Rate Adaptation Research Articles

BUFIT: FINE-GRAINED DYNAMIC BURST FAULT INJECTION TOOL FOR EMBEDDED field programmable gate array TESTING

Fault injection (FI) is a well-known method to attack embedded systems, particularly advanced FPGAs and microcontrollers physically. The FPGA-based embedded system constitutes SRAM for configuration data storage. Multiple-bit upset is a main threat for FPGAs due to technology scaling and complex application bit files. Space environments additionally incur radiation threats to these devices. This paper proposes burst error modeling and a burst fault injection tool (BUFIT) to address these issues. BUFIT has been proposed with fine-grained and coarse-grained circuits. Built-in instrumented FPGA-based FI is proposed for effectively injecting MBUs in configuration memory with space adaptive rate for accurately estimating soft errors. Evaluation of proposed BUFIT on Kintex-7 target FPGA to various OR 1200-based workloads is given to analyze the speed up of the proposed technique. Results on the OR 1200 processor show that BUFIT is three and two orders of magnitude faster than existing DPR and SCFIT techniques. It uses only 0.4 % CLB overhead and has negligible impact on FFs of target SFPGAs.

Individuals with anxiety and depression use atypical decision strategies in an uncertain world.

Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.

Individuals with anxiety and depression use atypical decision strategies in an uncertain world

Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.

Attention defines the context for implicit sensorimotor adaptation.

Movement errors are used to continuously recalibrate the sensorimotor map, a process known as sensorimotor adaptation. Here we examined how attention influences this automatic and obligatory learning process. Focusing first on spatial attention, we compared conditions in which the visual feedback that provided information about the movement outcome was either attended or unattended. Surprisingly, this manipulation had no effect on the rate of adaptation. We next used a dual-task methodology to examine the influence of attentional resources on adaptation. Here, again, we found no effect of attention, with the rate of adaptation similar under focused or divided attention conditions. Interestingly, we found that attention modulates adaptation in an indirect manner: Attended stimuli serve as cues that define the context for learning. The rate of adaptation was significantly attenuated when the attended stimulus changed from the end of one trial to the start of the next trial. In contrast, similar changes to unattended stimuli had no impact on adaptation. Together, these results suggest that visual attention defines the cues that establish the context for sensorimotor learning.

Exploring the relationship between child temperament, maternal psychiatric symptoms, family environment and infant feeding.

Infant temperament is assumed to be primarily innate. However, newer research suggests that maternal affection impacts ratings of temperament and environmental factors, including feeding method, can also influence infant temperament. This study investigates child temperament and its relationships with maternal psychiatric symptoms, environmental variables and feeding method longitudinally in a cohort of children followed from 6 to 72 months. Differences in temperament by feeding group are also investigated. We hypothesized that maternal psychiatric symptoms, environmental stressors, and impaired family dynamics would have negative impact on child temperament, whereas breastfeeding would have a positive impact on child temperament. Mothers' ratings of child's temperament, own psychiatric symptomatology, environmental stresses and family cohesion were obtained in 504 mother-infant dyads via rating scales completed by mothers. Infants were breastfeed (BF), fed soy-based infant formula (SF) or dairy-based infant formula (MF). Linear mixed effect models investigated the relationship of variables on child's temperament while controlling for significant covariates and repeated measurements. Mothers in this study did not endorse clinical-level psychiatric symptomatology; however, when adjusted for significant covariates, higher psychiatric symptomatology significantly correlated with environmental stressors, impaired family dynamics and elevations in temperament ratings of infants' adaptability and mood. There were no lasting differences for temperament between feeding groups. However, some significant transient increases in rhythmicity and adaptability were found between SF and BF children. Positive relationships between family environment stressors and maternal psychiatric ratings were found. Transient differences were found in child temperament based upon feeding method.

