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Artificial Intelligence (AI), particularly large language models (LLMs), holds the potential to bridge language and information gaps, which can benefit the economies of developing nations. However, our analysis of FLORES-200, FLORES+, Ethnologue, and World Development Indicators data reveals that these benefits largely favor English speakers. Speakers of languages in low-income and lower-middle-income countries face higher costs when using OpenAI's GPT models via APIs because of how the system processes the input -- tokenization. Around 1.5 billion people, speaking languages primarily from lower-middle-income countries, could incur costs that are 4 to 6 times higher than those faced by English speakers. Disparities in LLM performance are significant, and tokenization in models priced per token amplifies inequalities in access, cost, and utility. Moreover, using the quality of translation tasks as a proxy measure, we show that LLMs perform poorly in low-resource languages, presenting a ``double jeopardy" of higher costs and poor performance for these users. We also discuss the direct impact of fragmentation in tokenizing low-resource languages on climate. This underscores the need for fairer algorithm development to benefit all linguistic groups., Comment: Project GitHub repository at https://github.com/worldbank/double-jeopardy-in-llms

Modular soft robot arms (MSRAs) are composed of multiple independent modules connected in a sequence. Due to their modular structure and high degrees of freedom (DOFs), these modules can simultaneously bend at different angles in various directions, enabling complex deformation. This capability allows MSRAs to perform more intricate tasks than single module robots. However, the modular structure also induces challenges in accurate planning, modeling, and control. Nonlinearity, hysteresis, and gravity complicate the physical model, while the modular structure and increased DOFs further lead to accumulative errors along the sequence. To address these challenges, we propose a flexible task space planning and control strategy for MSRAs, named S2C2A (State to Configuration to Action). Our approach formulates an optimization problem, S2C (State to Configuration planning), which integrates various loss functions and a forward MSRA model to generate configuration trajectories based on target MSRA states. Given the model complexity, we leverage a biLSTM network as the forward model. Subsequently, a configuration controller C2A (Configuration to Action control) is implemented to follow the planned configuration trajectories, leveraging only inaccurate internal sensing feedback. Both a biLSTM network and a physical model are utilized for configuration control. We validated our strategy using a cable-driven MSRA, demonstrating its ability to perform diverse offline tasks such as position control, orientation control, and obstacle avoidance. Furthermore, our strategy endows MSRA with online interaction capability with targets and obstacles. Future work will focus on addressing MSRA challenges, such as developing more accurate physical models and reducing configuration estimation errors along the module sequence., Comment: 13 pages, 14 figures, 4 tables

Instructions for Build, Assembly, and Test (IBAT) refers to the process used whenever any operation is conducted on hardware, including tests, assembly, and maintenance. Currently, the generation of IBAT documents is time-intensive, as users must manually reference and transfer information from engineering diagrams and parts lists into IBAT instructions. With advances in machine learning and computer vision, however, it is possible to have an artificial intelligence (AI) model perform the partial filling of the IBAT template, freeing up engineer time for more highly skilled tasks. AiBAT is a novel system for assisting users in authoring IBATs. It works by first analyzing assembly drawing documents, extracting information and parsing it, and then filling in IBAT templates with the extracted information. Such assisted authoring has potential to save time and reduce cost. This paper presents an overview of the AiBAT system, including promising preliminary results and discussion on future work., Comment: 9 pages, 6 figures, 2 tables

In this paper we show the application of the new robotic multi-platform system HSURF to a specific use case of teleoperation, aimed at monitoring and inspection. The HSURF system, consists of 3 different kinds of platforms: floater, sinker and robotic fishes. The collaborative control of the 3 platforms allows a remotely based operator to control the fish in order to visit and inspect several targets underwater following a complex trajectory. A shared autonomy solution shows to be the most suitable, in order to minimize the effect of limited bandwidth and relevant delay intrinsic to acoustic communications. The control architecture is described and preliminary results of the acoustically teleoperated visits of multiple targets in a testing pool are provided.

