Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response

An intrusion detection system (IDS) perform postcompromise detection of security breaches whenever preventive measures such as firewalls do not avert an attack. However, these systems raise a vast number of alerts that must be analyzed and triaged by security analysts. This process is largely manual, tedious, and time-consuming. Alert correlation is a technique that reduces the number of intrusion alerts by aggregating alerts that are similar in some way. However, the correlation is performed outside the IDS through third-party systems and tools, after the IDS has already generated a high volume of alerts. These third-party systems add to the complexity of security operations. In this paper, we build on the highly researched area of alert and event correlation by developing a novel hierarchical event correlation model that promises to reduce the number of alerts issued by an intrusion detection system. This is achieved by correlating the events before the IDS classifies them. The proposed model takes the best features from similarity and graph-based correlation techniques to deliver an ensemble capability not possible by either approach separately. Further, we propose a correlation process for events rather than alerts as is the case in the current art. We further develop our own correlation and clustering algorithm which is tailor-made to the correlation and clustering of network event data. The model is implemented as a proof of concept with experiments run on standard intrusion detection sets. The correlation achieves an 87% data reduction through aggregation, producing nearly 21,000 clusters in about 30 s.
Scalable and Independent Learning of Nash Equilibrium Policies in n-Player Stochastic Games with Unknown Independent Chains

We study a subclass of $n$-player stochastic games, namely, stochastic games with independent chains and unknown transition matrices. In this class of games, players control their own internal Markov chains whose transitions do not depend on the states/actions of other players. However, players' decisions are coupled through their payoff functions. We assume players can receive only realizations of their payoffs, and that the players can not observe the states and actions of other players, nor do they know the transition probability matrices of their own Markov chain. Relying on a compact dual formulation of the game based on occupancy measures and the technique of confidence set to maintain high-probability estimates of the unknown transition matrices, we propose a fully decentralized mirror descent algorithm to learn an $\epsilon$-NE for this class of games. The proposed algorithm has the desired properties of independence, scalability, and convergence. Specifically, under no assumptions on the reward functions, we show the proposed algorithm converges in polynomial time in a weaker distance (namely, the averaged Nikaido-Isoda gap) to the set of $\epsilon$-NE policies with arbitrarily high probability. Moreover, assuming the existence of a variationally stable Nash equilibrium policy, we show that the proposed algorithm converges asymptotically to the stable $\epsilon$-NE policy with arbitrarily high probability. In addition to Markov potential games and linear-quadratic stochastic games, this work provides another subclass of $n$-player stochastic games that, under some mild assumptions, admit polynomial-time learning algorithms for finding their stationary $\epsilon$-NE policies.
The cohomology of homogeneous spaces in historical context

The real singular cohomology ring of a homogeneous space $G/K$, interpreted as the real Borel equivariant cohomology $H^*_K(G)$, was historically the first computation of equivariant cohomology of any nontrivial connected group action. After early approaches using the Cartan model for equivariant cohomology with real coefficients and the Serre spectral sequence, post-1962 work computing the groups and rings $H^*(G/K)$ and $H^*_H(G/K)$ with more general coefficient rings motivated the development of minimal models in rational homotopy theory, the Eilenberg-Moore spectral sequence, and A-infinity algebras. In this essay, we survey the history of these ideas and the associated results.
Persistent Directed Flag Laplacian

Topological data analysis (TDA) has had enormous success in science and engineering in the past decade. Persistent topological Laplacians (PTLs) overcome some limitations of persistent homology, a key technique in TDA, and provide substantial insight to the behavior of various geometric and topological objects. This work extends PTLs to directed flag complexes, which are an exciting generalization to flag complexes, also known as clique complexes, that arise naturally in many situations. We introduce the directed flag Laplacian and show that the proposed persistent directed flag Laplacian (PDFL) is a distinct way of analyzing these flag complexes. Example calculations are provided to demonstrate the potential of the proposed PDFL in real world applications.
Radio Jet Feedback on the Inner Disk of Virgo Spiral Galaxy Messier 58

Spitzer spectral maps reveal a disk of highly luminous, warm (>150 K) H2 in the center of the massive spiral galaxy Messier 58, which hosts a radio-loud active galactic nucleus (AGN). The inner 2.6 kpc of the galaxy appears to be overrun by shocks from the radio jet cocoon. Gemini NIRI imaging of the H2 1–0 S(1) emission line, Atacama Large Millimeter/submillimeter Array CO 2–1, and Hubble Space Telescope multiband imagery indicate that much of the molecular gas is shocked in situ, corresponding to lanes of dusty molecular gas that spiral toward the galactic nucleus. The CO 2–1 and ionized gas kinematics are highly disturbed, with velocity dispersion up to 300 km s−1. Dissipation of the associated kinetic energy and turbulence, likely injected into the interstellar medium by radio-jet-driven outflows, may power the observed molecular and ionized gas emission from the inner disk. The polycyclic aromatic hydrocarbon (PAH) fraction and composition in the inner disk appear to be normal, in spite of the jet and AGN activity. The PAH ratios are consistent with excitation by the interstellar radiation field from old stars in the bulge, with no contribution from star formation. The phenomenon of jet-shocked H2 may substantially reduce star formation and help to regulate the stellar mass of the inner disk and supermassive black hole in this otherwise normal spiral galaxy. Similarly strong H2 emission is found at the centers of several nearby spiral and lenticular galaxies with massive bulges and radio-loud AGNs.
Joint User Association and Resource Allocation for Multi-Cell Networks with Adaptive Semantic Communication

