Hasil untuk "Academies and learned societies"

Jingtao Wang, Qiwei Chen, C Ricardo Constante-Amores et al.

Here we demonstrate that the time-evolving interface observed during droplet formation, and consequently the resulting morphology nearing pinch-off, encode sufficient physical information for machine-learning (ML) frameworks to accurately infer key fluid properties, including viscosity and surface tension. Snapshots of dripping drops at the moment of break-up, together with their liquid properties and the flow rate, are used to form a data set for training ML algorithms. Experiments consisted of visualizing, using high-speed imaging, the process of droplet formation and identifying the frame closest to break-up. Experiments were conducted using Newtonian fluids under controlled flow conditions. In terms of the Reynolds (Re) and Ohnesorge (Oh) numbers, our conditions cover the domains 0.001< Re< 200 and 0.01 < Oh < 20, by using silicon oils, aqueous solutions of ethanol and glycerin, and methanol. For each case, flow parameters were recorded, along with images capturing the final stages of droplet break-up. Supervised regression models were trained to predict fluid parameters from the extracted contours of the breaking droplets. Our data set contains 840 examples. Our results demonstrate that the droplet geometry at pinch-off contains sufficient information to infer fluid properties by machine learning approaches. Our methods can predict surface tension, viscosity, or the droplet shape at pinch-off. These approaches provide alternatives to conventional methods to measure liquid properties while reducing measurement complexity and evaluation time and facilitating integration into automation. Unsupervised clustering is performed; the clusters represent regions in the Re-Oh and Bo-Oh planes, indicating that the latent representation may reveal physical properties and offering insight into droplet dynamics.

Eric Guiffo Kaigom

Robots are unrelentingly used to achieve operational efficiency in Industry 4.0 along with symbiotic and sustainable assistance for the work-force in Industry 5.0. As resilience, robustness, and well-being are required in anti-fragile manufacturing and human-centric societal tasks, an autonomous anticipation and adaption to thermal saturation and burns due to motors overheating become instrumental for human safety and robot availability. Robots are thereby expected to self-sustain their performance and deliver user experience, in addition to communicating their capability to other agents in advance to ensure fully automated thermally feasible tasks, and prolong their lifetime without human intervention. However, the traditional robot shutdown, when facing an imminent thermal saturation, inhibits productivity in factories and comfort in the society, while cooling strategies are hard to implement after the robot acquisition. In this work, smart digital twins endowed with generative AI, i.e., variational autoencoders, are leveraged to manage thermally anomalous and generate uncritical robot states. The notion of thermal difficulty is derived from the reconstruction error of variational autoencoders. A robot can use this score to predict, anticipate, and share the thermal feasibility of desired motion profiles to meet requirements from emerging applications in Industry 6.0 and Society 6.0.

Anoop Cherian, River Doyle, Eyal Ben-Dov et al.

Recent large language models (LLMs) are trained on diverse corpora and tasks, leading them to develop complementary strengths. Multi-agent debate (MAD) has emerged as a popular way to leverage these strengths for robust reasoning, though it has mostly been applied to language-only tasks, leaving its efficacy on multimodal problems underexplored. In this paper, we study MAD for solving vision-and-language reasoning problems. Our setup enables generalizing the debate protocol with heterogeneous experts that possess single- and multi-modal capabilities. To this end, we present Weighted Iterative Society-of-Experts (WISE), a generalized and modular MAD framework that partitions the agents into Solvers, that generate solutions, and Reflectors, that verify correctness, assign weights, and provide natural language feedback. To aggregate the agents' solutions across debate rounds, while accounting for variance in their responses and the feedback weights, we present a modified Dawid-Skene algorithm for post-processing that integrates our two-stage debate model. We evaluate WISE on SMART-840, VisualPuzzles, EvoChart-QA, and a new SMART-840++ dataset with programmatically generated problem instances of controlled difficulty. Our results show that WISE consistently improves accuracy by 2-7% over the state-of-the-art MAD setups and aggregation methods across diverse multimodal tasks and LLM configurations.