CLARA: A cluster-based node correlation for sampling rate adaptation and fault tolerance in sensor networks

Recently, wireless sensor networks (WSNs) have been proven as an efficient and low-cost solution for monitoring various kind of applications. However, the massive amount of data collected and transmitted by the sensor nodes, which are mostly redundant, will quickly consume their limited battery power, which is sometimes difficult to replace or recharge. Although the huge efforts made by researchers to solve such problem, most of the proposed techniques suffer from their accuracy and their complexity, which is not suitable for limited-resources sensors. Therefore, designing new data reduction techniques to reduce the raw data collected in such networks is becoming essential to increase their lifetime. In this paper, we propose a CLuster-based node correlation for sAmpling Rate adaptation and fAult tolerance, abbreviated CLARA, mechanism dedicated to periodic sensor network applications. Mainly, CLARA works on two stages: node correlation and fault tolerance. The first stage introduces a data clustering method that aims to search the correlation among neighboring nodes. Then, it accordingly adapts their sensing frequencies in a way to reduce the amount of data collected in such networks while preserving the information integrity at the sink. In the second stage, a fault tolerance model is proposed that allows the sink to regenerate the raw sensor data based on two methods: moving average (MA) and exponential smoothing (ES). We demonstrated the efficiency of our technique through both simulations and experiments. The best obtained results show that the first stage can reduce the sensor sampling rate, and accordingly the sensor energy, up to 64% while the second stage can accurately regenerate the raw data with an error loss less than 0.15.

Sigmoidal learning rate optimizer for deep neural network training using a two-phase adaptation approach

The optimization of machine learning models is an open research question since an optimal procedure to change the learning rate throughout training is still unknown. Manually defining a learning rate schedule involves troublesome, time-consuming try-and-error procedures to determine hyperparameters, such as learning rate decay epochs and learning rate decay rates, in order to navigate the intricate landscape of loss functions. Although adaptive learning rate optimizers automatize this process, recent studies suggest they may produce overfitting and reduce performance compared to fine-tuned learning rate schedules. Considering that the new machine learning loss function approaches present landscapes with much more saddle points than local minima, we proposed the Training Aware Sigmoidal Optimizer (TASO). This automated two-phase learning rate adaptation mechanism significantly reduces the need for manual hyperparameter tuning. The first phase uses a high learning rate to quickly traverse the numerous saddle points in the error surface, while the second phase uses a low learning rate to gradually approach the center of the local minimum previously found. We compared the proposed approach with commonly used adaptive learning rate schedules such as Adam, RMSProp, and Adagrad. The validation experiments were performed in image and text datasets and showed that TASO outperformed all competing methods in optimal (i.e., performing hyperparameter validation) and suboptimal (i.e., using default hyperparameters) scenarios. In our benchmark tests, TASO demonstrated promising performance, achieving an 8.32% increase in accuracy and a significant 46.62% decrease in training loss on average across various datasets and models. This remarkable performance positions TASO ahead of well-established adaptive optimizers, suggesting higher effectiveness and consistency in performance.

Efficient adaptive learning rate for convolutional neural network based on quadratic interpolation egret swarm optimization algorithm

Convolutional neural network (CNN) has recently become popular for addressing multi-domain image classification. However, most existing methods frequently suffer from poor performance, especially in performance and convergence for various datasets. Herein, we have proposed an algorithm for multi-domain image classification by introducing a novel adaptive learning rate rule to the conventional CNN. Specifically, we adopt the CNN to extract rich feature representations. Given that the hyperparameters of the learning rate have a positive effect on the prediction error, the Egret Swarm Optimization Algorithm (ESOA) is introduced to update the learning rate, which can jump out of local extrema during exploration. Therefore, combined with quadratic interpolation, the objective function can be approximated by a polynomial, thereby improving its prediction accuracy. To verify the robustness of the proposed algorithm, we conducted comprehensive experiments in five domain public datasets to fulfil the task of image classification. Meanwhile, the highest accuracy rate of 97.15 % was obtained on the test set. The performances of our method on 24 benchmark functions (CEC2017 and CEC2022) are compared with Particle Swarm Optimization (PSO), Genetic Algorithm(GA), Whale Optimization Algorithm(WOA), Catch Fish Optimization Algorithm(CFOA), GOOSE Algorithm(GO) and ESOA. In two benchmark sets, the performance metric values of our algorithm rank no. 1, especially in all unimodal functions in contrast with other baseline algorithms.

