Miracle-dz/UniFAR-FTUs huggingface dataset:
Navigating the seascape of ocean management: waypoints on the voyage toward sustainable use

Some societies have sustainably managed their local marine resources for centuries using traditional methods, but we are only beginning to learn how to do it at larger scales, including globally. A broad, deep and constantly growing body of ocean knowledge has developed, adding many new concepts, perspectives, management models and analytical tools into the knowledge base in a relatively short period. Such rapid growth has created a potentially confusing mash-up of ideas, acronyms, techniques, tools and regulations, demonstrated by recent titles such as, ‘Marine planning: tragedy of the acronyms’ (Ardron 2010), ‘Integrated marine science and management: wading through the morass’ (Elliott 2014), ‘Beyond rhetoric: navigating the conceptual tangle towards effective implementation of the ecosystem approach to oceans management ‘ (Engler 2015) and ‘Marine legislation – the ultimate ‘horrendogram’’ (Boyes and Elliott 2014, undated and 2016). The purpose of this paper is to assist policy makers, marine managers and those considering careers in this area by providing a short history of ocean management, its conceptual foundation, frameworks for modern management and examples of its application at different scales. Extensive literature exists to supplement the summarized information we present. We highlight the following terms as navigational markers through the ‘seascape’1 of marine management rhetoric: sustainability, ecosystem approach, ecosystem-based management, natural capital, ecosystem services, integrated ecosystem assessment, the causal framework DPSIR (Drivers, Pressures, States, Impacts, Responses) and its variants, indicators and reference points, marine area planning, marine spatial management (including decision support tools), adaptive ocean management and dynamic ocean management. We also point out the important roles of marine initiatives such as Blue Economy, the Ocean Health Index, Large Marine Ecosystems, Seascapes, Protected Areas and others. Understanding the similarities, differences, relationships and synergies among these activities increases the likelihood of achieving successful management processes or solutions. 1 We use ‘seascape’ (lower case) to describe the panorama of concepts, acronyms, techniques, tools and regulations germane to marine management. ‘Seascape’ and ‘Oceanscape’ (upper case) signify specific programs for integrated management at large-scales.
In major depression, complex intersections between adverse childhood experiences and negative life events impact neuroticism, brooding, suicidal tendencies, insomnia, and cognitive interference.

Background: There is evidence that adverse childhood experiences (ACEs) and negative life events (NLEs) are associated with major depression (MDD). Purpose: To determine whether ACEs affect all features of mild MDD, including suicidal tendencies, brooding, neuroticism, insomnia, cognitive deficits, severity of depression and anxiety, and cognitive deficits, and whether NLEs mediate these effects. Patients and methods: This study examines a cohort of 118 academic students, namely 74 students who satisfied the DSM-5-TR criteria for MDD and 44 normal control students. We assessed brooding, neuroticism, suicidal ideation and attempts, and the severity of depression, anxiety, insomnia, and the Stroop tests. Results: One validated factor could be extracted from brooding, neuroticism, current suicidal behaviors, and the severity of depression, anxiety, and insomnia, labeled the phenome of depression. A large part of the variance in the phenome of depression (55.0%) was explained by the combined effects of self-, relationships, and academic-related NLEs in conjunction with ACEs, including family dysfunction and abuse and neglect (both physical and emotional). The latter ACEs significantly interacted (moderating effect) with NLEs to impact the depression phenome. Although sexual abuse did not have direct effects on the phenome, its effects were mediated by NLEs. We discovered that increased sexual abuse, physical and emotional abuse and neglect, and ACEs related to family dysfunction predicted 22.5% of the variance in NLEs. Up to 18.5% of the variance in the Stroop test scores was explained by sexual abuse and the phenome of depression. The latter mediated the effects of NLEs and abuse, neglect, and family dysfunction on the Stroop test scores. Conclusion: Complex intersections between ACEs and NLEs impact the phenome of depression, which comprises neuroticism, brooding, suicidal tendencies, and the severity of insomnia, anxiety, and depression, while sexual abuse together with other ACEs and NLEs may impact cognitive interference inhibition.
Enhancing targeted transferability via feature space fine-tuning

Adversarial examples (AEs) have been extensively studied due to their potential for privacy protection and inspiring robust neural networks. Yet, making a targeted AE transferable across unknown models remains challenging. In this paper, to alleviate the overfitting dilemma common in an AE crafted by existing simple iterative attacks, we propose fine-tuning it in the feature space. Specifically, starting with an AE generated by a baseline attack, we encourage the features conducive to the target class and discourage the features to the original class in a middle layer of the source model. Extensive experiments demonstrate that only a few iterations of fine-tuning can boost existing attacks' targeted transferability nontrivially and universally. Our results also verify that the simple iterative attacks can yield comparable or even better transferability than the resource-intensive methods, which rest on training target-specific classifiers or generators with additional data. The code is available at: github.com/zengh5/TA_feature_FT.
A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model

