Miracle-dz/UniFAR-FTUs huggingface dataset:
AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection

This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. We also develop a custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including common rust, grey leaf spot and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops using advanced machine learning techniques and deep learning architectures.
Stock Market Price Prediction: A Hybrid LSTM and Sequential Self-Attention based Approach

One of the most enticing research areas is the stock market, and projecting stock prices may help investors profit by making the best decisions at the correct time. Deep learning strategies have emerged as a critical technique in the field of the financial market. The stock market is impacted due to two aspects, one is the geo-political, social and global events on the bases of which the price trends could be affected. Meanwhile, the second aspect purely focuses on historical price trends and seasonality, allowing us to forecast stock prices. In this paper, our aim is to focus on the second aspect and build a model that predicts future prices with minimal errors. In order to provide better prediction results of stock price, we propose a new model named Long Short-Term Memory (LSTM) with Sequential Self-Attention Mechanism (LSTM-SSAM). Finally, we conduct extensive experiments on the three stock datasets: SBIN, HDFCBANK, and BANKBARODA. The experimental results prove the effectiveness and feasibility of the proposed model compared to existing models. The experimental findings demonstrate that the root-mean-squared error (RMSE), and R-square (R2) evaluation indicators are giving the best results.
Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Review

Metaphor as an advanced cognitive modality works by extracting familiar concepts in the target domain in order to understand vague and abstract concepts in the source domain. This helps humans to quickly understand and master new domains and thus adapt to changing environments. With the continuous development of metaphor research in the natural language community, many studies using knowledge-assisted models to detect textual metaphors have emerged in recent years. Compared to not using knowledge, systems that introduce various kinds of knowledge achieve greater performance gains and reach SOTA in a recent study. Based on this, the goal of this paper is to provide a comprehensive review of research advances in the application of deep learning for knowledge injection in metaphor detection tasks. We will first systematically summarize and generalize the mainstream knowledge and knowledge injection principles. Then, the datasets, evaluation metrics, and benchmark models used in metaphor detection tasks are examined. Finally, we explore the current issues facing knowledge injection methods and provide an outlook on future research directions.
Auditory Attention Decoding with Task-Related Multi-View Contrastive Learning

The human brain can easily focus on one speaker and suppress others in scenarios such as a cocktail party. Recently, researchers found that auditory attention can be decoded from the electroencephalogram (EEG) data. However, most existing deep learning methods are difficult to use prior knowledge of different views (that is attended speech and EEG are task-related views) and extract an unsatisfactory representation. Inspired by Broadbent's filter model, we decode auditory attention in a multi-view paradigm and extract the most relevant and important information utilizing the missing view. Specifically, we propose an auditory attention decoding (AAD) method based on multi-view VAE with task-related multi-view contrastive (TMC) learning. Employing TMC learning in multi-view VAE can utilize the missing view to accumulate prior knowledge of different views into the fusion of representation, and extract the approximate task-related representation. We examine our method on two popular AAD datasets, and demonstrate the superiority of our method by comparing it to the state-of-the-art method.
Control of Vortex Dynamics using Invariants

Vortex-dominated flows are ubiquitous in engineering, and the ability to efficiently manipulate the dynamics of these vortices has broad applications, from wake shaping to mixing enhancement. However, the strongly nonlinear behavior of the vortex dynamics makes this a challenging task. In this work, we investigate the control of vortex dynamics by using a change of coordinates from the Biot-Savart equations into well-known invariants, such as the Hamiltonian, linear, and angular impulses, which are Koopman eigenfunctions. We then combine the resulting model with model predictive control to generate control laws that force the vortex system using"virtual cylinders". The invariant model is beneficial as it provides a linear, global description of the vortex dynamics through a recently developed Koopman control scheme for conserved quantities and invariants. The use of this model has not been well studied in the literature in the context of control. In this paper, we seek to understand the effect of changing each invariant individually or multiple invariants simultaneously. We use the 4-vortex system as our primary test bed, as it is the simplest configuration that exhibits chaotic behavior. We show that by controlling to specific invariant quantities, we can modify the transition from chaotic to quasiperiodic states. Finally, we computationally demonstrate the effectiveness of invariant control on a toy example of tracer mixing in the 4-vortex system.
Your Negative May not Be True Negative: Boosting Image-Text Matching with False Negative Elimination

