Unpredictable Target Research Articles

A Multi Moving Target Localization in Agricultural Farmlands by Employing Optimized Cooperative Unmanned Aerial Vehicle Swarm

The paper proposes an original method for employing optimised cooperative swarms of Unmanned Aerial Vehicles (UAVs) to localise multiple moving objects in agricultural farmlands. Crop Monitoring (CM), targeted fertilizer distribution, and Livestock Management (LM) are some of the Smart Farming (SF) applications of UAVs. However, the ever-changing nature of agricultural settings makes it challenging to set up UAV swarms. Detecting multiple evolving objectives in dynamic environments is complicated, and conventional methods are regularly optimized for single objectives, such as area or reduced Energy Consumption (EC), which is unsuitable. This research recommends a Multi-Objective Evolutionary Algorithm (MOEA) as a model for UAV swarms to balance task service, communication, and EC during the investigation. The approach paves the method for innovation in the agricultural sector by optimizing tasks in real-time, addressing unpredictable targets, boosting productivity, and reducing costs. The study’s findings present optimism for smart farm management and accurate SF by improving UAV systems’ response time and scalability.

Early parafoveal semantic integration in natural reading.

Humans can read and comprehend text rapidly, implying that readers might process multiple words per fixation. However, the extent to which parafoveal words are previewed and integrated into the evolving sentence context remains disputed. We investigated parafoveal processing during natural reading by recording brain activity and eye movements using MEG and an eye tracker while participants silently read one-line sentences. The sentences contained an unpredictable target word that was either congruent or incongruent with the sentence context. To measure parafoveal processing, we flickered the target words at 60 Hz and measured the resulting brain responses (i.e. Rapid Invisible Frequency Tagging, RIFT) during fixations on the pre-target words. Our results revealed a significantly weaker tagging response for target words that were incongruent with the previous context compared to congruent ones, even within 100ms of fixating the word immediately preceding the target. This reduction in the RIFT response was also found to be predictive of individual reading speed. We conclude that semantic information is not only extracted from the parafovea but can also be integrated with the previous context before the word is fixated. This early and extensive parafoveal processing supports the rapid word processing required for natural reading. Our study suggests that theoretical frameworks of natural reading should incorporate the concept of deep parafoveal processing.

Goal-Dependent Use of Temporal Regularities to Orient Attention under Spatial and Action Uncertainty.

The temporal regularities in our environments support the proactive dynamic anticipation of relevant events. In visual attention, one important outstanding question is whether temporal predictions must be linked to predictions about spatial locations or motor plans to facilitate behaviour. To test this, we developed a task for manipulating temporal expectations and task relevance of visual stimuli appearing within rapidly presented streams, while stimulus location and responding hand remained uncertain. Differently coloured stimuli appeared in one of two concurrent (left and right) streams with distinct temporal probability structures. Targets were defined by colour on a trial-by-trial basis and appeared equiprobably in either stream, requiring a localisation response. Across two experiments, participants were faster and more accurate at detecting temporally predictable targets compared to temporally unpredictable targets. We conclude that temporal expectations learned incidentally from temporal regularities can be called upon flexibly in a goal-driven manner to guide behaviour. Moreover, we show that visual temporal attention can facilitate performance in the absence of concomitant spatial or motor expectations in dynamically unfolding contexts.

Deep Q-Learning for Decentralized Multi-Agent Inspection of a Tumbling Target

As the number of on-orbit satellites increases, the ability to repair or de-orbit them is becoming increasingly important. The implicitly required task of on-orbit inspection is challenging due to coordination of multiple observer satellites, a highly nonlinear environment, a potentially unknown or unpredictable target, and time delays associated with ground-based control. There is a critical need for autonomous, robust, decentralized solutions. To achieve this, we consider a hierarchical, learned approach for the decentralized planning of multi-agent inspection of a tumbling target. Our solution consists of two components: a viewpoint or high-level planner trained using deep reinforcement learning, and a low-level planner that will handle the point-to-point maneuvering of the spacecraft. Operating under limited information, our trained multi-agent high-level policies successfully contextualize information within the global hierarchical environment and are correspondingly able to inspect over 90% of nonconvex tumbling targets, even in the absence of additional agent attitude control.