Far-field speech recognition is a challenging task that conventionally uses signal processing beamforming to attack noise and interference problem. But the performance has been found usually limited due to heavy reliance on environmental assumption. In this paper, we propose a unified multichannel far-field speech recognition system that combines the neural beamforming and transformer-based Listen, Spell, Attend (LAS) speech recognition system, which extends the end-to-end speech recognition system further to include speech enhancement. Such framework is then jointly trained to optimize the final objective of interest. Specifically, factored complex linear projection (fCLP) has been adopted to form the neural beamforming. Several pooling strategies to combine look directions are then compared in order to find the optimal approach. Moreover, information of the source direction is also integrated in the beamforming to explore the usefulness of source direction as a prior, which is usually available especially in multi-modality scenario. Experiments on different microphone array geometry are conducted to evaluate the robustness against spacing variance of microphone array. Large in-house databases are used to evaluate the effectiveness of the proposed framework and the proposed method achieve 19.26\% improvement when compared with a strong baseline.
On the order of Erd\H{o}s-Rogers functions

For an integer $n \geq 1$, the Erd\H{o}s-Rogers function $f_{s}(n)$ is the maximum integer $m$ such that every $n$-vertex $K_{s+1}$-free graph has a $K_s$-free subgraph with $m$ vertices. It is known that for all $s \geq 3$, $f_{s}(n) = \Omega(\sqrt{n\log n}/\log \log n)$ as $n \rightarrow \infty$. In this paper, we show that for all $s \geq 3$, \begin{equation*} f_{s}(n) = O(\sqrt{n}\, \log n). \end{equation*} This improves previous bounds of order $\sqrt{n} (\log n)^{2(s + 1)^2}$ by Dudek, Retter and R\"{o}dl.
Branched Variational Autoencoder Classifiers

This paper introduces a modified variational autoencoder (VAEs) that contains an additional neural network branch. The resulting branched VAE (BVAE) contributes a classification component based on the class labels to the total loss and therefore imparts categorical information to the latent representation. As a result, the latent space distributions of the input classes are separated and ordered, thereby enhancing the classification accuracy. The degree of improvement is quantified by numerical calculations employing the benchmark MNIST dataset for both unrotated and rotated digits. The proposed technique is then compared to and then incorporated into a VAE with fixed output distributions. This procedure is found to yield improved performance for a wide range of output distributions.
Nuclear mass predictions using machine learning models

The exploration of nuclear mass or binding energy, a fundamental property of atomic nuclei, remains at the forefront of nuclear physics research due to limitations in experimental studies and uncertainties in model calculations, particularly when moving away from the stability line. In this work, we employ two machine learning (ML) models, Gaussian Process Regression (GPR) and Support Vector Regression (SVR), to assess their performance in predicting nuclear mass excesses using available experimental data and a physics-based feature space. We also examine the extrapolation capabilities of these models using newly measured nuclei from AME2020 and by extending our calculations beyond the training and test set regions. Our results indicate that both GPR and SVR models perform quite well within the training and test regions when informed with a physics-based feature space. Furthermore, these ML models demonstrate the ability to make reasonable predictions away from the available experimental data, offering results comparable to the model calculations. Through further refinement, these models can be used as reliable and efficient ML tools for studying nuclear properties in the future.
Let's Get It Started: Fostering the Discoverability of New Releases on Deezer

This paper presents our recent initiatives to foster the discoverability of new releases on the music streaming service Deezer. After introducing our search and recommendation features dedicated to new releases, we outline our shift from editorial to personalized release suggestions using cold start embeddings and contextual bandits. Backed by online experiments, we discuss the advantages of this shift in terms of recommendation quality and exposure of new releases on the service.
On the numerical reliability of nonsmooth autodiff: a MaxPool case study

This paper considers the reliability of automatic differentiation (AD) for neural networks involving the nonsmooth MaxPool operation. We investigate the behavior of AD across different precision levels (16, 32, 64 bits) and convolutional architectures (LeNet, VGG, and ResNet) on various datasets (MNIST, CIFAR10, SVHN, and ImageNet). Although AD can be incorrect, recent research has shown that it coincides with the derivative almost everywhere, even in the presence of nonsmooth operations (such as MaxPool and ReLU). On the other hand, in practice, AD operates with floating-point numbers (not real numbers), and there is, therefore, a need to explore subsets on which AD can be numerically incorrect. These subsets include a bifurcation zone (where AD is incorrect over reals) and a compensation zone (where AD is incorrect over floating-point numbers but correct over reals). Using SGD for the training process, we study the impact of different choices of the nonsmooth Jacobian for the MaxPool function on the precision of 16 and 32 bits. These findings suggest that nonsmooth MaxPool Jacobians with lower norms help maintain stable and efficient test accuracy, whereas those with higher norms can result in instability and decreased performance. We also observe that the influence of MaxPool's nonsmooth Jacobians on learning can be reduced by using batch normalization, Adam-like optimizers, or increasing the precision level.
Novel End-to-End Production-Ready Machine Learning Flow for Nanolithography Modeling and Correction

