Miracle-dz/UniFAR-FTUs huggingface dataset:
The East Asian Erotic Picture Dataset and Gender Differences in Response to Opposite-Sex Erotic Stimuli in Chinese College Students

Understanding the processing of sexual stimuli has become a significant part of research on human sexuality. In addition to individual characteristics (gender and sexual orientation), empirical studies have shown that cultural factors play an important role in sexual stimuli processing. The attitudes toward sex have been reported to be more conservative in East Asian societies as compared to western countries, and significantly more sexual difficulties are observed among East Asian people. However, stimulus materials, which potentially facilitate human sexuality research on native East Asian people, are relatively not satisfactory. Erotic stimuli depicting East Asian figures are limited in the existing picture datasets. To address this issue, we present a collection of 237 erotic and 108 control pictures, accompanied by self-reported ratings of sexual arousal, pleasantness, and sexual attractiveness for opposite-sex erotic stimuli by heterosexual males and females (n = 40, divided into two equal-sized subsamples). This collection is divided into six categories, depending on their contents: dressed males (44), semi-nude males (65), nude males (64), dressed females (64), semi-nude females (52), and nude females (56). We showed gender differences in sexual arousal, pleasantness, and sexual attractiveness ratings in response to opposite-sex erotic pictures. Males reported the highest levels of sexual arousal, pleasantness, and sexual attractiveness for nude female pictures, whereas females reported the highest levels of sexual arousal, pleasantness, and sexual attractiveness for semi-nude male pictures. The erotic picture dataset may provide a useful resource of erotic stimuli that can be used as stimulus materials in experimental research on sexual function in East Asians.
Intraoperative Ultrasound-Assisted Extent of Resection Assessment in Pediatric Neurosurgical Oncology

Central nervous system tumors represent the most frequent solid malignancy in the pediatric population. Maximal safe surgical resection is a mainstay of treatment, with significant prognostic impact for the majority of histotypes. Intraoperative ultrasound (ioUS) is a widely available tool in neurosurgery to assist in intracerebral disease resection. Despite technical caveats, preliminary experiences suggest a satisfactory predictive ability, when compared to magnetic resonance imaging (MRI) studies. Most of the available evidence on ioUS applications in brain tumors derive from adult series, a scenario that might not be representative of the pediatric population. We present our preliminary experience comparing ioUS-assisted resection assessment to early post-operative MRI findings in 154 consecutive brain tumor resections at our pediatric neurosurgical unit. A high concordance was observed between ioUS and post-operative MRI. Overall ioUS demonstrated a positive predictive value of 98%, a negative predictive value of 92% in assessing the presence of tumor residue compared to postoperative MRI. Overall, sensibility and specificity were 86% and 99%, respectively. On a multivariate analysis, the only variable significantly associated to unexpected tumor residue on postoperative MRI was histology. Tumor location, patient positioning during surgery, age and initial tumor volume were not significantly associated with ioUS predictive ability. Our data suggest a very good predictive value of ioUS in brain tumor resective procedures in children. Low-grade glioma, high-grade glioma and craniopharyngioma might represent a setting deserving specific endeavours in order to improve intraoperative extent of resection assessment ability.
Temperature Induced Physiological Reaction Norms of the Coccolithophore Gephyrocapsa oceanica and Resulting Coccolith Sr/Ca and Mg/Ca Ratios

