ethicalabs/Research-Intent-Collab huggingface dataset:
PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track

We present a large-scale object detection system by team PFDet. Our system enables training with huge datasets using 512 GPUs, handles sparsely verified classes, and massive class imbalance. Using our method, we achieved 2nd place in the Google AI Open Images Object Detection Track 2018 on Kaggle.
Sounding-rocket microgravity experiments on alumina dust

AbstractAlumina (Al2O3) is believed to be the first major condensate to form in the gas outflow from oxygen-rich evolved stars because of the refractoriness and that α-Al2O3 (corundum, most stable polymorph) is a potential origin of a 13 μm feature that appears close to stars. However, no one has directly reproduced the 13 μm feature experimentally, and it has remained as a noteworthy unidentified infrared band. Here, we report nucleation experiments on Al2O3 nanoparticles monitored by a specially designed infrared spectrometer in the microgravity environment of a sounding rocket. The conditions approximate to those around asymptotic giant branch (AGB) stars. The measured spectra of the nucleated Al2O3 show a sharp feature at a wavelength of 13.55 μm and comparable in width to that observed near oxygen-rich AGB stars. Our finding that α-Al2O3 nucleates under certain condition provides a solid basis to elaborate condensation models of dust around oxygen-rich evolved stars.
INVESTIGATING THE INTERACTION BETWEEN E-TEST TYPE (ADAPTIVE VS. NON- ADAPTIVE) AND LEARNING STYLE (HOLISTIC VS. ANALYTICAL): IMPLICATIONS FOR SELF-EFFICACY AND STUDENTS' SATISFACTION WITH THE TEST AMONG EDUCATIONAL TECHNOLOGY STUDENTS

This study is to examine how self-efficacy and test satisfaction among students studying educational technology are impacted by the type of electronic exam (adaptive vs. non-adaptive) and learning style (holistic vs. analytical). Recent trends toward the application of artificial intelligence in the development of intelligent assessment technologies that improve measurement accuracy and adjust assessments to the unique needs of each learner make the study significant. Using a 2x2 factorial experimental design, the study included a group of students studying educational technology who were categorized based on their learning preferences and exam types. When compared to non-adaptive exams, the results showed statistically significant differences in favor of adaptive exams, which raised students' academic satisfaction and sense of self-efficacy. In terms of efficiency and performance, analytical learners did better than holistic learners. Furthermore, when adaptive exams were combined with analytical learning styles, the interaction between exam type and learning style demonstrated a favorable combination effect that improved assessment effectiveness. These results are in line with current developments that highlight the value of implementing AI-based educational tools, including deep learning chatbots, which support individualized instruction and inspire exceptionally motivated pupils. In order to enhance educational quality and foster students' self-efficacy, the study suggests implementing adaptive assessment models in online learning environments and combining them with AI-powered personalized learning tools.
Legal Aspects of Genetic Testing Regarding Insurance and Employment in Poland

In this article, the author analyses the framework for genetic testing in insurance and employment in the Polish legal system. The task is made difficult, however by the lack in Poland of any specific regulations in this area. For this reason, the general rules of insurance law, civil law, human rights law, medical law, and labour law are considered, along with data protection law. This paper highlights how the idea of a “special status” for genetic information is today no longer dominant, having been replaced with an understanding of genetic data simply as one type of medical data. As a consequence, the general rules applicable to the broader field apply to genetic data as well. Since there is no specific legal framework, the general rules on the duty to inform the patient are also used, along with the obligation to protect the privacy and other personality rights of both employees and patients undergoing genetic testing. The author also emphasizes that the genetic testing permitted cannot be conducted or applied in a way which infringes any anti-discrimination standards or the rules of confidentiality of sensitive personal data. The chapter concludes by stressing the desirability of introducing complex legislation on genetic testing both in insurance and employment.
Mini-Gemini: Mining the Potential of Multi-Modality Vision Language Models

Code and models are available at https://github.com/dvlab-research/MiniGemini
HIPAA, GDPR and Best Practice Guidelines for preserving data security and privacy - What Radiologists should know.