Climate change vulnerability assessment in the new urban planning process in Tanzania

Climate change vulnerability assessment is an essential tool for identifying regions that are most susceptible to the impacts of climate change and designing effective adaptation actions that can reduce vulnerability and enhance long-term resilience of these regions. This study explored a framework for climate change vulnerability assessment in the new urban planning process in Jangwani Ward, Tanzania. Specifically, taking flood as an example, this study highlighted the steps and methods for climate change vulnerability assessment in the new urban planning process. In the study area, 95 households were selected and interviewed through purposeful sampling. Additionally, 10 respondents (4 females and 6 males) were interviewed for Focus Group Discussion (FGD), and 3 respondents (1 female and 2 males) were selected for Key Informant Interviews (KII) at the Ministry of Lands, Housing and Human Settlements Development. This study indicated that climate change vulnerability assessment framework involves the assessment of climatic hazards, risk elements, and adaptive capacity, and the determination of vulnerability levels. The average hazard risk rating of flood was 2.3. Socioeconomic and livelihood activities and physical infrastructures both had the average risk element rating of 3.0, and ecosystems had the average risk element rating of 2.9. Adaptive capacity ratings of knowledge, technology, economy or finance, and institution were 1.6, 1.9, 1.4, and 2.2, respectively. The vulnerability levels of socioeconomic and livelihood activities and physical infrastructure were very high (4.0). Ecosystems had a high vulnerability level (3.8) to flood. The very high vulnerability level of socioeconomic and livelihood activities was driven by high exposure and sensitivity to risk elements and low adaptive capacity. The study recommends adoption of the new urban planning process including preparation, planning, implementation, and monitoring-evaluation-review phases that integrates climate change vulnerability assessment in all phases.

BBR-R: Improving BBR performance in multi-flow competition scenarios

The development of network infrastructures and the evolving demands of internet services impose higher requirements on congestion control algorithms. Although Google’s BBR algorithm achieves lower latency and higher goodput compared to traditional congestion control algorithms, it still has many issues. BBR sets the congestion window larger than the calculated ideal value to prevent transmission stalling in the presence of delayed and aggregated ACKs. However, in scenarios with multi-flow competition, this compromise on the congestion window leads to large amounts of queued data, causing increased latency and decreased fairness. Additionally, the ProbeRTT mechanism deviates from its original intent. In this study, we analyze the existing issues of the BBR algorithm from a theoretical standpoint and propose the BBR-R algorithm, which incorporates an adaptive sending rate adjustment mechanism and a new ProbeRTT triggering mechanism. While maintaining the ability for dynamic bandwidth exploration, the sending rate is adjusted based on a latency-related factor called Adaptive_RTprop to control the over-injected data. Coupled with the new ProbeRTT triggering mechanism, BBR-R reduces the frequency of entering the ProbeRTT phase and thereby improves transmission stability. In conducted experiments, BBR-R decreases the frequency of entering the ProbeRTT phase in many scenarios, achieves a 41.86% reduction in latency in the dual-flow competition scenario, and improves fairness by 22.79% in the five-flow competition scenario.

The Economic Impact Of The 1997 And 2008 Financial Crisis On Asia

This study investigates the factors that made Asian economies more resilient during the 2008 Global Financial Crisis (GFC) compared to the 1997 Asian Financial Crisis (AFC). The research addresses the question: What made Asian economies more resilient during the Global Financial Crisis than the Asian Financial Crisis? Through a comparative analysis, the research highlights the proactive reforms implemented after the AFC, such as the adoption of adaptable exchange rate regimes, enhanced banking supervision, and the development of deeper capital markets, which collectively fortified the region's financial systems. In this respect, it is observed that such reforms provided an enabling environment that, together with strong current account balances and large foreign exchange reserves, helped the Asian economies weather the GFC. The research has also attempted to explain how strategic international cooperation and financial policies updated were helpful in sustaining financial stability. Despite these successes, the research underscores ongoing vulnerabilities, such as high levels of corporate and household debt, that require attention to safeguard against future crises. The findings offer valuable insights into the importance of readiness, flexibility, and regional cooperation in ensuring economic stability and growth.

