Hasil untuk "Academies and learned societies"

Alexandre Henrique da Silva dos Santos

This study aims to recover the significance of Cartesian thought for the History of Geology. To this end, Descartes’s thesis is examined from two perspectives: the emergence of a new way of interpreting the planet—referred to as the Terraqueous Globe—and the Cartesian Natural Philosophy within the broader context of seventeenth‑century mechanism. The analysis shows that Descartes presents a coherent theory, transforming the Terraqueous Globe into an object of scientific inquiry by interpreting it mechanically and attributing to it a past accessible to reason. Such a proposal did not go unnoticed by his contemporaries and was able to influence philosophers and naturalists in the decades that followed.

Wenjing Zhai, Jianbin Zhang, Tao Liu

Existing multi-agent simulations often suffer from the "Agent-Centric Paradox": rules are hard-coded into individual agents, making complex social dynamics rigid and difficult to align with educational values. This paper presents EDU-MATRIX, a society-centric generative cognitive digital twin architecture that shifts the paradigm from simulating "people" to simulating a "social space with a gravitational field." We introduce three architectural contributions: (1) An Environment Context Injection Engine (ECIE), which acts as a "social microkernel," dynamically injecting institutional rules (Gravity) into agents based on their spatial-temporal coordinates; (2) A Modular Logic Evolution Protocol (MLEP), where knowledge exists as "fluid" capsules that agents synthesize to generate new paradigms, ensuring high dialogue consistency (94.1%); and (3) Endogenous Alignment via Role-Topology, where safety constraints emerge from the agent's position in the social graph rather than external filters. Deployed as a digital twin of a secondary school with 2,400 agents, the system demonstrates how "social gravity" (rules) and "cognitive fluids" (knowledge) interact to produce emergent, value-aligned behaviors (Social Clustering Coefficient: 0.72).

Bor Luen Tang

A paradox was observed with regard to an increase in gold open access publications despite the increase in financial constraints. While this was viewed positively by some as an indication of strategic adaptation and financial sacrifice to publish in open access journals with an impact factor instead of conference proceedings, there could be alternative explanations for the paradox. I propose views that reflect more negative issues with citations, peer review, and an arguably suboptimal mutually propagating publishing loop for gold open access publications.

Jintong Wu, Zhuang Shao, Junlei Zhao et al.

Silicon carbide (SiC) polymorphs are widely employed as nuclear materials, mechanical components, and wide-bandgap semiconductors. The rapid advancement of SiC-based applications has been complemented by computational modeling studies, including both ab initio and classical atomistic approaches. In this work, we develop a computationally efficient and general-purpose machine-learned interatomic potential (ML-IAP) capable of multimillion-atom molecular dynamics simulations over microsecond timescales. Using the ML-IAP, we systematically map the comprehensive pressure-temperature phase diagram and the threshold displacement energy distributions for the 2H and 3C polymorphs. Furthermore, collision cascade simulations provide in-depth insights into polymorph-dependent primary radiation damage clustering, a phenomenon that conventional empirical potentials fail to accurately capture.

Suvam Kumar Das, Suprio Ray

Indexes can significantly improve search performance in relational databases. However, if the query workload changes frequently or new data updates occur continuously, it may not be worthwhile to build a conventional index upfront for query processing. Adaptive indexing is a technique in which an index gets built on the fly as a byproduct of query processing. In recent years, research in database indexing has taken a new direction where machine learning models are employed for the purpose of indexing. These indexes, known as learned indexes, can be more efficient compared to traditional indexes such as B+-tree in terms of memory footprints and query performance. However, a learned index has to be constructed upfront and requires training the model in advance, which becomes a challenge in dynamic situations when workload changes frequently. To the best of our knowledge, no learned indexes exist yet for adaptive indexing. We propose a novel learned approach for adaptive indexing. It is built on the fly as queries are submitted and utilizes learned models for indexing data. To enhance query performance, we employ a query workload prediction technique that makes future workload projection based on past workload data. We have evaluated our learned adaptive indexing approach against existing adaptive indexes for various query workloads. Our results show that our approach performs better than others in most cases, offering 1.2x - 5.6x improvement in query performance.

Rodrigo Vinícius Luz da Silva, Karina Perrelli Randau

A família Peckolt foi composta por três gerações de farmacêuticos que dedicaram suas vidas ao estudo das plantas medicinais brasileiras, sendo considerados alguns dos profissionais mais importantes da área. Theodoro, Gustavo, Waldemar e Oswaldo tiveram grande relação com a flora nacional, a partir da qual estudaram milhares de espécies e publicaram centenas de trabalhos. Além disso, também foram responsáveis por fornecer novos gêneros e espécies vegetais à ciência, descobrir e isolar princípios ativos, elaborar medicamentos promissores e contribuir com organizações e publicações científicas. Diante disso, observa-se a importância de trabalhos como o dessa família sobre as plantas medicinais do Brasil.

