Koji Nakamura, Yua Murayama, Issei Horikoshi
et al.
The Low-Gain Avalanche Diode (LGAD) is a semiconductor detector capable of achieving excellent timing resolution (~20 ps) for minimum ionizing particles (MIPs). To realize a pixelated detector with both high timing precision and spatial resolution, we have been developing Capacitive-Coupled LGADs (ACLGADs) for future collider experiments, such as the latter phase of the High-Luminosity LHC. We have successfully fabricated a pixelated ACLGAD (ACLGADpix) with a 100 $μ$m %\times% 100 $μ$m pixel pitch, maintaining uniform timing performance across the active area. In this presentation, we will report recent measurement results from ACLGADpix prototypes using beta rays, an infrared laser, and a 3 GeV electron beam. We will also discuss potential readout electronics for future collider applications.
This paper introduces a new model for ML-augmented online decision making, called online algorithms with unreliable guidance (OAG). This model completely separates between the predictive and algorithmic components, thus offering a single well-defined analysis framework that relies solely on the considered problem. Formulated through the lens of request-answer games, an OAG algorithm receives, with each incoming request, a piece of guidance which is taken from the problem's answer space; ideally, this guidance is the optimal answer for the current request, however with probability $β$, the guidance is adversarially corrupted. The goal is to develop OAG algorithms that admit good competitiveness when $β= 0$ (a.k.a. consistency) as well as when $β= 1$ (a.k.a. robustness); the appealing notion of smoothness, that in most prior work required a dedicated loss function, now arises naturally as $β$ shifts from $0$ to $1$. We then describe a systematic method, called the drop or trust blindly (DTB) compiler, which transforms any online algorithm into a learning-augmented online algorithm in the OAG model. Given a prediction-oblivious online algorithm, its learning-augmented counterpart produced by applying the DTB compiler either follows the incoming guidance blindly or ignores it altogether and proceeds as the initial algorithm would have; the choice between these two alternatives is based on the outcome of a (biased) coin toss. As our main technical contribution, we prove (rigorously) that although remarkably simple, the class of algorithms produced via the DTB compiler includes algorithms with attractive consistency-robustness guarantees for three classic online problems: for caching and uniform metrical task systems our algorithms are optimal, whereas for bipartite matching (with adversarial arrival order), our algorithm outperforms the state-of-the-art.
Background: South African higher education institutions shifted from traditional to remote teaching and learning because of the coronavirus disease 2019 (COVID-19) national lockdown. Despite extensive research on remote teaching and learning in public universities, there is a noticeable gap in investigating how private higher education colleges, particularly their lecturers, navigated the transition to remote teaching.
Objectives: The objective of this study was to investigate lecturers’ experiences of remote teaching during the COVID-19 pandemic and national lockdown in private higher education colleges in Johannesburg, South Africa.
Methods: This qualitative study is based on an MA dissertation, but focuses specifically on data from semi-structured interviews with 10 lecturers employed at various private higher education colleges in Johannesburg. Thematic content analysis method was used to analyse data and present findings.
Results: The findings revealed several challenges faced by lecturers in transitioning and adapting to remote teaching. These challenges included unreliable internet connectivity, difficulty in monitoring student engagement during online sessions, teaching practical modules remotely, and the absence of conducive work environments during the national lockdown and work-from-home arrangements.
Conclusion: Lecturers at private higher education colleges faced significant challenges in adapting to remote teaching during the COVID-19 lockdown. These challenges highlight the need for better institutional support, targeted training in digital pedagogy, and improved infrastructure to enable more effective remote teaching.
Contribution: This study contributes to literature on remote teaching in private higher education institutions and highlights how remote teaching deepened lecturer–student distance, demonstrating the relevance of transactional distance theory (TDT).
