Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Detection of Late Blight Tomato Disease using Histogram Oriented Gradient based Support Vector Machine

The tomato is one of the most important fruits on earth. It plays an important and useful role in the agricultural production of any country. This research propose a novel smart technique for early detection of late blight diseases in tomatoes. This work improve the dataset with an increase in images from the field (the Plant Village dataset) and proposed a hybrid algorithm composed of support vector machines (SVM) and histogram-oriented gradients (HOG) for real-time detection of late blight tomato disease. To propose a HOG-based SVM model for early detection of late blight tomato leaf disease. To check the performance of the proposed model in terms of MSE, accuracy, precision, and recall as compared to Decision Tree and KNN. The integration of advanced technology in agriculture has the potential to revolutionize the industry, making it more efficient, sustainable, and profitable. This research work on the early detection of tomato diseases contributes to the growing importance of smart farming, the need for climate-smart agriculture, the rising need to more efficiently utilize natural resources, and the demand for higher crop yields. The proposed hybrid algorithm of SVM and HOG has significant potential for the early detection of late blight disease in tomato plants. The performance of the proposed model against decision tree and KNN algorithms and the results may assist in selecting the best algorithm for future applications. The research work can help farmers make data-driven decisions to optimize crop yield and quality while also reducing the environmental impact of farming practices.
Impact of electronic correlations on high-pressure iron: insights from time-dependent density functional theory

We present a comprehensive investigation of the electrical and thermal conductivity of iron under high pressures at ambient temperature, employing the real-time formulation of time-dependent density functional theory (RT-TDDFT). Specifically, we examine the influence of a Hubbard correction (+U) to account for strong electron correlations. Our calculations based on RT-TDDFT demonstrate that the evaluated electrical conductivity for both high-pressure body-centered cubic (BCC) and hexagonal close-packed (HCP) iron phases agrees well with experimental data. Furthermore, we explore the anisotropy in the thermal conductivity of HCP iron under high pressure, and our findings are consistent with experimental observations. Interestingly, we find that the incorporation of the +U correction significantly impacts the ground state and linear response properties of iron at pressures below 50 GPa, with its influence diminishing as pressure increases. This study offers valuable insights into the influence of electronic correlations on the electronic transport properties of iron under extreme conditions.
A reinforcement learning strategy for p-adaptation in high order solvers

Reinforcement learning (RL) has emerged as a promising approach to automating decision processes. This paper explores the application of RL techniques to optimise the polynomial order in the computational mesh when using high-order solvers. Mesh adaptation plays a crucial role in improving the efficiency of numerical simulations by improving accuracy while reducing the cost. Here, actor-critic RL models based on Proximal Policy Optimization offer a data-driven approach for agents to learn optimal mesh modifications based on evolving conditions. The paper provides a strategy for p-adaptation in high-order solvers and includes insights into the main aspects of RL-based mesh adaptation, including the formulation of appropriate reward structures and the interaction between the RL agent and the simulation environment. We discuss the impact of RL-based mesh p-adaptation on computational efficiency and accuracy. We test the RL p-adaptation strategy on a 1D inviscid Burgers' equation to demonstrate the effectiveness of the strategy. The RL strategy reduces the computational cost and improves accuracy over uniform adaptation, while minimising human intervention.
An effective gauge theory of fractons: perturbative and non-local aspects

We construct, from first principles, a covariant local model for scalar fractonic matter coupled to a symmetric tensor gauge field. The free gauge field action is just the one of Blasi-Maggiore model. The scalar sector is a non-trivial covariant generalization of Pretko's quartic model. Because the model has no quadratic term in the scalar field, a direct perturbative treatment fails. Remarkably, we demonstrate that the action can be driven to a perturbative effective action. However, with the price of carrying non-local interacting terms. We study the perturbative regime of the model and show that there are at least four distinct phases in the model: One with massive fractonic modes; one with massless fractons; a mixed phase with massive and massless fractons; and another one where fractons cannot occur at all in the physical spectrum.
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning

