Hasil untuk "Vocational guidance. Career development"

Oscar Bates, Carlos Cueto, Ciaran Coleman et al.

Transcranial ultrasound must overcome the significant challenge of the human skull, limiting both imaging and therapeutic applications. While high-fidelity numerical simulations can compensate for skull-induced distortions, they require precise skull templates (typically derived from Computed Tomography, CT) and spatial alignment to the patient's anatomy. Current alignment relies on concurrent Magnetic Resonance Imaging (MRI) for registration, introducing financial, logistical, and throughput barriers. To overcome these challenges, we present Manifold Optimisation for Full-Waveform Inversion (MOFI), a method to register skull templates without using a guidance image. Our method aligns the skull template by minimising the difference between simulated and observed radio-frequency acoustic data. We demonstrate that MOFI accurately recovers the position of skull templates in silico and in vitro, offering an alternative to MRI guidance-based registration. These results indicate that MOFI has the potential to be a practical alternative to MRI-guided approaches, reducing the barriers to wider clinical adoption of transcranial ultrasound.

Silvia Alvarado-Cordero, Carmen María Frías-Quesada, Viria Ureña-Salazar

Objetivo: Clarificar los principales cambios conceptuales que han surgido en la Orientación en el área vocacional, a partir de la segunda mitad del siglo pasado y las primeras décadas del siglo XXI, donde se transita de la orientación vocacional al desarrollo de carrera. Metodología: Se realizó una revisión bibliográfica de los conceptos vocación, llamado, carrera y desarrollo de la carrera, basándose en aportes teóricos de distintos autores y autoras reconocidos internacionalmente en el campo de la Orientación. Se incluye tanto quienes establecieron las ideas iniciales como quienes las transformaron y actualizaron para adaptarlas a los cambios del mundo actual, además se incorporan aportes de las investigadoras acerca de la realidad costarricense. Resultados: Se profundiza en los cuatro conceptos que surgieron con la disciplina y que luego fueron evolucionando. Tales modificaciones han representado una profunda transformación conceptual que a su vez han incidido en las intervenciones prácticas; por lo tanto, desde el punto de vista epistemológico el cambio, tiene implicaciones en la práctica profesional. Conclusiones: En Costa Rica se justifica la necesidad de que las instituciones que ofrecen servicios en el área vocacional, las instituciones formadoras en Orientación, así como los profesionales en ejercicio clarifiquen el significado que otorgan a cada concepto y las suposiciones teóricas que subyacen en estos; además, se requiere de un conocimiento profundo del contexto costarricense y sus condiciones.

Congjia Chen, Shen Yan, Yufu Qu

Point cloud registration is a fundamental task in 3D vision. Most existing methods only use geometric information for registration. Recently proposed RGB-D registration methods primarily focus on feature fusion or improving feature learning, which limits their ability to exploit image information and hinders their practical applicability. In this paper, we propose ViGG, a robust RGB-D registration method using mutual guidance. First, we solve clique alignment in a visual-geometric combination form, employing a geometric guidance design to suppress ambiguous cliques. Second, to mitigate accuracy degradation caused by noise in visual matches, we propose a visual-guided geometric matching method that utilizes visual priors to determine the search space, enabling the extraction of high-quality, noise-insensitive correspondences. This mutual guidance strategy brings our method superior robustness, making it applicable for various RGB-D registration tasks. The experiments on 3DMatch, ScanNet and KITTI datasets show that our method outperforms recent state-of-the-art methods in both learning-free and learning-based settings. Code is available at https://github.com/ccjccjccj/ViGG.

You-Jin Kim, Joshua Lu, Tobias Höllerer

Dynamic Theater explores the use of augmented reality (AR) in immersive theater as a platform for digital dance performances. The project presents a locomotion-based experience that allows for full spatial exploration. A large indoor AR theater space was designed to allow users to freely explore the augmented environment. The curated wide-area experience employs various guidance mechanisms to direct users to the main content zones. Results from our 20-person user study show how users experience the performance piece while using a guidance system. The importance of stage layout, guidance system, and dancer placement in immersive theater experiences are highlighted as they cater to user preferences while enhancing the overall reception of digital content in wide-area AR. Observations after working with dancers and choreographers, as well as their experience and feedback are also discussed.

