Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconstruction of regional 3D ocean temperature fields from re- 2 analysis data and real-time satellite and buoy surface measure- 3 ments 4

16 Despite advancements in computational science, nonlinear geophysical processes still present important modeling challenges. Physical sensors (e.g. satellites, AUVs, and buoys) can collect data at specific points, but are often sparse or inaccurate. We present a framework to build improved spatiotemporal models that combine dynamics inferred from high-fidelity models and sensor measurements. We are motivated by a temperature data set from sensors that are only able to make measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) at every ocean surface coordinate to a reanalysis data set of the time-evolving 3D temperature field. Next, a conditionally Gaussian model implemented through a temporal convolutional neural network (TCN) is built to predict the time coefficients of the PCA modes, and their variance, as a function of surface temperature. The 2D surface temperature field is estimated by a multi-fidelity Gaussian process regression scheme, for which buoy data have higher accuracy and precision (higher fidelity) compared to the satellite data. The surface temperature is then used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 3D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ◦ C. Overall, the proposed framework can build less expensive and more accurate conditionally Gaussian models in real time, and can be leveraged for optimal sampling and path planning.
Port Scanning: Techniques, Tools and Detection By Ali Mirza

—In cyber security, it has long been understood that some cyber security procedures can be utilised for malicious purposes as well as cyber defence. The focus of this paper lies on one such procedure called port scanning. In port scanning, vulnerabilities are discovered through searching hosts and networks for open ports and closed ports. In this paper, a thorough analysis of modern port scanning tools, techniques and detection methods is presented. Finally, this paper will discuss and propose an argument for the implementation of one specific port scanning detection technique aside from any other techniques.
Intrusion Detection Systems: Categories, Attack Detection and Response

— Cybercriminals' increasing expertise makes it very difficult to detect intrusions with any degree of precision. Any organisation that does not successfully prevent invasions may face significant repercussions. This study provides an overview of a wide variety of intrusion detection approaches, some of which are Signature-based IDS and Anomaly-based IDS. The report reviews IDS’ history, the dangers of intrusions, various detection methods, the challenges associated with managing IDSs
Ecological Ethics Thinking of Green Economy in the Era of Big Data

The rapid development of information technology marks the arrival of the “era of big data”, but in the era of big data, the ecological ethics of green economy is often ignored. In the era of big data, the distorted ecological ethics of human and nature alienated by the logic of capital still exist, and are highlighted as body-centered and ecological imperialism under the digital existence, which is directly related to the basic ethical issues of “human thinking in green economy” and “green problem governance responsibility”. Facing the two ethical problems of green economy in the era of big data, we should adhere to the stand of Marxist ecological ethics and absorb the essence of western environmental ethics. To be speciﬁc, in the innovation of body paradigm, we should change from “data” to “life”, and in the transformation of responsibility thinking, we should change from “solipsism” to “otherness”.
Cognition Aware Multi-modal Sarcasm Detection

Sarcasm is a complex linguistic construct with incongruity at its very core. Detecting sarcasm depends on the actual content spoken and tonal-ity, facial expressions, the context of an utter-ance, and personal traits like language proficiency and cognitive capabilities. In this paper, we propose the utilization of synthetic gaze data to improve the task performance for multimodal sarcasm detection in a conversational setting. We enrich an existing multimodal conversational dataset, i.e., MUStARD++ with gaze features. With the help of human participants, we collect gaze features for < 20% of data instances, and we investigate various meth-ods for gaze feature prediction for the rest of the dataset. We perform extrinsic and intrinsic evaluations to assess the quality of the predicted gaze features. We observe a performance gain of up to 6.6% points by adding a new modality, i.e., collected gaze features. When both collected and predicted data are used, we observe a performance gain of 2.3% points on the complete dataset. Interestingly, with only predicted gaze features, too, we observe a gain in performance (1.9% points). We retain and use the feature prediction model, which maximally correlates with collected gaze features. Our model trained on combining collected and synthetic gaze data achieves SoTA performance on the MUStARD++ dataset. To the best of our knowledge, ours is the first predict-and-use model for sarcasm detection. We publicly release the code, gaze data, and our best models for further research.
Emergent nearest-neighbor attraction in the fully renormalized interactions of the single-band repulsive Hubbard model at weak coupling

