Miracle-dz/UniFAR-FTUs huggingface dataset:
Text to multi-object images synthesis based on non-local self-attention

Deep Convolutional Network (CNN) can make the pictures generated by GAN more reasonable, but limited by the local receptive field of CNN, there are still many unreasonable places in the authenticity and semantics of the multi-object images generated from text. Therefore, a GAN-based method that incorporates a non-local self-attention mechanism is proposed. By embedding a non-local self-attention structure in the network, the network obtains global semantic information and detailed features, and uses the obtained information to perform level-by-level encoding to generate the final relatively reasonable image. The amount of parameters and calculation of the entire model is also reduced a lot. The proposed method is verified on the public COCO-stuff dataset and uses multiple indicators such as Inception Score, FID and classification accuracy score to evaluate the authenticity and diversity of the generated images. Experimental results show that the quality of the generated images is superior to that of previously proposed methods.
Role of Olive Bioactive Compounds in Respiratory Diseases

Respiratory diseases recently became the leading cause of death worldwide, due to the emergence of COVID-19. The pathogenesis of respiratory diseases is centred around inflammation and oxidative stress. Plant-based alongside synthetic drugs were considered as therapeutics due to their proven nutraceutical value. One such example is the olive, which is a traditional symbol of the MedDiet. Olive bioactive compounds are enriched with antioxidant, anti-inflammatory, anticancer and antiviral properties. However, there are few studies relating to the beneficial effect of olive bioactive compounds on respiratory diseases. A vague understanding of its molecular action, dosage and bioavailability limits its usefulness for clinical trials about respiratory infections. Hence, our review aims to explore olive bioactive compound’s antioxidant, anti-inflammatory and antiviral properties in respiratory disease defence and treatment. Molecular insight into olive compounds’ potential for respiratory system protection against inflammation and ensuing infection is also presented. Olive bioactive compounds mainly protect the respiratory system by subsiding proinflammatory cytokines and oxidative stress.
Optimizing emissions for machine learning training

Modern machine learning models consume massive amounts of energy. In a widely cited paper [9], the authors compare the estimated CO2 emissions from training common NLP models like BERT [5], GPT-2 [1], ELMO [8] and transformers [10]. Similarly, in [7], the authors compare other models like Meena [2] and GPT3 [4]. For example, GPT-3 training consumes around 550 metric tons of carbon. Together, data center use including machine learning model creation is projected to grow exponentially in the coming years [6]. As machine learning becomes used by more and more organizations for their business processes, it is imperative new paradigms for efficient model training and inferencing are developed. Our research project is motivated from a recent flight search which showed multiple flight options along with their estimated carbon emissions (Figure 1) . It is left upon the discretion of the traveller to pick a suitable flight based on CO2 emissions, comfort and convenience. We extrapolate the same for training a batch of ML jobs and create a scheduler which can appropriately allocate jobs to different datacenters at different times. In addition, based on the data characteristics and ML task (classification, regression etc.), we can also recommend the appropriate model to be used.
Anti-Disturbance of Scale-Free Spiking Neural Network against Impulse Noise

The bio-brain presents robustness function to external stimulus through its self-adaptive regulation and neural information processing. Drawing from the advantages of the bio-brain to investigate the robustness function of a spiking neural network (SNN) is conducive to the advance of brain-like intelligence. However, the current brain-like model is insufficient in biological rationality. In addition, its evaluation method for anti-disturbance performance is inadequate. To explore the self-adaptive regulation performance of a brain-like model with more biological rationality under external noise, a scale-free spiking neural network(SFSNN) is constructed in this study. Then, the anti-disturbance ability of the SFSNN against impulse noise is investigated, and the anti-disturbance mechanism is further discussed. Our simulation results indicate that: (i) our SFSNN has anti-disturbance ability against impulse noise, and the high-clustering SFSNN outperforms the low-clustering SFSNN in terms of anti-disturbance performance. (ii) The neural information processing in the SFSNN under external noise is clarified, which is a dynamic chain effect of the neuron firing, the synaptic weight, and the topological characteristic. (iii) Our discussion hints that an intrinsic factor of the anti-disturbance ability is the synaptic plasticity, and the network topology is a factor that affects the anti-disturbance ability at the level of performance.
Guest Editors’ Introduction for Special Issue on Applications of Computational Linguistics in Multimedia IoT Services

