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In recent years, privacy concerns have become a serious issue for companies wishing to protect economic models and comply with end-user expectations. In the same vein, some countries now impose, by law, constraints on data use and protection. Such context thus encourages machine learning to evolve from a centralized data and computation approach to decentralized approaches. Specifically, Federated Learning (FL) has been recently developed as a solution to improve privacy, relying on local data to train local models, which collaborate to update a global model that improves generalization behaviors. However, by definition, no computer system is entirely safe. Security issues, such as data poisoning and adversarial attack, can introduce bias in the model predictions. In addition, it has recently been shown that the reconstruction of private raw data is still possible. This paper presents a comprehensive study concerning various privacy and security issues related to federated learning. Then, we identify the state-of-the-art approaches that aim to counteract these problems. Findings from our study confirm that the current major security threats are poisoning, backdoor, and Generative Adversarial Network (GAN)-based attacks, while inference-based attacks are the most critical to the privacy of FL. Finally, we identify ongoing research directions on the topic. This paper could be used as a reference to promote cybersecurity-related research on designing FL-based solutions for alleviating future challenges.

To explain how populations with distinct warning signals coexist in close parapatry, we experimentally assessed intrinsic mechanisms acting as reproductive barriers within three poison-frog species from the Peruvian Amazon belonging to a Müllerian mimetic ring ( Ranitomeya variabilis , Ranitomeya imitator and Ranitomeya fantastica ). We tested the role of prezygotic and postzygotic isolation barriers between phenotypically different ecotypes of each species, using no-choice mating experiments and offspring survival analysis. Our results show that prezygotic mating preference did not occur except for one specific ecotype of R. imitator , and that all three species were able to produce viable inter-population F1 hybrids. However, while R. variabilis and R. imitator hybrids were able to produce viable F2 generations, we found that for R. fantastica , every F1 hybrid males were sterile while females remained fertile. This unexpected result, echoing with Haldane’s rule of speciation, validated phylogenetic studies which tentatively diagnose these populations of R. fantastica as two different species. Our work suggests that postzygotic genetic barriers likely participate in the extraordinary phenotypic diversity observed within Müllerian mimetic Ranitomeya populations, by maintaining species boundaries.

We offer a new in‐depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining sampling robot named ROMIE. GPP is essential for ROMIE's optimal performance, which is translated into solving the traveling salesman problem, a complex graph theory challenge that is crucial for determining the most effective route to cover all sampling locations in a mining field. This problem is central to enhancing ROMIE's operational efficiency and competitiveness against human labor by optimizing cost and time. The primary aim of this research is to advance GPP by developing, evaluating, and improving a cost‐efficient software and web application. We delve into an extensive comparison and analysis of Google operations research (OR)‐Tools optimization algorithms. Our study is driven by the goal of applying and testing the limits of OR‐Tools capabilities by integrating Reinforcement Learning techniques for the first time. This enables us to compare these methods with OR‐Tools, assessing their computational effectiveness and real‐world application efficiency. Our analysis seeks to provide insights into the effectiveness and practical application of each technique. Our findings indicate that Q‐Learning stands out as the optimal strategy, demonstrating superior efficiency by deviating only 1.2% on average from the optimal solutions across our datasets. Advancing global path planning algorithm is studied for transforming geochemical mining sampling in autonomous vehicles. Cutting‐edge algorithms are harnessed to solve the intricate traveling salesman problem, optimizing route efficiency. A novel analysis of operations research‐tools and reinforcement learning techniques is investigated, demonstrating Q‐learning's superior efficiency (codes provided for benchmarking). Technological advancements with a new benchmark for autonomous mining operations are provided.

Abstract Amphibians are ideal for studying visual system evolution because their biphasic (aquatic and terrestrial) life history and ecological diversity expose them to a broad range of visual conditions. Here, we evaluate signatures of selection on visual opsin genes across Neotropical anurans and focus on three diurnal clades that are well-known for the concurrence of conspicuous colors and chemical defense (i.e., aposematism): poison frogs (Dendrobatidae), Harlequin toads (Bufonidae: Atelopus), and pumpkin toadlets (Brachycephalidae: Brachycephalus). We found evidence of positive selection on 44 amino acid sites in LWS, SWS1, SWS2, and RH1 opsin genes, of which one in LWS and two in RH1 have been previously identified as spectral tuning sites in other vertebrates. Given that anurans have mostly nocturnal habits, the patterns of selection revealed new sites that might be important in spectral tuning for frogs, potentially for adaptation to diurnal habits and for color-based intraspecific communication. Furthermore, we provide evidence that SWS2, normally expressed in rod cells in frogs and some salamanders, has likely been lost in the ancestor of Dendrobatidae, suggesting that under low-light levels, dendrobatids have inferior wavelength discrimination compared to other frogs. This loss might follow the origin of diurnal activity in dendrobatids and could have implications for their behavior. Our analyses show that assessments of opsin diversification in across taxa could expand our understanding of the role of sensory system evolution in ecological adaptation.

