SEEKING A COMMON THEME: A STUDY OF CERAMIC EFFIGY ARTIFACTS IN THE PRE-HISPANIC AMERICAN SOUTHWEST AND NORTHERN MEXICO USING COMPUTER IMAGE PATTERN RECOGNITION AND PHYLOGENETIC ANALYSIS

Effigy artifacts are found throughout the Pre-Hispanic American Southwest and Northern Mexico (PHASNM), as well as in other cultures around the world, with many sharing the same forms and design features. The earliest figurines within the PHASNM were partial anthropomorphic figurines made from fired clay, dating to between A.D. 287 and A.D. 312 (Morss 1954:27). They were found in a pit house village of Bluff Ruin in the Forestdale Valley of eastern Arizona, and they appeared to be associated with the Mogollon culture. The temporal range of the samples examined in this study is from approximately 200 A.D. to 1650 A.D., and the geographical range includes the Southwestern United States (Arizona, New Mexico, Texas, Colorado, and Utah) and the northcentral section of Mexico (Casas Grandes and the surrounding area). This research looks at the similarities among the markings of ceramic effigy artifacts from the PHASNM, using computer image pattern recognition, design analysis, and phylogenetics, to determine whether their ceramic traditions share a common theme and whether the specific method of social learning responsible for the transmission of information relating to ceramic effigy decoration can be identified. Transmission is possible in one of three ways: vertical transmission, where parents/teachers distribute information by encouraging imitation and sharing learned traditions with children/students (Richerson and Boyd 2005; Shennan 2002); horizontal transmission, where information is transmitted among peers, either from within the individual’s group or from interaction with peers from neighboring populations (Borgerhoff Mulder et al. 2006), and where the individual comes into contact with a wide range of attributes related to the item of interest and then adopts those that allow for the fastest, most economical methods of production and distribution (Eerkens et al 2006; Rogers 1983); and oblique transmission, where information is transmitted by adults, masters, or institutions of elite or higher social status, either internally or externally to the adopting cultural Type (Jensen 2016; Jordan 2014), and where particular traits are adopted or left out in disproportionate ways, creating patterns in localized traditions that can be empirically identified. Horizontal transmission can be broken into two types: unlimited, where contact is not confined to a particular group; and limited, where contact is restricted to a particular set of contacts. Using criteria for each of the categories as set forth by the New Mexico Office of Archaeological Studies Pottery Typology Project, the samples were classified in terms of cultural area (culture), branch, tradition, ware, and type. The research v group consisted of 360 photographic samples represented by 868 images that were resized to a 640x640 pixel format. The images were then examined through computer image pattern recognition (using YOLOv5) and through manual observation. This study resulted in a database representing 230 traits. These traits were assembled into groups by cultural area, branch, tradition, ware, and type, and phylogenetic analysis was applied to show how the different entities transfer information among each other

AI Knowledge Transfer from the University to Society

AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors brings together examples from the "Innovative Ecosystem with Artificial Intelligence for Andalusia 2025" project at the University of Seville, a series of sub-projects composed of research groups and different institutions or companies that explore the use of Artificial Intelligence in a variety of high-impact sectors to lead innovation and assist in decision-making. Key Features Includes chapters on health and social welfare, transportation, digital economy, energy efficiency and sustainability, agro-industry, and tourism Great diversity of authors, expert in varied sectors, belonging to powerful research groups from the University of Seville with proven experience in the transfer of knowledge to the productive sector and agents attached to the Andalucía TECH Campu

Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project.This work has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955558. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Germany, France, Italy, Poland, Switzerland and Norway. In Spain, it has received complementary funding from MCIN/AEI/10.13039/501100011033, Spain and the European Union NextGenerationEU/PRTR (contracts PCI2021-121957, PCI2021-121931, PCI2021-121944, and PCI2021-121927). In Germany, it has received complementary funding from the German Federal Ministry of Education and Research (contracts 16HPC016K, 6GPC016K, 16HPC017 and 16HPC018). In France, it has received financial support from Caisse des dépôts et consignations (CDC) under the action PIA ADEIP (project Calculateurs). In Italy, it has been preliminary approved for complimentary funding by Ministero dello Sviluppo Economico (MiSE) (ref. project prop. 2659). In Norway, it has received complementary funding from the Norwegian Research Council, Norway under project number 323825. In Switzerland, it has been preliminary approved for complimentary funding by the State Secretariat for Education, Research, and Innovation (SERI), Norway. In Poland, it is partially supported by the National Centre for Research and Development under decision DWM/EuroHPCJU/4/2021. The authors also acknowledge financial support by MCIN/AEI /10.13039/501100011033, Spain through the “Severo Ochoa Programme for Centres of Excellence in R&D” under Grant CEX2018-000797-S, the Spanish Government, Spain (contract PID2019-107255 GB) and by Generalitat de Catalunya, Spain (contract 2017-SGR-01414). Anna Queralt is a Serra Húnter Fellow.With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2018-000797-S)