Sameer Sethi, Donald Martin, Emmanuel Klu

This paper presents SYMBIOSIS, an AI-powered framework and platform designed to make Systems Thinking accessible for addressing societal challenges and unlock paths for leveraging systems thinking frameworks to improve AI systems. The platform establishes a centralized, open-source repository of systems thinking/system dynamics models categorized by Sustainable Development Goals (SDGs) and societal topics using topic modeling and classification techniques. Systems Thinking resources, though critical for articulating causal theories in complex problem spaces, are often locked behind specialized tools and intricate notations, creating high barriers to entry. To address this, we developed a generative co-pilot that translates complex systems representations - such as causal loop and stock-flow diagrams - into natural language (and vice-versa), allowing users to explore and build models without extensive technical training. Rooted in community-based system dynamics (CBSD) and informed by community-driven insights on societal context, we aim to bridge the problem understanding chasm. This gap, driven by epistemic uncertainty, often limits ML developers who lack the community-specific knowledge essential for problem understanding and formulation, often leading to ill informed causal assumptions, reduced intervention effectiveness and harmful biases. Recent research identifies causal and abductive reasoning as crucial frontiers for AI, and Systems Thinking provides a naturally compatible framework for both. By making Systems Thinking frameworks more accessible and user-friendly, SYMBIOSIS aims to serve as a foundational step to unlock future research into responsible and society-centered AI. Our work underscores the need for ongoing research into AI's capacity to understand essential characteristics of complex adaptive systems paving the way for more socially attuned, effective AI systems.

Hanzhong Zhang, Muhua Huang, Jindong Wang

This paper introduces CMASE, a framework for Computational Multi-Agent Society Experiments that integrates generative agent-based modeling with virtual ethnographic methods to support researcher embedding, interactive participation, and mechanism-oriented intervention in virtual social environments. By transforming the simulation into a simulated ethnographic field, CMASE shifts the researcher from an external operator to an embedded participant. Specifically, the framework is designed to achieve three core capabilities: (1) enabling real-time human-computer interaction that allows researchers to dynamically embed themselves into the system to characterize complex social intervention processes; (2) reconstructing the generative logic of social phenomena by combining the rigor of computational experiments with the interpretative depth of traditional ethnography; and (3) providing a predictive foundation with causal explanatory power to make forward-looking judgments without sacrificing empirical accuracy. Experimental results show that CMASE can not only simulate complex phenomena, but also generate behavior trajectories consistent with both statistical patterns and mechanistic explanations. These findings demonstrate CMASE's methodological value for intervention modeling, highlighting its potential to advance interdisciplinary integration in the social sciences. The official code is available at: https://github.com/armihia/CMASE .

Can Liu, Jin Wang, and Yipeng Zhou et al.

Federated learning (FL) empowers privacypreservation in model training by only exposing users' model gradients. Yet, FL users are susceptible to gradient inversion attacks (GIAs) which can reconstruct ground-truth training data such as images based on model gradients. However, reconstructing high-resolution images by existing GIAs faces two challenges: inferior accuracy and slow-convergence, especially when duplicating labels exist in the training batch. To address these challenges, we present an Accurate and Fast-convergent Gradient Inversion attack algorithm, called AFGI, with two components: Label Recovery Block (LRB) which can accurately restore duplicating labels of private images based on exposed gradients; VME Regularization Term, which includes the total variance of reconstructed images, the discrepancy between three-channel means and edges, between values from exposed gradients and reconstructed images, respectively. The AFGI can be regarded as a white-box attack strategy to reconstruct images by leveraging labels recovered by LRB. In particular, AFGI is efficient that accurately reconstruct ground-truth images when users' training batch size is up to 48. Our experimental results manifest that AFGI can diminish 85% time costs while achieving superb inversion quality in the ImageNet dataset. At last, our study unveils the shortcomings of FL in privacy-preservation, prompting the development of more advanced countermeasure strategies.

Christopher H. Wortley

Ozdemir Cetin, Murat Cakiroglu, Cüneyt Bayılmış et al.

In today's world, many technologically advanced countries have realized that real power lies not in physical strength but in educated minds. As a result, every country has embarked on restructuring its education system to meet the demands of technology. As a country in the midst of these developments, we cannot remain indifferent to this transformation in education. In the Information Age of the 21st century, rapid access to information is crucial for the development of individuals and societies. To take our place among the knowledge societies in a world moving rapidly towards globalization, we must closely follow technological innovations and meet the requirements of technology. This can be achieved by providing learning opportunities to anyone interested in acquiring education in their area of interest. This study focuses on the advantages and disadvantages of internet-based learning compared to traditional teaching methods, the importance of computer usage in internet-based learning, negative factors affecting internet-based learning, and the necessary recommendations for addressing these issues. In today's world, it is impossible to talk about education without technology or technology without education.

Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan et al.

In recent years, by leveraging more data, computation, and diverse tasks, learned optimizers have achieved remarkable success in supervised learning, outperforming classical hand-designed optimizers. Reinforcement learning (RL) is essentially different from supervised learning, and in practice, these learned optimizers do not work well even in simple RL tasks. We investigate this phenomenon and identify two issues. First, the agent-gradient distribution is non-independent and identically distributed, leading to inefficient meta-training. Moreover, due to highly stochastic agent-environment interactions, the agent-gradients have high bias and variance, which increases the difficulty of learning an optimizer for RL. We propose pipeline training and a novel optimizer structure with a good inductive bias to address these issues, making it possible to learn an optimizer for reinforcement learning from scratch. We show that, although only trained in toy tasks, our learned optimizer can generalize to unseen complex tasks in Brax.

Khikayah Khikayah, Heru Prastyo

Penelitian ini bertujuan untuk mengetahui aktivitas dan habituasi serta kontribusi keagamaan Siswa SDIT Nidaul Hikmah Tahun 2018. Jenis penelitian ini adalah termasuk penelitian kualitatif. Pengumpulan data ini menggunakan metode wawancara, observasi, dan dokumentasi. Teknik analisis data menggunakan analisis data interaktif, yaitu : reduksi data, display data, dan kesimpulan atau verifikasi Hasil penelitian memaparkan bahwa aktifitas keagamaan yang dilakukan di SDIT Nidaul Hikmah Kota Salatiga Tahun 2018 berupa: ceramah keagamaan, PHBI dan pengamalan ajaran agama berupa: shalat dhuha, tahfidz, dan shalat berjamaah (Dhuhur, dan Azhar). Aktivitas keagamaan berkontribusi bagi diri siswa antara lain :1) menanamkan pembiasaan beribadah, 2) menumbuhkan kedisiplinan, dan membiasakan siswa hidup dekat dengan agama, 3) pembiasaan anak berperilaku sesuai nilai- nilai ajaran Islam, jujur, bertanggung jawab dan berkarakter. Kesimpulan penelitian ini adalah aktivitas dan habituasi memberikan kontribusi yang positif pada diri siswa di SDIT Nidaul Hikmah yang berupa kedisiplinan, kejujuran, tanggung jawab dan perilaku sesuai dengan nilai-nilai ajaran Islam.

Muladi Muladi, Yuni Rahmawati, I Made Wirawan et al.

Community service with SME Sakinah Bakery is carried out to increase the production capacity and quality. Sakinah Bakery produces sponge cake and pastries that are very popular within the community and spread across 500 vendor shops in Malang City and Regency. Consumer demands has not been fully fulfilled due to low production capacity. About 10% of the product are undercooked or overcooked. These low quality products reduce the taste and aroma, breaks easily and becomes mushy quickly. Based on surveys and interviews, partner needs an large capacity oven that can produce well-cooked biscuits. The production tool that determine the cake ripeness is oven. This problem is overcomed by making an oven with electronic controls to stabilize the temperature and baking time. Temperature control with sensor feedback mounted on the combustion chamber forms a stable closed loop control system. Proper heating is obtained by controlling the LPG main valve. The three furnaces are spread out to get an even heating. The oven was able to increase 60% the production capacity per bake. The number of fail product decreases by 5.19% indicated that the product quality increases. Technology transfer in the form of training is carried out to maintain the quality.

David Garcia, Max Pellert, Jana Lasser et al.

Social media generate data on human behaviour at large scales and over long periods of time, posing a complementary approach to traditional methods in the social sciences. Millions of texts from social media can be processed with computational methods to study emotions over time and across regions. However, recent research has shown weak correlations between social media emotions and affect questionnaires at the individual level and between static regional aggregates of social media emotion and subjective well-being at the population level, questioning the validity of social media data to study emotions. Yet, to date, no research has tested the validity of social media emotion macroscopes to track the temporal evolution of emotions at the level of a whole society. Here we present a pre-registered prediction study that shows how gender-rescaled time series of Twitter emotional expression at the national level substantially correlate with aggregates of self-reported emotions in a weekly representative survey in the United Kingdom. A follow-up exploratory analysis shows a high prevalence of third-person references in emotionally-charged tweets, indicating that social media data provide a way of social sensing the emotions of others rather than just the emotional experiences of users. These results show that, despite the issues that social media have in terms of representativeness and algorithmic confounding, the combination of advanced text analysis methods with user demographic information in social media emotion macroscopes can provide measures that are informative of the general population beyond social media users.