Vocational guidance. Career development, Social Sciences
Anton Ludwig Bonin, Pawel Robert Smolinski, Jacek Winiarski
This study investigates the impact of Generative AI on software development within the IT sector through a mixed-method approach, utilizing a survey developed based on expert interviews. The preliminary results of an ongoing survey offer early insights into how Generative AI reshapes personal productivity, organizational efficiency, adoption, business strategy and job insecurity. The findings reveal that 97% of IT workers use Generative AI tools, mainly ChatGPT. Participants report significant personal productivity gain and perceive organizational efficiency improvements that correlate positively with Generative AI adoption by their organizations (r = .470, p < .05). However, increased organizational adoption of AI strongly correlates with heightened employee job security concerns (r = .549, p < .001). Key adoption challenges include inaccurate outputs (64.2%), regulatory compliance issues (58.2%) and ethical concerns (52.2%). This research offers early empirical insights into Generative AI's economic and organizational implications.
Learning with guidance has proven effective across a wide range of machine learning systems. Guidance may, for example, come from annotated datasets in supervised learning, pseudo-labels in semi-supervised learning, and expert demonstration policies in reinforcement learning. However, guidance signals can be noisy due to domain shifts and limited data availability and may not generalize well. Blindly trusting such signals when they are noisy, incomplete, or misaligned with the target domain can lead to degraded performance. To address these challenges, we propose Adaptive Conformal Guidance (AdaConG), a simple yet effective approach that dynamically modulates the influence of guidance signals based on their associated uncertainty, quantified via split conformal prediction (CP). By adaptively adjusting to guidance uncertainty, AdaConG enables models to reduce reliance on potentially misleading signals and enhance learning performance. We validate AdaConG across diverse tasks, including knowledge distillation, semi-supervised image classification, gridworld navigation, and autonomous driving. Experimental results demonstrate that AdaConG improves performance and robustness under imperfect guidance, e.g., in gridworld navigation, it accelerates convergence and achieves over $6\times$ higher rewards than the best-performing baseline. These results highlight AdaConG as a broadly applicable solution for learning under uncertainty.
Dar-Yen Chen, Hmrishav Bandyopadhyay, Kai Zou
et al.
Negative guidance -- explicitly suppressing unwanted attributes -- remains a fundamental challenge in diffusion models, particularly in few-step sampling regimes. While Classifier-Free Guidance (CFG) works well in standard settings, it fails under aggressive sampling step compression due to divergent predictions between positive and negative branches. We present Normalized Attention Guidance (NAG), an efficient, training-free mechanism that applies extrapolation in attention space with L1-based normalization and refinement. NAG restores effective negative guidance where CFG collapses while maintaining fidelity. Unlike existing approaches, NAG generalizes across architectures (UNet, DiT), sampling regimes (few-step, multi-step), and modalities (image, video), functioning as a \textit{universal} plug-in with minimal computational overhead. Through extensive experimentation, we demonstrate consistent improvements in text alignment (CLIP Score), fidelity (FID, PFID), and human-perceived quality (ImageReward). Our ablation studies validate each design component, while user studies confirm significant preference for NAG-guided outputs. As a model-agnostic inference-time approach requiring no retraining, NAG provides effortless negative guidance for all modern diffusion frameworks -- pseudocode in the Appendix!
This paper presents an input-output feedback linearization (IOL)-based guidance law to ensure interception in a pursuer-evader engagement scenario. A point-mass dynamic model for both the pursuer and the evader is considered. An IOL guidance law is derived using range and line-of-sight (LOS) rate measurements. It is found that the range-based IOL guidance law exhibits a singularity under certain conditions. To address this issue, a fuzzy logic system is employed to smoothly blend the IOL guidance with the classical proportional guidance law, thereby avoiding the singularity. In contrast, the LOS-based IOL guidance law is free of singularities but suffers from divergence issues due to angle-related complications. To resolve this, a simple correction function is introduced to ensure consistent interception behavior. Results from Monte Carlo simulations indicate that both modifications of the IOL guidance laws cause interception with control limits applied.
Iskander Azangulov, Peter Potaptchik, Qinyu Li
et al.
Guidance is a cornerstone of modern diffusion models, playing a pivotal role in conditional generation and enhancing the quality of unconditional samples. However, current approaches to guidance scheduling--determining the appropriate guidance weight--are largely heuristic and lack a solid theoretical foundation. This work addresses these limitations on two fronts. First, we provide a theoretical formalization that precisely characterizes the relationship between guidance strength and classifier confidence. Second, building on this insight, we introduce a stochastic optimal control framework that casts guidance scheduling as an adaptive optimization problem. In this formulation, guidance strength is not fixed but dynamically selected based on time, the current sample, and the conditioning class, either independently or in combination. By solving the resulting control problem, we establish a principled foundation for more effective guidance in diffusion models.