Modeling of an Adaptive HHO Gas Controller Based on Fuzzy Logic and Polynomial Function Controls to Improve Engine Torque of Gasoline Engine

Typical hydrogen-hydrogen-oxygen gas (HHO) usage to improve gasoline engine performance refers to the fixed HHO flow rate method, providing 0.25‒0.5 liters/minute of HHO for every 1000 cc engine size. However, the arising hypothesis expresses that the fixed HHO flow rate method does not optimally improve engine torque for various loads. The research objective is to propose an adaptive HHO gas controller that manages the HHO generator to produce an appropriate HHO flow rate for engine operation by adapting to load and engine speed variations. Hence, the engine torque improvement optimally occurs for various loads. The adaptive HHO controller combines fuzzy logic and polynomial function controls involving real-time engine data, such as mass airflow (MAF), air-fuel ratio (AFR), and the commanded AFR from the engine control unit (ECU), whose values vary with load and engine speed. A system simulation based on Matlab-Simulink investigates engine performance improvement due to the controller. The results show that the adaptive HHO flow rate due to the proposed adaptive HHO controller improves engine torque during small, medium, and big-loaded engine operations, respectively, by 1.5%‒4.7%, 6.8%‒26.8%, and 21.1%‒72.8% depending on engine speed 2500 rpm‒4000 rpm) and the commanded AFR (12.6‒15.4). Conversely, under the same condition, the fixed HHO flow rate with 0.75 liters/minute HHO for a 1500 cc engine, used for comparison, improves the engine torque by 0%‒0.6%, 0.3%‒14.2%, and 9.3%‒50.1%, respectively. The data show that the adaptive HHO controller provides better improvement. Moreover, the adaptive HHO controller improves engine thermal efficiency and reduces AFR error against the commanded AFR.

Prioritized adjustments in posture stabilization and adaptive reaching during neuromuscular fatigue of lower-limb muscles.

Neuromuscular fatigue (NMF) induces temporary reductions in muscle force production capacity, affecting various aspects of motor function. Although studies have extensively explored NMF's impact on muscle activation patterns and postural stability, its influence on motor adaptation processes remains less understood. This article investigates the effects of localized NMF on motor adaptation during upright stance, focusing on reaching tasks. Using a force-field perturbation paradigm, participants performed reaching movements while standing upright before and after inducing NMF in the ankle dorsiflexor muscles. Results revealed that despite maintained postural stability, participants in the NMF group exhibited larger movement errors during reaching tasks, suggesting impaired motor adaptation. This was evident in both initial and terminal phases of adaptation, indicating a disruption in learning processes rather than a decreased adaptation rate. Analysis of electromyography activation patterns highlighted distinct strategies between groups, with the NMF group showing altered activation of both fatigued and nonfatigued muscles. In addition, differences in coactivation patterns suggested compensatory mechanisms to prioritize postural stability despite NMF-induced disruptions. These findings underscore the complex interplay between NMF, motor adaptation, and postural control, suggesting a potential role for central nervous system mechanisms in mediating adaptation processes. Understanding these mechanisms has implications for sports performance, rehabilitation, and motor skill acquisition, where NMF may impact the learning and retention of motor tasks. Further research is warranted to elucidate the transient or long-term effects of NMF on motor adaptation and its implications for motor rehabilitation interventions.NEW & NOTEWORTHY We assessed motor adaptation during force-field reaching following exercise-induced neuromuscular fatigue (NMF) on postural muscles. NMF impaired adaptation in performance. Similarly, diverging activation strategies were observed in the muscles. No effects were seen on measures of postural control. These results suggest the remodulation of motor commands to the muscles in the presence of NMF, which may be relevant in settings where participants could be exposed to NMF while learning, such as sports and rehabilitation.

