Abstract Successful career development requires increased career self-management and contemporary career orientations accordingly stress the importance of being self-directed, values-driven, and flexible. This paper provides an overview of key perspectives on contemporary career orientations in relation to career self-management (CSM), as well as a systematic review of these two streams of literatures. With a focus on highly influential classic and recent papers as well as on all papers published in the Journal of Vocational Behavior on these topics, we aim to integrate the literatures on career orientations and CSM and advance future research. To this purpose, we present an integrative framework of career self-regulation which views CSM as a dynamic process consisting of goal setting and development, information seeking, planning and execution of behaviors, and monitoring and feedback processing. This process is influenced by, and subsequently affects, individual career orientations. We finish the paper by providing several directions for future research in terms of examining more dynamic and self-regulatory processes, unpacking the role of context, integrating the larger proactivity literature, applying a work-nonwork perspective, and developing and testing interventions.
Lingchen Sun, Rongyuan Wu, Zhengqiang Zhang
et al.
Recent works such as REPA have shown that guiding diffusion models with external semantic features (e.g., DINO) can significantly accelerate the training of diffusion transformers (DiTs). However, the use of pretrained external features as guidance signals introduces additional dependencies. We argue that DiTs actually have the power to guide the training of themselves, and propose SelfTranscendence, an effective method that achieves fast convergence using internal feature supervision only. The desired internal guidance features should meet two requirements: structurally clean to help shallow blocks separate noise from signal, and semantically discriminative to help shallow layers learn effective representations. With this consideration, we first align the DiT features with the clean VAE latent features, a native component of latent diffusion, for a short training phase (e.g., 40 epochs) to improve their structural representations, then apply the classifier-free guidance to the intermediate features, enhancing their discriminative capability and semantic expressiveness. These enriched internal features, learned entirely within the model, are used as supervision signals to guide a new DiT training from scratch. Compared to existing self-contained methods, our approach achieves a significant performance boost. It can even surpass REPA, which uses the external DINO features as guidance, in both generation quality and convergence speed for both class-to-image and text-to-image generation tasks. The source code of our method can be found at https://github.com/csslc/Self-Transcendence.
Generative audio requires fine-grained controllable outputs, yet most existing methods require model retraining on specific controls or inference-time controls (\textit{e.g.}, guidance) that can also be computationally demanding. By examining the bottlenecks of existing guidance-based controls, in particular their high cost-per-step due to decoder backpropagation, we introduce a guidance-based approach through selective TFG and Latent-Control Heads (LatCHs), which enables controlling latent audio diffusion models with low computational overhead. LatCHs operate directly in latent space, avoiding the expensive decoder step, and requiring minimal training resources (7M parameters and $\approx$ 4 hours of training). Experiments with Stable Audio Open demonstrate effective control over intensity, pitch, and beats (and a combination of those) while maintaining generation quality. Our method balances precision and audio fidelity with far lower computational costs than standard end-to-end guidance. Demo examples can be found at https://zacharynovack.github.io/latch/latch.html.
Alicja Maksymiuk, Alexandre Duplessis, Michael Bronstein
et al.
Macrocycles are ring-shaped molecules that offer a promising alternative to small-molecule drugs due to their enhanced selectivity and binding affinity against difficult targets. Despite their chemical value, they remain underexplored in generative modeling, likely owing to their scarcity in public datasets and the challenges of enforcing topological constraints in standard deep generative models. We introduce MacroGuide: Topological Guidance for Macrocycle Generation, a diffusion guidance mechanism that uses Persistent Homology to steer the sampling of pretrained molecular generative models toward the generation of macrocycles, in both unconditional and conditional (protein pocket) settings. At each denoising step, MacroGuide constructs a Vietoris-Rips complex from atomic positions and promotes ring formation by optimizing persistent homology features. Empirically, applying MacroGuide to pretrained diffusion models increases macrocycle generation rates from 1% to 99%, while matching or exceeding state-of-the-art performance on key quality metrics such as chemical validity, diversity, and PoseBusters checks.
