Reviewerly/exHarmony huggingface dataset:
Using sky-classification to improve the short-term prediction of irradiance with sky images and convolutional neural networks

Clouds moving in front or away from the sun are the leading cause of irradiance variability. These variations have a repercussion on the electricity production of photovoltaic systems. Predicting such changes is essential for proper control of these systems and for maintaining grid stability. Images from the sky have proven to help with short-term solar irradiance forecasting, especially when combined with artificial intelligence. Nevertheless, these models tend to smooth the irradiance fluctuations. We propose a forecasting model to predict the clear-sky index in a forecast horizon of 20 min with a 1-minute resolution. Our model, based on a classifier to determine the sky conditions and, on an optical flow, applies an artificial intelligence model explicitly trained on each class of sky conditions. This strategy has an equivalent performance to an unclassified model and a forecast skill between 5 and 20% with respect to the smart persistence model for most classes of sky conditions while requiring considerably less training data. Although our model reduces the overall predicting error, it still has difficulties predicting irradiance changes and mainly overcast days. Our classifying strategy can be applied to other models targeting different objectives to predict sudden changes in either irradiance or power related to photovoltaic systems.
Indium concentrations as a potential indicator of orebody-intrusion distance in a skarn Pb-Zn system, elucidated by the Fozichong Orefield (South China)

Critical metal indium tends to be enriched in skarn Pb-Zn systems, but the spatiotemporal distribution pattern of indium and its controls in a specific skarn Pb-Zn deposit remains unclear. To address this problem, we conducted a combination of geological, mineralogical, and trace element geochemical investigations on the Fozichong skarn Pb-Zn Orefield (South China) that contains two individual Pb-Zn deposits with different distances from ore-causative intrusion (the proximal Longwan and the distal Guyi deposits). Three types of sphalerites with different mineralogical characteristics have been identified that is Sp1 (in the early sulfide stage with chalcopyrite solid solution), Sp2 (with pyrrhotite inclusions), and Sp3 (in the late sulfide stage without solid solution). Further analyses by EPMA (Electron Probe Micro-Analyzer) and LA-ICP-MS (Laser Ablation Inductively Coupled Plasma Mass Spectrometer) reveal that the sphalerite from the two Pb-Zn deposits have different indium concentrations. That is, indium is significantly enriched in the proximal Longwan sphalerite, varying from 1.20 to 287.69 ppm (mean = 78.67 ppm). By contrast, indium is relatively barren in the distal Guyi sphalerite, ranging from 0.04 to 4.79 ppm (mean = 2.11 ppm). The calculated GGIMFis sphalerite geothermometer confirms that the temperatures of proximal Longwan (283 – 392 °C) are higher than those of the distal Guyi (295 – 336 °C). Collectively, the sphalerite proximal to the magmatic intrusion with higher temperatures is concentrated into more indium, but indium in the distal sphalerite significantly decreases by one order of magnitude. Furthermore, the indium contents in sphalerite can be used as a robust indicator of the distance between Pb-Zn orebodies and magmatic intrusion in the skarn Pb-Zn system.
EESSO: Exploiting Extreme and Smooth Signals via Omni-frequency learning for Text-based Person Retrieval

Most of the existing methods manage to tackle the problem of text-based person retrieval from the spatial-wise perspective. In this paper, we manage to address the problem of this task from a novel perspective, namely, the frequency-wise perspective. To this end, we propose to Exploit Extreme and Smooth Signals via Omni-frequency learning (EESSO) through a jointly optimized multi-stream architecture. It consists of a Spatial Information Stream (SIS), an Extreme Signal Stream (ESS) and a Smooth Signal Stream (SSS). EESSO aims to excavate the complementary effect between spatial-wise and frequency-wise features, so as to achieve a superior performance. A novel Uncertainty-Guided Mutual Learning Mechanism (UG-MLM) is utilized during training, which not only enables the three streams to communicate with and learn from each other, but also models the data-related heteroscedastic uncertainty as a weight for knowledge transference, and hence enables each stream to adaptively allocate knowledge from the other two streams. A large number of experiments are carried out on the widely-used CUHK-PEDES, RSTPReid and ICFG-PEDES datasets to verify the effectiveness of EESSO. Through the experimental results, it may not be hard to find that EESSO has achieved the state-of-the-art performance in supervised, weakly supervised and cross-domain text-based person retrieval settings.
Variation-aware semantic image synthesis