An Image Adaptive Rate Mechanism in Semantic Communication for Image Endogenous Semantics

An Image Adaptive Rate Mechanism in Semantic Communication for Image Endogenous Semantics

Analysis of adaptation level of farmers affected by floods in paddy fields in Karang Agung Village, Jejawi District, Ogan Komering Ilir regency

The objective of this research is to: (1) To find out the impact of flooding occurring on the wilderness in the village of Karang Agung (2) To know the analysis of the degree of adaptation to the influence of the flood on the land of the farmers (3) to know the adaptation efforts of farmers through clusterization analysis. This research was done in the village of Karang Agung. Research methods used are survey methods and purposive sampling methods. The total sample taken is 31 farmers showing that: the impact of the flood on the management of the farm, the cultivation and marketing statistics and the strategy of financing the farm still affected by the floods, as well as the farmers' rate of adaptation is still low this is reinforced by the low result of the clustering of farmers.

A Review of Vision-Based Pothole Detection Methods Using Computer Vision and Machine Learning.

Potholes and other road surface damages pose significant risks to vehicles and traffic safety. The current methods of in situ visual inspection for potholes or cracks are inefficient, costly, and hazardous. Therefore, there is a pressing need to develop automated systems for assessing road surface conditions, aiming to efficiently and accurately reconstruct, recognize, and locate potholes. In recent years, various methods utilizing (a) computer vision, (b) three-dimensional (3D) point clouds, or (c) smartphone data have been employed to map road surface quality conditions. Machine learning and deep learning techniques have increasingly enhanced the performance of these methods. This review aims to provide a comprehensive overview of cutting-edge computer vision and machine learning algorithms for pothole detection. It covers topics such as sensing systems for acquiring two-dimensional (2D) and 3D road data, classical algorithms based on 2D image processing, segmentation-based algorithms using 3D point cloud modeling, machine learning, deep learning algorithms, and hybrid approaches. The review highlights that hybrid methods combining traditional image processing and advanced machine learning techniques offer the highest accuracy in pothole detection. Machine learning approaches, particularly deep learning, demonstrate superior adaptability and detection rates, while traditional 2D and 3D methods provide valuable baseline techniques. By reviewing and evaluating existing vision-based methods, this paper clarifies the current landscape of pothole detection technologies and identifies opportunities for future research and development. Additionally, insights provided by this review can inform the design and implementation of more robust and effective systems for automated road surface condition assessment, thereby contributing to enhanced roadway safety and infrastructure management.

Virulence Adaptation by Rice Planthoppers and Leafhoppers to Resistance Genes and Loci: A Review.

In recent decades, research on developing and deploying resistant rice has accelerated due to the availability of modern molecular tools and, in particular, advances in marker-assisted selection. However, progress in understanding virulence adaptation has been relatively slow. This review tracks patterns in virulence adaptation to resistance genes (particularly Bph1, bph2, Bph3, and bph4) and examines the nature of virulence based on selection experiments, responses by virulent populations to differential rice varieties (i.e., varieties with different resistance genes), and breeding experiments that interpret the genetic mechanisms underlying adaptation. The review proposes that varietal resistance is best regarded as a combination of minor and major resistance traits against which planthoppers develop partial or complete virulence through heritable improvements that are reversable or through evolutionary adaptation, respectively. Agronomic practices, deployment patterns, and herbivore population pressures determine the rates of adaptation, and there is growing evidence that pesticide detoxification mechanisms can accelerate virulence adaptation. Research to delay adaptation has mainly focused on gene pyramiding (i.e., including ≥ two major genes in a variety) and multilines (i.e., including ≥ two resistant varieties in a field or landscape); however, these strategies have not been adequately tested and, if not managed properly, could inadvertently accelerate adaptation compared to sequential deployment. Several research gaps remain and considerable improvements in research methods are required to better understand and manage virulence adaptation.