Carina Hübner, Thomas Bienengräber, Silvia Greiten

The transition from school to work is a significant phase in adolescent development. For students with special educational needs (SEN), preparing for this process involves many requirements, making vocational orientation particularly challenging. In Germany, vocational orientation programmes are established in all federal states and schools. Studies indicate that longer practical phases are particularly important from the perspective of students with SEN. However, it is unclear what objectives and expectations these students have for practical experience and how these experiences influence their pathway after school. In this exploratory qualitative study, 14 former students with learning disabilities (LD) were asked about the relevance of various internships for their vocational orientation process. The interviews were analysed using qualitative content analysis. The statements emphasise that several and longer practical phases are relevant. The analysis shows that interviewees retrospectively describe how undertaking various internships allows a process of verification and/or falsification, significantly influencing their vocational orientation. This contrasts with students without SEN, who generally complete fewer internships. Overall, it is evident that students with SEN rely on diverse practical experiences during their school years and that the frequency, duration, and quality of practical experience have a lasting influence on their vocational orientation. Abstrakt Övergången mellan skola och arbetsliv utgör en central fas i ungas utveckling och har stor betydelse för deras framtida yrkesmässiga etablering. För elever med särskilda utbildningsbehov (SOU) innebär förberedelserna inför denna process många krav, vilket gör yrkesorienteringen särskilt utmanande. Forskning visar att längre praktikperioder är av särskilt betydelse för elever med SOU ur ett elevperspektiv. Det är dock fortfarande oklart vilka mål och förväntningar dessa elever har på sina praktiska erfarenheter samt på vilket sätt dessa erfarenheter påverkar deras fortsatta väg efter avslutad skolgång. I denna explorativa kvalitativa studie tillfrågades 14 före detta tyska elever med inlärningssvårigheter (IS) om relevansen av olika praktikperioder för deras yrkesorienteringsprocess. Intervjuerna analyserades med hjälp av kvalitativ innehållsanalys. Studenternas utsagor betonar att flera och längre praktikperioder är relevanta. Analysen visar att de intervjuade retrospektivt beskriver hur olika praktikplatser möjliggör en process av verifiering och/eller förfalskning, vilket har en betydande inverkan på deras yrkesorientering. Detta står i kontrast till elever utan behov av särskilt stöd, som i allmänhet genomför färre praktikperioder. Sammantaget framstår det som tydligt att elever med SOU är beroende av olika praktiska erfarenheter under sin skoltid och att frekvensen, varaktigheten samt kvaliteten på dessa erfarenheterna har ett varaktigt inflytande på deras yrkesinriktning. Nyckelord: praktikperioder; yrkesorienteringsprocess; kul; elever med inlärningssvårigheter; övergångsprocess från skola till arbetsliv

Shenhao Zhu, Junming Leo Chen, Zuozhuo Dai et al.

In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques. The methodology utilizes the SMPL(Skinned Multi-Person Linear) model as the 3D human parametric model to establish a unified representation of body shape and pose. This facilitates the accurate capture of intricate human geometry and motion characteristics from source videos. Specifically, we incorporate rendered depth images, normal maps, and semantic maps obtained from SMPL sequences, alongside skeleton-based motion guidance, to enrich the conditions to the latent diffusion model with comprehensive 3D shape and detailed pose attributes. A multi-layer motion fusion module, integrating self-attention mechanisms, is employed to fuse the shape and motion latent representations in the spatial domain. By representing the 3D human parametric model as the motion guidance, we can perform parametric shape alignment of the human body between the reference image and the source video motion. Experimental evaluations conducted on benchmark datasets demonstrate the methodology's superior ability to generate high-quality human animations that accurately capture both pose and shape variations. Furthermore, our approach also exhibits superior generalization capabilities on the proposed in-the-wild dataset. Project page: https://fudan-generative-vision.github.io/champ.

Junkai Yan, Yipeng Gao, Qize Yang et al.

