Hasil untuk "Instruments and machines"

Jun Zhang, Chuan Zhang, Mingtao Zhang

ABSTRACT This study integrates embodied intelligence (EI) with a two‐stage two‐sided Hotelling duopoly model to reveal how physical intelligence reshapes digital platform equilibrium in intelligent logistics. By embedding EI‐driven efficiency parameters into market cost functions, the model demonstrates that improved perception and coordination reduce the effective transportation cost and transform pricing dynamics between competing platforms. Experiments in a digital twin warehouse show that when EI strength η increases from 0 to 0.6, throughput rises by 37.5%, Dock‐to‐Stock time decreases by 30.9%, and unit energy consumption drops by 7%–8%, verifying that EI directly enhances operational and economic efficiency. Further analysis confirms that asymmetric advantages in action or data lead to discriminatory pricing as the optimal strategy. Complementary encryption experiments indicate that lightweight security algorithms such as SHA‐1/SHA‐256 add less than 3% latency overhead, maintaining real‐time performance.

Kamilla L. Lomborg, Carolina Cucart-Mora, Jan-Olaf Reschke et al.

In a world of drastic climatic and ecological changes, our knowledge of how the environment influenced hominin behaviour is of the utmost importance. Archaeology plays a key role in this domain, as it is the only discipline that studies empirical evidence of past societies’ responses to environmental change. Computational models generating predictions about past climatic and ecological conditions are vital for understanding the archaeological record and how these factors shaped the dispersal of hominins out of Africa and into Eurasia during the Early and early Middle Pleistocene. In this paper, various models for past reconstructions of climatic and ecological conditions and simulation techniques are presented to provide an overview of the diverse approaches, possibilities, advantages and constraints of using computational reconstructions in archaeological research. Focusing on studies of hominin dispersals out of Africa and into Eurasia during the Early and early Middle Pleistocene, this paper discusses the links between environmental factors and hominin dispersal behaviour. The use of simulation techniques to represent hominin populations, such as cellular automata or agent-based modelling, can contribute to connecting small-scale environment-induced influences on hominins to large-scale patterns, supported by ecological theories of species survival and spatial behaviour. Collectively, these approaches provide an elaborate foundation for understanding environmental influences on past hominin dispersals.

Diego Hitzges, Guillaume Sagnol

Deep learning has been effectively applied to many discrete optimization problems. However, learning-based scheduling on unrelated parallel machines remains particularly difficult to design. Not only do the numbers of jobs and machines vary, but each job-machine pair has a unique processing time, dynamically altering feature dimensions. We propose a novel approach with a neural network tailored for offline deterministic scheduling of arbitrary sizes on unrelated machines. The goal is to minimize a complex objective function that includes the makespan and the weighted tardiness of jobs and machines. Unlike existing online approaches, which process jobs sequentially, our method generates a complete schedule considering the entire input at once. The key contribution of this work lies in the sophisticated architecture of our model. By leveraging various NLP-inspired architectures, it effectively processes any number of jobs and machines with varying feature dimensions imposed by unrelated processing times. Our approach enables supervised training on small problem instances while demonstrating strong generalization to much larger scheduling environments. Trained and tested on instances with 8 jobs and 4 machines, costs were only 2.51% above optimal. Across all tested configurations of up to 100 jobs and 10 machines, our network consistently outperformed an advanced dispatching rule, which incurred 22.22% higher costs on average. As our method allows fast retraining with simulated data and adaptation to various scheduling conditions, we believe it has the potential to become a standard approach for learning-based scheduling on unrelated machines and similar problem environments.

Shun Yoshida, Qingchuan Song, Bastian E. Rapp et al.

Despite their often perceived static and slow nature, some plants can move faster than the blink of an eye. The rapid snap closure motion of the Venus flytrap (Dionaea muscipula) has long captivated the interest of researchers and engineers alike, serving as a model for plant-inspired soft machines and robots. The translation of the fast snapping closure has inspired the development of various artificial Venus flytrap (AVF) systems. However, translating both the closing and reopening motion of D. muscipula into an autonomous plant inspired soft machine has yet to be achieved. In this study, we present an AVF that autonomously closes and reopens, utilizing novel thermo-responsive UV-curable shape memory materials for soft robotic systems. The life-sized thermo-responsive AVF exhibits closing and reopening motions triggered in a naturally occurring temperature range. The doubly curved trap lobes, built from shape memory polymers, close at 38°C, while reopening initiates around 45°C, employing shape memory elastomer strips as antagonistic actuators to facilitate lobe reopening. This work represents the first demonstration of thermo-responsive closing and reopening in an AVF with programmed sequential motion in response to increasing temperature. This approach marks the next step toward autonomously bidirectional moving soft machines/robots.