3D Gaussian Splatting (3DGS) has recently emerged as a promising approach in novel view synthesis, combining photorealistic rendering with real-time efficiency. However, its success heavily relies on dense camera coverage; under sparse-view conditions, insufficient supervision leads to irregular Gaussian distributions, characterized by globally sparse coverage, blurred background, and distorted high-frequency areas. To address this, we propose HeroGS, Hierarchical Guidance for Robust 3D Gaussian Splatting, a unified framework that establishes hierarchical guidance across the image, feature, and parameter levels. At the image level, sparse supervision is converted into pseudo-dense guidance, globally regularizing the Gaussian distributions and forming a consistent foundation for subsequent optimization. Building upon this, Feature-Adaptive Densification and Pruning (FADP) at the feature level leverages low-level features to refine high-frequency details and adaptively densifies Gaussians in background regions. The optimized distributions then support Co-Pruned Geometry Consistency (CPG) at parameter level, which guides geometric consistency through parameter freezing and co-pruning, effectively removing inconsistent splats. The hierarchical guidance strategy effectively constrains and optimizes the overall Gaussian distributions, thereby enhancing both structural fidelity and rendering quality. Extensive experiments demonstrate that HeroGS achieves high-fidelity reconstructions and consistently surpasses state-of-the-art baselines under sparse-view conditions.
General Background: Vocational education plays a crucial role in workforce development by equipping graduates with the necessary skills for employment. However, ensuring that vocational training aligns with labor market demands remains a global challenge. Specific Background: In Uzbekistan, ongoing leadership reforms aim to bridge the gap between vocational education and industry needs. Despite these efforts, structural disconnects persist, affecting graduates' employability and professional development. Knowledge Gap: Existing research lacks comprehensive insights into the specific factors that influence the professional skill progression of vocational graduates in Uzbekistan. There is also limited empirical analysis on how labor market structures and educational frameworks interact to shape employment outcomes. Aims: This study analyzes the factors affecting vocational education graduates’ skill development and their transition into the workforce. By employing a systematic analytical approach, the research evaluates the effectiveness of vocational training institutions, labor market readiness, and socio-economic mechanisms that regulate employment. Results: Findings indicate that while vocational education centers successfully develop workforce readiness, their curricula do not sufficiently align with industry needs, limiting employment optimization. Financial constraints, restricted access to skill development programs, and inadequate career counseling were identified as major barriers. Additionally, job availability and entrepreneurial opportunities vary significantly across geographical regions. Novelty: This study integrates statistical data analysis, sociological surveys, and economic modeling to provide a multidimensional perspective on vocational training effectiveness. The use of data triangulation enhances the reliability of findings, offering a nuanced understanding of employment trends, skill shortages, and market-driven educational improvements. Implications: Strengthening educational-industrial collaborations, enhancing career guidance services, and implementing entrepreneurial development programs can significantly improve graduate employability. Policymakers should consider these insights to formulate targeted vocational training reforms that address industry demands and labor market dynamics effectively. Highlights: Vocational education misalignment with labor market needs. kill gaps, financial barriers, and uneven job opportunities. Strengthen industry collaboration, career counseling, and entrepreneurial programs. Keywords: graduates of vocational education, professional abilities, employment, working conditions, new jobs.
INTRODUCTION This three-wave longitudinal study examined the reciprocal relationships among perceived parental support, career exploration, and career aspiration trajectories in adolescents, with a focus on collectivist cultural context. The research aimed to clarify how these variables dynamically influence one another during high school and whether gender differences exist in their developmental patterns. METHODS Participants included 3233 Chinese high school students (50% male, 50% female; Mage = 15.11 years) from Shandong and Henan provinces. Data were collected across three waves (2018-2020) using validated scales: the Career-Related Parental Support Scale, Vocational Exploration Questionnaire, and Career Aspiration Questionnaire. Latent growth curve modeling (LGCM) was employed to analyze developmental trajectories and bidirectional effects, while gender differences were tested via structural equation modeling. RESULTS Results revealed significant upward trends in parental support and career exploration, while career aspirations remained relatively stable across the three waves. Higher initial parental support predicted elevated initial levels (β = 0.529-0.964, p < 0.01) and accelerated growth in career exploration and aspirations. Reciprocally, adolescents' career exploration positively predicted subsequent parental support development (β = 0.430-1.204, p < 0.05). Gender differences were nonsignificant except for a minor disparity in initial career exploration favoring males (β = -0.049, p < 0.01). CONCLUSIONS The findings underscore bidirectional dynamics between parental support and adolescents' career development, emphasizing early parental engagement as pivotal for fostering career preparedness. Despite cultural expectations, gender differences were minimal, suggesting evolving egalitarian norms. Limitations include geographically restricted sampling and reliance on self-reports. This study advocates for family-based interventions to strengthen career guidance and highlights the importance of open parent-adolescent communication in educational systems.