The nonlinearity and hysteresis of soft robot motions have posed challenges in accurate soft robot control. Neural networks, especially recurrent neural networks (RNNs), have been widely leveraged for this issue due to their nonlinear activation functions and recurrent structures. Although they have shown satisfying accuracy in most tasks, these black-box approaches are not explainable, and hence, they are unsuitable for areas with high safety requirements, like robot-assisted surgery. Based on the RNN controllers, we propose a data-driven explainable controller (DDEC) whose parameters can be updated online. We discuss the Jacobian controller and kinematics controller in theory and demonstrate that they are only special cases of DDEC. Moreover, we utilize RNN, the Jacobian controller, the kinematics controller, and DDECs for trajectory following tasks. Experimental results have shown that our approach outperforms the other controllers considering trajectory following errors while being explainable. We also conduct a study to explore and explain the functions of each DDEC component. This is the first interpretable soft robot controller that overcomes the shortcomings of both NN controllers and interpretable controllers. Future work may involve proposing different DDECs based on different RNN controllers and exploiting them for high-safety-required applications., Comment: 10 pages, 8 figures, 5 tables

Animal behavior serves as a reliable indicator of the adaptation of organisms to their environment and their overall well-being. Through rigorous observation of animal actions and interactions, researchers and observers can glean valuable insights into diverse facets of their lives, encompassing health, social dynamics, ecological relationships, and neuroethological dimensions. Although state-of-the-art deep learning models have demonstrated remarkable accuracy in classifying various forms of animal data, their adoption in animal behavior studies remains limited. This survey article endeavors to comprehensively explore deep learning architectures and strategies applied to the identification of animal behavior, spanning auditory, visual, and audiovisual methodologies. Furthermore, the manuscript scrutinizes extant animal behavior datasets, offering a detailed examination of the principal challenges confronting this research domain. The article culminates in a comprehensive discussion of key research directions within deep learning that hold potential for advancing the field of animal behavior studies.

Deformable object manipulation is a classical and challenging research area in robotics. Compared with rigid object manipulation, this problem is more complex due to the deformation properties including elastic, plastic, and elastoplastic deformation. In this paper, we describe a new deformable object manipulation method including soft contact simulation, manipulation learning, and sim-to-real transfer. We propose a novel approach utilizing Vision-Based Tactile Sensors (VBTSs) as the end-effector in simulation to produce observations like relative position, squeezed area, and object contour, which are transferable to real robots. For a more realistic contact simulation, a new simulation environment including elastic, plastic, and elastoplastic deformations is created. We utilize RL strategies to train agents in the simulation, and expert demonstrations are applied for challenging tasks. Finally, we build a real experimental platform to complete the sim-to-real transfer and achieve a 90% success rate on difficult tasks such as cylinder and sphere. To test the robustness of our method, we use plasticine of different hardness and sizes to repeat the tasks including cylinder and sphere. The experimental results show superior performances of deformable object manipulation with the proposed method.

In this study, we investigate the feasibility of designing reconfigurable transmitting metasurfaces through the use of Drude-like scatterers with purely electric response. Theoretical and numerical analyses are provided to demonstrate that the response of spherical Drude-like scatterers can be tailored to achieve complete transmission, satisfying a generalized Kerker's condition at half of their plasma frequency. This phenomenon, which arises from the co-excitation of the electric dipole and the electric quadrupole within the scatterer, also exhibits moderate broadband performance. Subsequently, we present the application of these particles as meta-atoms in the design of reconfigurable multipolar Huygens metasurfaces, outlining the technical prerequisites for achieving effective beam-steering capabilities. Finally, we explore a plausible implementation of these low-loss Drude-like scatterers at microwave frequencies using plasma discharges. Our findings propose an alternative avenue for Huygens metasurface designs, distinct from established approaches relying on dipolar meta-atoms or on core-shell geometries. Unlike these conventional methods, our approach fosters seamless integration of reconfigurability strategies in beam-steering devices.