Jan Corazza, Ivan Gavran, Daniel Neider

Reward machines are an established tool for dealing with reinforcement learning problems in which rewards are sparse and depend on complex sequences of actions. However, existing algorithms for learning reward machines assume an overly idealized setting where rewards have to be free of noise. To overcome this practical limitation, we introduce a novel type of reward machines, called stochastic reward machines, and an algorithm for learning them. Our algorithm, based on constraint solving, learns minimal stochastic reward machines from the explorations of a reinforcement learning agent. This algorithm can easily be paired with existing reinforcement learning algorithms for reward machines and guarantees to converge to an optimal policy in the limit. We demonstrate the effectiveness of our algorithm in two case studies and show that it outperforms both existing methods and a naive approach for handling noisy reward functions.

Yiannis Kiouvrekis, Theodor Panagiotakopoulos

Electromagnetic field (EMF) exposure mapping is increasingly important for ensuring compliance with safety regulations, supporting the deployment of next-generation wireless networks, and addressing public health concerns. While numerous surveys have addressed specific aspects of radio propagation or radio environment maps, a comprehensive and unified overview of EMF mapping methodologies has been lacking. This review bridges that gap by systematically analyzing computational, geospatial, and machine learning approaches used for EMF exposure mapping across both wireless communication engineering and public health domains. A novel taxonomy is introduced to clarify overlapping terminology—encompassing radio maps, radio environment maps, and EMF exposure maps—and to classify construction methods, including analytical models, model-based interpolation, and data-driven learning techniques. In addition, the review highlights domain-specific challenges such as indoor versus outdoor mapping, data sparsity, and model generalization, while identifying emerging opportunities in hybrid modeling, big data integration, and explainable AI. By combining perspectives from communication engineering and public health, this work provides a broader and more interdisciplinary synthesis than previous surveys, offering a structured reference and roadmap for advancing robust, scalable, and socially relevant EMF mapping frameworks.