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

DeepTMH: Multimodal Semi-supervised framework leveraging Affective and Cognitive engagement for Telemental Health

To aid existing telemental health services, we propose DeepTMH, a novel framework that models telemental health session videos by extracting latent vectors corresponding to Affective and Cognitive features frequently used in psychology literature. Our approach leverages advances in semi-supervised learning to tackle the data scarcity in the telemental health session video domain and consists of a multimodal semi-supervised GAN to detect important mental health indicators during telemental health sessions. We demonstrate the usefulness of our framework and contrast against existing works in two tasks: Engagement regression and Valence-Arousal regression, both of which are important to psychologists during a telemental health session. Our framework reports 40% improvement in RMSE over SOTA method in Engagement Regression and 50% improvement in RMSE over SOTA method in Valence-Arousal Regression. To tackle the scarcity of publicly available datasets in telemental health space, we release a new dataset, MEDICA, for mental health patient engagement detection. Our dataset, MEDICA consists of 1299 videos, each 3 seconds long. To the best of our knowledge, our approach is the first method to model telemental health session data based on psychology-driven Affective and Cognitive features, which also accounts for data sparsity by leveraging a semi-supervised setup

Learning to Recover Spectral Reflectance from RGB Images

This paper tackles spectral reflectance recovery (SRR) from RGB images. Since capturing ground-truth spectral reflectance and camera spectral sensitivity are challenging and costly, most existing approaches are trained on synthetic images and utilize the same parameters for all unseen testing images, which are suboptimal especially when the trained models are tested on real images because they never exploit the internal information of the testing images. To address this issue, we adopt a self-supervised meta-auxiliary learning (MAXL) strategy that fine-tunes the well-trained network parameters with each testing image to combine external with internal information. To the best of our knowledge, this is the first work that successfully adapts the MAXL strategy to this problem. Instead of relying on naive end-to-end training, we also propose a novel architecture that integrates the physical relationship between the spectral reflectance and the corresponding RGB images into the network based on our mathematical analysis. Besides, since the spectral reflectance of a scene is independent to its illumination while the corresponding RGB images are not, we recover the spectral reflectance of a scene from its RGB images captured under multiple illuminations to further reduce the unknown. Qualitative and quantitative evaluations demonstrate the effectiveness of our proposed network and of the MAXL. Our code and data are available at https://github.com/Dong-Huo/SRR-MAXL

AI Knowledge Transfer from the University to Society

AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors brings together examples from the "Innovative Ecosystem with Artificial Intelligence for Andalusia 2025" project at the University of Seville, a series of sub-projects composed of research groups and different institutions or companies that explore the use of Artificial Intelligence in a variety of high-impact sectors to lead innovation and assist in decision-making. Key Features Includes chapters on health and social welfare, transportation, digital economy, energy efficiency and sustainability, agro-industry, and tourism Great diversity of authors, expert in varied sectors, belonging to powerful research groups from the University of Seville with proven experience in the transfer of knowledge to the productive sector and agents attached to the Andalucía TECH Campu

Toward a user-centered architecture

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2010.Includes bibliographical references.As more companies encourage users to participate in the design of personalized products through online configuration tools, a new kind of user-centered business model emerges. One of the outcomes of this transformation, is the restructuring of a company's products - from a one-size-fits-all to a kit-of-parts - allowing customers to mix-n-match. A similar process is taking place in architectural design, as more research projects and a few commercial applications employ mass-customization techniques to allow users to design and build their own living solutions. In this thesis, I propose a framework for user-centered architecture, called UDesign, and describe its implementation as a web application that allows users to design their own custom apartment. UDesign includes a sample one-bedroom apartment which users can customize through a kit-of-parts approach, i.e., a catalog of rooms (called assemblies), that can be combined to create a complete floor plan solution. While available configuration tools in architecture require the user to think like an expert, e.g., integrate form and function, UDesign takes a novel approach by deploying a suite of machine learning algorithms coupled with data from Facebook to model users' design preferences and match them with design solutions. Users can take advantage of these recommendations as their design starting point and continue to explore other alternatives by dragging and dropping rooms from the catalog on to the sample floor plan. As users explore design solutions, UDesign updates its recommendations to guide users through the design space and help them find solutions that best fit their needs. Finally, UDesign's integration with Facebook, allows users to share their designs, making UDesign part of their social network.by Yaniv Corem (Ophir).S.M