Philipp Vaeth, Dibyanshu Kumar, Benjamin Paassen
et al.
Classifier guidance is intended to steer a diffusion process such that a given classifier reliably recognizes the generated data point as a certain class. However, most classifier guidance approaches are restricted to robust classifiers, which were specifically trained on the noise of the diffusion forward process. We extend classifier guidance to work with general, non-robust, classifiers that were trained without noise. We analyze the sensitivity of both non-robust and robust classifiers to noise of the diffusion process on the standard CelebA data set, the specialized SportBalls data set and the high-dimensional real-world CelebA-HQ data set. Our findings reveal that non-robust classifiers exhibit significant accuracy degradation under noisy conditions, leading to unstable guidance gradients. To mitigate these issues, we propose a method that utilizes one-step denoised image predictions and implements stabilization techniques inspired by stochastic optimization methods, such as exponential moving averages. Experimental results demonstrate that our approach improves the stability of classifier guidance while maintaining sample diversity and visual quality. This work contributes to advancing conditional sampling techniques in generative models, enabling a broader range of classifiers to be used as guidance classifiers.
Objetivo: Este estudio se enfocó en analizar los procesos psicosociales inherentes a las dinámicas de pareja de estudiantes matriculados en la carrera de Ingeniería en Sistemas de Información en la Universidad Nacional, Costa Rica.
Metodología: Se adoptó una metodología cualitativa de corte narrativo, y la investigación incluyó cinco parejas participantes. Para la recolección de datos, se emplearon entrevistas semiestructuradas y el método de historia de vida durante el año 2021, permitiendo una profundización significativa en las experiencias personales de las personas participantes.
Resultados: El análisis de la información reveló que la comunicación entre las parejas se caracteriza por una orientación predominantemente positiva, evidenciando patrones de apego seguro y una marcada reciprocidad en la atracción interpersonal.
Conclusiones: Los hallazgos subrayan la importancia de promover investigaciones adicionales en el campo de la Orientación que profundicen en las relaciones de pareja, principalmente en contextos académicos especializados, para así fortalecer el cuerpo de conocimiento disponible que sustenta las dinámicas afectivas en entornos universitarios.
Classifier-free guidance (CFG) is widely used in diffusion models but often introduces over-contrast and over-saturation artifacts at higher guidance strengths. We present EP-CFG (Energy-Preserving Classifier-Free Guidance), which addresses these issues by preserving the energy distribution of the conditional prediction during the guidance process. Our method simply rescales the energy of the guided output to match that of the conditional prediction at each denoising step, with an optional robust variant for improved artifact suppression. Through experiments, we show that EP-CFG maintains natural image quality and preserves details across guidance strengths while retaining CFG's semantic alignment benefits, all with minimal computational overhead.
An important field of research in defense-related technology is the design of guidance laws. A guided missile is generally challenging to intercept if its trajectory becomes unpredictable. In this short communication, we have discussed a possible application of the chaos theory in developing an advanced guided missile, where the guidance law is based upon a robust chaotic map. This type of guided missile may be almost impossible to intercept by existing missile defense systems due to its unpredictable trajectory.
When using a diffusion model for image editing, there are times when the modified image can differ greatly from the source. To address this, we apply a dual-guidance approach to maintain high fidelity to the original in areas that are not altered. First, we employ text-guided optimization, using text embeddings to direct latent space and classifier-free guidance. Second, we use perceptual similarity guidance, optimizing latent vectors with posterior sampling via Tweedie formula during the reverse process. This method ensures the realistic rendering of both the edited elements and the preservation of the unedited parts of the original image.
AbstractThe incidence of app-based gig work is expanding rapidly in developed global north countries. Many app-based gig workers are migrants from developing global south countries searching for a better life in their resettlement countries. App-based gig work, however, is insecure, irregular and potentially precarious. Access to decent work is vital for migrants’ integration after resettlement and also their career development. In the context of the decent work agenda, this article explores the intersections of migration, app-based gig work, and southern migrants’ career development in the global north and considers the implications for career practice and research.