J. Santander-Vela, M. Bartolini, and et al.

The Square Kilometre Array telescopes have recently started their construction phase, after years of pre-construction effort. The new SKA Observatory (SKAO) intergovernmental organisation has been created, and the start of construction ($\mathrm{T_0}$) has already happened. In this talk, we summarise the construction progress of our facility, and the role that agile software development and open-source collaboration, and in particular the development of our TANGO-based control system, is playing.

Fredrik Thomasson

The article traces Johan David Åkerblad’s contributions to one of the 19th century’s most famous scientific triumphs, the decipherment of hieroglyphs. Egyptology often counts its birth from 1822 when hieroglyphs first could be read; the focus here are on the preceding decades of Egyptian studies. Åkerblad, the son of a mirror-maker, was born in Stockholm in 1763. That he was born a commoner and supported radical political ideas was an important factor in his less than successful Swedish career. He was a precocious oriental linguist who already before leaving Uppsala university was well versed in classical languages as well as in Arabic and Turkish. Throughout his diplomatic postings in Constantinople between 1784 and 1797 he travelled widely in the Eastern Mediterranean. He knew the local languages well enough to travel in disguise. While in Rome during the French occupation in 1798–1799 Åkerblad studied Coptic. He was invited to join the French invasion of Egypt in 1798 on the grounds of his exceptional knowledge of languages, but chose to decline. He was an acute observer of European expansionist intentions and commented ironically on both French and British ambitions in the Eastern Mediterranean. In Paris in 1802 he was given access to a print of the Demotic inscription from the Rosetta stone that had been found in the Nile Delta. The same year he published a treatise on the Demotic inscription and become instantaneously famous in orientalist circles and was in 1803 elected member of the Institut National (today’s Institut de France). Åkerblad managed to read some signs and words in the Demotic text and proved that ancient Egyptian was the ancestor of Coptic. He was, however, hindered by his conviction that Demotic was purely phonetic; this hurdle was only overcome when Jean-François Champollion completed the decipherment of hieroglyphs. It was then understood that the Demotic script was a mixture between phonetic and logographic signs. Åkerblad was ordered to leave Paris when the Swedish government broke off diplomatic relations with France in 1804, but instead of returning to Sweden he went to Italy where he died in Rome in 1819. During his adult life he only spent a few years in Sweden. The article argues that pre-decipherment Egyptology can not be divided from the turbulent decades in the wake of the French revolution. The stages in the decipherment coincided almost perfectly with the Napoleonic wars and both international and national politics played a pivotal role in the development of oriental and Egyptian studies. Åkerblad continued his Egyptian studies in Italy and corresponded with the French orientalists Silvestre de Sacy and Champollion and the British polymath Thomas Young. Nevertheless, he never managed to publish his Egyptian treatises and it is evident how national goals of academies and learned societies were highly influential in both science in general and for individual careers. Early Egyptology has sometimes been used as an example of international scientific co-operation, a claim refuted here. It is further argued that the history of Egyptology may benefit from being contextualized within a larger context of oriental studies. By briefly discussing the debate concerning Edward Said’s concept of ”orientalism”, as well as referring to recent Eastern Mediterranean historiography, such a context is suggested.

Cecília Hulshof

Neste artigo, discutiremos a representação da Idade Média no filme O Nome da Rosa, de Jean-Jacques Annaud. Procuraremos responder, dentre outras questões, de que forma o filme retrata a relação deste período histórico, e principalmente da Igreja, com a ciência e o conhecimento de sua própria época e de épocas anteriores, e deste modo, esclarecer se a imagem da Idade Média transmitida questiona ou reforça a ideia de Idade das Trevas, estabelecida pelo senso comum. A análise destas questões será feita dentro de um debate maior sobre a adequação das mídias visuais à transmissão da História, e, portanto, também como instrumento de ensino.

