Miracle-dz/UniFAR-FTUs huggingface dataset:
The limits of evolutionary convergence in sympatry: reproductive interference and historical constraints leading to local diversity in warning traits

Mutualistic interactions between defended species represent a striking case of evolutionary convergence in sympatry, driven by the increased protection against predators brought by mimicry in warning traits. However, such convergence is often limited: sympatric defended species frequently display different or imperfectly similar warning traits. The phylogenetic distance between sympatric species may indeed prevent evolution towards the exact same signal. Moreover, warning traits are also involved in mate recognition, so that trait convergence might result in heterospecific courtship and mating. Here, we develop a mathematical model to investigate the strength and direction of evolution of warning trait in defended species with different ancestral traits. Specifically, we determine the effect of phenotypic distances between ancestral trait states of sympatric defended species and of costs of heterospecific sexual interactions on imperfect mimicry and trait divergence. Our analytical results confirm that reproductive interference and historical constraints limit the convergence of warning traits, leading to either complete divergence, or imperfect mimicry. Our model reveals that imperfect mimicry evolves only when ancestral trait values differ between species due to historical constraints and highlights the importance of female and predator discriminations in the evolution of such imperfect mimicry. Our study thus provides new predictions on how reproductive interference interacts with historical constraints and may promote the emergence of novel warning traits, enhancing mimetic diversity.
Category-specific memory encoding in the medial temporal lobe and beyond: the role of reward

The medial temporal lobe (MTL), including the hippocampus (HC), perirhinal cortex (PRC), and parahippocampal cortex (PHC), is central to memory formation. Reward enhances memory through interplay between the HC and substantia nigra/ventral tegmental area (SNVTA). While the SNVTA also innervates the MTL cortex and amygdala (AMY), their role in reward-enhanced memory is unclear. Prior research suggests category specificity in the MTL cortex, with the PRC and PHC processing object and scene memory, respectively. It is unknown, however, whether reward modulates category-specific memory processes. Furthermore, no study has demonstrated clear category specificity in the MTL for encoding processes contributing to subsequent recognition memory. To address these questions, we had 39 healthy volunteers (27 for all memory-based analyses) undergo functional magnetic resonance imaging while performing an incidental encoding task pairing objects or scenes with high or low reward, followed by a next-day recognition test. Behaviorally, high reward preferably enhanced object memory. Neural activity in the PRC and PHC reflected successful encoding of objects and scenes, respectively. Importantly, AMY encoding effects were selective for high-reward objects, with a similar pattern in the PRC. The SNVTA and HC showed no clear evidence of successful encoding. This behavioral and neural asymmetry may be conveyed through an anterior–temporal memory system, including the AMY and PRC, potentially in interplay with the ventromedial prefrontal cortex.
Investigating densities of Symbiodiniaceae in two species of Antipatharians (black corals) from Madagascar

Here, we report the first methodological approach to investigate the presence and estimate the density of Symbiodiniaceae cells in corals of the order Antipatharia subclass Hexacorallia, known as black corals. Antipatharians are understudied ecosystem engineers of shallow (<30 m depth), mesophotic (30-150 m) and deep-sea (>200 m) reefs. They provide habitat to a vast number of marine fauna, enhancing and supporting coral reefs biodiversity globally. Nonetheless, little biological and ecological information exists on antipatharians, including the extent at which global change disturbances are threatening these corals. The assumption that they were exempted from threats related to climate change was challenged by findings of high density of dinoflagellates within three antipatharian colonies. Further methodical studies were necessary to investigate the regularity of these findings. An integrated design combining microscopy and molecular techniques was used to investigate the presence and estimate density of Symbiodiniaceae cells within two antipatharians species -Cupressopathes abies and Stichopathes maldivensis -from shallow and mesophotic reefs of SW Madagascar. Symbiodiniaceae-like cells were found within the two species from both shallow and mesophotic reefs, although the overall cell density was very low (0-4 cell mm-3). These findings suggest that high abundance of Symbiodiniaceae is not characteristic of antipatharians, which has relevant implications considering disruptions associated to climate change affecting other corals. However, the high densities of dinoflagellates found in antipatharian colonies exposed to higher light irradiance in other studies should be further examined.
Population dynamics of the primary malaria vector Nyssorhynchus darlingi in a high transmission setting dominated by fish farming in western Amazonian Brazil

