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"Torres, Ricardo"
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No more free lunch : reflections on the Cuban economic reform process and challenges for transformation
In September 2010, the Cuban government decided to embark on an economic reform program, unprecedented after the Revolution in 1959. This opened up opportunities for Cuban economists and scholars to participate in the development of the reform program. Thanks to grants from SSRC (Social Sciences Research Council, New York) and the Norwegian Ministry of Foreign Affairs, several researchers from the Cuban think tank CEEC (Center for Studies of the Cuban Economy, Havana) got an opportunity to visit countries that could be of interest for the reform process, notably Vietnam, but also Brazil, South Africa and Norway. The result of these field visits and a subsequent workshop involving contributions from Cuban as well as non-Cuban scholars, this volume showcases unprecedented new insights into the process and prospects for reform along many dimensions, including foreign direct investment, import substitution, entrepreneurship and business creation, science and technology development, and fiscal policies. The resulting analysis, in a comparative perspective, provides a framework for future research as well as for business practice and policymaking.
Book
Litter Detection with Deep Learning: A Comparative Study
Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.
Journal Article
Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function
With the widespread use of biometric authentication comes the exploitation of presentation attacks, possibly undermining the effectiveness of these technologies in real-world setups. One example takes place when an impostor, aiming at unlocking someone else's smartphone, deceives the built-in face recognition system by presenting a printed image of the user. In this work, we study the problem of automatically detecting presentation attacks against face authentication methods, considering the use-case of fast device unlocking and hardware constraints of mobile devices. To enrich the understanding of how a purely software-based method can be used to tackle the problem, we present a solely data-driven approach trained with multi-resolution patches and a multi-objective loss function crafted specifically to the problem. We provide a careful analysis that considers several user-disjoint and cross-factor protocols, highlighting some of the problems with current datasets and approaches. Such analysis, besides demonstrating the competitive results yielded by the proposed method, provides a better conceptual understanding of the problem. To further enhance efficacy and discriminability, we propose a method that leverages the available gallery of user data in the device and adapts the method decision-making process to the user's and the device's own characteristics. Finally, we introduce a new presentation-attack dataset tailored to the mobile-device setup, with real-world variations in lighting, including outdoors and low-light sessions, in contrast to existing public datasets.
Journal Article
Magnetite pollution nanoparticles in the human brain
Biologically formed nanoparticles of the strongly magnetic mineral, magnetite, were first detected in the human brain over 20 y ago [Kirschvink JL, Kobayashi-Kirschvink A, Woodford BJ (1992) Proc Natl Acad Sci USA 89(16):7683–7687]. Magnetite can have potentially large impacts on the brain due to its unique combination of redox activity, surface charge, and strongly magnetic behavior. We used magnetic analyses and electron microscopy to identify the abundant presence in the brain of magnetite nanoparticles that are consistent with high-temperature formation, suggesting, therefore, an external, not internal, source. Comprising a separate nanoparticle population from the euhedral particles ascribed to endogenous sources, these brain magnetites are often found with other transition metal nanoparticles, and they display rounded crystal morphologies and fused surface textures, reflecting crystallization upon cooling from an initially heated, iron-bearing source material. Such high-temperature magnetite nanospheres are ubiquitous and abundant in airborne particulate matter pollution. They arise as combustion-derived, iron-rich particles, often associated with other transition metal particles, which condense and/or oxidize upon airborne release. Those magnetite pollutant particles which are <∼200 nm in diameter can enter the brain directly via the olfactory bulb. Their presence proves that externally sourced iron-bearing nanoparticles, rather than their soluble compounds, can be transported directly into the brain, where they may pose hazard to human health.
Journal Article
Nearest neighbors distance ratio open-set classifier
In this paper, we propose a novel multiclass classifier for the open-set recognition scenario. This scenario is the one in which there are no a priori training samples for some classes that might appear during testing. Usually, many applications are inherently open set. Consequently, successful closed-set solutions in the literature are not always suitable for real-world recognition problems. The proposed open-set classifier extends upon the Nearest-Neighbor (NN) classifier. Nearest neighbors are simple, parameter independent, multiclass, and widely used for closed-set problems. The proposed Open-Set NN (OSNN) method incorporates the ability of recognizing samples belonging to classes that are unknown at training time, being suitable for open-set recognition. In addition, we explore evaluation measures for open-set problems, properly measuring the resilience of methods to unknown classes during testing. For validation, we consider large freely-available benchmarks with different open-set recognition regimes and demonstrate that the proposed OSNN significantly outperforms their counterparts in the literature.