We compute the perturbative expansion for the effective interaction $W$ of the half-filled 2-dimensional Hubbard model. We derive extensions of standard RPA resummations that include arbitrarily high order contributions in the $W_{\uparrow\uparrow}$ and $W_{\uparrow\downarrow}$ basis. Using algorithmic tools we explore the static $Q$-dependent interaction as well as the same-time quantity both in momentum- and real-space. We emphasize the absence of screening in the Hubbard interaction where we find an enhanced repulsive local $W_{\uparrow\downarrow}$ with a non-zero attractive $W_{\uparrow\uparrow}$. Finally, starting from only a locally repulsive bare interaction find an emergent non-local nearest-neighbor attraction for low temperatures at sufficiently large values of $U/t$ which may be key to understanding pairing processes in the model.
Learning Feature Matching via Matchable Keypoint-Assisted Graph Neural Network

Accurately matching local features between a pair of images is a challenging computer vision task. Previous studies typically use attention based graph neural networks (GNNs) with fully-connected graphs over keypoints within/across images for visual and geometric information reasoning. However, in the context of feature matching, considerable keypoints are non-repeatable due to occlusion and failure of the detector, and thus irrelevant for message passing. The connectivity with non-repeatable keypoints not only introduces redundancy, resulting in limited efficiency, but also interferes with the representation aggregation process, leading to limited accuracy. Targeting towards high accuracy and efficiency, we propose MaKeGNN, a sparse attention-based GNN architecture which bypasses non-repeatable keypoints and leverages matchable ones to guide compact and meaningful message passing. More specifically, our Bilateral Context-Aware Sampling Module first dynamically samples two small sets of well-distributed keypoints with high matchability scores from the image pair. Then, our Matchable Keypoint-Assisted Context Aggregation Module regards sampled informative keypoints as message bottlenecks and thus constrains each keypoint only to retrieve favorable contextual information from intra- and inter- matchable keypoints, evading the interference of irrelevant and redundant connectivity with non-repeatable ones. Furthermore, considering the potential noise in initial keypoints and sampled matchable ones, the MKACA module adopts a matchability-guided attentional aggregation operation for purer data-dependent context propagation. By these means, we achieve the state-of-the-art performance on relative camera estimation, fundamental matrix estimation, and visual localization, while significantly reducing computational and memory complexity compared to typical attentional GNNs.
Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data

Synthetic data is emerging as one of the most promising solutions to share individual-level data while safeguarding privacy. While membership inference attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset. This is often seen as a very strong assumption in practice, especially as the proposed main use cases for synthetic tabular data (e.g. medical data, financial transactions) are very specific and don't have any reference datasets directly available. We here show how this assumption can be removed, allowing for MIAs to be performed using only the synthetic data. Specifically, we developed three different scenarios: (S1) Black-box access to the generator, (S2) only access to the released synthetic dataset and (S3) a theoretical setup as upper bound for the attack performance using only synthetic data. Our results show that MIAs are still successful, across two real-world datasets and two synthetic data generators. These results show how the strong hypothesis made when auditing synthetic data releases - access to an auxiliary dataset - can be relaxed, making the attacks more realistic in practice.
Failure of the curvature-dimension condition in sub-Finsler manifolds

The Lott-Sturm-Villani curvature-dimension condition $\mathsf{CD}(K,N)$ provides a synthetic notion for a metric measure space to have curvature bounded from below by $K$ and dimension bounded from above by $N$. It has been recently proved that this condition does not hold in sub-Riemannian geometry for every choice of the parameters $K$ and $N$. In this paper, we extend this result to the context sub-Finsler geometry, showing that the $\mathsf{CD}(K,N)$ condition is not well-suited to characterize curvature in this setting. Firstly, we show that this condition fails in (strict) sub-Finsler manifolds equipped with a smooth strongly convex norm and with a positive smooth measure. Secondly, we focus on the sub-Finsler Heisenberg group, proving that curvature-dimension bounds can not hold also when the reference norm is less regular, in particular when it is of class $C^{1,1}$. The strategy for proving these results is a non-trivial adaptation of the work of Juillet [Rev. Mat. Iberoam. , 37(1):177-188, 2021], and it requires the introduction of new tools and ideas of independent interest. Finally, we demonstrate the failure of the (weaker) measure contraction property $\mathsf{MCP}(K,N)$ in the sub-Finsler Heisenberg group, equipped with a singular strictly convex norm and with a positive smooth measure. This result contrasts with what happens in the \sr Heisenberg group, which instead satisfies $\mathsf{MCP}(0,5)$.
A Versatile Hub Model For Efficient Information Propagation And Feature Selection