Text-to-3D generation, which synthesizes 3D assets according to an overall text description, has significantly progressed. However, a challenge arises when the specific appearances need customizing at designated viewpoints but referring solely to the overall description for generating 3D objects. For instance, ambiguity easily occurs when producing a T-shirt with distinct patterns on its front and back using a single overall text guidance. In this work, we propose DreamView, a text-to-image approach enabling multi-view customization while maintaining overall consistency by adaptively injecting the view-specific and overall text guidance through a collaborative text guidance injection module, which can also be lifted to 3D generation via score distillation sampling. DreamView is trained with large-scale rendered multi-view images and their corresponding view-specific texts to learn to balance the separate content manipulation in each view and the global consistency of the overall object, resulting in a dual achievement of customization and consistency. Consequently, DreamView empowers artists to design 3D objects creatively, fostering the creation of more innovative and diverse 3D assets. Code and model will be released at https://github.com/iSEE-Laboratory/DreamView.

Hefeng Wang, Jiale Cao, Jin Xie et al.

Text-to-image diffusion models have shown powerful ability on conditional image synthesis. With large-scale vision-language pre-training, diffusion models are able to generate high-quality images with rich texture and reasonable structure under different text prompts. However, it is an open problem to adapt the pre-trained diffusion model for visual perception. In this paper, we propose an implicit and explicit language guidance framework for diffusion-based perception, named IEDP. Our IEDP comprises an implicit language guidance branch and an explicit language guidance branch. The implicit branch employs frozen CLIP image encoder to directly generate implicit text embeddings that are fed to diffusion model, without using explicit text prompts. The explicit branch utilizes the ground-truth labels of corresponding images as text prompts to condition feature extraction of diffusion model. During training, we jointly train diffusion model by sharing the model weights of these two branches. As a result, implicit and explicit branches can jointly guide feature learning. During inference, we only employ implicit branch for final prediction, which does not require any ground-truth labels. Experiments are performed on two typical perception tasks, including semantic segmentation and depth estimation. Our IEDP achieves promising performance on both tasks. For semantic segmentation, our IEDP has the mIoU$^\text{ss}$ score of 55.9% on AD20K validation set, which outperforms the baseline method VPD by 2.2%. For depth estimation, our IEDP outperforms the baseline method VPD with a relative gain of 11.0%.

Yi Wang, Bingxian Mu

This paper introduces Bidirectional Guidance Informed Trees (BIGIT*),~a new asymptotically optimal sampling-based motion planning algorithm. Capitalizing on the strengths of \emph{meet-in-the-middle} property in bidirectional heuristic search with a new lazy strategy, and uniform-cost search, BIGIT* constructs an implicitly bidirectional preliminary motion tree on an implicit random geometric graph (RGG). This efficiently tightens the informed search region, serving as an admissible and accurate bidirectional guidance heuristic. This heuristic is subsequently utilized to guide a bidirectional heuristic search in finding a valid path on the given RGG. Experiments show that BIGIT* outperforms the existing informed sampling-based motion planners both in faster finding an initial solution and converging to the optimum on simulated abstract problems in $\mathbb{R}^{16}$. Practical drone flight path planning tasks across a campus also verify our results.

Tianrui Huang, Pu Cao, Lu Yang et al.

Diffusion-based image editing is a composite process of preserving the source image content and generating new content or applying modifications. While current editing approaches have made improvements under text guidance, most of them have only focused on preserving the information of the input image, disregarding the importance of editability and alignment to the target prompt. In this paper, we prioritize the editability by proposing a zero-shot image editing method, named \textbf{E}nhance \textbf{E}ditability for text-based image \textbf{E}diting via \textbf{E}fficient \textbf{C}LIP guidance (\textbf{E4C}), which only requires inference-stage optimization to explicitly enhance the edibility and text alignment. Specifically, we develop a unified dual-branch feature-sharing pipeline that enables the preservation of the structure or texture of the source image while allowing the other to be adapted based on the editing task. We further integrate CLIP guidance into our pipeline by utilizing our novel random-gateway optimization mechanism to efficiently enhance the semantic alignment with the target prompt. Comprehensive quantitative and qualitative experiments demonstrate that our method effectively resolves the text alignment issues prevalent in existing methods while maintaining the fidelity to the source image, and performs well across a wide range of editing tasks.