چکیده:هدف: پژوهش حاضر با هدف تبیین ابعاد و نشانگرهای پیشایندها و پسایندهای سکوت سازمانی معلمان انجام شده است.روش: پژوهش با استفاده از راهبرد پدیدار شناسی توصیفی انجام شد. مشارکتکنندگان 21 نفر از خبرگان آموزشی بودند که به روش هدفمند انتخاب شدند و به روش گلوله برفی به اشباع رسیدند. داده ها به وسیله مصاحبه نیمه ساختاریافته گردآوری شد و با استفاده از کدگذاری در سه سطح باز، محوری و منتخب تحلیل شد. برای اعتباریابی یافته ها از روش بررسی توسط اعضا و بازبینی توسط همکاران استفاده شد. یافته ها: یافته ها نشان داد که پیشایندهای سکوت سازمانی معلمان، 41 نشانگر هستند که در چهار بعد: عوامل مربوط به سطح مدیریت ارشد، عوامل سازمانی، عوامل مدیریتی و عوامل مربوط به زمینه اجتماعی مدرسه طبقه بندی می شوند. همچنین پسایندهای سکوت سازمانی معلمان، 15 نشانگر هستند که در دو بعد عملکرد معلم و تاثیر آموزشی ـ تربیتی بر دانش آموزان طبقه بندی می شوند. در نهایت اعتباریابی یافته ها نشان داد که الگوی پیشایندها و پسایندهای سکوت سازمانی معلمان از اعتبار لازم برخوردار است.نتیجه گیری: یافته های این پژوهش ضمن اینکه نوآوری پژوهشی دارد، می تواند برای تصمیم سازان و دست اندرکاران آموزشی در مدیریت سکوت معلم در مدرسه مفید باشد.
The importance of vocational guidance is growing, and the focus on modern tools enabling the development of competences such as flexibility, the ability to identify trends shaping the labour market and considering different scenarios of its development seem to be indispensable to survive in the world of work. The goal of the paper is to demonstrate a methodology and the outcomes of a research focused on the difference between the ideal characteristics and impressions of completed education by career counsellors in Poland. The research methods used are: the literature review, bibliometric analysis and the analysis and logical construction method. To diagnose the educational offerings the authors applied a modified SERVQUAL model. The observed gaps demonstrate the presence of significant discrepancies between the evaluation of the completed courses and the expectations of the respondents. Therefore, the expectations of those who took part in the survey about the quality of education in the field of vocational guidance are not fulfilled. The results of the analysis allow also to conclude that curricula only take little account of trend and scenario analysis tools specific to Industry 4.0; and this demonstrates a major challenge for including this curriculum content in the area of vocational guidance.
First published online 01 April 2022
زنان برای کسب موفقیت در انجام فعالیتهای اقتصادی به توانمندسازی با رویکرد کارآفرینی نیاز دارند. این پژوهش توصیفی-همبستگی با هدف بررسی تأثیر قابلیتهای کارآفرینی زنان روستایی بر میزان توانمندسازی آنها انجام شد. افراد مشارکتکننده در این پژوهش زنان روستایی خانهدار و سرپرست خانوار استان کرمانشاه به تعداد 221358 تن بودند که بر اساس جدول کرجسی و مورگان تعداد 354 تن از آنها به روش نمونهگیری تصادفی چند مرحلهای انتخاب شدند. ابزار پژوهش پرسشنامه محققساخته با سه بخش ویژگیهای فردی، میزان توانمندسازی با سه بعد اقتصادی، اجتماعی، رهبری و قابلیتهای کارآفرینی با چهار بعد استقلالطلبی، نوآوری، ریسکپذیری و فراکنشی عمل کردن بود که روایی و پایایی آن توسط روایی همگرا، ضریب آلفای کرونباخ و پایایی ترکیبی تأیید شد. برای تحلیل دادهها از نرمافزار SPSSWIN20 و PLSWIN3 استفاده شد. بر اساس نتایج، زنان روستایی در دو بعد اقتصادی و اجتماعی از توانمندی متوسط و در بعد رهبری از توانمندی مطلوب برخوردار بودند. آنها از لحاظ قابلیت کارآفرینی در بعد نوآوری بسیار ضعیف، در بعد فراکنشی عمل کردن، تقریباً ضعیف و در دو بعد استقلالطلبی و ریسکپذیری متوسط بودند. بر اساس نتایج مدلسازی معادلات ساختاری، قابلیتهای کارآفرینی بر روی توانمندسازی تأثیر مثبت و معناداری داشت. بر مبنای نتایج پیشنهاد میشود که برنامههای ترویجی برای توسعه فعالیتهای کارآفرینانه زنان روستایی توسعه یابد تا در نهایت محیطی کارآفرینانه برای آنان حاکم گردد.