Hub structure, characterized by a few highly interconnected nodes surrounded by a larger number of nodes with fewer connections, is a prominent topological feature of biological brains, contributing to efficient information transfer and cognitive processing across various species. In this paper, a mathematical model of hub structure is presented. The proposed method is versatile and can be broadly applied to both computational neuroscience and Recurrent Neural Networks (RNNs) research. We employ the Echo State Network (ESN) as a means to investigate the mechanistic underpinnings of hub structures. Our findings demonstrate a substantial enhancement in performance upon incorporating the hub structure. Through comprehensive mechanistic analyses, we show that the hub structure improves model performance by facilitating efficient information processing and better feature extractions.
Noise reduction for functional time series

A novel method for noise reduction in the setting of curve time series with error contamination is proposed, based on extending the framework of functional principal component analysis (FPCA). We employ the underlying, finite-dimensional dynamics of the functional time series to separate the serially dependent dynamical part of the observed curves from the noise. Upon identifying the subspaces of the signal and idiosyncratic components, we construct a projection of the observed curve time series along the noise subspace, resulting in an estimate of the underlying denoised curves. This projection is optimal in the sense that it minimizes the mean integrated squared error. By applying our method to similated and real data, we show the denoising estimator is consistent and outperforms existing denoising techniques. Furthermore, we show it can be used as a pre-processing step to improve forecasting.
Spatial Organization of Slit-Confined Melts of Ring Polymers with Nonconserved Topology: A Lattice Monte Carlo Study

We present Monte Carlo computer simulations for melts of semiflexible randomly knotted and randomly concatenated ring polymers on the fcc lattice and in slit confinement. Through systematic variation of the slit width at fixed melt density, we explore the influence of confinement on single-chain conformations and interchain interactions. We demonstrate that confinement makes chains globally larger and more elongated while enhancing both contacts and knottedness propensities. As for multichain properties, we show that ring–ring contacts decrease with the confinement, yet neighboring rings overlap more as confinement grows. These aspects are accompanied by a marked decrease in the links formed between pairs of neighboring rings. In connection with the quantitative relation between links and entanglements in polymer melts recently established by us [UbertiniM. A. ; RosaA.Macromolecules2023, 56, 3354–336237181245], we propose that confinement can be used to set polymer networks that act softer under mechanical stress and suggest a viable experimental setup to validate our results.
Self-similar solution for fractional Laplacian in cones

We construct a self-similar solution of the heat equation for the fractional Laplacian with Dirichlet boundary conditions in every fat cone. As applications, we give the Yaglom limit and entrance law for the corresponding killed isotropic stable L\'{e}vy process and precise large-time asymptotics for solutions of the Cauchy problem in the cone.
Vacuum polarisation without infinities

In honour of Detlef D\"urr, we report on a mathematical rigorous computation of the electric vacuum polarisation current and extract the well-known expression for the second order perturbation. Intermediate steps in the presented calculation demonstrate, to the knowledge of the authors for the first time, mathematical rigorous versions of the combined dimensional and Pauli-Villars regularisation schemes. These are employed as computational tools to infer convenient integral representations during the computation. The said second order expression is determined up to a remaining degree of freedom of a real number -- without ill-defined terms from start to end.
Optimal Transmit Antenna Deployment and Power Allocation for Wireless Power Supply in an Indoor Space