Plasmid-encoded insertion sequences promote rapid adaptation in clinical enterobacteria.

Plasmids are extrachromosomal genetic elements commonly found in bacteria. They are known to fuel bacterial evolution through horizontal gene transfer, and recent analyses indicate that they can also promote intragenomic adaptations. However, the role of plasmids as catalysts of bacterial evolution beyond horizontal gene transfer is poorly explored. In this study, we investigated the impact of a widespread conjugative plasmid, pOXA-48, on the evolution of several multidrug-resistant clinical enterobacteria. Combining experimental and within-patient evolution analyses, we unveiled that plasmid pOXA-48 promotes bacterial evolution through the transposition of plasmid-encoded insertion sequence 1 (IS1) elements. Specifically, IS1-mediated gene inactivation expedites the adaptation rate of clinical strains in vitro and fosters within-patient adaptation in the gut microbiota. We deciphered the mechanism underlying the plasmid-mediated surge in IS1 transposition, revealing a negative feedback loop regulated by the genomic copy number of IS1. Given the overrepresentation of IS elements in bacterial plasmids, our findings suggest that plasmid-mediated IS1 transposition represents a crucial mechanism for swift bacterial adaptation.

Enhancing autonomous pavement crack detection: Optimizing YOLOv5s algorithm with advanced deep learning techniques

To enhance the safety and comfort of vehicle travel, detecting pavement cracks is a critical task in road management. This article introduces an advanced single-stage target detection method utilizing the YOLOv5s algorithm to enhance real-time performance and accuracy. Initially, Squeeze-and-Excitation Networks are integrated into the model to facilitate self-learning for improved crack characterization. Subsequently, anchors computed through the K-means clustering algorithm are closely aligned with the fracture dataset, achieving an adaptation rate of 99.9 % and enhancing the recall rate of the model. Furthermore, the inclusion of the SimSPPF module from YOLOv6 diminishes memory usage and expedites detection speed. By replacing the original nearest up-sampling method with transposed convolution, optimization of up-sampling for crack datasets is achieved. Performance assessments reveal that the refined YOLOv5s algorithm attains an F1 score of 91 %, a mean Average Precision (mAP) of 93.6 %, and a 1.54 % increase in frames per second (fps) for pavement crack detection. This enhancement in detection technology signifies a substantial advancement in the maintenance and longevity of road infrastructure.

Epigenetic responses of trees to environmental stress in the context of climate change.

In long-lived tree populations, when environmental change outpaces rates of evolutionary adaptation, plasticity in traits related to stress tolerance, dormancy, and dispersal may be vital for preventing extinction. While a population's genetic background partly determines its ability to adapt to a changing environment, so too do the many types of epigenetic modifications that occur within and among populations, which vary on timescales orders of magnitude faster than the emergence of new beneficial alleles. Consequently, phenotypic plasticity driven by epigenetic modification may be especially critical for sessile, long-lived organisms such as trees that must rely on this plasticity to keep pace with rapid anthropogenic environmental change. While studies have reported large effects of DNA methylation, histone modification, and non-coding RNAs on the expression of stress-tolerance genes and resulting phenotypic responses, little is known about the role of these effects in non-model plants and particularly in trees. Here, we review new findings in plant epigenetics with particular relevance to the ability of trees to adapt to or escape stressors associated with rapid climate change. Such findings include specific epigenetic influences over drought, heat, and salinity tolerance, as well as dormancy and dispersal traits. We also highlight promising findings concerning transgenerational inheritance of an epigenetic 'stress memory' in plants. As epigenetic information is becoming increasingly easy to obtain, we close by outlining ways in which ecologists can use epigenetic information better to inform population management and forecasting efforts. Understanding the molecular mechanisms behind phenotypic plasticity and stress memory in tree species offers a promising path towards a mechanistic understanding of trees' responses to climate change.