Vale do Rio Juruá in western Acre, Brazil, has reported highest malaria numbers since 2005, and is considered persistent transmission hotspot. Fish farming development was encouraged to improve standard of living, resulting in productive breeding sites for Amazonian malaria vector species, including Nyssorhynchus darlingi that, combined with the high human density and mobility, adds to the local malaria burden. This study reports entomological profile of immature and adult Ny. darlingi at three sites in Mâncio Lima, Acre, during the rainy and dry season (February to September, 2017). From 63 fishponds, 10,859 larvae were collected, including 5,512 first-instar Anophelinae larvae and 4,927 second, third and fourth-instars, of which 8.5% (n = 420) were Ny. darlingi. This species was most abundant in not-abandoned fishponds and in the presence of emerging aquatic vegetation. Seasonal analysis of immatures in urban landscapes found no significant difference in the numbers of Ny. darlingi, corresponding to equivalent population density during the rainy to dry transition period. However, in the rural landscape, significantly higher numbers of Ny. darlingi larvae were collected in August (IRR = 5.80, p = 0.037) and September (IRR = 6.62, p = 0.023) (dry season), compared to February (rainy season), suggesting important role of fishponds for vector population maintenance during the seasonal transition in this landscape type. Adult sampling detected mainly Ny. darlingi (~93%), with similar outdoor feeding behavior, but different abundance according to landscape profile: urban site 1 showed higher peaks of human biting rate in May (46 bites/person/hour), than February (4) and September (15), while rural site 3 shows similar HBR during the same sampling period (22, 24 and 21, respectively). This study contributes to a better understanding of the larvae biology of the main malaria vector in the Vale do Rio Juruá region and, ultimately will support vector control efforts.
ADAMTS13 and von Willebrand factor assessment in steady state and acute vaso‐occlusive crisis of sickle cell disease

Sickle cell disease (SCD) is characterized by vaso‐occlusive crisis (VOC), acute chest syndrome (ACS) and multiorgan failure (MOF) complicated by thrombosis. Von Willebrand factor (VWF) is a strong marker of SCD‐related endothelial injury.
Effect of Surfactant Solutions on the Drag and the Flow Pattern of a Circular Cylinder

The effect of surfactant solutions on the flow past a circular cylinder was investigated in the Reynolds number (Re) ranging between 10 and 105 by measuring the drag and by flow visualization. Surfactant solutions were aqueous solutions of oleyl-bihydroxyethyl-methyl-ammonium-chloride in the concentration range of 50 to 200 ppm, to which sodium salicylate was added as a counterion. It was clarified that the drag coefficient of the cylinder in surfactant solutions increases, comparing that in water in a lower Re range for constant cylinder diameter. On the contrary, the drag coefficient decreases in higher Re range. The Re range in which drag reduction occurs increases with increasing diameter. The flow visualization results show that the drag increases because of the existence of the wide stagnation zone around the cylinder. This zone disappears in the Re range in which drag reduction occurs. In this case, the separation point moves rearward and the velocity defect in the wake becomes smaller. © 2005 American Institute of Chemical Engineers AIChE J, 2006
Presentation of fatal stroke due to SARS‐CoV‐2 and dengue virus coinfection

Herein, we report a case of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and dengue coinfection, presented as a fatal stroke in our hospital, in São José do Rio Preto, São Paulo State, a Brazilian city hyperendemic for dengue viruses and other arthropod‐borne viruses (arboviruses) and currently facing a surge of SARS‐CoV‐2 cases. This case is the first described in the literature and contributes to the better understanding of clinical presentations of two important diseases in a tropical setting.
Physical Determinants of Vitamin D Photosynthesis: A Review

Vitamin D synthesis by exposure of skin to solar ultraviolet radiation (UVR) provides the majority of this hormone that is essential for bone development and maintenance but may be important for many other health outcomes. This process, which is the only well‐established benefit of solar UVR exposure, depends on many factors including genetics, age, health, and behavior. However, the most important factor is the quantity and quality of UVR reaching the skin. Vitamin D synthesis specifically requires ultraviolet B (UVB) radiation that is the minority component (<5%) of solar UVR. This waveband is also the most important for the adverse effects of solar exposure. The most obvious of which is sunburn (erythema), but UVB is also the main cause of DNA damage to the skin that is a prerequisite for most skin cancers. UVB at the Earth's surface depends on many physical and temporal factors such as latitude, altitude, season, and weather. Personal, cultural, and behavioral factors are also important. These include skin melanin, clothing, body surface area exposed, holiday habits, and sunscreen use. There is considerable disagreement in the literature about the role of some of these factors, possibly because some studies have been done by researchers with little understanding of photobiology. It can be argued that vitamin D supplementation obviates the need for solar exposure, but many studies have shown little benefit from this approach for a wide range of health outcomes. There is also increasing evidence that such exposure offers health benefits independently of vitamin D: the most important of which is blood‐pressure reduction. In any case, public health advice must optimize risk versus benefit for solar exposure. It is fortunate that the individual UVB doses necessary for maintaining optimal vitamin D status are lower than those for sunburn, irrespective of skin melanin. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC. on behalf of American Society for Bone and Mineral Research.
Optimum Graft Density for Dispersing Particles in Polymer Melts