Thompson sampling (TS) is widely used in sequential decision making due to its ease of use and appealing empirical performance. However, many existing analytical and empirical results for TS rely on restrictive assumptions on reward distributions, such as belonging to conjugate families, which limits their applicability in realistic scenarios. Moreover, sequential decision making problems are often carried out in a batched manner, either due to the inherent nature of the problem or to serve the purpose of reducing communication and computation costs. In this work, we jointly study these problems in two popular settings, namely, stochastic multi-armed bandits (MABs) and infinite-horizon reinforcement learning (RL), where TS is used to learn the unknown reward distributions and transition dynamics, respectively. We propose batched $\textit{Langevin Thompson Sampling}$ algorithms that leverage MCMC methods to sample from approximate posteriors with only logarithmic communication costs in terms of batches. Our algorithms are computationally efficient and maintain the same order-optimal regret guarantees of $\mathcal{O}(\log T)$ for stochastic MABs, and $\mathcal{O}(\sqrt{T})$ for RL. We complement our theoretical findings with experimental results.
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks in the Physical World

Adversarial attacks, particularly patch attacks, pose significant threats to the robustness and reliability of deep learning models. Developing reliable defenses against patch attacks is crucial for real-world applications, yet current research in this area is unsatisfactory. In this paper, we propose DIFFender, a novel defense method that leverages a text-guided diffusion model to defend against adversarial patches. DIFFender includes two main stages: patch localization and patch restoration. In the localization stage, we find and exploit an intriguing property of the diffusion model to precisely identify the locations of adversarial patches. In the restoration stage, we employ the diffusion model to reconstruct the adversarial regions in the images while preserving the integrity of the visual content. Thanks to the former finding, these two stages can be simultaneously guided by a unified diffusion model. Thus, we can utilize the close interaction between them to improve the whole defense performance. Moreover, we propose a few-shot prompt-tuning algorithm to fine-tune the diffusion model, enabling the pre-trained diffusion model to adapt to the defense task easily. We conduct extensive experiments on image classification, face recognition, and further in the physical world, demonstrating that our proposed method exhibits superior robustness under strong adaptive attacks and generalizes well across various scenarios, diverse classifiers, and multiple patch attack methods.
Two-Way Semantic Transmission of Images without Feedback

As a competitive technology for 6G, semantic communications can significantly improve transmission efficiency. However, many existing semantic communication systems require information feedback during the training coding process, resulting in a significant communication overhead. In this article, we consider a two-way semantic communication (TW-SC) system, where information feedback can be omitted by exploiting the weight reciprocity in the transceiver. Particularly, the channel simulator and semantic transceiver are implemented on both TW-SC nodes and the channel distribution is modeled by a conditional generative adversarial network. Simulation results demonstrate that the proposed TW-SC system performs closing to the state-of-the-art one-way semantic communication systems but requiring no feedback between the transceiver in the training process.
TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter

Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), a comprehensive dataset encompassing over 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes), available at https://zenodo.org/records/8031785. Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and natural images is inversely correlated with the number of likes. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images.
AINA Safety Code, A Review of Loss of Plasma Control Transients in ITER: Sudden Increase in Fuelling Rate, Sudden Increase of Auxiliary Heating

In this contribution, two ITER loss of plasma control events are investigated. The first one is a failure of pumps and/or pellet injection system which fuel the plasma, producing an overfuelling event. The second examines an increase of external heating during the steady state operation of ITER. For both events, initial simulation parameters are scanned in order to find out which could lead to highest fusion power. Extensive simulation work has been done with AINA-1.0 (July-2007), the safety code developed by Fusion Energy Engineering Laboratory (FEEL-UPC) on the basis of SAFALY. AINA is a hybrid code comprising a zero-dimensional plasma dynamics and radial and poloidal thermal analyses of in-vessel components, and is intended to the quantitative investigation of plasma events in nuclear fusion reactors such as ITER. FEEL-UPC research group has been developing AINA code since 2004. AINA code brings significant improvements in relation to the original SAFALY code, and FEEL-UPC aims to continue improving it in the near future, with new models for plasma wall interactions, blanket thermal analysis, and plasma edge and core.
Neural Fields with Hard Constraints of Arbitrary Differential Order

While deep learning techniques have become extremely popular for solving a broad range of optimization problems, methods to enforce hard constraints during optimization, particularly on deep neural networks, remain underdeveloped. Inspired by the rich literature on meshless interpolation and its extension to spectral collocation methods in scientific computing, we develop a series of approaches for enforcing hard constraints on neural fields, which we refer to as Constrained Neural Fields (CNF). The constraints can be specified as a linear operator applied to the neural field and its derivatives. We also design specific model representations and training strategies for problems where standard models may encounter difficulties, such as conditioning of the system, memory consumption, and capacity of the network when being constrained. Our approaches are demonstrated in a wide range of real-world applications. Additionally, we develop a framework that enables highly efficient model and constraint specification, which can be readily applied to any downstream task where hard constraints need to be explicitly satisfied during optimization.
MuRS: Mutant Ranking and Suppression using Identifier Templates