Coccolithophores are one of the major contributors to the pelagic production of calcium carbonate and their fossilized remains are a key component of the biogeochemical cycles of calcium (Ca), magnesium (Mg), and other divalent cations present in the intracellular precipitated calcitic structures (coccoliths). The geochemical signature of coccoliths (e.g., Sr/Ca and Mg/Ca ratios) is used as paleoproxy to reconstruct past environmental conditions and to understand the underlying physiological precipitation kinetics. Here, we present the elemental fractionation of Sr and Mg in calcite of the coccolithophore Gephyrocapsa oceanica from controlled laboratory experiments applying an extended temperature gradient (12 to 27°C). The physiological reaction norm of G. oceanica, in terms of growth rate, exhibited optimum behavior while the partition coefficient of Sr (DSr) was linearly correlated with temperature and DMg indicated no specific trend. Our results indicate: (1) a presumably secondary physiological control of DSr, and (2) the importance of calibrating coccolithophore-based proxies using experiments that include the full physiological reaction norms (i.e., a possible non-linear response) to environmental drivers (e.g., temperature, salinity, and pH, etc.). The presented results contribute to an improved understanding of the underlying physiological kinetics involved in regulating coccolith elemental fractionation and give additional implications for designing future laboratory experiments to calibrate and apply coccolithophore based paleoproxies on the fossil sediment record.
AaPKAc Regulates Differentiation of Infection Structures Induced by Physicochemical Signals From Pear Fruit Cuticular Wax, Secondary Metabolism, and Pathogenicity of Alternaria alternata

Alternaria alternata, the casual agent of black rot of pear fruit, can sense and respond to the physicochemical cues from the host surface and form infection structures during infection. To evaluate the role of cyclic AMP-dependent protein kinase (cAMP-PKA) signaling in surface sensing of A. alternata, we isolated and functionally characterized the cyclic adenosine monophosphate-dependent protein kinase A catalytic subunit gene (AaPKAc). Gene expression results showed that AaPKAc was strongly expressed during the early stages of appressorium formation on hydrophobic surfaces. Knockout mutants ΔAaPKAc were generated by replacing the target genes via homologous recombination events. We found that intracellular cAMP content increased but PKA content decreased in ΔAaPKAc mutant strain. Appressorium formation and infection hyphae were reduced in the ΔAaPKAc mutant strain, and the ability of the ΔAaPKAc mutant strain to recognize and respond to high hydrophobicity surfaces and different surface waxes was lower than in the wild type (WT) strain. In comparison with the WT strain, the appressorium formation rate of the ΔAaPKAc mutant strain on high hydrophobicity and fruit wax extract surface was reduced by 31.6 and 49.3% 4 h after incubation, respectively. In addition, AaPKAc is required for the hypha growth, biomass, pathogenicity, and toxin production of A. alternata. However, AaPKAc negatively regulated conidia formation, melanin production, and osmotic stress resistance. Collectively, AaPKAc is required for pre-penetration, developmental, physiological, and pathological processes in A. alternata.
Self-Organized Criticality in Economic Fluctuations: The Age of Maturity

Self-Organized Criticality (SOC) has been proposed as a paradigm that may rationalize the emergence of macrofinancial fluctuations. The wave of innovative thinking sparked by this proposal continues to produce interesting contributions in many areas of economics, ranging from macroeconomics to finance. In this review, we propose a guided tour to these achievements, highlighting that analysis of SOC equilibria is a promising avenue to establish a nexus between i) a statistical equilibrium characterized by the spontaneous emergence of dynamic critical fluctuations and ii) a strategic equilibrium concept modeling a large number of interacting players. The critical state is the stable outcome arising from a trade-off between cooperation and competition.
QoE-centric control and management of multimedia services in software defined and virtualized networks