Background: Student educational psychologists must learn to navigate the unfathomable depths of human experience with nuanced insight. However, a diagnostic checklist approach is increasingly dominating psychological practice, emphasising biomedical symptoms and subsequent pharmacological treatment above deeper psychological insight. A phenomenological approach to experience may serve as a buffer against the reductionist medicalisation of ordinary lifeworld matters. The genogram’s inherently embodied character renders it an appropriate teaching tool for developing phenomenological consciousness.
Objectives: This article is a self-reflective narrative on how I propose using the career-focused genogram to increase phenomenological consciousness among student educational psychologists.
Methods: This exploratory investigation used a self-reflective narrative research approach to understand the career-focused genogram as a pedagogical strategy to encourage phenomenological consciousness among student educational psychologists. Reflective teaching journal entries and teaching notes serve as the foundation for this investigation.
Results: My teaching experiences using a Deweyan framework of analysis revealed three major themes: the genogram as a metaphorical function of the phenomenological orientation; the career-focused genogram as an integrated life-career ecology; and the self-constructed career-focused genogram as an embodied engagement activity.
Conclusion: The career-focused genogram as an enactment of the phenomenological condition of embeddedness can be used to promote a pluralistic psychology education that values both scientific and philosophically orientated approaches towards understanding and appreciating the depth and nuance of matters related to the lifeworld.
Contribution: This article offers a contextual perspective to existing literature on the importance of a philosophically orientated educational psychology curriculum as an alternative to a technicist diagnose-and-treat curriculum.
Vocational guidance. Career development, Social Sciences
Objective: To describe and analyze the demands of ninth-grade students related to educational and career guidance at the Instituto Profesional Técnico Leonila Pinzón de Grimaldo and the Instituto Profesional Técnico e Industrial de Aguadulce, in Coclé, Panama, during the second semester of 2023.
Methodology: A basic, non-experimental, cross-sectional, retrospective, and quantitative study was conducted. This study was based on fieldwork through the administration of a structured survey to 60 students. The survey was validated. Descriptive statistics were presented, and analysis was performed using the chi-square test.
Results: Ninth-grade students articulated specific demands for educational guidance, emphasizing the need for support in personal, social, and vocational aspects. They expressed a preference for the use of Information and Communication Technologies (ICTs) and dynamic, interactive, and personalized guidance modalities.
Conclusions: Educational guidance faces the challenge of adapting to a complex environment, where the significant incorporation of ICTs requires not only infrastructure but also the development of professional skills in guidance providers. Overcoming these limitations would allow for optimizing interventions and expanding the formative impact on the development of academic and professional careers.
The evolution of Large Language Models (LLMs) has significantly advanced multi-turn conversation systems, emphasizing the need for proactive guidance to enhance users' interactions. However, these systems face challenges in dynamically adapting to shifts in users' goals and maintaining low latency for real-time interactions. In the Baidu Search AI assistant, an industrial-scale multi-turn search system, we propose a novel two-phase framework to provide proactive guidance. The first phase, Goal-adaptive Supervised Fine-Tuning (G-SFT), employs a goal adaptation agent that dynamically adapts to user goal shifts and provides goal-relevant contextual information. G-SFT also incorporates scalable knowledge transfer to distill insights from LLMs into a lightweight model for real-time interaction. The second phase, Click-oriented Reinforcement Learning (C-RL), adopts a generate-rank paradigm, systematically constructs preference pairs from user click signals, and proactively improves click-through rates through more engaging guidance. This dual-phase architecture achieves complementary objectives: G-SFT ensures accurate goal tracking, while C-RL optimizes interaction quality through click signal-driven reinforcement learning. Extensive experiments demonstrate that our framework achieves 86.10% accuracy in offline evaluation (+23.95% over baseline) and 25.28% CTR in online deployment (149.06% relative improvement), while reducing inference latency by 69.55% through scalable knowledge distillation.