We investigate the dispersion of polymer-grafted particles suspended in a polymer matrix by viscoelastic measurements and electron microscopy. We find that, when the graft density is low, the particle dispersion improves with an increase of the graft density, while the particle dispersion becomes worse when the graft density is too high; i.e., there is an optimum graft density for dispersing the particles. We explain the phenomena by showing that the grafted polymers cause attraction for high graft density.
Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

The current approaches for energy consumption optimisation in buildings are mainly reactive or focus on scheduling of daily/weekly operation modes in heating. Machine Learning (ML)-based advanced control methods have been demonstrated to improve energy efficiency when compared to these traditional methods. However, placing of ML-based models close to the buildings is not straightforward. Firstly, edge-devices typically have lower capabilities in terms of processing power, memory, and storage, which may limit execution of ML-based inference at the edge. Secondly, associated building information should be kept private. Thirdly, network access may be limited for serving a large number of edge devices. The contribution of this paper is an architecture, which enables training of ML-based models for energy consumption prediction in private cloud domain, and transfer of the models to edge nodes for prediction in Kubernetes environment. Additionally, predictors at the edge nodes can be automatically updated without interrupting operation. Performance results with sensor-based devices (Raspberry Pi 4 and Jetson Nano) indicated that a satisfactory prediction latency (~7–9 s) can be achieved within the research context. However, model switching led to an increase in prediction latency (~9–13 s). Partial evaluation of a Reference Architecture for edge computing systems, which was used as a starting point for architecture design, may be considered as an additional contribution of the paper.
Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network

The effective detection of driver drowsiness is an important measure to prevent traffic accidents. Most existing drowsiness detection methods only use a single facial feature to identify fatigue status, ignoring the complex correlation between fatigue features and the time information of fatigue features, and this reduces the recognition accuracy. To solve these problems, we propose a driver sleepiness estimation model based on factorized bilinear feature fusion and a long- short-term recurrent convolutional network to detect driver drowsiness efficiently and accurately. The proposed framework includes three models: fatigue feature extraction, fatigue feature fusion, and driver drowsiness detection. First, we used a convolutional neural network (CNN) to effectively extract the deep representation of eye and mouth-related fatigue features from the face area detected in each video frame. Then, based on the factorized bilinear feature fusion model, we performed a nonlinear fusion of the deep feature representations of the eyes and mouth. Finally, we input a series of fused frame-level features into a long-short-term memory (LSTM) unit to obtain the time information of the features and used the softmax classifier to detect sleepiness. The proposed framework was evaluated with the National Tsing Hua University drowsy driver detection (NTHU-DDD) video dataset. The experimental results showed that this method had better stability and robustness compared with other methods.
Cyclists' Crossing Intentions When Interacting with Automated Vehicles: A Virtual Reality Study

Most of cyclists’ fatalities originate from collisions with motorized vehicles. It is expected that automated vehicles (AV) will be safer than human-driven vehicles, but this depends on the nature of interactions between non-automated road users, among them cyclists. Little research on the interactions between cyclists and AVs exists. This study aims to determine the main factors influencing cyclists’ crossing intentions when interacting with an automated vehicle as compared to a conventional vehicle (CV) using a 360° video-based virtual reality (VR) method. The considered factors in this study included vehicle type, gap size between cyclist and vehicle, vehicle speed, and right of way. Each factor had two levels. In addition, cyclist’s self-reported behavior and trust in automated vehicles were also measured. Forty-seven participants experienced 16 different crossing scenarios in a repeated measures study using VR. These scenarios are the result of combinations of the studied factors at different levels. In total, the experiment lasted 60 min. The results show that the gap size and the right of way were the primary factors affecting the crossing intentions of the individuals. The vehicle type and vehicle speed did not have a significant effect on the crossing intentions. Finally, the 360° video-based VR method scored relatively high as a research method and comparable with the results of a previous study investigating pedestrians’ crossing intentions confirming its suitability as a research methodology to study cyclists’ crossing intentions.
Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark

Facial expressions are the most common medium for expressing human emotions. Due to the wide range of real-world applications, facial expression understanding has received extensive attention from researchers. One of the most vital issues of facial expression recognition is the extraction and modeling of the temporal dynamics of facial emotions from videos. Additionally, the rapid growth of video data from various multimedia sources is becoming a serious concern. Therefore, to address these issues, in this paper, we introduce a novel approach on top of Spark for facial expression understanding from videos. First, we propose a new dynamic feature descriptor, namely, the local directional structural pattern from three orthogonal planes (LDSP-TOP), which analyzes the structural aspects of the local dynamic texture. Second, we design a 1-D convolutional neural network (CNN) to capture additional discriminative features. Third, a long short-term memory (LSTM) autoencoder is employed to learn the spatiotemporal features. Finally, an extensive experimental investigation is carried out to demonstrate the performance and scalability of the proposed framework.
A Thunderstorm Cloud Point Charge Localization Method Based on CEEMDAN and SG Filtering

