An effective hybrid search mode for multi-objective optimization with constraints

In this paper a new search strategy for multi-objective optimization (MOO) with constraints is proposed based on a hybrid search mode (HSM). The search processes for feasible solutions and optimal solutions are executed in a mixed way for the existing methods. With regard to HSM, a hybrid search mode is proposed, which consists of two processes: Feasibility search mode (FSM) and optimal search mode (OSM). The executions of these two search modes are independent relatively and also adjusted according to the population distribution. In the early stage, FSM plays the leading role for exploring the feasible space since most of the individuals are infeasible. With the increase of the feasible individuals, OSM is the primary operation for the search of optimal individuals. The proposed method is simple to implement and need few extra parameter tuning. The handing method of constraints is tested on several multi-objective optimization problems with constraints. The remarkable results demonstrate its effectiveness and good performance.

MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification

RGB–IR cross modality person re-identification (RGB–IR Re-ID) is an important task for video surveillance in poorly illuminated or dark environments. In addition to the common challenge of Re-ID, the large cross-modality variations between RGB and IR images must be considered. The existing RGB–IR Re-ID methods use different network structures to learn the global shared features associated with multi-modalities. However, most global shared feature learning methods are sensitive to background clutter, and contextual feature relationships are not considered among the mined features. To solve these problems, this paper proposes a dual-path attention network architecture MFCNet. SGA (Spatial-Global Attention) module embedded in MFCNet includes spatial attention and global attention branches to mine discriminative features. First, the SGA module proposed in this paper focuses on the key parts of the input image to obtain robust features. Next, the module mines the contextual relationships among features to obtain discriminative features and improve network performance. Finally, extensive experiments demonstrate that the performance of the network architecture proposed in this paper is better than that of state-of-the-art methods under various settings. In the all-search mode of the SYSU and RegDB data sets, the rank-1 accuracy reaches 51.64% and 69.76%, respectively.

Intravascular Crawling of Patrolling Monocytes: A Lèvy-Like Motility for Unique Search Functions?

Patrolling monocytes (PMo) are the organism’s preeminent intravascular guardians by their continuous search of damaged endothelial cells and harmful microparticles for their removal and to restore homeostasis. This surveillance is accomplished by PMo crawling on the apical side of the endothelium through regulated interactions of integrins and chemokine receptors with their endothelial ligands. We propose that the search mode governs the intravascular motility of PMo in vivo in a similar way to T cells looking for antigen in tissues. Signs of damage to the luminal side of the endothelium (local death, oxidized LDL, amyloid deposits, tumor cells, pathogens, abnormal red cells, etc.) will change the diffusive random towards a Lèvy-like crawling enhancing their recognition and clearance by PMo damage receptors as the integrin αMβ2 and CD36. This new perspective can help identify new actors to promote unique PMo intravascular actions aimed at maintaining endothelial fitness and combating harmful microparticles involved in diseases as lung metastasis, Alzheimer’s angiopathy, vaso-occlusive disorders, and sepsis.

Personalization of Search Results Representation of a Digital Library

The process of discovering appropriate resources in digital libraries within universities is important, as it can have a big effect on whether retrieved works are useful to the requester. The improvement of the user experience with the digital library of the University of Novi Sad dissertations (PHD UNS) through the personalization of search results representation is the aim of the research presented in this paper. There are three groups of PHD UNS digital library users: users from the academic community, users outside the academic community, and librarians who are in charge of entering dissertation data. Different types of textual and visual representations were analyzed, and representations which needed to be implemented for the groups of users of PHD UNS digital library were selected. After implementing these representations and putting them into operation in April 2017, the user interface was extended with functionality that allows users to select their desired style for representing search results using an additional module for storing message logs. The stored messages represent an explicit change in the results representation by individual users. Using these message logs and ELK technology stack, we analyzed user behavior patterns depending on the type of query, type of device, and search mode. The analysis has shown that the majority of users of the PHD UNS system prefer using the textual style of representation rather than the visual. Some users have changed the style of results representation several times and it is assumed that different types of information require a different representation style. Also, it has been established that the most frequent change to the visual results representation occurs after users perform a query which shows all the dissertations from a certain time period and which is taken from the advanced search mode; however, there is no correlation between this change and the client’s device used.