Gildo Magalhães

Jessica Woodgate, Paul Marshall, Nirav Ajmeri

Social norms are standards of behaviour common in a society. However, when agents make decisions without considering how others are impacted, norms can emerge that lead to the subjugation of certain agents. We present RAWL-E, a method to create ethical norm-learning agents. RAWL-E agents operationalise maximin, a fairness principle from Rawlsian ethics, in their decision-making processes to promote ethical norms by balancing societal well-being with individual goals. We evaluate RAWL-E agents in simulated harvesting scenarios. We find that norms emerging in RAWL-E agent societies enhance social welfare, fairness, and robustness, and yield higher minimum experience compared to those that emerge in agent societies that do not implement Rawlsian ethics.

Andrew Kyle Lampinen, Stephanie C. Y. Chan, Katherine Hermann

Representation learning, and interpreting learned representations, are key areas of focus in machine learning and neuroscience. Both fields generally use representations as a means to understand or improve a system's computations. In this work, however, we explore surprising dissociations between representation and computation that may pose challenges for such efforts. We create datasets in which we attempt to match the computational role that different features play, while manipulating other properties of the features or the data. We train various deep learning architectures to compute these multiple abstract features about their inputs. We find that their learned feature representations are systematically biased towards representing some features more strongly than others, depending upon extraneous properties such as feature complexity, the order in which features are learned, and the distribution of features over the inputs. For example, features that are simpler to compute or learned first tend to be represented more strongly and densely than features that are more complex or learned later, even if all features are learned equally well. We also explore how these biases are affected by architectures, optimizers, and training regimes (e.g., in transformers, features decoded earlier in the output sequence also tend to be represented more strongly). Our results help to characterize the inductive biases of gradient-based representation learning. We then illustrate the downstream effects of these biases on various commonly-used methods for analyzing or intervening on representations. These results highlight a key challenge for interpretability $-$ or for comparing the representations of models and brains $-$ disentangling extraneous biases from the computationally important aspects of a system's internal representations.

Charles-Étienne Joseph, Benjamin Thérien, Abhinav Moudgil et al.

Communication-efficient variants of SGD, specifically local SGD, have received a great deal of interest in recent years. These approaches compute multiple gradient steps locally on each worker, before averaging model parameters, helping relieve the critical communication bottleneck in distributed deep learning training. Although many variants of these approaches have been proposed, they can sometimes lag behind state-of-the-art adaptive optimizers for deep learning. In this work, we investigate if the recent progress in the emerging area of learned optimizers can potentially close this gap in homogeneous data and homogeneous device settings while remaining communication-efficient. Specifically, we meta-learn how to perform global updates given an update from local SGD iterations. Our results demonstrate that learned optimizers can substantially outperform local SGD and its sophisticated variants while maintaining their communication efficiency. Our learned optimizers can even generalize to unseen and much larger datasets and architectures, including ImageNet and ViTs, and to unseen modalities such as language modeling. We therefore show the potential of learned optimizers for improving communication-efficient distributed learning.

Djoko Rahardjo, Seta Nurhayati Mularum, Kukuh Madyaningrana et al.

Tanaman berkhasiat obat banyak dimanfaatkan oleh masyarakat di Indonesia secara turun-temurun untuk mendukung kesehatan. Inventarisasi dan pemanfaatan beragam tanaman obat mutlak dilakukan untuk pengembangan potensinya dalam menunjang kesehatan. Pemanfaatan jamu sebagai produk olahan tanaman obat mulai mendapatkan perhatian lebih dari pemerintah melalui institusi kesehatan. Pengabdian ini bertujuan untuk membuat percontohan taman tanaman obat keluarga (Toga) di Dusun Ngelosari, Kecamatan Piyungan, Kabupaten Bantul yang memadukan rintisan ekowisata lereng bukit dan pengolahan tanaman obat keluarga. Kegiatan pengabdian ini didasarkan pada analisis situasi dan permasalahan kesehatan di lingkup Puskesmas Piyungan. Berdasarkan identifikasi masalah dan analisis kebutuhan yang dilakukan bersama puskesmas dan kader kesehatan maka pendekatan pengembangan percontohan taman toga, pelatihan dan pendampingan pada kelompok wanita tani (KWT) dipilih sebagai pendekatan yang diharapkan mampu menyelesaikan permasalahan kesehatan dan ekonomi masyarakat. Program terealisir dalam bentuk persiapan dan observasi lokasi taman Toga, pengembangan taman Toga percontohan, pelatihan budidaya tanaman obat, pasca panen dan pengolahan, serta pengemasan dan pemasaran produk herbal. Kegiatan ini mempunyai hasil berupa Taman Toga Ngupoyo Sehat yang menjadi sumber bahan baku produk jamu yang dihasilkan oleh KWT setempat. Selain meningkatkan pendapatan KWT setempat, pelaksanaan program dapat dipergunakan sebagai model sinergisme pengembangan konservasi dan peningkatan kesehatan masyarakat.