Multimedia services consumption has increased tremendously since the deployment of 4G/LTE networks. Mobile video services (e.g., YouTube and Mobile TV) on smart devices are expected to continue to grow with the emergence and evolution of future networks such as 5G. The end user’s demand for services with better quality from service providers has triggered a trend towards Quality of Experience (QoE) centric network management through efficient utilization of network resources. However, existing network technologies are either unable to adapt to diverse changing network conditions, or limited in available resources. This has posed challenges to service providers for provisioning of QoE-centric multimedia services. New networking solutions such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) can provide better solutions in terms of QoE control and management of multimedia services in emerging and future networks. The features of SDN, such as adaptability, programmability and cost effectiveness make it suitable for bandwidth intensive multimedia applications such as live video streaming, 3D/HD video and video gaming. However, the delivery of multimedia services over SDN/NFV networks to achieve optimized QoE, and the overall QoE-centric network resource management remain an open question especially in the advent development of future softwarized networks. The work in this thesis intends to investigate, design and develop novel approaches for QoE-centric control and management of multimedia services (with a focus on video streaming services) over software defined and virtualized networks. First, a video quality management scheme based on the traffic intensity under Dynamic Adaptive Video Streaming over HTTP (DASH) using SDN is developed. The proposed scheme can mitigate virtual port queue congestion which may cause buffering or stalling events during video streaming, thus, reducing the video quality. A QoE-driven resource allocation mechanism is designed and developed for improving the end user’s QoE for video streaming services. The aim of this approach is to find the best combination of network node functions that can provide an optimized QoE level to end-users through network node cooperation. Furthermore, a novel QoE-centric management scheme is proposed and developed, which utilizes Multipath TCP (MPTCP) and Segment Routing (SR) to enhance QoE for video streaming services over SDN/NFV-based networks. The goal of this strategy is to enable service providers to route network traffic through multiple disjointed bandwidth-satisfying paths and meet specific service QoE guarantees to the end-users. Extensive experiments demonstrated that the proposed schemes in this work improve the video quality significantly compared with the state-ofthe-art approaches. The thesis further proposes the path protections and linkfailure free MPTCP/SR-based architecture that increases Survivability , resilience,
ezc3d: An easy C3D file I/O cross-platform solution for C++, Python and MATLAB

Summary This work introduces the open source ezc3d library which provides a light and comprehensive API to easily read and write c3d files. The C++ core provides a fast file I/O library, and convenient MATLAB and Python3 interfaces are also provided so researchers can smoothly implement ezc3d in their current workflow. It supports c3d files from the main biomechanics software, namely: Vicon, Qualisys, Optotrak, BTS and XSens. In addition, since the c3d standard allows for multiple ways to store force platform data, a force platform analysis module is provided. The main feature of this module is to express forces and moments in more common reference frames—that is, expressed in the global reference frame calculated at either the origin or at the centre of pressure—so they can be directly interpreted by the user.
Gated Recurrent Unit with RSSIs from Heterogeneous Network for Mobile Positioning

Recently, research studies on Location-Based Services (LBSs) based on networks including cellular network and Wi-Fi network have gradually become popular. Received Signal Strength Indicators (RSSIs) from the network can be detected and collected by mobile devices to estimate the locations without adopting the Global Positioning System (GPS). Previous research studies utilized the RSSIs of only cellular network or only Wi-Fi network to estimate location, which leads to a two-fold predicament involving error limits of cellular network-based methods and environmental constraints of Wi-Fi network-based methods. In addition, accommodating a highly temporal dependence of RSSI series data, this paper proposed a mobile positioning system based on Gated Recurrent Unit (GRU) with RSSIs from the heterogeneous network. GRU learns the temporal correlation of RSSIs and the relationship between RSSIs and GPS coordinates to estimate the locations of mobile devices. A large number of real experiments have been carried out to verify the performance of the proposed method, and experimental results demonstrate that the proposed method has lower errors (i.e., 5.86 m and 75% of errors within 4 m) compared with Neural Network (NN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM).
End to end system for hazy image classification and reconstruction based on mean channel prior using deep learning network

: Outdoor images are having several applications including autonomous vehicles, geo-mapping, and surveillance. It is a common phenomenon that the images captured outdoor are prone to noise, which arises due to natural and manmade extreme atmospheric conditions such as haze, fog, and smog. Importantly in autonomous vehicle navigation, it is very important to recover the ground truth image to get the better decision by the system. Estimation of the transmission map and air-light is very crucial in recovering the ground truth image. In this study, the authors proposed a new method to estimate the transmission map based on a mean channel prior (MCP), which represents the depth map to estimate the transmission map. The authors proposed a deep neural network to identify the hazy image for the further dehazing process. In this study, the authors presented, two novel contributions, first an MCP-based image dehazing and second, a deep neural network-based identification of hazy images as a pre-processing block in the proposed end to end system. The proposed deep learning network using the TensorFlow platform provided validation accuracy of 93.4% for hazy image classification. Finally, the proposed MCP-based dehazing network showed better performance in terms of peak-signal-to-noise ratio, structural similarity index, and computational time than that of existing methods.
Compact S-transform for analysing local spectrum