The Internet of Multimedia Things (IoMT) [4] is the combination of interfaces, protocols, and associated multimedia-related information, which enables advanced services and applications based on the human-to-device and device-to-device interactions in physical and virtual environments. The rapid growth in multimedia-on-demand traffic that refers to audio, video, and images has drastically shifted on the vision of the Internet of Things (IoT) [1, 5] from scalar to IoMT, which is an integral part of multimedia services such as real-time content delivery, online games, and video conferencing on the global Internet [3, 4]. Complementarily, Computational Linguistics (CL) [2] is an interdisciplinary research field concerned with the processing of languages by computers. Since machine translation began to emerge in the early 1970s, CL has grown and developed exponentially. Nevertheless, the combination of IoT-based multimedia with CL services has received less attention so far and has emerged as a new research paradigm for future computing applications. The future of smart IoMT devices with NLP is more important in real-time systems such as speech understanding, emotion recognition, and home automation. There are several issues and technical challenges that need attention from the research community. The rapid growth of multimedia IoT services (data abstraction, data sharing, data mining) has led the way to incorporating CL techniques to meet its requirements. This special issue presents multimedia IoT services in real-time systems and highlights the open research challenges to get advantageous use of CL.
Internal gravity waves generated by oscillations of a sphere

We consider the radiation of internal gravity waves from a spherical body oscillating vertically in a stratified incompressible fluid. A near-field solution (under the Boussinesq approximation) is obtained by separation of variables in an elliptic problem, followed by analytic continuation to the frequencies ω < N of internal wave radiation. Matched expansions are used to relate this solution to a far-field solution in which non-Boussinesq terms are retained. In the outer near field there are parallel conical wavefronts between characteristic cones tangent to the body, but with a wavelength found to be shorter than that for oscillations of a circular cylinder. It is also found that there are caustic pressure singularities above and below the body where the characteristics intersect. Far from the source, non-Boussinesq effects cause a diffraction of energy out of the cones. The far-field wave-fronts are hyperboloidal, with horizontal axes. The case of horizontal oscillations of the sphere is also examined and is shown to give rise to the same basic wave structure. The related problem of a pulsating sphere is then considered, and it is concluded that certain features of the wave pattern, including the caustic singularities near the source, are common to a more general class of oscillating sources.
Optical coherence tomography: Physical principles and applications

A method to construct optical images of objects hidden in light-scattering media (optical coherence tomography) using low-coherence radiation and an optical interferometer with an anisotropic fiber is realized. An experimental basis for realization of this method in various branches of science and medicine is created. Based on the achievements of fiber-optic interferometry, low-coherence semiconductor sources, and the application of modern electronics and software, a series of portable fast-scanning computerized optical tomographs adapted to clinical applications is developed. The clinical studies (including endoscopic studies) were carried out using optical coherence tomography. Experimental methods are developed and equipment is created for high-accuracy measurements based on optical low-coherence interferometry using single-mode polarization-maintaining optical fibers. In the interference measurements, theoretical limits of sensitivity are achieved. The parameters (dynamic range, spatial resolution, and scanning rate) needed for the tomographic applications of the method proposed are obtained. The complexes for color optical coherence tomography and polarization coherence tomography are realized owing to the upgrade of the original devices. Tomographs are used in experimental clinical studies. Based on the results of these studies, the first albums of optical images of human cover tissues, including mucosae of internal organs, are produced.
Processing and recognition of gastric cancer pathological images based on Resnet

In order to solve the problem that manual discrimination of gastric cancer pathological images is time-consuming and laborious and requires high relevant medical expertise, this paper proposes a method based on improved convolutional neural network to assist recognition of gastric cancer pathological images. In this paper, residual network (Resnet) was used as the basis for optimization and improvement, and the network structure was reasonably adjusted to improve the adaptability of the recognition task. The two-dimension attention mechanism of Convolutional Block Attention Module (CBAM) is introduced to improve the accuracy of the model. The knowledge of transfer learning is used to accelerate the training process and the fitting of network training. In order to increase the generalization of the network model, the experimental images were data enhanced. The experimental results show that in the fifth layer network structure CONV5, replacing the first convolution layer with the attention mechanism module is better than replacing the second convolution layer and replacing both convolution layers at the same time, and the attention mechanism module is a serial arrangement of CAM+SAM. Compared with VGG16, Alextnet and Googlenet, the improved network structure has the highest accuracy of 96.58%.
Informal employment on domestic care platforms: a study on the individualisation of risk and unpaid labour in mature market contexts