Learning objectives Background Findings and procedure details Conclusion Personal information and conflict of interest References
A Framework for Transportation Network Optimization for Real-World Logistics: Extensions and Applications

Transportation network optimization is traditionally stated as a classical transportation problem in which supply is allocated to demand at minimum cost. While elegant in an analytical sense, the underlying formulation lacks aspects that are now dominating the real world of logistics: time dependence, binding capacities, multimodal transfers and uncertainty. This paper proposes an integrated framework which retains the interpretability of minimum-cost flow and adds extra dimensions to it along four realism axes. In the baseline, explicit penalty terms for surplus and unmet demand are included to reflect pragmatic trade-offs when exact balance is infeasible. First, spatial routing is linked with intertemporal decisions under inventory carryover and storage costs in a transshipment model. Second, capacity constraints are placed on arcs and facilities so as to obviate a bounded operationally credible feasible set. Third, multimodal routing is added using mode-specific arcs and transfer rules that represent physical compatibilities and service frictions. Fourth, uncertainty in costs, capacities and demands is factored in by separating anticipative decision from adaptive recourse. A dual-price-based diagnostic layer, parametric ranges and scenario testing are used to transform the results of the optimization into management actions such as expanding capacity, mode shifting and buffer-stock. The proposed formulation is a trade-off between tractability and operational fidelity, and provides the building block for scalable solution strategies (exact when possible and heuristic when necessary) that are appropriate for large-scale, practical logistics systems.
Subspace Diffusion Generative Models

Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process. We question whether it is necessary to run this entire process at high dimensionality and incur all the inconveniences thereof. Instead, we restrict the diffusion via projections onto subspaces as the data distribution evolves toward noise. When applied to state-of-the-art models, our framework simultaneously improves sample quality -- reaching an FID of 2.17 on unconditional CIFAR-10 -- and reduces the computational cost of inference for the same number of denoising steps. Our framework is fully compatible with continuous-time diffusion and retains its flexible capabilities, including exact log-likelihoods and controllable generation. Code is available at https://github.com/bjing2016/subspace-diffusion.
Navigating Crises and Ethical Evolution: The Role of Crisis Management and Corporate Social Responsibility in Modern Business Practices

This study explores the influence of demographic factors—age, gender, and occupation—on employees’ perceptions of crisis management and corporate social responsibility (CSR). Using ANOVA analysis, the research evaluates how these factors impact opinions on organizational crises, management performance, communication strategies during crises, and the role of CSR in business operations. The findings indicate that occupation significantly affects perceptions of management crises and CSR initiatives, while age and gender have minimal impact. The study highlights the need for organizations to adopt tailored approaches to crisis management and CSR, considering diverse occupational perspectives. It also emphasizes that strategic crisis response and CSR integration are essential for business resilience and sustainability. Future research could explore the impact of cultural and sectoral differences, as well as the influence of digital transformation and ESG practices. These insights will help organizations enhance their global impact through adaptive crisis management and socially responsible practices.
Optical mode division multiplexing inspired photonic neural network accelerator

Abstract         On-chip photonic neural network (PNN) is emerging as an attractive solution for artificial neural networks due to its high computing density, low energy consumption, and compact size. Matrix-vector multiplication (MVM) plays a key role in on-chip PNN, which can achieve high-speed multiply-accumulate operation. Most of the current schemes implement MVM by adopting wavelength division multiplexing technology to accumulate the power of different wavelengths together, resulting in using a mass of laser sources. Additionally, real-number-field MVM is inevitable for realizing precise PNNs, while limited by the nature of light, effective solutions to perform negative value computing are still inadequate. Here, we propose and demonstrate a PNN accelerator based on wavelength and mode hybrid multiplexing technology to reduce the use of multi-wavelength lasers, which can satisfactorily play real-number-field computing (including positive and negative domain) based on a newly presented transformation mapping approach, avoiding the demanding experimental setup and the sacrificing weight modulation depth. As a proof-of-concept, a fabricated accelerator for image convolution and letter pattern detection has been demonstrated successfully, achieving a computing density of 1.37 TOPS/mm2 under the 22.38 Gbaud modulation rate.
RETRACTED: Leadership behavior and organizational change management in selected public universities of Ethiopia: Exploring the impact of leadership influences and change processes

The study explored the impact of leadership behavior on organizational change management process in selected public Universities in Ethiopia. The authors utilized a concurrent mixed Triangulation design, combining quantitative and qualitative methods. The Quantitative data collected from 581 questionnaires were distributed to 379 academic staff and 202 academic leaders. Besides, qualitative data was collected from 12 top management bodies and 48 student representatives by using interviews and focus group discussions respectively. This ensured that a representative sample was obtained from the target population. The researchers used mean, standard deviation, independent sample t-test, correlation and regression to analyze and interpret the data collected. The study findings revealed a positive and significant impact of autocratic, democratic, laissez-faire, and transactional leadership behaviors on organizational change management process. However, there was a negative and significant impact of transformational leadership behavior on organizational change management process. Indeed, the study identified significant differences in the perceptions of leadership behaviors and organizational change management between academic staff and academic leaders. Based on the study findings, the authors recommended that leaders in public Universities customize and apply the identified positive leadership behaviors (autocratic, democratic, laissez-faire, and transactional) to effectively manage organizational change.
Optical computing: Photonic neural networks