A detailed knowledge of the decay properties of the so called Hoyle state in the $^{12}$C nucleus ($E_x=7.654$ MeV, $0^+$) is required to calculate the rate at which carbon is forged in typical red-giant stars. This paper reports on a new almost background-free measurement of the radiative decay branching ratio of the Hoyle state using advanced charged particle coincidence techniques. The exploitation, for the first time in a similar experiment, of a bidimensional map of the coincidence efficiency allows to reach an unitary value and, consequently, to strongly reduce sources of systematic uncertainties. The present results suggest a value of the radiative branching ratio of $\Gamma_{rad}/\Gamma_{tot}=4.2(6)\cdot10^{-4}$. This finding helps to resolve the tension between recent data published in the literature.

Modular soft robots have shown higher potential in sophisticated tasks than single-module robots. However, the modular structure incurs the complexity of accurate control and necessitates a control strategy specifically for modular robots. In this paper, we introduce a data collection strategy and a novel and accurate bidirectional LSTM configuration controller for modular soft robots with module number adaptability. Such a controller can control module configurations in robots with different module numbers. Simulation cable-driven robots and real pneumatic robots have been included in experiments to validate the proposed approaches, and we have proven that our controller can be leveraged even with the increase or decrease of module number. This is the first paper that gets inspiration from the physical structure of modular robots and utilizes bidirectional LSTM for module number adaptability. Future work may include a planning method that bridges the task and configuration spaces and the integration of an online controller., Comment: 10 figures, 4 tables

An investigation of the quasiprojectile breakup channel in semiperipheral and peripheral collisions of $^{58,64}$Ni+$^{58,64}$Ni at 32 and 52 MeV/nucleon is presented. Data have been acquired in the first experimental campaign of the INDRA-FAZIA apparatus in GANIL. The effect of isospin diffusion between projectile and target in the two asymmetric reactions has been highlighted by means of the isospin transport ratio technique, exploiting the neutron-to-proton ratio of the quasiprojectile reconstructed from the two breakup fragments. We found evidence that, for the same reaction centrality, a higher degree of relaxation of the initial isospin imbalance is achieved in the breakup channel with respect to the more populated binary output, possibly indicating the indirect selection of specific dynamical features. We have proposed an interpretation based on different average projectile-target contact times related to the two exit channels under investigation, with a longer interaction for the breakup channel. The time information has been extracted from AMD simulations of the studied systems coupled to GEMINI++: the model calculations support the hypothesis hereby presented.

Maize, a crucial crop globally cultivated across vast regions, especially in sub-Saharan Africa, Asia, and Latin America, occupies 197 million hectares as of 2021. Various statistical and machine learning models, including mixed-effect models, random coefficients models, random forests, and deep learning architectures, have been devised to predict maize yield. These models consider factors such as genotype, environment, genotype-environment interaction, and field management. However, the existing models often fall short of fully exploiting the complex network of causal relationships among these factors and the hierarchical structure inherent in agronomic data. This study introduces an innovative approach integrating random effects into Bayesian networks (BNs), leveraging their capacity to model causal and probabilistic relationships through directed acyclic graphs. Rooted in the linear mixed-effects models framework and tailored for hierarchical data, this novel approach demonstrates enhanced BN learning. Application to a real-world agronomic trial produces a model with improved interpretability, unveiling new causal connections. Notably, the proposed method significantly reduces the error rate in maize yield prediction from 28% to 17%. These results advocate for the preference of BNs in constructing practical decision support tools for hierarchical agronomic data, facilitating causal inference., Comment: 34 pages, 6 figures

In 1929, Enrico Fermi wrote "Problemi attuali della fisica" ("Contemporary Problems of Physics"), a short article in Italian published in the magazine "Annali dell'istruzione media". The magazine was sponsored by the Italian Ministry of Schooling and Education, and it was addressed to teachers and principals of middle and high schools. This short text written by Fermi had been forgotten for a long time. However, in recent years it has been republished in Italy; moreover, the "Annali dell'istruzione media" have been digitized and made freely available online. It is unclear why Fermi wrote an article of a clearly divulgative nature for a non-technical journal, but is still interesting to to read his words and appreciate his skills as a great scientific communicator. In this work we include the transcription of the original article in Italian, and we also propose an English translation to make the text available world-wide and accessible to a broader public.