Luma F. Ali, Hussein Togun, Abdellatif M. Sadeq

Practical applications such as solar power energy systems, electronic cooling, and the convective drying of vented enclosures require continuous developments to enhance fluid and heat flow. Numerous studies have investigated the enhancement of heat transfer in L-formed vented cavities by inserting heat-generating components, filling the cavity with nanofluids, providing an inner rotating cylinder and a phase-change packed system, etc. Contemporary work has examined the thermal performance of L-shaped porous vented enclosures, which can be augmented by using metal foam, using nanofluids as a saturated fluid, and increasing the wall surface area by corrugating the cavity’s heating wall. These features are not discussed in published articles, and their exploration can be considered a novelty point in this work. In this study, a vented cavity was occupied by a copper metal foam with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>P</mi><mi>I</mi><mo>=</mo><mn>10</mn></mrow></semantics></math></inline-formula> and saturated with a copper–water nanofluid. The cavity walls were well insulated except for the left wall, which was kept at a hot isothermal temperature and was either non-corrugated or corrugated with rectangular waves. The Darcy–Brinkman–Forchheimer model and local thermal non-equilibrium models were adopted in momentum and energy-governing equations and solved numerically by utilizing commercial software. The influences of various effective parameters, including the Reynolds number (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>20</mn><mo>≤</mo><mi>R</mi><mi>e</mi><mo>≤</mo><mn>1000</mn></mrow></semantics></math></inline-formula>), the nanoparticle volume fraction (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>%</mo><mo>≤</mo><mi>φ</mi><mo>≤</mo><mn>20</mn><mo>%</mo></mrow></semantics></math></inline-formula>), the inflow and outflow vent aspect ratios (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.1</mn><mo>≤</mo><mrow><mrow><mi>D</mi></mrow><mo>/</mo><mrow><mi>H</mi></mrow></mrow><mo>≤</mo><mn>0.4</mn></mrow></semantics></math></inline-formula>), the rectangular wave corrugation number (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>=</mo><mn>5</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>=</mo><mn>10</mn></mrow></semantics></math></inline-formula>), and the corrugation dimension ratio (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>R</mi><mo>=</mo><mn>1</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>R</mi><mo>=</mo><mn>0.5</mn></mrow></semantics></math></inline-formula>) were determined. The results indicate that the flow field and heat transfer were affected mainly by variations in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>e</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mrow><mi>D</mi></mrow><mo>/</mo><mrow><mi>H</mi></mrow></mrow></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>φ</mi></mrow></semantics></math></inline-formula> for a non-corrugated left wall; they were additionally influenced by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>R</mi></mrow></semantics></math></inline-formula> when the wall was corrugated. The fluid- and solid-phase temperatures of the metal foam increased with an increase in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>e</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mrow><mi>D</mi></mrow><mo>/</mo><mrow><mi>H</mi></mrow></mrow></mrow></semantics></math></inline-formula>. The fluid-phase Nusselt number near the hot left sidewall increased with an increase in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>φ</mi></mrow></semantics></math></inline-formula> by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfenced separators="|"><mrow><mn>25</mn><mo>–</mo><mn>60</mn></mrow></mfenced><mo>%</mo></mrow></semantics></math></inline-formula>, while the solid-phase Nusselt number decreased by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfenced separators="|"><mrow><mn>10</mn><mo>–</mo><mn>30</mn></mrow></mfenced><mo>%</mo></mrow></semantics></math></inline-formula>, and these numbers rose by around <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.5</mn></mrow></semantics></math></inline-formula> times when the Reynolds number increased from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>20</mn></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1000</mn></mrow></semantics></math></inline-formula>. For the corrugated hot wall, the Nusselt numbers of the two metal foam phases increased with an increase in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>e</mi></mrow></semantics></math></inline-formula> and decreased with an increase in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mrow><mi>D</mi></mrow><mo>/</mo><mrow><mi>H</mi></mrow></mrow></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>R</mi></mrow></semantics></math></inline-formula>, or <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi></mrow></semantics></math></inline-formula> by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>10</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>19</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>37</mn><mo>%</mo></mrow></semantics></math></inline-formula>. The original aspect of this study is its use of a thermal, non-equilibrium, nanofluid-saturated metal foam in a corrugated L-shaped vented cavity. We aimed to investigate the thermal performance of this system in order to reinforce the viability of applying this material in thermal engineering systems.

Milena Tsvetkova, Taha Yasseri, Niccolo Pescetelli et al.

From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behaviour alone. Under this paradigm, we review recent research and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open collaboration community and a discussion forum. To ensure more robust and resilient human-machine communities, we require a new sociology of humans and machines. Researchers should study these communities using complex system methods; engineers should explicitly design artificial intelligence for human-machine and machine-machine interactions; and regulators should govern the ecological diversity and social co-development of humans and machines.

Nicolò Oreste Pinciroli Vago, Francesca Forbicini, Piero Fraternali

Non-neural Machine Learning (ML) and Deep Learning (DL) models are often used to predict system failures in the context of industrial maintenance. However, only a few researches jointly assess the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three data sets concerning the operation of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously, and (3) a nitrogen generator working continuously. The problem is formulated as a binary classification task that assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM, and Transformers) are compared using multivariate telemetry time series. The results indicate that, in the considered scenarios, the dimension of the prediction windows plays a crucial role and highlight the effectiveness of DL approaches at classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches at classifying similar and repetitive patterns preceding a failure.

LIU Shuxian, XU Huan, WANG Wei, DENG Le

Social network provides a medium for information dissemination,leading to the rapid development of public opinion.Controlling the development direction of public opinion is one of the core issues of public opinion dynamics.However,the public opinion dynamics model mainly studies the way of updating the opinions of the subject so as to deduce the law of public opinion evolution.This paper classifies the current public opinion dynamics models,analyzes their advantages and disadvantages,and their applications in different fields,and summarizes the future research direction of public opinion dynamics.It is helpful to understand the law of the evolution of public opinion,so as to provide better guidance for the government and other institutions to control the direction of public opinion.