Diff-based mutation testing is a mutation testing approach that only mutates lines affected by a code change under review. This approach scales independently of the code-base size and introduces test goals (mutants) that are directly relevant to an engineer’s goal such as fixing a bug, adding a new feature, or refactoring existing functionality. Google’s mutation testing service integrates diff-based mutation testing into the code review process and continuously gathers developer feedback on mutants surfaced during code review. To enhance the developer experience, the mutation testing service uses a number of manually-written rules that suppress not-useful mutants—mutants that have consistently received negative developer feedback. However, while effective, manually implementing suppression rules requires significant engineering time. This paper proposes and evaluates MuRS, an automated approach that groups mutants by patterns in the source code under test and uses these patterns to rank and suppress future mutants based on historical developer feedback on mutants in the same group. To evaluate MuRS, we conducted an A/B testing study, comparing MuRS to the existing mutation testing service. Despite the strong baseline, which uses manually-written suppression rules, the results show a statistically significantly lower negative feedback ratio of 11.45% for MuRS versus 12.41% for the baseline. The results also show that MuRS is able to recover existing suppression rules implemented in the baseline. Finally, the results show that statement-deletion mutant groups received both the most positive and negative developer feedback, suggesting a need for additional context that can distinguish between useful and not-useful mutants in these groups. Overall, MuRS is able to recover existing suppression rules and automatically learn additional, finer-grained suppression rules from developer feedback.
Generalized Abel equations and applications to translation invariant Radon transforms

Abstract Generalized Abel equations have been employed in the recent literature to invert Radon transforms which arise in a number of important imaging applications, including Compton Scatter Tomography (CST), Ultrasound Reflection Tomography (URT), and X-ray CT. In this paper, we present novel injectivity results and inversion methods for generalized Abel operators. We apply our theory to a new Radon transform, R j \mathcal{R}_{j} , of interest in URT, which integrates a square integrable function of compact support, 𝑓, over ellipsoid and hyperboloid surfaces with centers on a plane. Using our newly established theory on generalized Abel equations, we show that R j \mathcal{R}_{j} is injective and provide an inversion method based on Neumann series. In addition, using algebraic methods, we present image phantom reconstructions from R j ⁢ f \mathcal{R}_{j}f data with added pseudo-random noise.
Learning to Rank when Grades Matter

Graded labels are ubiquitous in real-world learning-to-rank applications, especially in human rated relevance data. Traditional learning-to-rank techniques aim to optimize the ranked order of documents. They typically, however, ignore predicting actual grades. This prevents them from being adopted in applications where grades matter, such as filtering out ``poor'' documents. Achieving both good ranking performance and good grade prediction performance is still an under-explored problem. Existing research either focuses only on ranking performance by not calibrating model outputs, or treats grades as numerical values, assuming labels are on a linear scale and failing to leverage the ordinal grade information. In this paper, we conduct a rigorous study of learning to rank with grades, where both ranking performance and grade prediction performance are important. We provide a formal discussion on how to perform ranking with non-scalar predictions for grades, and propose a multiobjective formulation to jointly optimize both ranking and grade predictions. In experiments, we verify on several public datasets that our methods are able to push the Pareto frontier of the tradeoff between ranking and grade prediction performance, showing the benefit of leveraging ordinal grade information.
The Unexpected Optical and Ultraviolet Variability of the Standard Star α Sex (HD 87887)

The analysis of the available Transiting Exoplanet Survey Satellite (TESS) light curves of α Sex (HD 87887) reveals low-frequency pulsations with a period of about 9.1 hr in this spectroscopic A0 III standard star. The IUE observations in 1992 December reveal large flux variations both in the far-UV and in the mid-UV, which are accompanied by variations of the brightness in the V band recorded by the the Fine Error Sensor on board IUE. The ultraviolet variability could be due to an eclipse by an hitherto undetected companion of smaller radius, possibly 2.5 R ⊙, but this needs confirmation by further monitoring possibly with TESS. An abundance determination yields solar abundances for most elements. Only carbon and strontium are underabundant and titanium, vanadium, and baryum mildly overabundant. Identification is provided for most of the lines absorbing more than 2% in the optical spectrum of α Sex. Stellar evolution modeling shows that α Sex is near the terminal-age main sequence, and its mass, radius, and age are estimated to be M = 2.57 ± 0.32 M ⊙, R = 3.07 ± 0.90 R ⊙, A = 385 ± 77 Myr, respectively.
Contextual Font Recommendations based on User Intent