Optical lithography is the main enabler to semiconductor manufacturing. It requires extensive processing to perform the Resolution Enhancement Techniques (RETs) required to transfer the design data to a working Integrated Circuits (ICs). The processing power and computational runtime for RETs tasks is ever increasing due to the continuous reduction of the feature size and the expansion of the chip area. State-of-the-art research sought Machine Learning (ML) technologies to reduce runtime and computational power, however they are still not used in production yet. In this study, we analyze the reasons holding back ML computational lithography from being production ready and present a novel highly scalable end-to-end flow that enables production ready ML-RET correction.
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing

Self-supervised frameworks for representation learning have recently stirred up interest among the remote sensing community, given their potential to mitigate the high labeling costs associated with curating large satellite image datasets. In the realm of multimodal data fusion, while the often used contrastive learning methods can help bridging the domain gap between different sensor types, they rely on data augmentations techniques that require expertise and careful design, especially for multispectral remote sensing data. A possible but rather scarcely studied way to circumvent these limitations is to use a masked image modelling based pretraining strategy. In this paper, we introduce Fus-MAE, a self-supervised learning framework based on masked autoencoders that uses cross-attention to perform early and feature-level data fusion between synthetic aperture radar and multispectral optical data - two modalities with a significant domain gap. Our empirical findings demonstrate that Fus-MAE can effectively compete with contrastive learning strategies tailored for SAR-optical data fusion and outperforms other masked-autoencoders frameworks trained on a larger corpus.
AdvSQLi: Generating Adversarial SQL Injections Against Real-World WAF-as-a-Service

As the first defensive layer that attacks would hit, the web application firewall (WAF) plays an indispensable role in defending against malicious web attacks like SQL injection (SQLi). With the development of cloud computing, WAF-as-a-service, as one kind of Security-as-a-service, has been proposed to facilitate the deployment, configuration, and update of WAFs in the cloud. Despite its tremendous popularity, the security vulnerabilities of WAF-as-a-service are still largely unknown, which is highly concerning given its massive usage. In this paper, we propose a general and extendable attack framework, namely AdvSQLi, in which a minimal series of transformations are performed on the hierarchical tree representation of the original SQLi payload, such that the generated SQLi payloads can not only bypass WAF-as-a-service under black-box settings but also keep the same functionality and maliciousness as the original payload. With AdvSQLi, we make it feasible to inspect and understand the security vulnerabilities of WAFs automatically, helping vendors make products more secure. To evaluate the attack effectiveness and efficiency of AdvSQLi, we first employ two public datasets to generate adversarial SQLi payloads, leading to a maximum attack success rate of 100% against state-of-the-art ML-based SQLi detectors. Furthermore, to demonstrate the immediate security threats caused by AdvSQLi, we evaluate the attack effectiveness against 7 WAF-as-a-service solutions from mainstream vendors and find all of them are vulnerable to AdvSQLi. For instance, AdvSQLi achieves an attack success rate of over 79% against the F5 WAF. Through in-depth analysis of the evaluation results, we further condense out several general yet severe flaws of these vendors that cannot be easily patched.
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning

Recent Newton-type federated learning algorithms have demonstrated linear convergence with respect to the communication rounds. However, communicating Hessian matrices is often unfeasible due to their quadratic communication complexity. In this paper, we introduce a novel approach to tackle this issue while still achieving fast convergence rates. Our proposed method, named as Federated Newton Sketch methods (FedNS), approximates the centralized Newton's method by communicating the sketched square-root Hessian instead of the exact Hessian. To enhance communication efficiency, we reduce the sketch size to match the effective dimension of the Hessian matrix. We provide convergence analysis based on statistical learning for the federated Newton sketch approaches. Specifically, our approaches reach super-linear convergence rates w.r. t. the communication rounds for the first time. We validate the effectiveness of our algorithms through various experiments, which coincide with our theoretical findings.
SpatialVisVR: An Immersive, Multiplexed Medical Image Viewer With Contextual Similar-Patient Search