Yingfan Liu, Renyu Zhu, Ming Gao

With the rapid development of big data and AI technology, programming is in high demand and has become an essential skill for students. Meanwhile, researchers also focus on boosting the online judging system's guidance ability to reduce students' dropout rates. Previous studies mainly targeted at enhancing learner engagement on online platforms by providing personalized recommendations. However, two significant challenges still need to be addressed in programming: C1) how to recognize complex programming behaviors; C2) how to capture intrinsic learning patterns that align with the actual learning process. To fill these gaps, in this paper, we propose a novel model called Programming Exercise Recommender with Learning Style (PERS), which simulates learners' intricate programming behaviors. Specifically, since programming is an iterative and trial-and-error process, we first introduce a positional encoding and a differentiating module to capture the changes of consecutive code submissions (which addresses C1). To better profile programming behaviors, we extend the Felder-Silverman learning style model, a classical pedagogical theory, to perceive intrinsic programming patterns. Based on this, we align three latent vectors to record and update programming ability, processing style, and understanding style, respectively (which addresses C2). We perform extensive experiments on two real-world datasets to verify the rationality of modeling programming learning styles and the effectiveness of PERS for personalized programming guidance.

David Wan, Jaemin Cho, Elias Stengel-Eskin et al.

Highlighting particularly relevant regions of an image can improve the performance of vision-language models (VLMs) on various vision-language (VL) tasks by guiding the model to attend more closely to these regions of interest. For example, VLMs can be given a "visual prompt", where visual markers such as bounding boxes delineate key image regions. However, current VLMs that can incorporate visual guidance are either proprietary and expensive or require costly training on curated data that includes visual prompts. We introduce Contrastive Region Guidance (CRG), a training-free guidance method that enables open-source VLMs to respond to visual prompts. CRG contrasts model outputs produced with and without visual prompts, factoring out biases revealed by the model when answering without the information required to produce a correct answer (i.e., the model's prior). CRG achieves substantial improvements in a wide variety of VL tasks: When region annotations are provided, CRG increases absolute accuracy by up to 11.1% on ViP-Bench, a collection of six diverse region-based tasks such as recognition, math, and object relationship reasoning. We also show CRG's applicability to spatial reasoning, with 10% improvement on What'sUp, as well as to compositional generalization -- improving accuracy by 11.5% and 7.5% on two challenging splits from SugarCrepe -- and to image-text alignment for generated images, where we improve by up to 8.4 AUROC and 6.8 F1 points on SeeTRUE. When reference regions are absent, CRG allows us to re-rank proposed regions in referring expression comprehension and phrase grounding benchmarks like RefCOCO/+/g and Flickr30K Entities, with an average gain of 3.2% in accuracy. Our analysis explores alternative masking strategies for CRG, quantifies CRG's probability shift, and evaluates the role of region guidance strength, empirically validating CRG's design choices.

Zhicheng Sun, Zhenhao Yang, Yang Jin et al.

Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem. The prevalent approaches typically require training on extensive domain-specific images to achieve identity preservation, which lacks flexibility across different use cases. To address this issue, we exploit classifier guidance, a training-free technique that steers diffusion models using an existing classifier, for personalized image generation. Our study shows that based on a recent rectified flow framework, the major limitation of vanilla classifier guidance in requiring a special classifier can be resolved with a simple fixed-point solution, allowing flexible personalization with off-the-shelf image discriminators. Moreover, its solving procedure proves to be stable when anchored to a reference flow trajectory, with a convergence guarantee. The derived method is implemented on rectified flow with different off-the-shelf image discriminators, delivering advantageous personalization results for human faces, live subjects, and certain objects. Code is available at https://github.com/feifeiobama/RectifID.

Zicheng Duan, Yuxuan Ding, Chenhui Gou et al.