Adobe Fonts has a rich library of over 20,000 unique fonts that Adobe users utilize for creating graphics, posters, composites etc. Due to the nature of the large library, knowing what font to select can be a daunting task that requires a lot of experience. For most users in Adobe products, especially casual users of Adobe Express, this often means choosing the default font instead of utilizing the rich and diverse fonts available. In this work, we create an intent-driven system to provide contextual font recommendations to users to aid in their creative journey. Our system takes in multilingual text input and recommends suitable fonts based on the user's intent. Based on user entitlements, the mix of free and paid fonts is adjusted. The feature is currently used by millions of Adobe Express users with a CTR of>25%.
Two-temperature principle for evaluating electrothermal performance of GaN HEMTs

Self-heating effects in Gallium nitride (GaN) high-electron-mobility transistors (HEMTs) can adversely impact both device reliability and electrical performance. Despite this, a holistic understanding of the relationship among heat transport mechanisms, device reliability, and degradation of electrical performance has yet to be established. This Letter presents an in-depth analysis of self-heating effects in GaN HEMTs using technology computer-aided design and phonon Monte Carlo simulations. We examine the differential behaviors of the maximum channel temperature (Tmax) and the equivalent channel temperature (Teq) in response to non-Fourier heat spreading processes, highlighting their respective dependencies on bias conditions and phonon ballistic effects. Our study reveals that Tmax, a crucial metric for device reliability, is highly sensitive to both heat source-related and cross-plane ballistic effects, especially in the saturation regime. In contrast, Teq, which correlates with drain current degradation, shows minimal bias dependence and is predominantly influenced by the cross-plane ballistic effect. These findings emphasize the importance of optimizing device designs to mitigate both Tmax and Teq, with a particular focus on thermal designs influenced by the heat source size. This work contributes to a deeper understanding of self-heating phenomena in GaN HEMTs and provides valuable insights for enhancing device performance and reliability.
Distributed Trust Through the Lens of Software Architecture

Distributed trust is a nebulous concept that has evolved from different perspectives in recent years. While one can attribute its current prominence to blockchain and cryptocurrency, the distributed trust concept has been cultivating progress in federated learning, trustworthy and responsible AI in an ecosystem setting, data sharing, privacy issues across organizational boundaries, and zero trust cybersecurity. This paper will survey the concept of distributed trust in multiple disciplines. It will take a system/software architecture point of view to look at trust redistribution/shift and the associated tradeoffs in systems and applications enabled by distributed trust technologies.
From Driver to Supervisor: Comparing Cognitive Load and EEG-based Attention Allocation across Automation Levels

With increasing automation, drivers’ role progressively transitions from active operators to passive system supervisors, aﬀecting their behaviour and cognitive processes. This study aims to understand better attention allocation and perceived cognitive load in manual, L2, and L3 driving in a realistic environment. We conducted a test-track experiment with 30 participants. While driving a prototype automated vehicle, participants were exposed to a passive auditory oddball task and their EEG was recorded. We studied the P3a ERP component elicited by novel environmental cues, an index of attention resources used to process the stimuli. The self-reported cognitive load was assessed using the NASA-TLX. Our ﬁndings revealed no signiﬁcant diﬀerence in perceived cognitive load between manual and L2 driving, with L3 driving demonstrating a lower self-reported cognitive load. Despite this, P3a mean amplitude was highest during manual driving, in-dicating greater attention allocation towards processing environmental sounds compared to L2 and L3 driving. We argue that the need to integrate environmental information might be attenuated in L2 and L3 driving. Further empirical evidence is necessary to understand whether the decreased processing of environmental stimuli is due to top-down attention control leading to attention withdrawal or a lack of available resources due to high cognitive load. To the best of our knowledge, this study is the ﬁrst attempt to use the passive oddball paradigm outside the laboratory. The insights of this study have signiﬁcant implications for automation safety and user interface design.