Semantic image synthesis (SIS) aims to produce photorealistic images aligning to given conditional semantic layout and has witnessed a significant improvement in recent years. Although the diversity in image-level has been discussed heavily, class-level mode collapse widely exists in current algorithms. Therefore, we declare a new requirement for SIS to achieve more photorealistic images, variation-aware, which consists of inter- and intra-class variation. The inter-class variation is the diversity between different semantic classes while the intra-class variation stresses the diversity inside one class. Through analysis, we find that current algorithms elusively embrace the inter-class variation but the intra-class variation is still not enough. Further, we introduce two simple methods to achieve variation-aware semantic image synthesis (VASIS) with a higher intra-class variation, semantic noise and position code. We combine our method with several state-of-the-art algorithms and the experimental result shows that our models generate more natural images and achieve slightly better FIDs and/or mIoUs than the counterparts. Our codes and models will be publicly available.
Anomaly detection for synthetic diamond grain using monocular depth estimation

This paper studies the problem of estimating the maximum depth from a single two-dimensional monocular image of a grain of synthetic diamond to detect anomalies that would breach the quality criteria. To target this problem, we propose two models that employ different approaches to the depth map predicted by the zero-shot monocular depth estimation model, MiDaS, in combination with EfficientNet. The first model utilizes a normal map generated by differentiating the MiDaS-predicted depth map, taking an RGB image as input. The inclusion of the normal map provides valuable information about the pseudo-roughness of the diamond grain, aiding in the identification of anomalies. The second model incorporates a weighting scheme that enhances the MiDaS-predicted depth map through pointwise convolution with the RGB image. By assigning appropriate weights, the model highlights crucial features of the image, thus improving the overall performance. The main objective of both approaches is to improve the accuracy of the models by incorporating the per-pixel relative depth information extracted by MiDaS. Both of these proposed models outperformed existing state-of-the-art models in detecting anomalous synthetic diamond particles. Through this research, we aim to contribute to the advancement of anomaly detection in the field of synthetic diamond quality assessment, leveraging the potential of monocular depth estimation and innovative model architectures.
Soil organic matter content prediction based on two-branch convolutional neural network combining image and spectral features

Soil organic matter (SOM) is the main source of soil nutrients. Rapid determination of SOM content is of great significance for guiding field management. The change of SOM content will change the physical properties of soil, such as color and porosity, and then lead to obvious differences in soil reflectance in the visible and near infrared range. In this study, 417 topsoil (5–15 cm) samples were collected from five experimental fields in North China, and spectral data in the range of 350–1700nm and image data of sampling points were obtained. Multi-CNN network was established to realize the feature fusion of image and spectrum based on two-branch CNN to predict SOM content. Firstly, the spectral feature extraction module based on the improved CNN was constructed, and the spectral information from 768 to 140 bands was compressed by using Auto Encoder, and the feature was extracted by CNN. The feature extraction of the soil image is realized by the ResNet50 network, and finally the feature information of the image and the spectrum is fused by the bilinear pooling fusion module in the fully connected layer to complete the SOM content prediction and output the results. After experimental verification, BLMulti CNN had the highest accuracy in predicting organic matter models, in which the prediction set R2 is 0.910, 0.017 higher than that of the Multi-CNN based on concat, 0.075 higher than that of the prediction method of AE-CNN and 0.10 higher than that of the image prediction model based on ResNet50. The BLMulti-CNN effectively improves the prediction accuracy of the model and provides technical guidance for the rapid detection of SOM content in the field.
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models

The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.
Adaptive learning on hierarchical data streams using window-weighted Gaussian probabilities