Zero-shot personalized image generation models aim to produce images that align with both a given text prompt and subject image, requiring the model to incorporate both sources of guidance. Existing methods often struggle to capture fine-grained subject details and frequently prioritize one form of guidance over the other, resulting in suboptimal subject encoding and imbalanced generation. In this study, we uncover key insights into overcoming such drawbacks, notably that 1) the choice of the subject image encoder critically influences subject identity preservation and training efficiency, and 2) the text and subject guidance should take effect at different denoising stages. Building on these insights, we introduce a new approach, EZIGen, that employs two main components: leveraging a fixed pre-trained Diffusion UNet itself as subject encoder, following a process that balances the two guidances by separating their dominance stage and revisiting certain time steps to bootstrap subject transfer quality. Through these two components, EZIGen, initially built upon SD2.1-base, achieved state-of-the-art performances on multiple personalized generation benchmarks with a unified model, while using 100 times less training data. Moreover, by further migrating our design to SDXL, EZIGen is proven to be a versatile model-agnostic solution for personalized generation. Demo Page: zichengduan.github.io/pages/EZIGen/index.html

Rinat Gold, Azgad Gold

BACKGROUND The therapeutic process is fraught with various feelings. This research focused on a specific type of negative feeling, namely self-doubt (SD). AIM To explore and characterize the nature of SD among speech and language therapists (SLTs) (the frequency of SD, situations that trigger SD, emotions and thoughts related to SD, and coping strategies) in various stages of occupational experience. METHODS & PROCEDURES A total of 267 SLTs answered an online survey. Respondents represented SLTs in all stages of vocational experience, with varying academic degrees, from a variety of employment settings. The survey addressed situations that trigger SD, thoughts, and emotions associated with SD and the background information of the respondents. Frequency distributions of the responses of the participants were determined, and independent-samples Kruskal-Wallis tests were conducted to examine if there were differences between groups that differed in their occupational experience on the frequency of SD, attitudes towards SD and emotions related to SD. OUTCOMES & RESULTS Differences were found between SLTs in various stages of professional development in several aspects of SD. Novice SLTs reported significantly higher levels of SD compared with experienced SLTs. In the face of SD, novice SLTs consider career abandonment significantly more than do experienced SLTs. They perceive themselves as a failure when experiencing SD to a significantly greater extent than do more experienced SLTs. In addition, SD is associated with various negative emotions. CONCLUSIONS & IMPLICATIONS Self-doubt is a natural professional feeling. It may be harmful especially in the early stages of professional development. Our findings call for support and guidance in the face of SD. WHAT THIS PAPER ADDS What is already known on the subject Healthcare professionals report feeling SD. This feeling may have deleterious effects on well-being and career satisfaction and is especially harmful in young therapists. What this paper adds to existing knowledge This study characterizes the nature of SD among SLTs in various stages of occupational experience. Our findings indicate that SD is reported among SLTs at all career stages, especially in novice SLTs. Self-doubt is associated with a range of negative thoughts and emotions, and it may be triggered by various situations. Nonetheless, it is a topic that our respondents rarely learn about. What are the potential or actual clinical implications of this work? Normalising and validating SD is important to SLTs' resilience and may facilitate coping. This may be achieved by learning about the subject of SD in graduate programmes. In addition, mentors should create a safe learning culture to allow sharing SD and challenging situations, especially in the first years of occupational experience.