Gustavo Santos Giacomini

No início da idade moderna, novas concepções astronômicas e físicas fomentaram ideias relativas a outros mundos e vida extraterrestre, de modo que forneceram um  território inédito para a imaginação científica e literária, a saber, o novo mundo na Lua. Em vista disso, empregamos a obra Somnium, de Johannes Kepler, como vestígio e indício, com a finalidade de examinar a ligação existente entre ciência e literatura no princípio da modernidade. Procuramos destacar como as ideias relativas à pluralidade e habitabilidade dos mundos e à possibilidade de viagens lunares e cósmicas são exploradas nesse contato íntimo entre ciência e literatura. Nosso objetivo é identificar as persistências e metamorfoses dos resquícios na obra de Kepler e, com isso, delinear as linhas primevas de um novo gênero literário, isto é, a ficção científica. Ao propor um exercício de hermenêutica histórica sobre o Somnium, pretendemos levar em conta, pelo menos em parte, as transmissões e recepções do tema enquanto trajetória de uma ideia no tempo.

Zaenal Arifin, Buang Budi Wahono, Dias Prihatmoko et al.

Livelihoods as fishermen are the backbone for people who live near the coast, it is not much different from the people in Jobokuto village, Jepara Regency who also work as fishermen, so far the catches of fishermen after getting catches of fish are directly sold to middlemen or local fish auction sites, so that fishermen cannot increase fish catches to be processed into commodities that have high economic value so that their economy can increase. Based on this description, service activities are carried out to fishermen by providing training ranging from the potential of fisheries, the superiority of GPS and Sounder technology, increased catches on processed fish and stabilization of processed fish equipment ranging from fish shredder, shredded cooker, shredded slicer, bender, spinner as well as tools fish smoke. Some of these activities are expected to result in the end that fishermen SMEs can develop their businesses, increase catches and sales, and improve the welfare of fishermen in Jepara Regency. The activity is also expected to be a pilot project for other fishermen in the district in implementing fishing technology using a fish finder with the use of appropriate technology that is environmentally friendly.

Ryan Marcus, Andreas Kipf, Alexander van Renen et al.

Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified benchmark that compares well-tuned implementations of three learned index structures against several state-of-the-art "traditional" baselines. Using four real-world datasets, we demonstrate that learned index structures can indeed outperform non-learned indexes in read-only in-memory workloads over a dense array. We also investigate the impact of caching, pipelining, dataset size, and key size. We study the performance profile of learned index structures, and build an explanation for why learned models achieve such good performance. Finally, we investigate other important properties of learned index structures, such as their performance in multi-threaded systems and their build times.

Pooja Guhan, Naman Awasthi, and Kathryn McDonald et al.

We discuss MET, a learning-based algorithm proposed for perceiving a patient's level of engagement during telehealth sessions. We leverage latent vectors corresponding to Affective and Cognitive features frequently used in psychology literature to understand a person's level of engagement in a semi-supervised GAN-based framework. We showcase the efficacy of this method from the perspective of mental health and more specifically how this can be leveraged for a better understanding of patient engagement during telemental health sessions. To further the development of similar technologies that can be useful for telehealth, we also plan to release a dataset MEDICA containing 1299 video clips, each 3 seconds long and show experiments on the same. Our framework reports a 40% improvement in RMSE (Root Mean Squared Error) over state-of-the-art methods for engagement estimation. In our real-world tests, we also observed positive correlations between the working alliance inventory scores reported by psychotherapists. This indicates the potential of the proposed model to present patient engagement estimations that aligns well with the engagement measures used by psychotherapists.

Carina Prunkl, Jess Whittlestone

One way of carving up the broad "AI ethics and society" research space that has emerged in recent years is to distinguish between "near-term" and "long-term" research. While such ways of breaking down the research space can be useful, we put forward several concerns about the near/long-term distinction gaining too much prominence in how research questions and priorities are framed. We highlight some ambiguities and inconsistencies in how the distinction is used, and argue that while there are differing priorities within this broad research community, these differences are not well-captured by the near/long-term distinction. We unpack the near/long-term distinction into four different dimensions, and propose some ways that researchers can communicate more clearly about their work and priorities using these dimensions. We suggest that moving towards a more nuanced conversation about research priorities can help establish new opportunities for collaboration, aid the development of more consistent and coherent research agendas, and enable identification of previously neglected research areas.