Most existing image-text matching methods adopt triplet loss as the optimization objective, and choosing a proper negative sample for the triplet of is important for effectively training the model, e.g., hard negatives make the model learn efficiently and effectively. However, we observe that existing methods mainly employ the most similar samples as hard negatives, which may not be true negatives. In other words, the samples with high similarity but not paired with the anchor may reserve positive semantic associations, and we call them false negatives. Repelling these false negatives in triplet loss would mislead the semantic representation learning and result in inferior retrieval performance. In this paper, we propose a novel False Negative Elimination (FNE) strategy to select negatives via sampling, which could alleviate the problem introduced by false negatives. Specifically, we first construct the distributions of positive and negative samples separately via their similarities with the anchor, based on the features extracted from image and text encoders. Then we calculate the false negative probability of a given sample based on its similarity with the anchor and the above distributions via the Bayes' rule, which is employed as the sampling weight during negative sampling process. Since there may not exist any false negative in a small batch size, we design a memory module with momentum to retain a large negative buffer and implement our negative sampling strategy spanning over the buffer. In addition, to make the model focus on hard negatives, we reassign the sampling weights for the simple negatives with a cut-down strategy. The extensive experiments are conducted on Flickr30K and MS-COCO, and the results demonstrate the superiority of our proposed false negative elimination strategy. The code is available at https://github.com/LuminosityX/FNE.
Visual Saliency Detection in Advanced Driver Assistance Systems

Visual Saliency refers to the innate human mechanism of focusing on and extracting important features from the observed environment. Recently, there has been a notable surge of interest in the field of automotive research regarding the estimation of visual saliency. While operating a vehicle, drivers naturally direct their attention towards specific objects, employing brain-driven saliency mechanisms that prioritize certain elements over others. In this investigation, we present an intelligent system that combines a drowsiness detection system for drivers with a scene comprehension pipeline based on saliency. To achieve this, we have implemented a specialized 3D deep network for semantic segmentation, which has been pretrained and tailored for processing the frames captured by an automotive-grade external camera. The proposed pipeline was hosted on an embedded platform utilizing the STA1295 core, featuring ARM A7 dual-cores, and embeds an hardware accelerator. Additionally, we employ an innovative biosensor embedded on the car steering wheel to monitor the driver drowsiness, gathering the PhotoPlethysmoGraphy (PPG) signal of the driver. A dedicated 1D temporal deep convolutional network has been devised to classify the collected PPG time-series, enabling us to assess the driver level of attentiveness. Ultimately, we compare the determined attention level of the driver with the corresponding saliency-based scene classification to evaluate the overall safety level. The efficacy of the proposed pipeline has been validated through extensive experimental results.
Purely magnetic tunnelling between radial magnetic wells

This article is devoted to the semiclassical spectral analysis of the magnetic Laplacian in two dimensions. Assuming that the magnetic field is positive and has two symmetric radial wells, we establish an accurate tunnelling formula, that is a one-term estimate of the spectral gap between the lowest two eigenvalues. This gap is exponentially small when the semiclassical parameter goes to zero, but positive.
Numerical analysis and experimental verification of optical scattering from microplastics

Accurate and fast characterization of the micron-sized plastic particles in aqueous media requires an in-depth understanding of light interaction with these particles. Due to the complexity of Mie scattering theory, the features of the scattered light have rarely been related to the physical properties of these tiny objects. To address this problem, we reveal the relation of the wavelength-dependent optical scattering patterns with the size and refractive index of the particles by numerically studying the angular scattering features. We subsequently present a low-cost setup to measure the optical scattering of the particles. Theoretical investigation shows that the angular distribution of the scattered light by microplastics carries distinct signatures of the particle size and the refractive index. The results can be used to develop a portable, low-cost setup to detect microplastics in water.
Artificial intelligence for the classification of focal liver lesions in ultrasound – a systematic review