Putting language switching in context: Effects of sentence context and interlocutors on bilingual switching.

Many bilinguals switch languages in daily-life conversations. Although this usually happens within sentence context and with another speaker, most research on the cognitive mechanisms underlying the production of language switches has studied individual words. Here, we examined how context influences both switching frequency and the temporal cost associated with it. Sixty Bulgarian-English bilinguals named pictures in their language of choice without any context, in a sentence context, and in interaction with another (recorded) bilingual. Switching frequency was lower, and costs higher, when bilinguals switched languages with context than without context. This suggests switching costs were not an artifact of tasks without context. Furthermore, both switching frequency and costs correlated across the tasks. In addition, we examined the potential influence of sentence context and the conversation partner. Predictability in sentence context has previously been argued to reduce language competition, which in turn could influence switching. We therefore compared sentences with a predictable or unpredictable target word. As hypothesized, bilinguals were less likely to switch languages when a word was predictable in its sentence context, potentially because words in the other language were less active. The conversation partner's overall switching behaviour had little impact on a bilingual's general switching rate, showing relatively low global alignment. However, local alignment was observed as switching was influenced by the partner's switching in the immediately preceding utterance. Together, these findings show that while production tasks without context can reliably measure switching costs, studying effects of context is necessary to better capture a bilingual's language-switching behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Temporal unpredictability increases error monitoring as revealed by EEG-EMG investigation.

Reacting in an unpredictable context increases error monitoring as evidenced by greater error-related negativity (ERN), an electrophysiological marker linked to an evaluation of response outcomes. We investigated whether ERN also increased when participants evaluated their responses to events that appeared in unpredictable versus predictable moments in time. We complemented electroencephalographic (EEG) analysis of cortical activity by measuring performance monitoring processes at the peripheral level using electromyography (EMG). Specifically, we used EMG data to quantify how temporal unpredictability would affect motor time (MT), the interval between the onset of muscle activity, and the mechanical response. MT increases following errors, indexing online error detection, and an attempt to stop incorrect actions. In our temporally cued version of the stop-signal task, symbolic cues predicted (temporally predictable condition) or not (temporally unpredictable condition) the onset of a target. In 25% of trials, an auditory signal occurred shortly after the target presentation, informing participants that they should inhibit their response completely. Response times were slower, and fewer inhibitory errors were made during temporally unpredictable than predictable trials, indicating enhanced control of unwanted actions when target onset time was unknown. Importantly, the ERN to inhibitory errors was greater in temporally unpredictable relative to temporally predictable conditions. Similarly, EMG data revealed prolonged MT when reactions to temporally unpredictable targets had not been stopped. Taken together, our results show that a temporally unpredictable environment increases the control of unwanted actions, both at cortical and peripheral levels, suggesting a higher subjective cost of maladaptive responses to temporally uncertain events.

Motor synchronization and impulsivity in pediatric borderline personality disorder with and without attention-deficit hyperactivity disorder: an eye-tracking study of saccade, blink and pupil behavior.

Shifting motor actions from reflexively reacting to an environmental stimulus to predicting it allows for smooth synchronization of behavior with the outside world. This shift relies on the identification of patterns within the stimulus - knowing when a stimulus is predictable and when it is not - and launching motor actions accordingly. Failure to identify predictable stimuli results in movement delays whereas failure to recognize unpredictable stimuli results in early movements with incomplete information that can result in errors. Here we used a metronome task, combined with video-based eye-tracking, to quantify temporal predictive learning and performance to regularly paced visual targets at 5 different interstimulus intervals (ISIs). We compared these results to the random task where the timing of the target was randomized at each target step. We completed these tasks in female pediatric psychiatry patients (age range: 11-18 years) with borderline personality disorder (BPD) symptoms, with (n = 22) and without (n = 23) a comorbid attention-deficit hyperactivity disorder (ADHD) diagnosis, against controls (n = 35). Compared to controls, BPD and ADHD/BPD cohorts showed no differences in their predictive saccade performance to metronome targets, however, when targets were random ADHD/BPD participants made significantly more anticipatory saccades (i.e., guesses of target arrival). The ADHD/BPD group also significantly increased their blink rate and pupil size when initiating movements to predictable versus unpredictable targets, likely a reflection of increased neural effort for motor synchronization. BPD and ADHD/BPD groups showed increased sympathetic tone evidenced by larger pupil sizes than controls. Together, these results support normal temporal motor prediction in BPD with and without ADHD, reduced response inhibition in BPD with comorbid ADHD, and increased pupil sizes in BPD patients. Further these results emphasize the importance of controlling for comorbid ADHD when querying BPD pathology.