In modern pathology, multiplexed immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) bring both vast opportunities and challenges. These techniques illuminate complex tumor microenvironment interactions, necessitating intuitive visualization tools. With the rise of electronic health records (EHR) and information overload for physicians, integrating advanced technologies becomes essential. Enter SpatialVisVR: a versatile VR platform for comparing medical images, adaptable for data privacy on embedded hardware. Clinicians can capture pathology slides in real-time via mobile, then SpatialVisVR employs a deep learning algorithm to match and display similar mIF images. This interface allows for adding or removing up to 100 multiplexed protein channels, aiding immuno-oncology decisions. Ultimately, SpatialVisVR aims to refine diagnostic processes, promoting a holistic, efficient approach to immuno-oncology research and treatment.
MalModel: Hiding Malicious Payload in Mobile Deep Learning Models with Black-box Backdoor Attack

Mobile malware has become one of the most critical security threats in the era of ubiquitous mobile computing. Despite the intensive efforts from security experts to counteract it, recent years have still witnessed a rapid growth of identified malware samples. This could be partly attributed to the newly-emerged technologies that may constantly open up under-studied attack surfaces for the adversaries. One typical example is the recently-developed mobile machine learning (ML) framework that enables storing and running deep learning (DL) models on mobile devices. Despite obvious advantages, this new feature also inadvertently introduces potential vulnerabilities (e.g., on-device models may be modified for malicious purposes). In this work, we propose a method to generate or transform mobile malware by hiding the malicious payloads inside the parameters of deep learning models, based on a strategy that considers four factors (layer type, layer number, layer coverage and the number of bytes to replace). Utilizing the proposed method, we can run malware in DL mobile applications covertly with little impact on the model performance (i.e., as little as 0.4% drop in accuracy and at most 39ms latency overhead).
Mixing"Magnetic'' and"Electric'' Ehlers--Harrison transformations: The Electromagnetic Swirling Spacetime and Novel Type I Backgrounds

In this paper, we obtain a complete list of stationary and axisymmetric spacetimes, generated from a Minkowski spacetime using the Ernst technique. We do so by operating on the associated seed potentials with a composition of Ehlers and Harrison transformations. In particular, assigning an additional ``electric'' or ``magnetic'' tag to the transformations, we investigate the new spacetimes obtained either via a composition of magnetic Ehlers and Harrison transformations (first part) or via a magnetic-electric combination (second part). In the first part, the resulting type D spacetime, dubbed electromagnetic swirling universe, features key properties, separately found in swirling and (Bonnor--)Melvin spacetimes, the latter recovered in appropriate limits. A detailed analysis of the geometry is included, and subtle issues are addressed. A detailed proof that the spacetime belongs to the Kundt family, is included, and a notable relation to the planar-Reissner-Nordstr\"om-NUT black hole is also meticulously worked out. This relation is further exploited to reverse-engineer the form of the solution in the presence of a nontrivial cosmological constant. A Schwarzschild black hole embedded into the new background is also discussed. In the second part, we present four novel stationary and axisymmetric asymptotically nonflat type I spacetimes, which are naively expected to be extensions of the Melvin or swirling solution including a NUT parameter or electromagnetic charges. We actually find that they are, under conditions, free of curvature and topological singularities, with the physical meaning of the electric transformation parameters in these backgrounds requiring further investigation.
Optimizing Dataflow Systems for Scalable Interactive Visualization

Supporting the interactive exploration of large datasets is a popular and challenging use case for data management systems. Traditionally, the interface and the back-end system are built and optimized separately, and interface design and system optimization require different skill sets that are difficult for one person to master. To enable analysts to focus on visualization design, we contribute VegaPlus, a system that automatically optimizes interactive dashboards to support large datasets. To achieve this, VegaPlus leverages two core ideas. First, we introduce an optimizer that can reason about execution plans in Vega, a back-end DBMS, or a mix of both environments. The optimizer also considers how user interactions may alter execution plan performance, and can partially or fully rewrite the plans when needed. Through a series of benchmark experiments on seven different dashboard designs, our results show that VegaPlus provides superior performance and versatility compared to standard dashboard optimization techniques.
Tackling Electrode Shift In Gesture Recognition with HD-EMG Electrode Subsets

sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across sessions. Multi-channel surface EMG, also referred to as high-density sEMG (HD-sEMG) systems, have been used to improve performance with the information collected through the use of additional electrodes. However, a lack of robustness is ever present due to limited datasets and the difficulties in addressing sources of variability, such as electrode placement. In this study, we propose training on a collection of input channel subsets and augmenting our training distribution with data from different electrode locations, simultaneously targeting electrode shift and reducing input dimensionality. Our method increases robustness against electrode shift and results in significantly higher intersession performance across subjects and classification algorithms.