As Internet of Things (IoT) devices proliferate, sustainable methods for powering them are becoming indispensable. The wireless provision of power enables battery-free operation and is crucial for complying with weight and size restrictions. For the energy harvesting components of these devices to be small, a high operating frequency is necessary. In conjunction with an electrically large antenna, the receivers may be located in the radiating near-field (Fresnel) region, e.g., in indoor scenarios. In this paper, we propose a wireless power transfer system to ensure a reliable supply of power to an arbitrary number of mobile, low-power, and single-antenna receivers, which are located in a three-dimensional cuboid room. To this end, we formulate a max-min optimisation problem to determine the optimal allocation of transmit power among an infinite number of radiating elements of the system's transmit antenna array. Thereby, the optimal deployment, i.e, the set of transmit antenna positions that are allocated non-zero transmit power according to the optimal allocation, is obtained implicitly. Generally, the set of transmit antenna positions corresponding to the optimal deployment has Lebesgue measure zero and the closure of the set has empty interior. Moreover, for a one-dimensional transmit antenna array, the set of transmit antenna positions is proven to be finite. The proposed optimal solution is validated through simulation. Simulation results indicate that the optimal deployment requires a finite number of transmit antennas and depends on the geometry of the environment and the dimensionality of the transmit antenna array. The robustness of the solution, which is obtained under a line-of-sight (LoS) assumption between the transmitter and receiver, is assessed in an isotropic scattering environment containing a strong LoS component.
Market Making of Options via Reinforcement Learning

Market making of options with different maturities and strikes is a challenging problem due to its high dimensional nature. In this paper, we propose a novel approach that combines a stochastic policy and reinforcement learning-inspired techniques to determine the optimal policy for posting bid-ask spreads for an options market maker who trades options with different maturities and strikes. When the arrival of market orders is linearly inverse to the spreads, the optimal policy is normally distributed.
A renormalization group improvement for thermally resummed effective potential

We propose a novel method for renormalization group improvement of thermally resummed effective potential. In our method, $\beta$-functions are temperature dependent as a consequence of the divergence structure in resummed perturbation theory. In contrast to the ordinary $\overline{\text{MS}}$ scheme, the renormalization group invariance of the resummed finite-temperature effective potential holds order by order, which significantly mitigates a notorious renormalization scale dependence of phase transition quantities such as a critical temperature even at the one-loop order. We also devise a tractable method that enables one to incorporate temperature-dependent higher-order corrections by fully exploiting the renormalization group invariance.
A novel method for fabrication of size-controlled metallic nanoparticles by laser ablation

Time resolved experimental investigation of laser produced plasma-induced shockwaves has been carried out in the presence of confining walls placed along the lateral directions using a Mach Zehnder interferometer in air ambient. Copper was used as target material. The primary and the reflected shock waves and their effects on the evolution of medium density and the plasma density have been studied. The reflected shock wave has been seen to be affecting the shape and density of the plasma plume in the confined geometry. The same experiments were performed with water and isopropyl alcohol as the ambient liquids and the produced nanoparticles were characterised for size and size distribution. Significant differences in the size and size distribution are seen in case of the nanoparticles produced from the ablation of the targets with and without confining boundary. The observed trend has been attributed to the presence of confining boundary and the way it affects the thermalisation time of the plasma plume. The experiments also show the effect of medium density on the mean size of the copper nanoparticles produced.
Monotone twist maps and Dowker-type theorems

Given a planar oval, consider the maximal area of inscribed $n$-gons resp. the minimal area of circumscribed $n$-gons. One obtains two sequences indexed by $n$, and one of Dowker's theorems states that the first sequence is concave and the second is convex. In total, there are four such classic results, concerning areas resp. perimeters of inscribed resp. circumscribed polygons, due to Dowker, Moln\'ar, and Eggleston. We show that these four results are all incarnations of the convexity property of Mather's $\beta$-function (the minimal average action function) of the respective billiard-type systems. We then derive new geometric inequalities of similar type for various other billiard system. Some of these billiards have been thoroughly studied, and some are novel. Moreover, we derive new inequalities (even for conventional billiards) for higher rotation numbers.
Filter bubbles in recommender systems: Fact or fallacy—A systematic review

A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems (RSs). This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in RSs. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in RSs. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in RSs, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and RSs. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.
Android Malware Detection using Machine learning: A Review

Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning(ML) techniques have been shown to be effective at detecting malware for Android, a comprehensive analysis of the methods used is required. We review the current state of Android malware detection us ing machine learning in this paper. We begin by providing an overview of Android malware and the security issues it causes. Then, we look at the various supervised, unsupervised, and deep learning machine learning approaches that have been utilized for Android malware detection. Addi tionally, we present a comparison of the performance of various Android malware detection methods and talk about the performance evaluation metrics that are utilized to evaluate their efficacy. Finally, we draw atten tion to the drawbacks and difficulties of the methods that are currently in use and suggest possible future directions for research in this area. In addition to providing insights into the current state of Android malware detection using machine learning, our review provides a comprehensive overview of the subject.