O. Vasyl’yeva, L. Horoshkova, S. Shvydka

Relevance of the research topic. The structural transformations of the national economy, competition intensification, and unfavourable demographic changes lead to dramatic changes in the labor market, which is characterized by a mismatch between the demand and the supply of labour and vocational qualification and educational levels as well as types of economic activity. The consequences of the full-scale military aggression of the russian federation in Ukraine weakens the potential of the labor market. Firstly, there are barriers to the free movement of production factors, including workforce. Secondly, there are losses of production facilities and infrastructure. Thirdly, as a result of forced migration, there are negative trends in employment and income. Furthermore, the structure of the labor market has changed significantly: there is an imbalance between labor supply and demand, and regional disparities in the concentration of labor resources deepens. Formulation of the problem. The stabilization of the national labor market, growth in employment, redistribution of the workforce for the post-war recovery of Ukraine's economy require the development of effective labor market management mechanisms in the context of training specialists, formation of special skills, the development of professional education system to minimize educational-professional and regional imbalances. Analysis of recent research and publications. The trends in demand and supply on the labor market, the influence of various factors on its structure are investigated by domestic (V. Antoniuk,  V. Brych, V. Heiets,  A. Hrishnova, L. Ilich, A. Kolot,  M. Krymova, E. Libanova, A. Novikova, I. Petrova, N. Rushchyshyn,  Z. Smutchak,  L. Shaulska,  N. Yakymova) and foreign scientists (D. Alpisbaeva, H. Andersen, G. Becker, G. Brisese, M. Kali, S. McGuinness, M. Popp, A. Robay, P. Sloan, G. Fields, R. Freeman). The results of research of educational and qualification disproportions in the labor sphere are reflected in the works of N. Azmuk, V. Twin, O. Kupets, L. Lisohor, V. Sarioglo, L. Fedunichik, who study the disproportionality of labour supply and demand in terms of the uneven distribution of workers in terms of occupations and economic activities, due to the imbalance between the available and the required levels of professional competence of employees. The development of the system of vocational and technical education as a source of formation of the labour market of vocational professions is described in the scientific developments of A. Amoshi, I. Hnibidenko, M. Dolishny, V. Kutsenko, M. Semikina, V. Shmatova and others. Selection of unexplored parts of the general problem. Despite some progress in resolving these problems, the impact of the vocational training system on the sectoral and regional disparities in the national labour market is not sufficiently investigated; as well as the risks and threats caused by the military actions in Ukraine are still not taken into account. Setting the task, the purpose of the study. The above-mentioned circumstances make it expedient to assess educational, vocational and sectoral imbalances in the labour market and identify guidelines for redressing the imbalance between labour supply and demand. Method or methodology for conducting research. In the process of research general scientific (analysis and synthesis, abstract-logical, generalization and system analysis) and special methods of studying economic phenomena and processes are used. Presentation of the main material (results of work). The paper analyses the dynamics of the labour market, indicating the steady trend towards the decrease of the number of employed population. An assessment of the structure of demand and supply in the labour market by economic activity and occupational group reveals an excess in the labour market of the economically active population, which associates itself with the following areas of the economy: agriculture, trade and vehicle repair, public administration. There is an unmet demand for workers in the manufacturing industry, transport, health, education. Among the professional groups, a large proportion of the unemployed are trade and service workers, employees and managers, and the most demanded in the labour market are skilled vocational professionals. The results of the study highlight that the current problem of the national labour market and the existing imbalance between the supply of labour and the demand for it is the mismatch between educational services of the vocational training system to the needs of the labour market, insufficient level of vocational education, imperfect state and regional order for skilled workers, lack of effective interaction between stakeholders, insufficient motivation of young people for vocational professions. Conclusion according to the article. In order to overcome the existing negative trends, it is necessary to increase vocational guidance among schoolchildren. This guidance should focus on popularization of relevant and promising professions. It should contribute to strengthening the capacity of public employment services; expand the range of services and improve their quality. Career guidance ought to strengthen the practical component of training of workers and develop the system of dual education. There should be introduction of the program “job security for young people”, initiation of the research on formation of specialties, skills and qualifications, taking into account the strategic outlook of the labour market.  Social dialogue in the context of creating conditions for continuing vocational education should be created. There should be promotion of the development of small and medium-sized enterprises as well as constant content of educational programmes to meet the needs of the regional labour market and expand the competencies of skilled workers. State standards for specific occupations on a modular and competency basis should be introduced. There is a strong demand for improvement of the material and technical base of vocational schools as well as modernization of the network of educational establishments. The implementation of these directions will contribute to the formation and development of innovative human capital, restoration of the quality of the workforce, and overcoming the imbalance in the labor market. The balancing of the labor market is the main need for post-war development, and effective employment must be an integral part of post-war reconstruction social policy.

Leon Pietschmann, Paul-David Zuercher, Erik Bubík et al.