Joost C. F. de Winter, Dimitra Dodou, Yke Bauke Eisma

The processes underlying human cognition are often divided into System 1, which involves fast, intuitive thinking, and System 2, which involves slow, deliberate reasoning. Previously, large language models were criticized for lacking the deeper, more analytical capabilities of System 2. In September 2024, OpenAI introduced the <i>o1</i> model series, designed to handle System 2-like reasoning. While OpenAI’s benchmarks are promising, independent validation is still needed. In this study, we tested the <i>o1-preview</i> model twice on the Dutch ‘Mathematics B’ final exam. It scored a near-perfect 76 and 74 out of 76 points. For context, only 24 out of 16,414 students in the Netherlands achieved a perfect score. By comparison, the <i>GPT-4o</i> model scored 66 and 62 out of 76, well above the Dutch students’ average of 40.63 points. Neither model had access to the exam figures. Since there was a risk of model contamination (i.e., the knowledge cutoff for <i>o1-preview</i> and <i>GPT-4o</i> was after the exam was published online), we repeated the procedure with a new Mathematics B exam that was published after the cutoff date. The results again indicated that <i>o1-preview</i> performed strongly (97.8th percentile), which suggests that contamination was not a factor. We also show that there is some variability in the output of <i>o1-preview</i>, which means that sometimes there is ‘luck’ (the answer is correct) or ‘bad luck’ (the output has diverged into something that is incorrect). We demonstrate that the self-consistency approach, where repeated prompts are given and the most common answer is selected, is a useful strategy for identifying the correct answer. It is concluded that while OpenAI’s new model series holds great potential, certain risks must be considered.

ZHANG Yifan, SONG Wei

When solving multi-objective optimization problems, particle swarm optimization algorithms usually employ preset example selection methods and search strategies, which cannot be adjusted according to specific optimization states. In the face of different optimization problems, inappropriate search strategies cannot effectively guide the population, resulting in low search performance of the population. To solve the above problems, a multi-objective particle swarm optimization algorithm guided by extreme learning decision network (ELDN-PSO) is proposed. First of all, the multi-objective optimization problem is decomposed into several scalar subproblems, and an extreme learning decision network is constructed. The network takes the particle position as input, and selects appropriate search actions for each particle according to the optimization state. The fitness change of a particle on the subproblem is obtained as the training sample for the reinforcement learning, and the training speed is improved by extreme learning machine. In the process of optimization, the network is automatically adjusted according to the optimization states, and it selects the appropriate search strategy for the particles at different search stages. Secondly, the non-dominated solutions in the multi-objective optimization problem are difficult to compare. Thus, the leadership of each solution is quantified into a comparable value, so that the examples are more clearly selected for the particles. In addition, an external archive is used to store better particles to maintain the quality of the solutions and guide the population. Comparative experiments are carried out on the ZDT and DTLZ test functions. The results show that ELDN-PSO can effectively cope with different Pareto front shapes, improving the optimization speed as well as the convergence and diversity of the solutions.

T. Anitha Kumari, Sanket Mishra

The integration of sensors in the monitoring of essential bodily measurements, air quality, and energy consumption in buildings demonstrates the importance of the Internet of Things (IoT) in everyday life. These security breaches are caused by rudimentary and immature security protocols that are implemented on IoT devices. An intrusion detection system is used to detect security threats and system-level applications to detect malicious activities. This paper introduces Tachyon, a combination of various statistical and tree-based Artificial Intelligence (AI) techniques, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Bidirectional Auto-Regressive Transformers (BART), Logistic Regression (LR), Multivariate Adaptive Regression Splines (MARS), Decision Tree (DT), and a top k stack ensemble to distinguish between normal and malicious attacks in a binary classification setting. The IoTID2020 dataset used in this study consists of 6,25,783 samples with 83 features. An initial examination of the data reveals its unbalanced nature. To create a balanced dataset, a range of sampling techniques were used, including Oversampling, Undersampling, Synthetic Minority Oversampling Technique (SMOTE), Random Oversampling Examples (ROSE), Borderline Synthetic Minority Oversampling Technique (b-SMOTE), and Adaptive Synthetic (ADASYN). In addition, principal component analysis (PCA) and partial least squares (PLS) were used to determine the most significant features. The experimental results demonstrate that the stacked ensemble achieved a performance of 99.8%, which is better than the baseline approaches. An ablation study of ensemble models was also conducted to assess the performance of the proposed model in various scenarios.