The current thunderstorm monitoring methods ignore the nonlinear and non-stationary characteristics of atmospheric electric field signals, which has a negative effect on the monitoring results. Based on complementary ensemble empirical mode decomposition with adaptive noise and Savitzky-Golay filtering (CEEMDAN-SG), a point charge localization method for the thunderstorm cloud is proposed. After CEEMDAN is used to decompose the electric field signal into a series of intrinsic mode function (IMF) components, the signal is reconstructed after SG filtering of those noise-dominant components. Then, the reconstructed signal is used for point charge localization correction. By changing the signal samples, the decomposition order and SNR of CEEMDAN-SG, CEEMDAN, etc. are compared, and the performance of the method is analyzed. Experiments show that compared to the SNR before reconstruction, the SNR after reconstruction is improved by about 3%. At the same time, the results can match the radar chart well on the time scale. After using the CNN-LSTM network model, it is found that compared with the original signal, the amplitude of the absolute error of the reconstructed signal is larger, so that the signal characteristics can be more displayed. This once again proves the localization effects.
Evolution of Bulletin Board & its application to E-Voting - A Survey

—The voting process is fundamental to any democratic system – be it a country or a company’s boardroom. Nearly forty years ago, e-voting was theoretically perceived as a more efﬁcient replacement of the widely existing paper-based traditional voting system. Several research works have been carried out to ensure more security and efﬁciency in different settings for e-voting schemes. One of the fundamental building blocks of e-voting systems is the public Bulletin Board through which several security properties are achieved. After introducing Blockchain technology, the bulletin board has found a new meaningful and concrete way of distributed way of implementation. Before Blockchain technology, either such a system was theoretically assumed or perceived as a public broadcast channel with memory. In this survey, we present a concise survey of bulletin boards’ evolution with a typical application to the e-voting systems. We note that bulletin boards have other applications in other joint computation areas. Still, we are interested in evolving e-voting systems based on bulletin board and how several desired security properties are realized through bulletin boards.
On Algebraic Embedding for Unstructured Lattices

Lattice-based cryptography, the study of cryptographic primitives whose security is based on the hardness of so-called lattice problems, has taken center stage in cryptographic research in recent years. It potentially oﬀers favorable security features, even against quantum algorithms. One of the main obstacles for wide adoption of this type of cryptography is its unsatisfactory eﬃciency. To address this point, eﬃcient lattice-based cryptography usually relies on the intractability of problems on lattices with additional algebraic structure (such as so-called ideal-lattices or module-lattices). It is an important open question to evaluate the hardness of such lattice problems, and their relation to the hardness of problems on unstructured lattices.
The Computational Complexity of Understanding Binary Classifier Decisions

For a d-ary Boolean function Φ: {0, 1}d → {0, 1} and an assignment to its variables x = (x1, x2, . . . , xd) we consider the problem of finding those subsets of the variables that are sufficient to determine the function value with a given probability δ. This is motivated by the task of interpreting predictions of binary classifiers described as Boolean circuits, which can be seen as special cases of neural networks. We show that the problem of deciding whether such subsets of relevant variables of limited size k ≤ d exist is complete for the complexity class NPPP and thus, generally, unfeasible to solve. We then introduce a variant, in which it suffices to check whether a subset determines the function value with probability at least δ or at most δ − γ for 0 < γ < δ. This promise of a probability gap reduces the complexity to the class NPBPP. Finally, we show that finding the minimal set of relevant variables cannot be reasonably approximated, i.e. with an approximation factor d1−α for α > 0, by a polynomial time algorithm unless P = NP. This holds even with the promise of a probability gap.
FuNet: A Novel Road Extraction Network with Fusion of Location Data and Remote Sensing Imagery

Road semantic segmentation is unique and difficult. Road extraction from remote sensing imagery often produce fragmented road segments leading to road network disconnection due to the occlusion of trees, buildings, shadows, cloud, etc. In this paper, we propose a novel fusion network (FuNet) with fusion of remote sensing imagery and location data, which plays an important role of location data in road connectivity reasoning. A universal iteration reinforcement (IteR) module is embedded into FuNet to enhance the ability of network learning. We designed the IteR formula to repeatedly integrate original information and prediction information and designed the reinforcement loss function to control the accuracy of road prediction output. Another contribution of this paper is the use of histogram equalization data pre-processing to enhance image contrast and improve the accuracy by nearly 1%. We take the excellent D-LinkNet as the backbone network, designing experiments based on the open dataset. The experiment result shows that our method improves over the compared advanced road extraction methods, which not only increases the accuracy of road extraction, but also improves the road topological connectivity.