The combination of Visual Guidance and Extended Reality (XR) technology holds the potential to greatly improve the performance of human workforces in numerous areas, particularly industrial environments. Focusing on virtual assembly tasks and making use of different forms of supportive visualisations, this study investigates the potential of XR Visual Guidance. Set in a web-based immersive environment, our results draw from a heterogeneous pool of 199 participants. This research is designed to significantly differ from previous exploratory studies, which yielded conflicting results on user performance and associated human factors. Our results clearly show the advantages of XR Visual Guidance based on an over 50\% reduction in task completion times and mistakes made; this may further be enhanced and refined using specific frameworks and other forms of visualisations/Visual Guidance. Discussing the role of other factors, such as cognitive load, motivation, and usability, this paper also seeks to provide concrete avenues for future research and practical takeaways for practitioners.

Niket Patel, Luis Salamanca, Luis Barba

Diffusion models have emerged as a pivotal advancement in generative models, setting new standards to the quality of the generated instances. In the current paper we aim to underscore a discrepancy between conventional training methods and the desired conditional sampling behavior of these models. While the prevalent classifier-free guidance technique works well, it's not without flaws. At higher values for the guidance scale parameter $w$, we often get out of distribution samples and mode collapse, whereas at lower values for $w$ we may not get the desired specificity. To address these challenges, we introduce an updated loss function that better aligns training objectives with sampling behaviors. Experimental validation with FID scores on CIFAR-10 elucidates our method's ability to produce higher quality samples with fewer sampling timesteps, and be more robust to the choice of guidance scale $w$. We also experiment with fine-tuning Stable Diffusion on the proposed loss, to provide early evidence that large diffusion models may also benefit from this refined loss function.

Binbin Li, Tianxin Meng, Xiaoming Shi et al.

It is becoming increasingly emphasis on the importance of LLM participating in clinical diagnosis decision-making. However, the low specialization refers to that current medical LLMs can not provide specific medical advice, which are more like a medical Q\&A. And there is no suitable clinical guidance tree data set that can be used directly with LLM. To address this issue, we first propose LLM-executavle clinical guidance tree(CGT), which can be directly used by large language models, and construct medical diagnostic decision-making dataset (MedDM), from flowcharts in clinical practice guidelines. We propose an approach to screen flowcharts from medical literature, followed by their identification and conversion into standardized diagnostic decision trees. Constructed a knowledge base with 1202 decision trees, which came from 5000 medical literature and covered 12 hospital departments, including internal medicine, surgery, psychiatry, and over 500 diseases.Moreover, we propose a method for reasoning on LLM-executable CGT and a Patient-LLM multi-turn dialogue framework.

Margarida Martins Barroso

In this article we analyze the educational and work trajectories of middle managers at the same multinational company in Sweden and Portugal. Based on the analysis of the company’s documentation and on qualitative interviews with middle managers in both countries, results show four different types of trajectories in this group of professionals: linear specialist, linear generalist, reoriented and disrupted. In the Portuguese establishment, all interviewed managers had a higher education degree in areas related to management, and most of them had a reoriented type of trajectory. In Sweden, the educational levels and fields of study were more diversified and most of the interviewees had a disrupted trajectory. The article discusses the co-existence of different educational paths leading to similar professional outcomes and the effects of the institutional context in shaping individual trajectories and company strategy. Abstrakt I den här artikeln analyserar vi utbildnings- och yrkeskarriärerna för mellanchefer i samma multinationella företag i Sverige och Portugal. Baserat på analysen av företagsdokumentation och kvalitativa intervjuer med mellanchefer i båda länderna visar resultaten på fyra olika typer av banor i denna yrkesgrupp: linjär specialist, linjär generalist, omorienterad och avbruten. I det portugisiska företaget hade alla intervjuade chefer en högre utbildning inom områden med anknytning till förvaltning, och de flesta av dem hade en omorienterad typ av karriär. I Sverige var utbildningsnivåerna och studieområdena mer diversifierade och de flesta av de intervjuade hade en avbruten karriär. I artikeln diskuteras samexistensen av olika utbildningsvägar som leder till liknande yrkesmässiga resultat och effekterna av det institutionella sammanhanget när det gäller att forma individuella banor och företagsstrategier för rekrytering. Nyckelord: Utbildningsvägar; Förvaltning; Ledarkarriär; Portugal; Sverige