Robert Lee Poe, Soumia Zohra El Mestari

The definition and implementation of fairness in automated decisions has been extensively studied by the research community. Yet, there hides fallacious reasoning, misleading assertions, and questionable practices at the foundations of the current fair machine learning paradigm. Those flaws are the result of a failure to understand that the trade-off between statistically accurate outcomes and group similar outcomes exists as independent, external constraint rather than as a subjective manifestation as has been commonly argued. First, we explain that there is only one conception of fairness present in the fair machine learning literature: group similarity of outcomes based on a sensitive attribute where the similarity benefits an underprivileged group. Second, we show that there is, in fact, a trade-off between statistically accurate outcomes and group similar outcomes in any data setting where group disparities exist, and that the trade-off presents an existential threat to the equitable, fair machine learning approach. Third, we introduce a proof-of-concept evaluation to aid researchers and designers in understanding the relationship between statistically accurate outcomes and group similar outcomes. Finally, suggestions for future work aimed at data scientists, legal scholars, and data ethicists that utilize the conceptual and experimental framework described throughout this article are provided.

Siddharth Maharana, Ramya M. Anche, A. N. Ramaprakash et al.

The Wide-Area Linear Optical Polarimeter (WALOP)-South instrument is an upcoming wide-field and high-accuracy optical polarimeter to be used as a survey instrument for carrying out the Polar-Areas Stellar Imaging in Polarization High Accuracy Experiment (PASIPHAE) program. Designed to operate as a one-shot four-channel and four-camera imaging polarimeter, it will have a field of view of $35\times 35$ arcminutes and will measure the Stokes parameters $I$, $q$, and $u$ in a single exposure in the SDSS-r broadband filter. The design goal for the instrument is to achieve an overall polarimetric measurement accuracy of 0.1 % over the entire field of view. We present here the complete polarimetric modeling of the instrument, characterizing the amount and sources of instrumental polarization. To accurately retrieve the real Stokes parameters of a source from the measured values, we have developed a calibration method for the instrument. Using this calibration method and simulated data, we demonstrate how to correct instrumental polarization and obtain 0.1 % accuracy in the degree of polarization, $p$. Additionally, we tested and validated the calibration method by implementing it on a table-top WALOP-like test-bed polarimeter in the laboratory.

Hao CHEN, Feng WANG, Weiming ZHANG et al.

With the development of multimedia techniques, the demand for copyright protection of digital products has also gradually risen.Digital watermarking is an effective means to protect the copyright of digital products.It is generally made by adding important identification information (i.e., digital watermark) to a digital carrier (e.g., text, image, etc.), so that the carrier carries the identification information but does not affect the normal use of the carrier.The common digital watermark embedding scheme is to embed the watermark information by modifying the carrier via specific algorithms.In the actual application scenarios, there are many images or objects to be protected (such as art paintings, etc.) that are not allowed to be modified.Based on this background, a new carrier-independent deep optical watermarking algorithm was proposed, which can realize watermark information embedding without modifying the original carrier and achieve the purpose of copyright protection.Specifically, a new watermark template expression scheme at the embedding end was proposed, which expressed the watermark information by visible light modulation.By analyzing the visual system of human eyes, a watermark template pattern based on alternating projection was proposed to embed the watermark information, which made the embedding process neither require modification of the original carrier nor affect the visual senses of human eyes.At the extraction end, a watermark extraction network based on residual connection was designed, and the captured watermarked images were fed into this network after perspective transformation to extract the watermark information.The experiments were conducted under various conditions and comparisons with three baseline algorithms were made.The experimental results show that the proposed algorithm generates watermarked images with less visual distortion and is robust to the ＆quot;projecting-shooting＆quot; process.The watermark extraction network has high accuracy in extracting watermark information at different distances, angles and illumination conditions, and has certain advantages over other general networks.