: The Fourier transform of a N point time series is a N point complex series, while the S-transform (ST) of the same time series is a N × N 2D time–frequency complex matrix. The computation and storage of N 2 − N additional points are a major drag on the usage of ST. In this study the compact S-transform (cST) is presented, with efficiencies brought about through computation of only selected voices (frequencies). The cST spectrum has uncomputed voice gaps that increase in width towards the higher frequencies. Plot of the cST magnitude spectrum is virtually indistinguishable from the ST magnitude plot. Local spectrum at any spot on the cST can be quickly examined in detail through interpolation. The cST requires the computation of approximately 3 N voices compared to ⌊ N /2⌋ + 1 for the ST. The proportion of computed voices decrease for larger N. For N = 1024 , ∼ 20% of the voices in the time-frequency spectrum is computed; for N = 2048 only 14% of the voices is computed. For applications, such as audio and speech signal processing where segments of one million samples are not uncommon, <1% of the voices are computed, thereby reducing the computation time by ∼ 99%.
Research on the Application of Multimedia Elements in Visual Communication Art under the Internet Background

With the rapid development of Internet technology, the dissemination of information is undergoing rapid changes. The Internet has become an indispensable part of people’s study, work, and life. In the context of the Internet, this paper analyzes the application of multimedia elements in visual communication art design, elaborates the basic concepts, visual features, and design principles of the visual communication art design, and analyzes the multifaceted aspects of multimedia elements. This paper discusses the basic methods and general rules of the application of multimedia elements in visual communication art design under the background of the Internet, as well as the research on the emotional factors and interactions of multimedia elements on people. Brain-like computing is the meaning of imitation brain. First is the structure, then the function, then from the structure design and analysis of the brain, and finally use the brain's thinking ability to solve problems. It summarizes the text, graphic images, and colors in the Internet background. The innovative application of multimedia elements such as page layout and animation in visual communication art design explains the influence of multimedia elements on the overall visual effect in visual communication art design. It explores how visual information can be conveyed more reasonably and effectively. In the context of the Internet, the application of multimedia elements has brought a new possibility for developing visual communication art design, providing a new platform for the traditional visual communication art design.
lidar: A Python package for delineating nested surface depressions from digital elevation data

lidar is a Python package for delineating the nested hierarchy of surface depressions in digital elevation models (DEMs). In traditional hydrological modeling, surface depressions in a DEM are commonly treated as artifacts and thus filled and removed to create a depressionless DEM, which can then be used to generate continuous stream networks. In reality, however, surface depressions in DEMs are commonly a combination of spurious and actual terrain features (Lindsay & Creed, 2006). Fine-resolution DEMs derived from Light Detection and Ranging (LiDAR) data can capture and represent actual surface depressions, especially in glaciated (Wu & Lane, 2016) and karst landscapes (Wu et al., 2016). During the past decades, various algorithms have been developed to identify and delineate surface depressions, such as depression filling (Wang & Liu, 2006), depression breaching (Lindsay & Dhun, 2015), hybrid breaching-filling (Lindsay, 2016), and contour tree method (Wu et al., 2015). More recently, a level-set method based on graph theory was proposed to delineate the nested hierarchy of surface depressions (Wu et al., 2019). The lidar Python package implements the level-set method and makes it possible for delineating the nested hierarchy of surface depressions as well as elevated terrain features. It also provides an interactive Graphical User Interface (GUI) that allows users to run the program with minimal coding.
Efficient Computation Offloading for Service Workflow of Mobile Applications in Mobile Edge Computing