Delivering wildlife habitat on productive agricultural lands cost‐effectively: The case of migratory shorebirds on California rice lands

AbstractPromoting wildlife habitat on working agricultural land is a growing conservation priority, and agri‐environment schemes are using payments for environmental services (PES) to reach these privately owned lands. PES can be ecologically effective, but also expensive, putting pressure on these schemes to be cost‐effective, maximizing the conservation value of their limited resources. This study assesses the cost‐effectiveness of four PES schemes in California that paid rice growers to provide temporary flood habitat on their working lands in support of shorebirds. It examines whether the schemes (1) paid for flood habitat only when needed (targeting); (2) paid for habitat that was actually delivered and would not otherwise be provided (additionality); (3) sacrificed as little habitat as possible when the habitat competed with production (ag/wildlife balance); and (4) fostered a commitment among growers to maintain the habitat after payments ended (permanence). Results show that the schemes fell short in each of the four goals, and they expose several factors that undermined their cost‐effectiveness. First, variable weather patterns altered the date in which the shorebird‐habitat gap emerged year‐to‐year, creating a dynamic and unpredictable target for the schemes to address. Second, growers' views on the compatibility of the flood habitat with their rice production varied widely and changed rapidly as the agricultural calendar progressed, making it a challenge to mitigate potential ag/wildlife conflicts. Third, water for flood habitat is expensive, and growers proved unwilling to shoulder that expense once payments ended. These results highlight the need for schemes to adopt design elements that can add flexibility, such as cancelation clauses, so that they can adjust to dynamic targets and adapt to changing ag/wildlife relationships. Results also suggest that PES schemes operating on working lands may require long‐term external support. That finding amplifies the imperative that schemes use their conservation resources cost‐effectively.

Reading fluency and statistical learning across modalities and domains: Online and offline measures.

The vulnerability of statistical learning has been demonstrated in reading difficulties in both the visual and acoustic modalities. We examined segmentation abilities of Hungarian speaking adolescents with different levels of reading fluency in the acoustic verbal and visual nonverbal domains. We applied online target detection tasks, where the extent of learning is reflected in differences between reaction times to predictable versus unpredictable targets. Explicit judgments of well-formedness were also elicited in an offline two-alternative forced choice (2AFC) task. Learning was evident in both the acoustic verbal and visual nonverbal tasks, both in online and offline measures, but learning effects were larger both in online and offline tasks in the verbal acoustic condition. We haven't found evidence for a significant relationship between statistical learning and reading fluency in adolescents in either modality. Together with earlier findings, these results suggest that the relationship between reading and statistical learning is dependent on the domain, modality and nature of the statistical learning task, on the reading task, on the age of participants, and on the specific language. The online target detection task is a promising tool which can be adapted to a wider set of tasks to further explore the contribution of statistical learning to reading acquisition in participants from different populations.

Prediction of semantic features is modulated by global prediction reliability: Evidence from the N400 effect

Lexical predictability has been shown to be modulated by the global context, but it is unclear whether the global context has a similar modulating effect on the prediction of semantic features. Event-related potentials (ERP) should be helpful in addressing this question, as the N400 effect is sensitive to both lexical predictability and the prediction of semantic features during sentence comprehension. The present study manipulated the semantic similarity between unpredictable target words and expected words in experimental sentences. Different types of filler sentences (predictable vs. incongruous) were used to manipulate the global prediction reliability (GPR). The ERP results showed that, in the predictable filler block, the N400 was reduced when there was high semantic similarity between the unpredictable target word and expected word in the experimental sentences. However, this association was absent in the incongruous filler block, in which participants were discouraged from predicting the upcoming information. These results suggest that the prediction of semantic features of upcoming words could be modulated by the global context. • N400 effect sensitive to lexical predictability was modulated by global context. • Semantic similarity between target words and expected words was manipulated. • Pre-activated semantic features are modulated by global prediction reliability.