Diffusion models have achieved remarkable success as generative models. However, even a well-trained model can accumulate errors throughout the generation process. These errors become particularly problematic when arbitrary guidance is applied to steer samples toward desired properties, which often breaks sample fidelity. In this paper, we propose a general solution to address the off-manifold phenomenon observed in diffusion models. Our approach leverages a time predictor to estimate deviations from the desired data manifold at each timestep, identifying that a larger time gap is associated with reduced generation quality. We then design a novel guidance mechanism, `Temporal Alignment Guidance' (TAG), attracting the samples back to the desired manifold at every timestep during generation. Through extensive experiments, we demonstrate that TAG consistently produces samples closely aligned with the desired manifold at each timestep, leading to significant improvements in generation quality across various downstream tasks.
Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative models and, in particular, diffusion models. However, current molecular diffusion models are tailored towards a specific downstream task and lack adaptability. We introduce UniGuide, a framework for controlled geometric guidance of unconditional diffusion models that allows flexible conditioning during inference without the requirement of extra training or networks. We show how applications such as structure-based, fragment-based, and ligand-based drug design are formulated in the UniGuide framework and demonstrate on-par or superior performance compared to specialised models. Offering a more versatile approach, UniGuide has the potential to streamline the development of molecular generative models, allowing them to be readily used in diverse application scenarios.
This paper proposes a guidance strategy to achieve time-constrained interception of stationary targets, taking into account both the bounded field-of-view (FOV) of seeker-equipped interceptors and the actuator's physical constraints. Actuator saturation presents a significant challenge in real-world systems, often resulting in degraded performance. However, since these limitations are typically known in advance, incorporating them into the guidance design can enhance overall performance. To address the FOV constraint, a time-to-go error-based approach is adopted. Furthermore, to incorporate the lateral acceleration constraints, the engagement kinematics are augmented with an input saturation model. Subsequently, the guidance strategy that constrains the lateral acceleration and the time-to-go values within their respective bounds is derived using Lyapunov stability concepts and the backstepping technique. Furthermore, a multi-stage approach is suggested to expand the achievable range of impact time. Numerical simulations are performed to validate the efficacy of the proposed scheme for different initial engagement geometries.
This paper presents the design, implementation, and flight test results of two novel 3D path-following guidance algorithms based on nonlinear model predictive control (MPC), with specific application to fixed-wing small uncrewed aircraft systems. To enable MPC, control-augmented modelling and system identification of the RAAVEN small uncrewed aircraft is presented. Two formulations of MPC are then showcased. The first schedules a static reference path rate over the MPC horizon, incentivizing a constant inertial speed. The second, with inspiration from model predictive contouring control, dynamically optimizes for the reference path rate over the controller horizon as the system operates. This allows for a weighted tradeoff between path progression and distance from path, two competing objectives in path-following guidance. Both controllers are formulated to operate over general smooth 3D arc-length parameterized curves. The MPC guidance algorithms are flown over several high-curvature test paths, with comparison to a baseline lookahead guidance law. The results showcase the real-world feasibility and superior performance of nonlinear MPC for 3D path-following guidance at ground speeds up to 36 meters per second.
Amit Shivam, Manuel C. R. M. Fernandes, Fernando A. C. C. Fontes
et al.
This paper presents a geometric and theoretical study of an exponentially varying look-ahead parameter for UAV path-following guidance. Conventional guidance laws with a fixed look-ahead distance often drive the vehicle into turn-rate saturation when the heading or cross-track error is large, leading to constrained maneuvers and higher control effort. The proposed variable L0 strategy reshapes the look-ahead profile so that the guidance command adapts to the evolving tracking error geometry. A detailed investigation shows that this adaptation significantly enlarges the region in which the commanded turn rate remains unsaturated, allowing the vehicle to operate smoothly over a broader range of error conditions. For representative settings, the unsaturated operational envelope increases by more than 70% relative to the constant L0 formulation. These geometric insights translate to smoother trajectories, earlier recovery from saturation, and reduced control demand. Simulation studies on straight-line and elliptical paths demonstrate the merits of the variable look-ahead strategy, highlighting its control-efficient and reliable path-following performance.
Le-Anh Tran, Chung Nguyen Tran, Ngoc-Luu Nguyen
et al.