The hierarchical data stream classification task addresses challenges in both hierarchical and data stream classification primary areas. In these scenarios, machine learning models must simultaneously deal with class hierarchies and adapt to respond to nonstationary data. Given such a challenging set of traits, existing techniques are deficient, as they perform incremental learning and are slow to adapt to newer data, thus not capturing their dynamics in a timely fashion. In this study, we propose two novel adaptive Gaussian Naive Bayes classifiers tailored to classify hierarchical data streams. The models use window-weighted Gaussian probabilities to consider current and historical data and improve the adaptability of the classifiers, especially for nonstationary data streams. As a result of our research, we introduce a unified protocol for evaluating and comparing hierarchical data stream classifiers and establish a benchmark for the hierarchical data stream classification task encompassing the proposed methods and state-of-the-art classifiers. The results demonstrate that our proposed algorithms achieve better prediction correctness than their state-of-the-art counterparts while responding more swiftly to changes in data distribution.
CBReT: A Cluster-Based Resampling Technique for dealing with imbalanced data in code smell prediction

Code smell refers to substandard design patterns in software's source code that may lead to faults-prone implementation. Machine learning-based code smell prediction models suffer from data imbalance problems, i.e., one class contains significantly more instances than another. The existing oversampling approaches, such as SMOTE (Synthetic Minority Over-sampling Technique), have been used for balancing the code smell dataset by generating synthetic samples for the minority class. However, the distribution of classes of code smell datasets is overlapped; hence, randomly generated instances can damage the decision boundary between both classes. This paper addresses this issue and proposes a novel Cluster-Based Resampling Technique, CBReT, that generates synthetic instances by considering the distribution of the code smell data. The CBReT first formulates clusters (containing minority and majority instances) based on the data distribution using Gaussian Mixture Model (GMM). Next, each cluster is balanced separately by synthesizing minority instances. While balancing the clusters, the CBReT also checks the validity of the synthetic instances so that each synthetic instance holds similar properties as the other minority instances. To assess the performance of CBReT, extensive experiments have been conducted on the four publicly available benchmark code smell datasets. We have used various performance metrics to evaluate our model's performance. The experimental results show that the CBReT technique significantly increased the performance of the code smell prediction model by 0.18% (min) and 9.08% (max) compared to the state-of-the-art imbalance learning approaches.
Mineral prediction based on prototype learning

In the field of mineral resource prediction, acquiring labeled data and bearing high annotation costs pose significant challenges. Moreover, distinct characteristics are present in different types of data, including geophysical, geochemical, and geological data. However, conventional deep learning methods often treat all data uniformly, neglecting the specificities inherent in various domains of knowledge. To address these issues, we propose Geo-Meta, an optimized prototype learning model that combines prototype learning, metric learning, and meta-learning strategies to classify multi-source geological samples. Geo-Meta utilizes tailored network architectures for different data types to fuse features and compute prototypes in the metric space. Additionally, it employs a generative model to identify subtle anomaly features and incorporates a semi-supervised algorithm based on label propagation to refine the initial prototypes, enhancing their robustness and category-awareness. Furthermore, Geo-Meta incorporates a dynamic distance metric module to quantify the similarity between sample features and class prototypes, facilitating effective category partitioning. Extensive experimentation demonstrates that our proposed model achieves remarkable performance. It attains an impressive Area Under the Receiver Operator Curve (AUC) of 98% without data augmentation, using a significantly limited number of samples. Moreover, it accurately identifies approximately 93% of ore deposits within only 7% of the study area, surpassing similar models in both accuracy and efficiency. The mineral resource prediction map generated by Geo-Meta aligns with the spatial distribution of mineral resources and adheres to geological knowledge, providing valuable technical support for decision-making in target area exploration. Our proposed model enables the adaptation of deep learning algorithms to geological big data, offering substantial potential for cost and time reduction in mineral prediction. Thus, it holds great relevance and practicality for the mining industry.
Parrots and Paragrams: AI Language Models and Erasure Poetry