Soft robots have been leveraged in considerable areas like surgery, rehabilitation, and bionics due to their softness, flexibility, and safety. However, it is challenging to produce two same soft robots even with the same mold and manufacturing process owing to the complexity of soft materials. Meanwhile, widespread usage of a system requires the ability to replace inner components without highly affecting system performance, which is interchangeability. Due to the necessity of this property, a hybrid adaptive controller is introduced to achieve interchangeability from the perspective of control approaches. This method utilizes an offline-trained recurrent neural network controller to cope with the nonlinear and delayed response from soft robots. Furthermore, an online optimizing kinematics controller is applied to decrease the error caused by the above neural network controller. Soft pneumatic robots with different deformation properties but the same mold have been included for validation experiments. In the experiments, the systems with different actuation configurations and the different robots follow the desired trajectory with errors of 3.3 +- 2.9% and 4.3 +- 4.1% compared with the working space length, respectively. Such an adaptive controller also shows good performance on different control frequencies and desired velocities. This controller is also compared with a model-based controller in simulation. This controller endows soft robots with the potential for wide application, and future work may include different offline and online controllers. A weight parameter adjusting strategy may also be proposed in the future., Comment: accepted by IEEE Robotics and Automation Letters

Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently., Comment: 16 pages, 6 figures, 7tables, accepted by IEEE Transactions on Automation Science and Engineering on 11 March, 2024

Four different reactions, $^{32}$S+$^{12}$C and $^{20}$Ne+$^{12}$C at 25 and 50 MeV/nucleon, have been measured with the FAZIA detector capable of full isotopic identification of most forward emitted reaction products. Fragment multiplicities, angular distributions and energy spectra have been measured and compared with Monte Carlo simulations, i.e. the antisymmetrized molecular dynamics (AMD) and the heavy-ion phase space exploration (HIPSE) models. These models are combined with two different afterburner codes (HF$l$ and SIMON) to describe the decay of the excited primary fragments. In the case of AMD, the effect of including the clustering and inter-clustering processes to form bound particles and fragments is discussed. A clear confirmation of the role of cluster aggregation in the reaction dynamics and particle production for these light systems, for which the importance of the clustering process increases with bombarding energy, is obtained., Comment: 15 pages, 14 PDF figures

An analysis of the asymmetric reactions $^{40,48}$Ca+$^{12}$C at 25 and 40 MeV/nucleon is presented. Data have been collected with six modules of the FAZIA array. The analysis is focused on the breakup channel of sources produced in dissipative collisions, partially corresponding to incomplete fusion processes. The study has been performed both on detected fragments and on some resonances reconstructed by means of particle-fragment correlations, with a focus on the evolution of the breakup channel with the beam energy and the neutron content of the system, looking in particular at the relative velocity between the breakup fragments. Results show that also Carbon fragments reconstructed by means of particle correlations can be in large part interpreted as the light partner of a scission.

In April 1925, Enrico Fermi was only 23 years old and he had graduated less than three years earlier. Despite his age, Fermi was invited at the University of Rome by Federigo Enriques to give several lectures on the recent advances in physics at the Italian Mathematics Seminar. Enriques was also the editor of "Periodico di Matematiche", an important Italian journal concerned primarily with secondary school math teachers. Some of the articles published by "Periodico di Matematiche" correspond to the lectures which Fermi gave during seminars and meetings of Italian mathematicians. Here we propose an English translation for one of these articles, entitled "Sopra la teoria cinetica dei corpi solidi" and discussing the kinetic theory of solids. We believe that this Fermi's work offers a twofold interesting perspective: on one side, it allows us to explore the problems related to the structure of matter of emerging at the time as seen by the eyes of a bright, young physicist. On the other hand, it shows us the undoubtedly high scientific level of the articles read in Italy by the secondary school math teachers in the Twenties.