Resilience Enhancement of Pilot Protection in Power Systems

Concerns about the cybersecurity and resilience of power systems have heightened in electric utilities and regulatory agencies over the past decade mainly because of the unpredictable target, location and scale of cyberattacks and the potential severity of consequences. The cybersecurity of pilot protection is paramount in bulk power systems considering their prevalence and the crucial role they play in protecting critical assets and preventing large system disturbances and major blackouts. This paper investigates the resilience of pilot protection using a co-simulation platform based on OPAL-RT simulator and Riverbed Modeler. It is demonstrated that software-defined networking for operational technology (OT SDN) significantly improves the resilience of pilot protection to false data injection (FDI) attacks compared to traditional networks. Moreover, the resilience of OT SDN based pilot protection to denial of service (DoS) attack against the SDN controller is investigated both in the proactive and reactive modes of operation. The simulation results verified the resilience of OT SDN controller in OT SDN based pilot protection to DoS attacks in the proactive mode of operation.

Pupillary fluctuation amplitude preceding target presentation is linked to the variable foreperiod effect on reaction time in Psychomotor Vigilance Tasks.

Understanding temporally attention fluctuations can benefit scientific knowledge and real-life applications. Temporal attention studies have typically used the reaction time (RT), which can be measured only after a target presentation, as an index of attention level. We have proposed the Micro-Pupillary Unrest Index (M-PUI) based on pupillary fluctuation amplitude to estimate RT before the target presentation. However, the kind of temporal attention effects that the M-PUI reflects remains unclear. We examined if the M-PUI shows two types of temporal attention effects initially reported for RTs in the variable foreperiod tasks: the variable foreperiod effect (FP effect) and the sequential effect (SE effect). The FP effect refers to a decrease in the RT due to an increase in the foreperiod of the current trial, whereas the SE effect refers to an increase in the RT in the early part of the foreperiod of the current trial due to an increase in the foreperiod of the previous trial. We used a simple reaction task with the medium-term variable foreperiods (Psychomotor Vigilance Task) and found that the M-PUI primarily reflects the FP effect. Inter-individual analyses showed that the FP effect on the M-PUI, unlike other eye movement indices, is correlated with the FP effect on RT. These results suggest that the M-PUI is a potentially powerful tool for investigating temporal attention fluctuations for a partly unpredictable target.

Be there on time: Spatial-temporal regularities guide young children's attention in dynamic environments.

Children's ability to benefit from spatiotemporal regularities to detect goal‐relevant targets was tested in a dynamic, extended context. Young adults and children (from a low‐deprivation area school in the United Kingdom; N = 80; 5–6 years; 39 female; ethics approval did not permit individual‐level race/ethnicity surveying) completed a dynamic visual‐search task. Targets and distractors faded in and out of a display over seconds. Half of the targets appeared at predictable times and locations. Search performance in children was poorer overall. Nevertheless, they benefitted equivalently from spatiotemporal regularities, detecting more predictable than unpredictable targets. Children's benefits from predictions correlated positively with their attention. The study brings ecological validity to the study of attentional guidance in children, revealing striking behavioral benefits of dynamic experience‐based predictions.

Right place, right time: Spatiotemporal predictions guide attention in dynamic visual search.

Visual search is a fundamental element of human behavior and is predominantly studied in a laboratory setting using static displays. However, real-life search is often an extended process taking place in dynamic environments. We have designed a dynamic-search task in order to incorporate the temporal dimension into visual search. Using this task, we tested how participants learn and utilize spatiotemporal regularities embedded within the environment to guide performance. Participants searched for eight instances of a target that faded in and out of a display containing similarly transient distractors. In each trial, four of the eight targets appeared in a temporally predictable fashion with one target appearing in each of four spatially separated quadrants. The other four targets were spatially and temporally unpredictable. Participants’ performance was significantly better for spatiotemporally predictable compared to unpredictable targets (Experiments 1–4). The effects were reliable over different patterns of spatiotemporal predictability (Experiment 2) and primarily reflected long-term learning over trials (Experiments 3, 4), although single-trial priming effects also contributed (Experiment 4). Eye-movement recordings (Experiment 1) revealed that spatiotemporal regularities guide attention proactively and dynamically. Taken together, our results show that regularities across both space and time can guide visual search and this guidance can primarily be attributed to robust long-term representations of these regularities.