Ocean surface monitoring, emphasizing oil slick detection, has become essential due to its importance for oil exploration and ecosystem risk prevention. Automation is now mandatory since the manual annotation process of oil by photo-interpreters is time-consuming and cannot process the data collected continuously by the available spaceborne sensors. Studies on automatic detection methods mainly focus on Synthetic Aperture Radar (SAR) data exclusively to detect anthropogenic (spills) or natural (seeps) oil slicks, all using limited datasets. The main goal is to maximize the detection of oil slicks of both natures while being robust to other phenomena that generate false alarms, called “lookalikes”. To this end, this paper presents the automation of offshore oil slick detection on an extensive database of real and recent oil slick monitoring scenarios, including both types of slicks. It relies on slick annotations performed by expert photo-interpreters on Sentinel-1 SAR data over four years and three areas worldwide. In addition, contextual data such as wind estimates and infrastructure positions are included in the database as they are relevant data for oil detection. The contributions of this paper are: (i) A comparative study of deep learning approaches using SAR data. A semantic and instance segmentation analysis via FC-DenseNet and Mask R-CNN, respectively. (ii) A proposal for Fuse-FC-DenseNet, an extension of FC-DenseNet that fuses heterogeneous SAR and wind speed data for enhanced oil slick segmentation. (iii) An improved set of evaluation metrics dedicated to the task that considers contextual information. (iv) A visual explanation of deep learning predictions based on the SHapley Additive exPlanation (SHAP) method adapted to semantic segmentation. The proposed approach yields a detection performance of up to 94% of good detection with a false alarm reduction ranging from 14% to 34% compared to mono-modal models. These results provide new solutions to improve the detection of natural and anthropogenic oil slicks by providing tools that allow photo-interpreters to work more efficiently on a wide range of marine surfaces to be monitored worldwide. Such a tool will accelerate the oil slick detection task to keep up with the continuous sensor acquisition. This upstream work will allow us to study its possible integration into an industrial production pipeline. In addition, a prediction explanation is proposed, which can be integrated as a step to identify the appropriate methodology for presenting the predictions to the experts and understanding the obtained predictions and their sensitivity to contextual information. Thus it helps them to optimize their way of working.

Several metrics have been proposed in literature to assess the perceptual quality of two-dimensional images. However, no similar effort has been devoted to quality assessment of stereoscopic images. Therefore, in this paper, we review the different issues related to 3D visualization, and we propose a quality metric for the assessment of stereopairs using the fusion of 2D quality metrics and of the depth information. The proposed metric is evaluated using the SAMVIQ methodology for subjective assessment. Specifically, distortions deriving from coding are taken into account and the quality degradation of the stereopair is estimated by means of subjective tests.

This paper reports on a water flow energy harvester exploiting a horizontal axis turbine with distributed magnets of alternate polarities at the rotor periphery and air coils outside the pipe. The energy harvester operates down to 1.2L/min with an inlet section of 20mm of diameter and up to 25.2mW are provided at 20L/min in a 2.4V NiMH battery through a BQ25504 power management circuit. The pressure loss induced by the insertion of the energy harvester in the hydraulic circuit and by the extraction of energy has been limited to 0.05bars at 30L/min, corresponding to a minor loss coefficient of KEH=3.94.

Diffuse large B cell lymphoma (DLBCL) is successfully treated with combination immuno-chemotherapy, but relapse with resistant disease occurs in ~ 40% of patients. However, little is known regarding relapsed/refractory DLBCL (rrDLBCL) genetics and alternative therapies. Based on findings from other tumors, we hypothesized that RAS-MEK-ERK signaling would be upregulated in resistant tumors, potentially correlating with mutations in RAS, RAF, or associated proteins. We analyzed mutations and phospho-ERK levels in tumor samples from rrDLBCL patients. Unlike other tumor types, rrDLBCL is not mutated in any Ras or Raf family members, despite having increased expression of p-ERK. In paired biopsies comparing diagnostic and relapsed specimens, 33% of tumors gained p-ERK expression, suggesting a role in promoting survival. We did find mutations in several Ras-associating proteins, including GEFs, GAPs, and downstream effectors that could account for increased ERK activation. We further investigated mutations in one such protein, RASGRP4. In silico modeling indicated an increased interaction between H-Ras and mutant RASGRP4. In cell lines, mutant RASGRP4 increased basal p-ERK expression and lead to a growth advantage in colony forming assays when challenged with doxorubicin. Relapsed/refractory DLBCL is often associated with increased survival signals downstream of ERK, potentially corresponding with mutations in protein controlling RAS/MEK/ERK signaling.

Diffuse large B-cell lymphoma (DLBCL) relapses in approximately 40% of patients following frontline therapy. We reported that STAT6 D419  mutations are enriched in relapsed/refractory DLBCL (rrDLBCL) samples, suggesting that JAK/STAT signaling plays a role in therapeutic resistance. We hypothesized that STAT6 D419 mutations can improve DLBCL cell survival by reprogramming the microenvironment to sustain STAT6 activation. Thus, we investigated the role of STAT6 D419 mutations on DLBCL cell growth and its microenvironment. We found that phospho-STAT6 D419N was retained in the nucleus longer than phospho-STAT6 WT following IL-4 stimulation, and STAT6 D419N recognized a more restricted DNA-consensus sequence than STAT6 WT. Upon IL-4 induction, STAT6 D419N expression led to a higher magnitude of gene expression changes, but in a more selective list of gene targets compared with STAT WT . The most significantly expressed genes induced by STAT6 D419N were those implicated in survival, proliferation, migration, and chemotaxis, in particular CCL17. This chemokine, also known as TARC, attracts helper T-cells to the tumor microenvironment, especially in Hodgkin’s lymphoma. To this end, in DLBCL, phospho-STAT6 + rrDLBCL cells had a greater proportion of infiltrating CD4 + T-cells than phospho-STAT6 − tumors. Our findings suggest that STAT6 D419 mutations in DLBCL lead to cell autonomous changes, enhanced signaling, and altered composition of the tumor microenvironment.