This article explains how digitally mediated provision of domestic care services perpetuates the invisibility and informality of such work through individualising risk, which we operationalise by one of its dimensions, that of unpaid labour. We understand unpaid labour as the cost of the risk borne individually by workers at the intersection of the social (inter-personal) and economic (monetary) spheres. Drawing on the experiences of domestic care workers providing their services through platforms, the study shows how platforms have made their way into the labour markets and welfare structures of two mature economies, Belgium and France. Via their (digital) rules, they pursue ‘regulatory compliance’ and ‘disruption’ as distinct strategies for establishing platform dominance, albeit with country-based differences. Platform-mediated employment outcomes remain generally unrecognised, undocumented and informal, with unpaid labour characterising the cost of the individualisation of risk.
Extremal Degree-Product Indices of Graphs with Fixed Number of Pendant Vertices and Cyclomatic Number

The Narumi-Katayama index and first multiplicative Zagreb index of a graph are defined as the product of the degrees of the vertices of and the product of square of the degrees of the vertices of , respectively. The second multiplicative Zagreb index is defined as . In this paper, we compute the extremal , and for the graphs with given order, number of pendant vertices and cyclomatic number.
Extracellular Polysaccharide Receptor and Receptor-Binding Proteins of the Rhodobacter capsulatus Bacteriophage-like Gene Transfer Agent RcGTA

A variety of prokaryotes produce a bacteriophage-like gene transfer agent (GTA), and the alphaproteobacterial Rhodobacter capsulatus RcGTA is a model GTA. Some environmental isolates of R. capsulatus lack the ability to acquire genes transferred by the RcGTA (recipient capability). In this work, we investigated the reason why R. capsulatus strain 37b4 lacks recipient capability. The RcGTA head spike fiber and tail fiber proteins have been proposed to bind extracellular oligosaccharide receptors, and strain 37b4 lacks a capsular polysaccharide (CPS). The reason why strain 37b4 lacks a CPS was unknown, as was whether the provision of a CPS to 37b4 would result in recipient capability. To address these questions, we sequenced and annotated the strain 37b4 genome and used BLAST interrogations of this genome sequence to search for homologs of genes known to be needed for R. capsulatus recipient capability. We also created a cosmid-borne genome library from a wild-type strain, mobilized the library into 37b4, and used the cosmid-complemented strain 37b4 to identify genes needed for a gain of function, allowing for the acquisition of RcGTA-borne genes. The relative presence of CPS around a wild-type strain, 37b4, and cosmid-complemented 37b4 cells was visualized using light microscopy of stained cells. Fluorescently tagged head spike fiber and tail fiber proteins of the RcGTA particle were created and used to measure the relative binding to wild-type and 37b4 cells. We found that strain 37b4 lacks recipient capability because of an inability to bind RcGTA; the reason it is incapable of binding is that it lacks CPS, and the absence of CPS is due to the absence of genes previously shown to be needed for CPS production in another strain. In addition to the head spike fiber, we found that the tail fiber protein also binds to the CPS.
Relationship between EMG and fNIRS during Dynamic Movements

In the scientific literature focused on surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS), which have been described together and separately many times, presenting different possible applications, researchers have explored a diverse range of topics related to these advanced physiological measurement techniques. However, the analysis of the two signals and their interrelationships continues to be a focus of study in both static and dynamic movements. The main purpose of this study was to determine the relationship between signals during dynamic movements. To carry out the analysis described, the authors of this research paper chose two sports exercise protocols: the Astrand–Rhyming Step Test and the Astrand Treadmill Test. In this study, oxygen consumption and muscle activity were recorded from the gastrocnemius muscle of the left leg of five female participants. This study found positive correlations between EMG and fNIRS signals in all participants: 0.343–0.788 (median-Pearson) and 0.192–0.832 (median-Spearman). On the treadmill, the signal correlations between the participants with the most active and least active lifestyle achieved the following medians: 0.788 (Pearson)/0.832 (Spearman) and 0.470 (Pearson)/0.406 (Spearman), respectively. The shapes of the changes in the EMG and fNIRS signals during exercise suggest a mutual relationship during dynamic movements. Furthermore, during the treadmill test, a higher correlation was observed between the EMG and NIRS signals in participants with a more active lifestyle. Due to the sample size, the results should be interpreted with caution.
Monte Carlo Methods in Climate Science