LCDNet: Light-Weighted Cloud Detection Network for High-Resolution Remote Sensing Images

Cloud detection is one of the important links in high-resolution remote sensing image processing. Cloud detection methods can be mainly divided into three categories: threshold methods, clustering methods based on machine learning, and deep learning methods. The traditional threshold method needs cumbersome manual calibration, which has high cost and poor universality. The generalization of clustering based method is very poor. In addition, existing deep learning methods tend to have many model parameters and high training costs. To solve the above problems, a light-weighted cloud detection network (LCDNet) based on deep learning method is proposed. The network can complete the task of high-precision segmentation with less parameters and computation. Its light-weighted bottleneck (LB) layer can quickly capture the multi-scale feature information in the image and segment the cloud with high efficiency. The gated channel excitation (GCE) module can effectively reduce the feature redundancy in the network, so as to highlight the details of the cloud layer and improve the detection accuracy. The function of light-weighted self attention (LSA) module is to quickly establish the spatial location information in the feature map, so as to locate the unpredictable targets and reduce the false detection rate. The experimental results show that the model shows excellent performance on cloud and cloud shadow datasets with 353.76 k parameters and 456.28Mmac computation. In addition, the training results on GF1_WHU and L8 Biome datasets further show that our model has excellent generalization performance, which is of great significance for the efficient implementation of cloud detection.

Special treatment of prediction errors in autism spectrum disorder

For autistic individuals, sensory stimulation can be experienced as overwhelming. Models of predictive coding postulate that cortical mechanisms disamplify predictable information and amplify prediction errors that surpass a defined precision level. In autism, the neuronal processing is putting an inflexibly high precision on prediction errors according to the HIPPEA theory (High, Inflexible Precision of Prediction Errors in Autism). We used an apparent motion paradigm to test this prediction. In apparent motion paradigms, the illusory motion of an object creates a prediction about where and when an internally generated token would be moving along the apparent motion trace. This illusion facilitates the perception of a flashing stimulus (target) appearing in-time with the apparent motion token and is perceived as a predictable event (predictable target). In contrast, a flashing stimulus appearing out-of-time with the apparent motion illusion is an unpredictable target that is less often detected even though it produces a prediction error signal. If a prediction error does not surpass a given precision threshold the stimulation event is discounted and therefore less often detected than predictable tokens. In autism, the precision threshold is lower and the same prediction errors (unpredictable target) triggers a detection similar to that of a predictable flash stimulus. To test this hypothesis, we recruited 11 autistic males and 9 neurotypical matched controls. The participants were tasked to detect flashing stimuli placed on an apparent motion trace either in-time or out-of-time with the apparent motion illusion. Descriptively, 66% (6/9) of neurotypical and 64% (7/11) of autistic participants were better at detecting predictable targets. The prediction established by illusory motion appears to assist autistic and neurotypical individuals equally in the detection of predictable over unpredictable targets. Importantly, 55% (6/11) of autistic participants had faster responses for unpredictable targets, whereas only 22% (2/9) of neurotypicals had faster responses to unpredictable compared to predictable targets. Hence, these tentative results suggest that for autistic participants, unpredictable targets produce an above threshold prediction error, which leads to faster response. This difference in unpredictable target detection can be encapsulated under the HIPPEA theory, suggesting that precision setting could be aberrant in autistic individuals with respect to prediction errors. These tentative results should be considered in light of the small sample. For this reason, we provide the full set of materials necessary to replicate and extend the results.