Semantic communication is a promising communication paradigm that utilizes Deep Neural Networks (DNNs) to extract the information relevant to downstream tasks, hence significantly reducing the amount of transmitted data. In current practice, the semantic communication transmitter for a specific task is typically pre-trained and shared by all users. However, due to user heterogeneity, it is desirable to use different transmitters according to the available computational and communication resources of users. In this paper, we first show that it is possible to dynamically adjust the computational and communication overhead of DNN-based transmitters, thereby achieving adaptive semantic communication. After that, we investigate the user association and resource allocation problem in a multi-cell network where users are equipped with adaptive semantic communication transmitters. To solve this problem, we decompose it into three subproblems involving the scheduling of each user, the resource allocation of each base station (BS), and the user association between users and BSs. Then we solve each problem progressively based on the solution of the previous subproblem. The final algorithm can obtain near-optimal solutions in polynomial time. Numerical results show that our algorithm outperforms benchmarks under various situations.
Localizing and Assessing Node Significance in Default Mode Network using Sub-Community Detection in Mild Cognitive Impairment

Our study aims to utilize fMRI to identify the affected brain regions within the Default Mode Network (DMN) in subjects with Mild Cognitive Impairment (MCI), using a novel Node Significance Score (NSS). We construct subject-specific DMN graphs by employing partial correlation of Regions of Interest (ROIs) that make-up the DMN. For the DMN graph, ROIs are the nodes and edges are determined based on partial correlation. Four popular community detection algorithms (Clique Percolation Method (CPM), Louvain algorithm, Greedy Modularity and Leading Eigenvectors) are applied to determine the largest sub-community. NSS ratings are derived for each node, considering (I) frequency in the largest sub-community within a class across all subjects and (II) occurrence in the largest sub-community according to all four methods. After computing the NSS of each ROI in both healthy and MCI subjects, we quantify the score disparity to identify nodes most impacted by MCI. The results reveal a disparity exceeding 20% for 10 DMN nodes, maximally for PCC and Fusiform, showing 45.69% and 43.08% disparity. This aligns with existing medical literature, additionally providing a quantitative measure that enables the ordering of the affected ROIs. These findings offer valuable insights and could lead to treatment strategies aggressively targeting the affected nodes.
Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model

Both graph structures and textual information play a critical role in Knowledge Graph Completion (KGC). With the success of Pre-trained Language Models (PLMs) such as BERT, they have been applied for text encoding for KGC. However, the current methods mostly prefer to fine-tune PLMs, leading to huge training costs and limited scalability to larger PLMs. In contrast, we propose to utilize prompts and perform KGC on a frozen PLM with only the prompts trained. Accordingly, we propose a new KGC method named PDKGC with two prompts -- a hard task prompt which is to adapt the KGC task to the PLM pre-training task of token prediction, and a disentangled structure prompt which learns disentangled graph representation so as to enable the PLM to combine more relevant structure knowledge with the text information. With the two prompts, PDKGC builds a textual predictor and a structural predictor, respectively, and their combination leads to more comprehensive entity prediction. Solid evaluation on two widely used KGC datasets has shown that PDKGC often outperforms the baselines including the state-of-the-art, and its components are all effective. Our codes and data are available at https://github.com/genggengcss/PDKGC.
Distributed Bayesian Estimation in Sensor Networks: Consensus on Marginal Densities

In this paper, we aim to design and analyze distributed Bayesian estimation algorithms for sensor networks. The challenges we address are to (i) derive a distributed provably-correct algorithm in the functional space of probability distributions over continuous variables, and (ii) leverage these results to obtain new distributed estimators restricted to subsets of variables observed by individual agents. This relates to applications such as cooperative localization and federated learning, where the data collected at any agent depends on a subset of all variables of interest. We present Bayesian density estimation algorithms using data from non-linear likelihoods at agents in centralized, distributed, and marginal distributed settings. After setting up a distributed estimation objective, we prove almost-sure convergence to the optimal set of pdfs at each agent. Then, we prove the same for a storage-aware algorithm estimating densities only over relevant variables at each agent. Finally, we present a Gaussian version of these algorithms and implement it in a mapping problem using variational inference to handle non-linear likelihood models associated with LiDAR sensing.
Rejuvenating image-GPT as Strong Visual Representation Learners