T hey look placid lapping against the beach on a calm day, but the oceans are actually quite dynamic. The ocean currents act as conveyor belts, transporting heat both vertically between the water’s surface and the depths and laterally from one area of the globe to another. This effect is so significant that the temperature and precipitation patterns can change dramatically when currents do. For example: Shortly after the last ice age, northern Europe experienced a shocking change in climate from 10,800 to 9,500 BC. At the start of this period, temperatures plummeted in a matter of decades. It became 7° Celsius colder, and glaciers started forming in England! The cold spell lasted for more than a thousand years, but it ended as suddenly as it had begun. Why? The most popular theory is that a huge lake in North America formed by melting glaciers burst its bank—and in a massive torrent lasting for years, the water from this lake rushed out to the northern Atlantic Ocean. By fl oating atop the denser salt water, this fresh water blocked a major current: the Atlantic Meridional Overturning Circulation. This current brings warm water north and helps keep northern Europe warm. When it shut down, northern Europe was plunged into a deep freeze. Right now, global warming is causing ice sheets in Greenland to melt and release fresh water into the North Atlantic. Could this shut down the Atlantic Meridional Overturning Circulation and make the climate of northern Europe much colder? In 2010, Keller and Urban [4] tackled this question using a simple climate model, historical data, probability theory, and lots of computing power. Their goal was to understand the spectrum of possible futures compatible with what we know today. Let us look at some of the ideas underlying their work.
Visual processing is specific to animals

It is well known that animals have a special meaning for humans, and in biology humans have coexisted with animals for a long time. From the ancestors of human beings there has been an inseparable relationship with animals. And this relationship also makes the human visual system seem to have a more special visual processing mechanism for animals than other targets. To find out whether this mechanism exists only in animals, we performed an experiment. The experiment was a two forced choice (2-AFC) task. Since the scene has an effect on object recognition, we will use animal stimuli without background for comparison experiments with non-animal stimuli. We mixed animal stimulus images and non-animal stimulus images in a disordered manner to form stimulus image sets (50 images each), all of which were without background. Our results showed that subjects had faster reaction times for the animal stimulus pictures than for the non-animal stimulus images, with 524 ms for the animal stimulus pictures and 547 ms for the nonanimal stimulus pictures. The subjects' correct judgment rate for animal stimulus images was higher than that for nonanimal stimulus images, with 96.1% for animal stimulus images and 91.6% for non-animal stimulus images.
Ostwald ripening of nanoislands in semiconductor heterosystems and its influence on optical properties

Influence of the Ostwald ripening on character of the size distribution in semiconductor heterosystems with quantum dots as island films consisting of cylindrical, disclike islands of constant height is studied. Size distribution function and formulae for temporal changes of average island size are derived. Calculations are carried out within the LSW theory, in assumption of joint action of two (diffusion and Wagner) growing mechanisms. Comparison of the calculating results with experimental data proves validity of the accepted model approximations. Expectancy of correlation of the distribution function, growing mechanisms and properties of heterosystems is discussed.
The First Eigenvalue of a Compact Manifold

This paper discusses the first eigenvalue on a compact Riemann manifold with the negative lower bound Ricci curvature. Let M be a compact Riemann manifold with the Ricci curvature≥-R, R=const. ≥0 and d is the diameter of M. Our main result is that the first eigenvalue λ1 of M satisfies λ1≥π2/d2-0.518. R.
Expanding diversity (in) undergraduate classes with advancements in (the) teaching (of) economics: A symposium

The articles in this symposium originated from a program sponsored by the American Economic Association (AEA). Expanding diversity (in) undergraduate classes with advancements in (the) teaching (of) economics (EDUCATE) was co-developed by the AEA Task Force on Outreach to High School and College Students and the Committee on Economic Education (CEE). EDUCATE was designed to increase diversity in the economics profession by helping instructors create classrooms that are open and engaging to all the students they teach. While the current level of research on what is effective at achieving this is still limited, the design of the workshop is based on what is known to be effective, as documented in the literature on the scholarship of teaching and learning. There are three phases of the overall program: pre-workshop preparation activities, a 3-day interactive workshop, and post-workshop implementation. Preparatory activities include completing a small number of readings and reflective exercises that are designed to let the organizers learn about the participants and set the stage for the workshop. The workshop provides opportunities for instructors of undergraduate courses to take part in course design activities and experience pedagogical strategies that will engage all the students they teach. First, attendees learn the importance of clearly defining the learning outcomes they wish to achieve, as this not only informs students what is expected but also informs the other decisions an instructor must make in designing their class. Based on the economic-specific approach to learning outcomes (see Allgood and Bayer 2017), attendees have opportunities to identify learning objectives that focus on the students’ ability to “do economics. ” Building on this foundation, instructors then learn about an inclusive approach to learning outcomes and backward course design. Fink’s taxonomy of significant learning (2003) adds categories of human dimension, caring, and learning how to learn to more traditional foci on foundational knowledge, application, and integration. The backward course design process (Wiggins and McTighe 2005) guides the instructor to begin with identified learning outcomes and then proceed to construct assessments that will provide evidence of student achievement, before choosing pedagogic practices that will best enable students to achieve the outcomes. Participants are next introduced to pedagogical practices that enable students to be active participants in economic analysis