Attention expedites target selection by prioritizing the neural processing of distractor features

Whether doing the shopping, or driving the car – to navigate daily life, our brain has to rapidly identify relevant color signals among distracting ones. Despite a wealth of research, how color attention is dynamically adjusted is little understood. Previous studies suggest that the speed of feature attention depends on the time it takes to enhance the neural gain of cortical units tuned to the attended feature. To test this idea, we had human participants switch their attention on the fly between unpredicted target color alternatives, while recording the electromagnetic brain response to probes matching the target, a non-target, or a distracting alternative target color. Paradoxically, we observed a temporally prioritized processing of distractor colors. A larger neural modulation for the distractor followed by its stronger attenuation expedited target identification. Our results suggest that dynamic adjustments of feature attention involve the temporally prioritized processing and elimination of distracting feature representations.

The spatial diffusion of covid-19 in Texas

ABSTRACT The predominant narrative of covid-19 emphasizes its unpredictable origins, virulence, human targets, and spatial targets. Its international spread may be erratic, but at the regional level, diffusion research has been able to forecast its spread with some accuracy. This study reviews research showing how infectious disease spread over space may be predicted dependent upon a place’s size, distance from large cities and disease entry points, population density, and SES. Based on these characteristics, this analysis models the arrival of covid-19 in each of the 254 counties of Texas between March 6 (the index case) and 1 September 2020, using Texas Department of State Health Statistics data. A temporal gravity model, based solely on the population of a place divided by distance to the dominant city of its tributary region is able to explain two-thirds of the variation in arrival dates. For specific places that the model cannot predict well, the examination of their residuals suggests other variables that substantially improve predictions. This model and procedure serve local planners in the case of a resurgence of the virus or another variant of it in Texas.

Tracking statistical learning online: Word segmentation in a target detection task

Despite the essential role of statistical learning in shaping human behavior, there are still controversies concerning its measurement. In this paper, we present a novel online target-detection task in an acoustic word segmentation paradigm, which is able to track the process of learning and does not build on deliberation and decision making. Beside testing the novel online task, we also examined its relationship with two offline measures: the traditional two-alternative forced choice (2AFC) task, and the statistically-induced chunking recall (SICR) task (Isbilen et al., 2017). Participants showed a significant learning effect on the online task, reflected in the decrease of reaction times during training and in the differences between reaction times to predictable versus unpredictable targets. Online learning scores correlated with the 2AFC scores, but this association was only present when participants did not have explicit knowledge about stimuli. SICR scores were not associated with any of the other measures. The internal consistency was higher for online learning measures than for the other two tasks. These findings show that the online target detection task is a good tool for assessing statistical learning, and invite further research on its psychometric properties.

The influence of temporal predictability on express visuomotor responses.

Humans are able to generate target-directed visuomotor responses in less than 100 ms after stimulus onset. These "express" responses have been termed stimulus-locked responses (SLRs) and are proposed to be modulated by visuomotor transformations performed subcortically via the superior colliculus. Unfortunately, these responses have proven difficult to detect consistently across individuals. The recent report of an effective paradigm for generating SLRs in 100% of participants appears to change this. The task required the interception of a target moving at a constant velocity that emerged from behind a barrier. Here, we aimed to reproduce the efficacy of this paradigm for eliciting SLRs and to test the hypothesis that its effectiveness derives from the predictability of target onset time as opposed to target motion per se. In one experiment, we recorded surface electromyogram (EMG) from shoulder muscles as participants made reaches to intercept temporally predictable or unpredictable targets. Consistent with our hypothesis, predictably timed targets produced more frequent and stronger SLRs than unpredictably timed targets. In a second experiment, we compared different temporally predictable stimuli and observed that transiently presented targets produced larger and earlier SLRs than sustained moving targets. Our results suggest that target motion is not critical for facilitating the SLR expression and that timing predictability does not rely on extrapolation of a physically plausible motion trajectory. These findings provide support for a mechanism whereby an internal timer, probably located in cerebral cortex, primes the processing of both visual input and motor output within the superior colliculus to produce SLRs.NEW & NOTEWORTHY Express stimulus-driven responses in humans have been proposed to be originated subcortically via the superior colliculus. These short-latency responses are facilitated by the presentation of dynamic visual stimuli. Here, we show that this facilitation is related to the predictable target timing, regardless of its kinematic attributes. We propose that the superior colliculus can be primed to generate express stimulus-driven motor responses via cortical top-down projection.