Optical computers will be more interesting if they take advantage of phenomena that are unique to optics. In this respect, telecommunications hardware might have something to offer.
Inclusion and the Right to Access to Regular Classes for Students with Disabilities

In the past decades many countries have made progress toward increasing access to quality education for students with disabilities, yet The World Bank (2019) estimates that 85% of children with disabilities continue to lack access to any schooling. The right to access for K-12 students with disabilities has been recognized globally and locally in many countries across the world (United Nations Education, Scientific and Cultural Organization [UNESCO], 2016), with each country making decisions on how to increase access to regular classes based on local contexts and needs.
Gender, Business Ethics, and Corporate Social Responsibility: Assessing and Refocusing a Conversation

This article reviews a conversation between business ethicists and feminist scholars begun in the early 1990s and traces the development of that conversation in relation to feminist theory. A bibliographic analysis of the business ethics (BE) and corporate social responsibility (CSR) literatures over a twenty-five-year period elucidates the degree to which gender has been a salient concern, the methodologies adopted, and the ways in which gender has been analyzed (by geography, issue type, and theoretical perspective). Identifying significant limitations to the incorporation of feminist theory in these literatures, we discuss how feminist scholarship relating to behavior (through psychology and related fields), organizations (through feminist organization studies), and economics (through feminist economics) could be integrated. We suggest that a better integration of feminist theory would strengthen BE/CSR research, and point to new research directions and agendas arising from our approach.
TalkPhoto: A Versatile Training-Free Conversational Assistant for Intelligent Image Editing

Thanks to the powerful language comprehension capabilities of Large Language Models (LLMs), existing instruction-based image editing methods have introduced Multimodal Large Language Models (MLLMs) to promote information exchange between instructions and images, ensuring the controllability and flexibility of image editing. However, these frameworks often build a multi-instruction dataset to train the model to handle multiple editing tasks, which is not only time-consuming and labor-intensive but also fails to achieve satisfactory results. In this paper, we present TalkPhoto, a versatile training-free image editing framework that facilitates precise image manipulation through conversational interaction. We instruct the open-source LLM with a specially designed prompt template to analyze user needs after receiving instructions and hierarchically invoke existing advanced editing methods, all without additional training. Moreover, we implement a plug-and-play and efficient invocation of image editing methods, allowing complex and unseen editing tasks to be integrated into the current framework, achieving stable and high-quality editing results. Extensive experiments demonstrate that our method not only provides more accurate invocation with fewer token consumption but also achieves higher editing quality across various image editing tasks.
A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation

While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 Brightness-mode (B-mode) images consisting of sagittal slices of porcine spinal cords (N=25) before and after a contusion injury. We additionally benchmark the performance metrics of several state-of-the-art object detection algorithms to localize the site of injury and semantic segmentation models to label the anatomy for comparison and creation of task-specific architectures. Finally, we evaluate the zero-shot generalization capabilities of the segmentation models on human ultrasound spinal cord images to determine whether training on our porcine dataset is sufficient for accurately interpreting human data. Our results show that the YOLOv8 detection model outperforms all evaluated models for injury localization, achieving a mean Average Precision (mAP50-95) score of 0.606. Segmentation metrics indicate that the DeepLabv3 segmentation model achieves the highest accuracy on unseen porcine anatomy, with a Mean Dice score of 0.587, while SAMed achieves the highest Mean Dice score generalizing to human anatomy (0.445). To the best of our knowledge, this is the largest annotated dataset of spinal cord ultrasound images made publicly available to researchers and medical professionals, as well as the first public report of object detection and segmentation architectures to assess anatomical markers in the spinal cord for methodology development and clinical applications.
Restricted families of projections and random subspaces

We study the restricted families of orthogonal projections in $\mathbb{R}^3$. We show that there are families of random subspaces which admit a Marstrand- Mattila type projection theorem.
Stability of a critical nonlinear neutral delay differential equation