Edge computing has become a promising solution to overcome the user equipment (UE) constraints such as low computing capacity and limited energy. A key edge computing challenge is providing computing services with low service congestion and low latency, but the computing resources of edge servers were limited. User task randomness and network inhomogeneity brought considerable challenges to limited-resource MEC systems. To solve these problems, the presented paper proposed a blocking- and delay-aware schedule strategy for MEC environment service workflow offloading. First, the workflow was modeled in mobile applications and the buffer queue in servers. Then, the server collaboration area was divided through a collaboration area division method based on clustering. Finally, an improved particle swarm optimization scheduling method was utilized to solve this NP-hard problem. Many simulation results verified the effectiveness of the proposed scheme. This method was superior to existing methods, which effectively reduces the blocking probability and execution delay and ensures the quality of the experience of the user.
Identification of Volatile Compounds from a Brewery with SPME Technique

Abstract The paper presents results of the analysis of the volatile compounds arising from the production processes in a brewery. The investigated material comprised the unhoped brewer’s wort which was taken from the fermentation tanks during the industrial process. The identification of volatile compounds was conducted with the use of Solid-Phase MicroExtraction (SPME) technique by extracting the compounds from the headspace of the brewer’s wort (HS-SPME). The procedure was optimized by modifying the parameters potentially influencing the process efficiency. The analytes adsorbed on the fibers were subsequently placed in the injector of a gas chromatograph, where they were released in the course of thermal desorption. Three types of fibers were chosen for the experiments: 65 μm PDMS/DVB, 50/30 μm DVB/CAR/PDMS and 100 μm PDMS. The greatest number of peaks corresponding to compounds found in the examined material was observed on the 50/30 μm DVB/CAR/PDMS fiber while the lowest was identified on the 100 μm PDMS fiber. The detected compounds are mainly the derivatives of aliphatic, alicyclic and aromatic hydrocarbons with different functional groups e.g. carbonyl, aldehyde or ester and possessing O-heteroatom in their structure.
Two viewpoints based real-time recognition for hand gestures

: It is extremely challenging to accomplish excellent accuracy for gesture recognition using an approach where complexity in computation time for recognition is less. This study compares accuracy in hand gesture recognition of a single viewpoint set-up with proposed two viewpoint set-up for different classification techniques. The efficacy of the presented approach is verified practically with various image processing, feature extraction and classification techniques. Two camera system make geometry learning and three-dimensional (3D) view feasible compared to a single camera system. Geometrical features from additional viewpoint contribute to 3D view estimation of the hand gesture. It also improves the classification accuracy. Experimental results demonstrate that the proposed method show escalation in recognition rate compared to the single-camera system, and also has great performance using simple classifiers like the nearest neighbour and decision tree. Classification within 1 s is considered as real-time in this study.
SeismicMesh: Triangular meshing for seismology

SeismicMesh is a Python package for simplex mesh generation in two or three dimensions. As an implementation of DistMesh (Persson & Strang, 2004), it produces high-quality meshes at the expense of speed. For increased efficiency, the core package is written in C++, works in parallel, and uses the Computational Geometry Algorithms Library (Hert & Seel, 2020). SeismicMesh can also produce mesh-density functions from seismological data to be used in the mesh generator.
From condiment to metal–organic framework and its derived 3D architecture nanoporous carbon for supercapacitor electrodes

The crystalline metal-organic-framework (MOF) microrods from monosodium glutamate and zinc acetate dihydrate were spontaneously formed by mixing their solution at room temperature. After carbonization in an inert atmosphere, these MOF microrods are evolved into N and O co-doped nanoporous carbon with 3D-architecture. The model of gas bubbles is elucidated for the formation of these interconnected porous structure. As the electrode material for supercapacitor, the derived nanoporous carbon at the temperature of 800 °C exhibits good capacitance performance in alkali aqueous electrolyte.