Abstract Focal liver lesions are detected in about 15% of abdominal ultrasound examinations. The diagnosis of frequent benign lesions can be determined reliably based on the characteristic B-mode appearance of cysts, hemangiomas, or typical focal fatty changes. In the case of focal liver lesions which remain unclear on B-mode ultrasound, contrast-enhanced ultrasound (CEUS) increases diagnostic accuracy for the distinction between benign and malignant liver lesions. Artificial intelligence describes applications that try to emulate human intelligence, at least in subfields such as the classification of images. Since ultrasound is considered to be a particularly examiner-dependent technique, the application of artificial intelligence could be an interesting approach for an objective and accurate diagnosis. In this systematic review we analyzed how artificial intelligence can be used to classify the benign or malignant nature and entity of focal liver lesions on the basis of B-mode or CEUS data. In a structured search on Scopus, Web of Science, PubMed, and IEEE, we found 52 studies that met the inclusion criteria. Studies showed good diagnostic performance for both the classification as benign or malignant and the differentiation of individual tumor entities. The results could be improved by inclusion of clinical parameters and were comparable to those of experienced investigators in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in the studies and a lack of independent validation cohorts, the transfer of the results into clinical practice is limited.
Performance pay and non-native language comprehension: Can we learn to understand better when we’re paid to listen?

Abstract Non-native speech is difficult for native listeners to understand. While listeners can learn to understand non-native speech after exposure, it is unclear how to optimize this learning. Experimental subjects transcribed non-native speech and were paid either a flat rate or based on their performance. Participants who were paid based on performance demonstrated improved performance overall and faster learning than participants who were paid a flat rate. These results suggest that exposure alone is not sufficient to optimize learning of non-native speech and that current models of this process must be revised to account for the effects of motivation and incentive.
Responses of soil–plant C, N, and P concentrations and stoichiometry to contrasting application rates of biochar to subtropical paddy field

Summary Biochar is increasingly used in crop production as a fertilizer; however, its effects on nutrient cycling and stoichiometry in rice paddy soil–plant systems are unclear. We tested for effects of contrasting rates of biochar on soil and rice plant organ carbon (C), nitrogen (N), and phosphorus (P) concentrations and stoichiometry and soil physicochemical properties in early and late paddies. Overall, biochar reduced soil bulk density by an average of 7.4%, while application at 10, 20, and 40 t ha−1 increased soil C and N concentrations in early paddies by 31.6, 41.3, and 104.2%, respectively, and by 8.0, 5.0, and 21.8%, respectively; in late paddies, there were increases of 23.0, 94.1, and 117.0%, respectively, and 6.7, 15.4, and 18.0%, respectively (P < 0.05). Following biochar application at 10, 20, and 40 t ha−1, soil concentration of P decreased in early paddies by 10.9, 19.0, and 13.9%, respectively, and increased in late paddies by 4.3, 16.4, and 20.1%, respectively. Biochar increased ratios of soil C:N and C:P in early and late paddies (P < 0.05), and there was no effect on concentration and stoichiometry of soil available nutrients. Biochar reduced rice plant organ concentration of N and P in early rice and increased leaf N:P ratios. Despite the biochar application improved nutrient status in plant–soil system, we did not observe a significant increase in yield (P > 0.05). According to the N:P value of leaves between treatments, it was found that biochar alleviated the current situation of N limitation in paddy fields during the mature period and transformed the N limitation of early rice into a joint limitation of N and P. These results show that the addition of biochar to subtropical paddy soils leads to a short-term reduction in soil bulk density and increases in soil C and N concentrations and soil fertility. Thus, biochar applied at optimal rates is likely to improve the sustainability of subtropical paddy rice production.
Three Types of Tropical Waves Related to Tropical Cyclogenesis Over the Western North Pacific

Abstract The present study applies a space—time filter to identify three dominant types of tropical waves: Madden—Julian oscillations (MJOs), equatorial Rossby (ER) waves, and tropical depression (TD)-type disturbances. The impacts of these waves on tropical cyclones (TCs) were investigated based on 131 observations during the period 2000–07. The results suggest that 72% of TC geneses were related to the joint impacts of more than one type of wave. The composites for cases in different categories reveal that TCs related to the concurrence of the three types of waves have strong and large initial vortices at the time of TC genesis. In the absence of the MJO, ERand TD—related TC genesis, embedded in easterly flow, exhibits a relatively fast initiation process and gives rise to a relatively small scale vortex. In contrast, without the ER wave contribution, TCs associated with ER and TD waves did not require strong convection at the time of genesis because an initial vortex can rapidly develop in the MJO active phase through persistent energy transfer. The MJO—related TC geneses were scattered in geographic distribution, as opposed to the clustered and eastward shift observed for genesis cases without contributions from MJOs.
Recursive full convolutional network hybrid model based on industrial load data augmentation