This work deals with a scalar nonlinear neutral delay differential equation issued from the study of wave propagation. A critical value of the coefficients is considered, where only few results are known. The difficulty follows from the fact that the spectrum of the linear operator is asymptotically closed to the imaginary axis. An analysis based on the energy method provides new results about the asymptotic stability of the constant and periodic solutions. A complete analysis of the stability diagram is given. Lastly, existence of periodic solutions is discussed, involving a Diophantine condition on the period.
A dynamic modeling simulation for supply chain management inventory service

This paper presents a simulation approach to analyze the rental housing supply chain's inventory problem. Unlike most products, the rental housing unit is a "circulation type" product, and the inventory problem is complicated. In this paper, a systematic and flexible process is proposed that efficiently provides critical decision-making support to managers to help them understand and validate the inventory problem in the rental housing supply chain. The proposed procedure considered inventory impact factors, such as the forecasting method, the lead time, the initial inventory level, and the inventory filling indicator. In addition, the procedure is applied to an actual rental housing supply chain to confirm its effectiveness. Furthermore, the proposed method was found to be both practical and powerful in assisting managers with their continuous decision-making efforts.
The Right to Water and Sanitation in Conflict-Affected Communities: Digital Transformation and Emerging Challenges

This article examines The Right to Water and Sanitation in Conflict-Affected Communities: Digital Transformation and Emerging Challenges with a focused emphasis on Senegal within the field of Political Science. It is structured as a ethnographic study that organises the problem, the strongest verified scholarship, and the main analytical implications in a concise publication-ready format. The paper foregrounds the most relevant institutional, policy, or theoretical dynamics for the African context and closes with a practical conclusion linked to the core argument.
Assessing Leadership Potential with a Biographical Measure of Personality Traits

The aim of this study was to demonstrate the feasibility of biographical inventories free of the limitations common to many current biographical measures by constructing and validating an inventory composed of homogeneous scales, with item content that is factual and fair, to assess personality traits predictive of leadership. The experimental inventory, with tentative scales for Dominance, Emotional Stability, Need for Achievement, Self‐Confidence, and Sociability, was administered to incoming midshipmen at the US Naval Academy and item analyzed. The validity of the final scales was appraised against subsequent peer ratings of the traits and of leadership shortly after entering the Academy; military performance grades, and midshipman rank and leadership position at the Academy; and recommendations for promotion after graduating and serving as an officer. The Sociability, Dominance, and Need for Achievement scales demonstrated convergent and discriminant validity, and all of the scales except Self‐Confidence correlated with the leadership criteria. The Sociability scale’s consistent correlations with several of the leadership criteria, including a nontrivial correlation (r = 0.28) with the leadership peer rating, implies that this measure may be useful in assessing leadership potential.
Assessing Climate Vulnerability and Adaptive Capacity in Jharkhand, India: A Multi-Source Data Approach for Inclusive Adaptation and Resilience Planning

Abstract                 Socioeconomically disadvantaged groups are at serious risk from climate change, especially in areas with ecological fragility and structural inequality. A disaggregated, data-integrated strategy is necessary to evaluate climate risk in Jharkhand, India, due to overlapping vulnerabilities. The socio-spatial complexity of climate vulnerability has not been sufficiently evaluated in the majority of prior research, despite its crucial importance for creating focused adaptation plans that can successfully mitigate climate-related hazards. In this work, Earth observation data and microdata from the National Family Health Survey (NFHS-5) are combined to provide a district-level assessment of climate vulnerability in Jharkhand. According to the conceptual model outlined in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), vulnerability is understood here as the interaction of three basic elements: exposure, sensitivity, and adaptive capacity. The ERA5, MODIS, NFHS-5, and Census datasets were used to compile 31 variables to examine the vulnerability. Principal Component Analysis (PCA) was used to create a Climate Vulnerability Index, validated through the Equal Weighting Method (EWM), and the results were further examined by social groups. Results show spatial variation in urban-industrial districts like Dhanbad and Bokaro show evidence of resilience, while the tribal dominated districts of West Singhbhum, Khunti, Gumla, and Latehar demonstrate very high levels of vulnerability regarding climate change impacts. Scheduled Tribes (ST) were the most vulnerable social group, highlighting how structural marginalization shapes adaptive capacity and resilience against climate vulnerability. This study offers methodological innovation by integrating unit-level microdata from the National Family Health Survey (NFHS-5) and geospatial data to assess vulnerability and provide policy-relevant ideas for localized and socially inclusive adaptation and disaster risk reduction actions aligned with the Sustainable Development Goals (SDGs).