This paper enhances image-GPT (iGPT), one of the pioneering works that introduce autoregressive pretraining to predict next pixels for visual representation learning. Two simple yet essential changes are made. First, we shift the prediction target from raw pixels to semantic tokens, enabling a higher-level understanding of visual content. Second, we supplement the autoregressive modeling by instructing the model to predict not only the next tokens but also the visible tokens. This pipeline is particularly effective when semantic tokens are encoded by discriminatively trained models, such as CLIP. We introduce this novel approach as D-iGPT. Extensive experiments showcase that D-iGPT excels as a strong learner of visual representations: A notable achievement of D-iGPT is its compelling performance on the ImageNet-1K dataset -- by training on publicly available datasets, D-iGPT achieves 89.5\% top-1 accuracy with a vanilla ViT-Large model. This model also shows strong generalization on the downstream task and robustness on out-of-distribution samples. Code is avaiable at \href{https://github.com/OliverRensu/D-iGPT}{https://github.com/OliverRensu/D-iGPT}.
VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence

Current diffusion-based video editing primarily focuses on structure-preserved editing by utilizing various dense correspondences to ensure temporal consistency and motion alignment. However, these approaches are often ineffective when the target edit involves a shape change. To embark on video editing with shape change, we explore customized video subject swapping in this work, where we aim to replace the main subject in a source video with a target subject having a distinct identity and potentially different shape. In contrast to previous methods that rely on dense correspondences, we introduce the VideoSwap framework that exploits semantic point correspondences, inspired by our observation that only a small number of semantic points are necessary to align the subject's motion trajectory and modify its shape. We also introduce various user-point interactions (\eg, removing points and dragging points) to address various semantic point correspondence. Extensive experiments demonstrate state-of-the-art video subject swapping results across a variety of real-world videos.
Expected hitting time estimates on finite graphs

The expected hitting time from vertex $a$ to vertex $b$, $H(a,b)$, is the expected value of the time it takes a random walk starting at $a$ to reach $b$. In this paper, we give estimates for $H(a,b)$ when the distance between $a$ and $b$ is comparable to the diameter of the graph, and the graph satisfies a Harnack condition. We show that, in such cases, $H(a,b)$ can be estimated in terms of the volumes of balls around $b$. Using our results, we estimate $H(a,b)$ on various graphs, such as rectangular tori, some convex traces in $\mathbb{Z}^d$, and fractal graphs. Our proofs use heat kernel estimates.
Datos preliminares sobre el cultivo de larvas y juveniles de centolla Maja squinado Herbst, 1788 en el Centro Oceanográfico de Vigo del Instituto Español de Oceanografía

Preliminary data on rearing larvae and juveniles of the spider crab Maja squinado Herbst, 1788 at the Instituto Espanol de Oceanografia Vigo Oceanographic Centre (northwest Spain) Preliminary data on reproduction, larval culture and growth of juveniles of spider crab Maja squinado, Herbst 1788 are given. Data were obtained from culture trials carried out during 2000 and 2001 at the Instituto Espanol de Oceanografia (IEO) in Vigo (northwest Spain). Each female spawned 3 to 5 times. Larval culture was carried out using intensive and semi-extensive systems; in the case of the former, the larvae survival rate ranged from 8-13 % during the first month of life. As to the semi-extensive system, the growth rate of 50-day-old juveniles was high, reaching a carapace length of 65.48 ± 7.53 mm, with a survival rate of 66 % at 9.5 months. However, at the same age, those from the intensive system had reached only 32.13 ± 7.86 mm, with a mortality of 100 %.
A Challenging Multimodal Video Summary: Simultaneously Extracting and Generating Keyframe-Caption Pairs from Video

This paper proposes a practical multimodal video summarization task setting and a dataset to train and evaluate the task. The target task involves summarizing a given video into a predefined number of keyframe-caption pairs and displaying them in a listable format to grasp the video content quickly. This task aims to extract crucial scenes from the video in the form of images (keyframes) and generate corresponding captions explaining each keyframe's situation. This task is useful as a practical application and presents a highly challenging problem worthy of study. Specifically, achieving simultaneous optimization of the keyframe selection performance and caption quality necessitates careful consideration of the mutual dependence on both preceding and subsequent keyframes and captions. To facilitate subsequent research in this field, we also construct a dataset by expanding upon existing datasets and propose an evaluation framework. Furthermore, we develop two baseline systems and report their respective performance.
Second-Order Uncertainty Quantification: A Distance-Based Approach