Hundreds of classification algorithms have been proposed for the task of classifying time series. These methods are broadly classified into three types: distance-based, feature-based, and deep learning-based. Problems such as small and diverse time series data have led to the low accuracy of deep learning models in time series classification tasks. To address the above problems, a recursive full convolutional network hybrid model combined with data augmentation is proposed in this paper. Firstly, data augmentation is used to address the problem of small and diverse raw data. Secondly, compared with the traditional depth model, this paper uses a recursive full convolutional network hybrid model, which makes the model training faster and more accurate. Finally, the effectiveness of the proposed method is verified by using an industrial park load data and the largest publicly available TSC benchmark dataset UCRArchive2018 for assisted training, and the results show that the time series classification method based on recursive full convolutional network hybrid model proposed in this paper has obvious superiority compared with existing methods.
How sustainable is your menu? Designing and assessing an interactive artefact to support chefs’ sustainable recipe-planning practices

Rising sustainability concerns in the food industry have driven the need for innovative approaches in culinary operations. Redesigning the menus and recipes from a sustainability perspective is a promising approach to reducing restaurants' environmental impact. Chefs, as crucial decision-makers in menu and recipe planning practices, play a vital role in promoting sustainable food services. However, the literature lacks insights into chefs' sustainable recipe planning practices and how information and communication technologies (ICTs) could support these practices. This paper addresses this gap by conducting individual interview sessions (n=10) and recipe generation workshops (n=10) with 20 chefs in total. It reveals four dimensions of sustainable recipes (locality, seasonality, frugality, and food quality) based on semi-structured interviews. It presents a novel interactive recipe planning concept called KNOBIE, which was designed to support chefs' sustainable recipe planning practices by using insights that gathered from the interviews. Lastly, based on an assessment of this concept through online recipe generation sessions with chefs, it provides five design implications for integrating ICTs into the sustainable menu and recipe planning practices to promote sustainable food services in restaurants.
Who's On First?

There is a pattern of miscommunication bedeviling the people working on consciousness that is reminiscent of the classic Abbott and Costello ‘Who’s on First? ’ routine. With the best of intentions, people are talking past each other, seeing major disagreements when there are only terminological or tactical preferences — or even just matters of emphasis — that divide the sides. Since some substantive differences also lurk in this confusion, it is well worth trying to sort out. Much of the problem seems to have been caused by some misdirection in my apologia for heterophenomenology (Dennett, 1982; 1991), advertised as an explicitly third-person approach to human consciousness, so I will try to make amends by first removing those misleading signposts and sending us back to the real issues. On the face of it, the study of human consciousness involves phenomena that seem to occupy something rather like another dimension: the private, subjective, ‘first-person’ dimension. Everybody agrees that this is where we start. What, then, is the relation between the standard ‘third-person’ objective methodologies for studying meteors or magnets (or human metabolism or bone density), and the methodologies for studying human consciousness? Can the standard methods be extended in such a way as to do justice to the phenomena of human consciousness? Or do we have to find some quite radical or revolutionary alternative science? I have defended the hypothesis that there is a straightforward, conservative extension of objective science that handsomely covers the ground — all the ground — of human consciousness, doing justice to all the data without ever having to abandon the rules and constraints of the experimental method that have worked so well in the rest of science. This third-person methodology, dubbed heterophenomenology (phenomenology of another not oneself), is, I have claimed, the sound way to take the first person point of view as seriously as it can be taken.
The role of oxygen in the functioning of the Earth system: past, present and future

Abstract In the Solar System, the coming into existence of a peculiar, fully developed atmosphere on Earth was determined by the ‘Great Oxidation Event’ at the turn of the Proterozoic and Palaeozoic. Within about 600 million years, there were large changes in oxygen concentrations in this atmosphere, ranging from 15 to 35 per cent, having been determined by a combination of cosmic-climatic, tectonic-volcanic and biological phenomena. A particular environmental change occurred at the beginning of the 19th century, as a result of the overlap of the end of the natural Little Ice Age and the beginning of anthropogenic warming of the ‘industrial revolution’. According to the author, the rate of human impact on environmental changes is estimated at about 15 per cent. The appearance of mankind brought new changes in the natural environment, including the oxygen content of the air. The current scale of anthropogenic impact justifies the introduction of a new time slice in the planet’s history - the Anthropocene. The functioning of civilisation is conditioned by meeting energy needs, to be implemented by creating a system of energy generators, among which the heat of the Earth should be an important component. The energy generated from this inexhaustible and cost-free geo-resource should be seen as the most ecological among all currently used energy carriers.