Florian Marquardt

These brief lecture notes cover the basics of neural networks and deep learning as well as their applications in the quantum domain, for physicists without prior knowledge. In the first part, we describe training using backpropagation, image classification, convolutional networks and autoencoders. The second part is about advanced techniques like reinforcement learning (for discovering control strategies), recurrent neural networks (for analyzing time traces), and Boltzmann machines (for learning probability distributions). In the third lecture, we discuss first recent applications to quantum physics, with an emphasis on quantum information processing machines. Finally, the fourth lecture is devoted to the promise of using quantum effects to accelerate machine learning.

Jorge A. Amador, Johan Manuel Redondo, Gerard Olivar‐Tost et al.

The concept of Sustainable Development has given rise to multiple interpretations. In this article, it is proposed that Sustainable Development should be interpreted as the capacity of territory, community, or landscape to conserve the notion of well-being that its population has agreed upon. To see the implications of this interpretation, a Brander and Taylor model, to evaluate the implications that extractivist policies have over an isolated community and cooperating communities, is proposed. For an isolated community and through a bifurcation analysis in which the Hopf bifurcation and the heteroclinic cycle bifurcation are detected, 4 prospective scenarios are found, but only one is sustainable under different extraction policies. In the case of cooperation, the exchange between communities is considered by coupling two models such as the one defined for the isolated community, with the condition that their transfers of renewable resources involve conservation policies. Since human decisions do not occur in a continuum, but rather through jumps, the mathematical model of cooperation used is a Filippov System, in which the dynamics could involve two switching manifolds of codimension one and one switching manifold of codimension two. The exchange in the cooperation model, for specific parameter arrangements, exhibits n-periodic orbits and chaos. It is notable that, in the cases in which the system shows sliding, it could be interpreted as a recovery delay related to the time needed by the deficit community to recover, until its dependence on the other community stops. It is concluded (1) that a sustainability analysis depends on the way well-being is defined because every definition of well-being is not necessarily sustainable, (2) that sustainability can be visualized as invariant sets in the nonzero region of the space of states (equilibrium points, n-periodic orbits, and strange attractors), and (3) that exchange is key to the prevalence of the human being in time. The results question us on whether Sustainable Development is only to keep us alive or if it also implies doing it with dignity.

Arif Amrulloh, Enny Itje Sela

Penjadwalan mata kuliah merupakan permasalahan yang sering terjadi pada perguruan tinggi, di antaranya adalah bentrok waktu mengajar dosen, ruangan dan kelas mahasiswa. Kajian ini mengusulkan optimasi penjadwalan mata kuliah menggunakan algoritme genetika dan pencarian tabu. Algoritme genetika berfungsi untuk menghasilkan generasi terbaik kromosom yang tersusun atas gen dosen, hari, dan jam. Pencarian tabu digunakan untuk pembagian ruang perkuliahan. Penjadwalan dilakukan di fakultas Informatika yang mempunyai empat program studi dengan 65 dosen, 93 mata kuliah, 265 penugasan dosen, dan 65 kelas. Proses pembangkitan 265 jadwal membutuhkan waktu selama 561 detik dan tidak ada bentrok yang terjadi. Kombinasi algoritme genetika dan pencarian tabu dapat memproses jadwal mata kuliah yang cukup banyak dengan lebih cepat daripada cara manual.

K. C. Chen, W. Dai, C. Errando-Herranz et al.

A quantum random access memory (qRAM) is considered an essential computing unit to enable polynomial speedups in quantum information processing. Proposed implementations include the use of neutral atoms and superconducting circuits to construct a binary tree but these systems still require demonstrations of the elementary components. Here, we propose a photonic-integrated-circuit (PIC) architecture integrated with solid-state memories as a viable platform for constructing a qRAM. We also present an alternative scheme based on quantum teleportation and extend it to the context of quantum networks. Both implementations realize the two key qRAM operations, (1) quantum state transfer and (2) quantum routing, with already demonstrated components: electro-optic modulators, a Mach-Zehnder interferometer (MZI) network, and nanocavities coupled to artificial atoms for spin-based memory writing and retrieval. Our approaches furthermore benefit from built-in error detection based on photon heralding. Detailed theoretical analysis of the qRAM efficiency and query fidelity shows that our proposal presents viable near-term designs for a general qRAM.