In the past couple of years, various approaches to representing and quantifying different types of predictive uncertainty in machine learning, notably in the setting of classification, have been proposed on the basis of second-order probability distributions, i.e., predictions in the form of distributions on probability distributions. A completely conclusive solution has not yet been found, however, as shown by recent criticisms of commonly used uncertainty measures associated with second-order distributions, identifying undesirable theoretical properties of these measures. In light of these criticisms, we propose a set of formal criteria that meaningful uncertainty measures for predictive uncertainty based on second-order distributions should obey. Moreover, we provide a general framework for developing uncertainty measures to account for these criteria, and offer an instantiation based on the Wasserstein distance, for which we prove that all criteria are satisfied.
Thresholds for patterns in random permutations

We explore how the asymptotic structure of a random permutation of $[n]$ with $m$ inversions evolves, as $m$ increases, establishing thresholds for the appearance and disappearance of any classical, consecutive or vincular pattern. The threshold for the appearance of a classical pattern depends on the greatest number of inversions in any of its sum indecomposable components.
New aeromagnetic and gravity compilations from Norway and adjacent areas: methods and applications

Abstract The Geological Survey of Norway (NGU) has produced new aeromagnetic and gravity maps from Norway and adjacent areas, compiled from ground, airborne and satellite data. Petrophysical measurements on core samples, hand specimens and on in situ bedrock exposures are essential for the interpretation of these maps. Onshore, the most prominent gravity and magnetic anomalies are attributed to lower crustal rocks that have been brought closer to the surface. The asymmetry of the gravity anomalies along the Lapland Granulite Belt and Kongsberg–Bamble Complex, combined with the steep gradient, points to the overthrusted high-density granulites as being the main source of the observed anomalies. The Kongsberg–Bamble anomaly can be traced southwards through the Kattegat to southern Sweden. This concept of gravity field modelling can also be applied to the Mid-Norwegian continental shelf and could partially explain the observed high-density rocks occurring below the More and Voring basins and in the Lofoten area. Extrapolations of Late-Caledonian detachment structures occurring on the mainland can be traced on aeromagnetic and gravimetric images towards the NW across the continental margin. Subcropping Late Palaeozoic to Cenozoic sedimentary units along the mid-Norwegian coast produce a conspicuous magnetic anomaly pattern. The asymmetry of the low-amplitude anomalies, with a steep gradient and a negative anomaly to the east and a gentler gradient to the west, relates the anomalies to gently westward dipping strata. Recent aeromagnetic surveys in the Barents Sea have revealed negative magnetic anomalies associated with shallow salt diapirs. Buried Quaternary channels partly filled with gravel and boulders of crystalline rocks generate magnetic anomalies in the North Sea. The new maps also show that the opening of the Norwegian–Greenland Sea occurred along stable continental margins without offsets across minor fracture zones, or involving jumps in the spreading axis. A triple junction formed at 48 Ma between the Lofoten and Norway Basins.
Steerers: A framework for rotation equivariant keypoint descriptors

Image keypoint descriptions that are discriminative and matchable over large changes in viewpoint are vital for 3D reconstruction. However, descriptions output by learned descriptors are typically not robust to camera rotation. While they can be made more robust by, e.g., data augmentation, this degrades performance on upright images. Another approach is test-time augmentation, which incurs a significant increase in runtime. We instead learn a linear transform in description space that encodes rotations of the input image. We call this linear transform a steerer since it allows us to transform the descriptions as if the image was rotated. From representation theory we know all possible steerers for the rotation group. Steerers can be optimized (A) given a fixed descriptor, (B) jointly with a descriptor or (C) we can optimize a descriptor given a fixed steerer. We perform experiments in all of these three settings and obtain state-of-the-art results on the rotation invariant image matching benchmarks AIMS and Roto-360. We publish code and model weights at github.com/georg-bn/rotation-steerers.
Global bifurcation for Paneitz type equations and constant Q-curvature metrics

We consider the Paneitz-type equation $\Delta^2 u -\alpha \Delta u +\beta (u-u^q ) =0$ on a closed Riemannian manifold $(M,g)$. We reduce the equation to a fourth-order ordinary differential equation assuming that $(M,g)$ admits a proper isoparametric function. Assuming that $\alpha$ and $\beta$ are positive and $\alpha^2>4\beta$, we prove that the global nonconstant solutions of this ordinary differential equation only has nondegenerate critical points. Applying global bifurcation theory we prove multiplicity results for positive solutions of the equation. As an application and motivation we prove multiplicity results for conformal constant $Q$-curvature metrics. For example, consider closed positive Einstein manifolds $(M^n ,g )$ and $(X^m , h)$ of dimensions $n,m \geq 3$. Assuming that $M$ admits a proper isoparametric function (with a symmetry condition) we prove that as $\delta>0$ gets closer to 0, the number of constant $Q$-curvature metrics conformal to $g_{\delta} = g+\delta h$ goes to infinity.