Journal Article
Ultrafast and persistent photoinduced phase transition at room temperature monitored by streaming powder diffraction
Ultrafast photoinduced phase transitions at room temperature, driven by a single laser shot and persisting long after stimuli, represent emerging routes for ultrafast control over materials’ properties. Time-resolved studies provide fundamental mechanistic insight into far-from-equilibrium electronic and structural dynamics. Here we study the photoinduced phase transformation of the Rb
0.94
Mn
0.94
Co
0.06
[Fe(CN)
6
]
0.98
material, designed to exhibit a 75 K wide thermal hysteresis around room temperature between Mn
III
Fe
II
tetragonal and Mn
II
Fe
III
cubic phases. We developed a specific powder sample streaming technique to monitor by ultrafast X-ray diffraction the structural and symmetry changes. We show that the photoinduced polarons expand the lattice, while the tetragonal-to-cubic photoinduced phase transition occurs within 100 ps above threshold fluence. These results are rationalized within the framework of the Landau theory of phase transition as an elastically-driven and cooperative process. We foresee broad applications of the streaming powder technique to study non-reversible and ultrafast dynamics.
Photoinduced phase transitions occur in a variety of materials and allow for the optical control of the materials properties. Here, Herve et al present a streaming powder X-ray diffraction method allowing them to study the ultrafast photoinduced phase transition of Rb
0.94
Mn
0.94
Co
0.06
[Fe(CN)
6
]
0.9
within thermal hysteresis.
Journal Article
Azithromycin removal from water via adsorption on drinking water sludge-derived materials: Kinetics and isotherms studies
In this study, we utilized drinking water treatment sludge (WTS) to produce adsorbents through the drying and calcination process. These adsorbents were then evaluated for their ability to remove azithromycin (AZT) from aqueous solutions. The L-500 adsorbent, derived from the calcination (at 500°C) of WTS generated under conditions of low turbidity in the drinking water treatment plant, presented an increase in the specific surface area from 70.745 to 95.471 m 2 g -1 and in the total pore volume from 0.154 to 0.211 cm 3 g -1 , which resulted in a significant AZT removal efficiency of 65% in distilled water after 60 min of treatment. In synthetic wastewater, the rate of AZT removal increased to 80%, in comparison, in a real effluent of a municipal wastewater treatment plant, an AZT removal of 56% was obtained. Kinetic studies revealed that the experimental data followed the pseudo-second-order model (R 2 : 0.993–0.999, APE: 0.07–1.30%, and Δq: 0.10–2.14%) suggesting that chemisorption is the limiting step in the adsorption using L-500. This finding aligns with FTIR analysis, which indicates that adsorption mechanisms involve π-π stacking, hydrogen bonding, and electrostatic interactions. The equilibrium data were analyzed using the nonlinear Langmuir, Freundlich, and Langmuir-Freundlich isotherms. The Langmuir-Freundlich model presented the best fitting (R 2 : 0.93, APE: 2.22%, and Δq: 0.06%) revealing numerous interactions and adsorption energies between AZT and L-500. This adsorbent showed a reduction of 19% in its AZT removal after four consecutive reuse cycles. In line with the circular economy principles, our study presents an interesting prospect for the reuse and valorization of WTS. This approach not only offers an effective adsorbent for AZT removal from water but also represents a significant step forward in advancing sustainable water treatment solutions within the framework of the circular economy.
Journal Article
Evaluating the Removal of the Antibiotic Cephalexin from Aqueous Solutions Using an Adsorbent Obtained from Palm Oil Fiber
This study aimed to understand the adsorption process of cephalexin (CPX) from aqueous solution by a biochar produced from the fiber residue of palm oil. Scanning electron microscopy, Fourier transform infrared spectroscopy, Boehm titration, and the point of zero charge were used to characterize the morphology and surface functional groups of the adsorbent. Batch tests were carried out to evaluate the effects of the solution pH, temperature, and antibiotic structure. The adsorption behavior followed the Langmuir model and pseudo-second-order model with a maximum CPX adsorption capacity of 57.47 mg g−1. Tests on the thermodynamic behavior suggested that chemisorption occurs with an activation energy of 91.6 kJ mol−1 through a spontaneous endothermic process. Electrostatic interactions and hydrogen bonding represent the most likely adsorption mechanisms, although π–π interactions also appear to contribute. Finally, the CPX removal efficiency of the adsorbent was evaluated for synthetic matrices of municipal wastewater and urine. Promising results were obtained, indicating that this adsorbent can potentially be applied to purifying wastewater that contains trace antibiotics.
Journal Article