Distractor suppression leads to reduced flanker interference

Recent studies using the additional singleton paradigm have shown that regularities in distractor locations can cause biases in the spatial priority map, such that attentional capture by salient singletons is reduced for locations that are likely to contain distractors. It has been suggested that this type of suppression is proactive (i.e., occurring before display onset). The current study replicated the original findings using an online version of the task. To further assess the suppression of high-probability locations, we employed a congruence manipulation similar to the traditional flanker effect, where distractors could be either congruent or incongruent with the response to the target. Experiment 1 shows that through statistical learning distractor suppression reduces the interference from incongruent distractors, as participants made less errors in high-probability versus low-probability conditions. In Experiment 2, participants were forced to search for a specific target feature (the so-called feature-search mode), which is assumed to allow participants to ignore distractors in a top-down manner. Yet even when this “top-down” search mode was employed, there was still a congruence effect when the distractor singleton was presented at the low-probability but not at the high-probability location. The absence, but not reversal, of a congruence effect at the high-probability location also further indicates that this distractor suppression mechanism is proactive. The results indicate that regardless of the search mode used, there is suppression of the high-probability location indicating that this location competes less for attention within the spatial priority map than all other locations.

Robot global path planning using PSO algorithm based on the interaction mechanism between leaders and individuals

To overcome the disadvantages of low optimization accuracy and prematurity of the canonical PSO algorithm, we proposed an improved particle swarm optimization based on the interaction mechanism between leaders and individuals (PSO-IBLI), and used it to implement robot global path planning. In the PSO-IBLI algorithm, in different stages, each particle learns from the elites according to different regular. Moreover, the improved algorithm divides the execution state into two categories, where the parameters and the evaluation mechanisms are varied accordingly. In this way, the global best particles no longer walk randomly and have more learning objects. At the same time, other particles learn from not only the global best position, their historical best positions, but also the other elites. The learning strategy makes the search mode always in the adaptive adjustment, and it improves the speed of convergence and promotes this algorithm to find a more precise solution. The experimental results suggest that the precision and convergence speed of the PSO-IBLI algorithm is higher than the other three different algorithms. Additionally, some experiments are carried out to plan the robot’s entire collision-free path using the PSO-IBLI algorithm and the other three algorithms. The results show that the PSO-IBLI algorithm can obtain the shortest collision-free way in four algorithms.

Influences of luminance contrast and ambient lighting on visual context learning and retrieval

Invariant spatial context can guide attention and facilitate visual search, an effect referred to as “contextual cueing.” Most previous studies on contextual cueing were conducted under conditions of photopic vision and high search item to background luminance contrast, leaving open the question whether the learning and/or retrieval of context cues depends on luminance contrast and ambient lighting. Given this, we conducted three experiments (each contains two subexperiments) to compare contextual cueing under different combinations of luminance contrast (high/low) and ambient lighting (photopic/mesopic). With high-contrast displays, we found robust contextual cueing in both photopic and mesopic environments, but the acquired contextual cueing could not be transferred when the display contrast changed from high to low in the photopic environment. By contrast, with low-contrast displays, contextual facilitation manifested only in mesopic vision, and the acquired cues remained effective following a switch to high-contrast displays. This pattern suggests that, with low display contrast, contextual cueing benefited from a more global search mode, aided by the activation of the peripheral rod system in mesopic vision, but was impeded by a more local, fovea-centered search mode in photopic vision.

Visual Search Within a Limited Window Area: Scrolling Versus Moving Window

Every day we perceive pictures on our mobile phones and scroll through images within a limited space. At present, however, visual perception via image scrolling is not well understood. This study investigated the nature of visual perception within a small window frame. It compared visual search efficiency using three modes: scrolling, moving-window, and free-viewing. The item number and stimulus size varied. Results showed variations in search efficiency depending on search mode. The slowest search occurred under the scrolling condition, followed by the moving-window condition, and the fastest search occurred under the no-window condition. For the scrolling condition, the response time increased the least sharply in proportion to item number but most sharply in proportion to the stimulus size compared to the other two conditions. Analysis of the trace of scan revealed frequent pauses interjected with small and fast stimulus shifts for the scrolling condition, but slow and continuous window movements interjected with a few pauses for the moving-window condition. We concluded that searching via scrolling was less efficient than searching via a moving-window, reflecting differences in dynamic properties of participants’ scan.

Incorporating Reverse Search for Friend Recommendation with Random Walk

Recommending friends is an important mechanism for social networks to enhance their vitality and attractions to users. The huge user base as well as the sparse user relationships give great challenges to propose friends on social networks. Random walk is a classic strategy for recommendations, which provides a feasible solution for the above challenges. However, most of the existing recommendation methods based on random walk are only weighing the forward search, which ignore the significance of reverse social relationships. In this paper, we proposed a method to recommend friends by integrating reverse search into random walk. First, we introduced the FP-Growth algorithm to construct both web graphs of social networks and their corresponding transition probability matrix. Second, we defined the reverse search strategy to include the reverse social influences and to collaborate with random walk for recommending friends. The proposed model both optimized the transition probability matrix and improved the search mode to provide better recommendation performance. Experimental results on real datasets showed that the proposed method performs better than the naive random walk method which considered the forward search mode only.