This paper introduces a novel deep learning framework for low-light image enhancement, named the Encoder-Decoder Network with Illumination Guidance (EDNIG). Building upon the U-Net architecture, EDNIG integrates an illumination map, derived from Bright Channel Prior (BCP), as a guidance input. This illumination guidance helps the network focus on underexposed regions, effectively steering the enhancement process. To further improve the model's representational power, a Spatial Pyramid Pooling (SPP) module is incorporated to extract multi-scale contextual features, enabling better handling of diverse lighting conditions. Additionally, the Swish activation function is employed to ensure smoother gradient propagation during training. EDNIG is optimized within a Generative Adversarial Network (GAN) framework using a composite loss function that combines adversarial loss, pixel-wise mean squared error (MSE), and perceptual loss. Experimental results show that EDNIG achieves competitive performance compared to state-of-the-art methods in quantitative metrics and visual quality, while maintaining lower model complexity, demonstrating its suitability for real-world applications. The source code for this work is available at https://github.com/tranleanh/ednig.
At the core of reinforcement learning is the idea of learning beyond the performance in the data. However, scaling such systems has proven notoriously tricky. In contrast, techniques from generative modeling have proven remarkably scalable and are simple to train. In this work, we combine these strengths, by deriving a direct relation between policy improvement and guidance of diffusion models. The resulting framework, CFGRL, is trained with the simplicity of supervised learning, yet can further improve on the policies in the data. On offline RL tasks, we observe a reliable trend -- increased guidance weighting leads to increased performance. Of particular importance, CFGRL can operate without explicitly learning a value function, allowing us to generalize simple supervised methods (e.g., goal-conditioned behavioral cloning) to further prioritize optimality, gaining performance for "free" across the board.
This work introduces Transformer-based Successive Convexification (T-SCvx), an extension of Transformer-based Powered Descent Guidance (T-PDG), generalizable for efficient six-degree-of-freedom (DoF) fuel-optimal powered descent trajectory generation. Our approach significantly enhances the sample efficiency and solution quality for nonconvex-powered descent guidance by employing a rotation invariant transformation of the sampled dataset. T-PDG was previously applied to the 3-DoF minimum fuel powered descent guidance problem, improving solution times by up to an order of magnitude compared to lossless convexification (LCvx). By learning to predict the set of tight or active constraints at the optimal control problem's solution, Transformer-based Successive Convexification (T-SCvx) creates the minimal reduced-size problem initialized with only the tight constraints, then uses the solution of this reduced problem to warm-start the direct optimization solver. 6-DoF powered descent guidance is known to be challenging to solve quickly and reliably due to the nonlinear and non-convex nature of the problem, the discretization scheme heavily influencing solution validity, and reference trajectory initialization determining algorithm convergence or divergence. Our contributions in this work address these challenges by extending T-PDG to learn the set of tight constraints for the successive convexification (SCvx) formulation of the 6-DoF powered descent guidance problem. In addition to reducing the problem size, feasible and locally optimal reference trajectories are also learned to facilitate convergence from the initial guess. T-SCvx enables onboard computation of real-time guidance trajectories, demonstrated by a 6-DoF Mars powered landing application problem.
This study investigates the interconnected dynamics of mental well-being and self-efficacy among students enrolled at Shaanxi Vocational & Technical College in Shaanxi Province, China. Recognizing the unique challenges faced by students in vocational and technical education, the research adopts a qualitative approach, employing interviews with both students and teachers to gain comprehensive insights. The findings reveal that academic pressures significantly impact students' mental well-being, contributing to heightened stress and anxiety. Social support systems, particularly peer relationships, emerge as crucial in mitigating these challenges, emphasizing the importance of fostering a supportive community. Moreover, self-efficacy is closely tied to practical experiences and skills development within the vocational education system. The study highlights the pivotal role of teachers in shaping students' self-confidence through encouragement and constructive feedback. Career aspirations are identified as influential in shaping self-efficacy, with students having clear goals expressing higher levels of confidence. Based on the findings, the study proposes recommendations to strengthen support systems, promote social support networks, provide teacher training, offer comprehensive career guidance, and enhance the curriculum to incorporate more practical experiences. The recommendations aim to create an environment conducive to the holistic development of students, preparing them for academic success and future careers. The study concludes by emphasizing the importance of ongoing research and evaluation to ensure continuous improvement in support systems and educational practices. Implementing these recommendations can significantly contribute to the positive mental health and self-efficacy of students at Shaanxi Vocational & Technical College, ultimately enhancing their overall educational experience and future