Duleeka Knipe, Rachel Jewkes

Eric Burkholder, Mason Sake, Jiamin Zhang

It is well documented that students sometimes resist active learning techniques. A recent study showed how students believed that they learned less in active learning classrooms than they learned in lectures, even though they learned more. In this article, we describe a method for introducing active learning methods to college students that is based on a preparation for future learning approach. The students who received this introduction to active learning appeared to be more receptive to group work in the classroom than students who started the course with an explanation of the reasons and values of active learning.

Marta D'Elia, Hang Deng, Cedric Fraces et al.

The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research. Within the scope of ML and materials research, the goal of the workshop was to discuss the state-of-the-art in each community, promote crosstalk and accelerate multi-disciplinary collaborative research, and identify challenges and opportunities. As the end result, four topic areas were identified: ML in predicting materials properties, and discovery and design of novel materials, ML in porous and fractured media and time-dependent phenomena, Multi-scale modeling in heterogeneous porous materials via ML, and Discovery of materials constitutive laws and new governing equations. This workshop was part of the AmeriMech Symposium series sponsored by the National Academies of Sciences, Engineering and Medicine and the U.S. National Committee on Theoretical and Applied Mechanics.

Nonik Indrawatiningsih, Luthfi Hakim

The role of technology (science and technology) is currently very much needed as a means of conducting online learning. One of the technologies that can be applied in bold learning is the Zoom Meeting Cloud application. The purpose of this service is to provide training to kindergarten teachers in operating the Zoom Cloud Meeting application as a bold learning tool. The method used is in the form of training. The subject of the study are 6 orders of people and is a kindergarten teacher. The performance indicator in the implementation of this service program is 70% of participants can operate the Zoom Cloud Meeting application. Based on the results of the training, it shows that the training participants can be categorized into 2, namely participants who succeed in achieving 5 indicators (100%) with 2 participants and participants who reach 4 indicators (80%) with 4 participants in using the Zoom Cloud Meeting application. From this training the participants were greatly helped because the previous participants had not been able to operate the Zoom Cloud Meeting application but after the training participants were able to run the Zoom Cloud Meeting application.

Diego Pereira Rezende

Investigação que tem como objetivo analisar a fotografia “Explosivo fixa” (Man Ray, 1934) a partir do arcabouço da teoria quântica sustentado pela constatação da “dualidade onda-partícula”. Para isso, em um primeiro momento, abordaremos a edificação do “paradigma quântico” durante as primeiras décadas do século XX. Em um segundo momento, averiguaremos a potencialidade transdisciplinar consolidada por meio do desenvolvimento desse paradigma. E, em um terceiro momento, examinaremos a imagem surrealista criada por Man Ray e buscaremos explorá-la de acordo com o pensamento construído pelo “paradigma quântico” e pela transdisciplinaridade.

Jin Lu, Rui Niu, Shuai Wan et al.

A multimode microcavity sensor based on a self-interference microring resonator is demonstrated experimentally. The proposed multimode sensing method is implemented by recording wideband transmission spectra that consist of multiple resonant modes. It is different from the previous dissipative sensing scheme, which aims at measuring the transmission depth changes of a single resonant mode in a microcavity. Here, by combining the dissipative sensing mechanism and the machine learning algorithm, the multimode sensing information extracted from a broadband spectrum can be efficiently fused to estimate the target parameter. The multimode sensing method is immune to laser frequency noises and robust against system imperfection, thus our work presents a great step towards practical applications of microcavity sensors outside the research laboratory. The voltage applied across the microheater on the chip was adjusted to bring its influence on transmittance through the thermo-optic effects. As a proof-of-principle experiment, the voltage was detected by the multimode sensing approach. The experimental results demonstrate that the limit of detection of the multimode sensing by the general regression neural network is reduced to 6.7% of that of single-mode sensing within a large measuring range.

Bernardo Monechi, Miguel Ibáñez-Berganza, and Vittorio Loreto

The ongoing rapid urbanization phenomena make the understanding of the evolution of urban environments of utmost importance to improve the well-being and steer societies towards better futures. Many studies have focused on the emerging properties of cities, leading to the discovery of scaling laws mirroring, for instance, the dependence of socio-economic indicators on city sizes. Though scaling laws allow for the definition of city-size independent socio-economic indicators, only a few efforts have been devoted to the modeling of the dynamical evolution of cities as mirrored through socio-economic variables and their mutual influence. In this work, we propose a Maximum Entropy (ME), non-linear, generative model of cities. We write in particular a Hamiltonian function in terms of a few macro-economic variables, whose coupling parameters we infer from real data corresponding to French towns. We first discover that non-linear dependencies among different indicators are needed for a complete statistical description of the non-Gaussian correlations among them. Furthermore, though the dynamics of individual cities are far from being stationary, we show that the coupling parameters corresponding to different years turn out to be quite robust. The quasi time-invariance of the Hamiltonian model allows proposing an analytic model for the evolution in time of the macro-economic variables, based on the Langevin equation. Despite no temporal information about the evolution of cities has been used to derive this model, its forecast accuracy of the temporal evolution of the system is compatible to that of a model inferred using explicitly such information.