Vocational guidance. Career development, Agriculture (General)
The aim of this article is to describe and discuss professionalism and professionalisation of career guidance counselling in Sweden in relation to different logics of professional practice. The transformation of the labour market and the educational system in Sweden over the past decades has led to an increase in the importance of individual educational and occupational choices and development of career management skills in relation to individual trajectories based on personal interests. Also, individual agency has increased in importance in relation to the quantitative planning of secondary and tertiary education aiming to match supply and demand in the labour market. Within the dominant functionalistic technical-instrumental paradigm, which focus on individual agency and rational choices, the importance of career guidance and counselling has increasingly come into focus. Through the incorporation of market-oriented management logics in the welfare sector, the previously dominant logics of professional responsibility have in many professional groups been replaced by professional accountability. In Sweden, the process of professionalising the emerging semi-profession of career guidance and counselling is driven by policymakers. Rather than being defined by practising professionals, professionalism is externally defined in reports and policy documents shaped by elites and experts, outlining core competences in career guidance and counselling related to political objectives. The practice of career guidance and counselling is not strongly governed and there is little external evaluation. The professionals have autonomy and relatively high degrees of freedom in formulating problems and solutions, but little organisational support. Abstrakt Syftet med denna artikel är att diskutera professionalism och professionalisering av studie- och yrkesvägledning i Sverige i relation till olika logiker för organisering av professionellt arbete. Arbetsmarknaden och utbildningsystemen i Sverige har genomgått betydande strukturella förändringar under de senaste decennierna, vilka har medfört att betydelsen av individuella studie- och yrkesval samt utvecklingen av karriärkompetens har ökat, i relation till såväl individens livsbanor som matchningen mellan utbud och efterfrågan på arbetsmarknaden. Inom det dominerande funktionalistiska och tekniskt rationella paradigmet betonas betydelsen av individuella val samt studie- och yrkesvägledning. Med införandet av marknadslogik i välfärdssektorn har den tidigare dominerande logiken, vilken var baserad på yrkesprofessionalism och professionellt ansvar, i många professionella grupper ersatts av organisationsprofessionalism och redovisningsskyldighet. I Sverige har studie- och yrkesvägledningens professionalisering i första hand drivits på från den politiska arenan. Professionalismen har utformats av expertis i policydokument, där den professionella kompetensen har definierats utifrån politiska mål. Den centrala styrningen och externa utvärderingen av studie- och yrkesvägledningen är dock begränsad. Studie- och yrkesvägledare har relativt stora frihet att formulera problem och identifiera lösningar i den professionella praktiken, men de har svagt stöd inom organisationen.
With the vigorous development of mobile photography technology, major mobile phone manufacturers are scrambling to improve the shooting ability of equipments and the photo beautification algorithm of software. However, the improvement of intelligent equipments and algorithms cannot replace human subjective photography technology. In this paper, we propose the aesthetic language guidance of image (ALG). We divide ALG into ALG-T and ALG-I according to whether the guiding rules are based on photography templates or guidance images. Whether it is ALG-T or ALG-I, we guide photography from three attributes of color, lighting and composition of the images. The differences of the three attributes between the input images and the photography templates or the guidance images are described in natural language, which is aesthetic natural language guidance (ALG). Also, because of the differences in lighting and composition between landscape images and portrait images, we divide the input images into landscape images and portrait images. Both ALG-T and ALG-I conduct aesthetic language guidance respectively for the two types of input images (landscape images and portrait images).