This essay on poetry and computation borrows a provocative trope from an essay written by AI specialists who describe large language models as "stochastic parrots," copyists relying on probability statistics to generate answers. The history of poetry affords us many instances of textual borrowing that might provocatively be called parroting, repetition embedded in procedural selection from a source text, most recently in the minor genre of erasure poetry. This essay pursues analogies and differences between the digital systems and erasure poetics with the assistance of a poetic device known as the paragram, associated with eccentric searchers for hidden or occult messages in literary classics, as well as linguistic theorists such as Ferdinand de Saussure, Roman Jakobson, and Julia Kristeva. The essay centers on a prominent erasure poem, Voyager (2010) by Srikanth Reddy, and also discusses more briefly texts by Tom Phillips, Ronald Johnson, Janet Holmes, and Susan Howe.
Non-degenerate-pump four-wave mixing kinetic inductance travelling-wave parametric amplifiers

Abstract Kinetic inductance travelling-wave parametric amplifiers (KITWPAs) have been demonstrated to achieve high gain over broad bandwidths whilst achieving near quantum-limited noise performance, properties which are extremely important for many ultra-sensitive experiments. In early KITWPA designs, the requirement for phase-matching lead to the creation of a large zero-gain gap in the centre of the gain profile where the peak gain is, which also slightly narrows down the operational bandwidth of the device. This has been mitigated in more recent designs by introducing a DC bias to the KITWPA device, which allows the gap to be tuned away from the amplification band. However, the added DC biasing requires a more complicated experimental setup and potentially leads to unwanted heat leak in the cryogenic environment. Additionally, operation with a DC bias also become challenging at higher frequencies beyond the microwave regime. In this paper, we present the concept of a KITWPA operating in a non-degenerate-pump four-wave mixing (NP-4WM) regime, whereby the injection of two pump tones along with a weak signal results in a broad, flat gain profile that removes the zero-gain gap as well as eliminates the need for a DC bias and the complexities associated with it. We demonstrate how a NP-4WM KITWPA is feasible to achieve broadband amplification at a range of frequencies, first in the microwave range where most KITWPAs reported to-date have been successfully experimentally characterised. We then extend the designs to several millimetre (mm) bands to illustrate how we can use this technique to design a broadband front-end pre-amplifier that covers several Atacama Large Millimetre/sub-millimetre Array (ALMA) Bands.
Performance improvement in reservoir computing by using HfZrO<sub>2</sub> FeFETs through operating voltage optimization

Abstract We have investigated how the parameters of an input gate voltage ( V g ) waveform and a drain voltage ( V d ) impact the performance of reservoir computing (RC) using a Hf 0.5 Zr 0.5 O 2 ferroelectric FET (FeFET). The RC performance is maximized by the high swing amplitude of the V g and the most symmetrical polarization switching condition in the triangular-shaped input waveform, obtained by the center V g of 0.5 V, because of the enhanced polarization switching of the FeFETs. Regarding the V d dependence, the amount of the drain current and polarization switching have a trade-off relationship. As a result, a moderate V d of 1.0 V becomes optimum in terms of the RC performance because a difference in drain current responses between different gate input patterns is maximized with this V d . Furthermore, high computing capacities are achieved by combining the above optimal bias condition with drain current responses to both original and inverted gate input patterns.
Object aroused emotion analysis network for image sentiment analysis

From cognitive psychology, objects are closely related to emotions, and inherently possess the ability to arouse human emotions. Hence, fully utilizing the relationships between objects and emotions can help achieve more accurate visual emotion recognition. In this paper, we propose a novel object aroused emotion analysis network to realize image sentiment classification by investigating the interactions between objects and emotions. To quantify the various emotion potencies of each object, a novel object emotion distribution module is proposed to explore the mapping among objects and emotions, and quantitatively demonstrate how various objects arouse different emotions. An object emotion-modeling mapping module is proposed to analyze the effect of objects and object combinations with emotion information on image sentiment; this module maps common objects to emotion dimensions and improves image sentiment classification with abundant object combination information. Then, we fuse the object-emotion mapping relation and multi-model features using BiGRU, thus realizing more accurate emotion recognition. Extensive experiments on widely used emotion datasets prove that our proposed method achieves excellent performance and outperforms most state-of-the-art image sentiment classification methods.
Artificial Intelligence (AI) in Tourism

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.