conditions in a competition–colonisation trade‐off

Most of the classical theory on species coexistence has been based on species-level competitive trade-offs. However, it is becoming apparent that plant species display high levels of trait plasticity. The implications of this plasticity are almost completely unknown for most coexistence theory. Here, we model a competition-colonisation trade-off and incorporate trait plasticity to evaluate its effects on coexistence. Our simulations show that the classic competition-colonisation trade-off is highly sensitive to environmental circumstances, and coexistence only occurs in narrow ranges of conditions. The inclusion of plasticity, which allows shifts in competitive hierarchies across the landscape, leads to coexistence across a much broader range of competitive and environmental conditions including disturbance levels, the magnitude of competitive differences between species, and landscape spatial patterning. Plasticity also increases the number of species that persist in simulations of multispecies assemblages. Plasticity may generally increase the robustness of coexistence mechanisms and be an important component of scaling coexistence theory to higher diversity communities.

The Energy-Aware Matrix Completion-Based Data Gathering Scheme for Wireless Sensor Networks

In the Wireless Sensor Networks (WSNs), ensuring long-term survival of the sensor devices is crucial, especially for non-energy harvesting networks where the sensors have to deal with the available limited power. Thus, there is a huge need to efficiently select, in each time-slot, a small set of source nodes to monitor the network area and deliver their data to the sink. Note that there is a trade-off between energy efficiency, achieved through data-compression, and the informative quality received by the sink. Moreover, although applying a high data compression ratio extremely reduces the overall network energy consumption, the network lifetime is not necessarily extended due to the uneven energy depletion of the nodes' batteries. To this end, in this paper, we propose the Energy-Aware Matrix Completion based data gathering approach (EAMC), which designates the active nodes according to their residual energy levels. To collect data readings, the proposed EAMC relies on a nodes clustering phase and a MC based data sampling. Then, the interpolation of all the missing data is performed by the sink thanks to a Three-stage MC based recovery framework. Since we are interested in high data loss scenarios, the limited amount of delivered data must be sufficient in terms of informative quality it holds in order to reach a satisfactory recovery accuracy for the entire data. Hence, the EAMC selects the nodes depending on their inter-correlation as well as the network energy efficiency, with the use of a combined energy-aware and correlation-based metric. This introduced active node cost function changes with the type of application one wants to perform with the intention to reach a longer lifespan for the network. Therewith, the numerical results show that the EAMC achieves an attractive and competitive trade-off between the data reconstruction quality and the network lifetime for all the investigated scenarios.

Functional traits and phenotypic plasticity modulate species coexistence across contrasting climatic conditions

Functional traits are expected to modulate plant competitive dynamics. However, how traits and their plasticity in response to contrasting environments connect with the mechanisms determining species coexistence remains poorly understood. Here, we couple field experiments under two contrasting climatic conditions to a plant population model describing competitive dynamics between 10 annual plant species in order to evaluate how 19 functional traits, covering physiological, morphological and reproductive characteristics, are associated with species’ niche and fitness differences. We find a rich diversity of univariate and multidimensional associations, which highlight the primary role of traits related to water- and light-use-efficiency for modulating the determinants of competitive outcomes. Importantly, such traits and their plasticity promote species coexistence across climatic conditions by enhancing stabilizing niche differences and by generating competitive trade-offs between species. Our study represents a significant advance showing how leading dimensions of plant function connect to the mechanisms determining the maintenance of biodiversity.