Miracle-dz/UniFAR-FTUs huggingface dataset:
GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation Learning

Penetration testing (PT) is an efﬁcient network testing and vulnerability mining tool by simulating a hacker’s attack for valuable information applied in some areas, e.g., the host’s operating and database systems. Compared with manual PT, intelligent PT has become a dominating mainstream due to less time-consuming and lower labor costs. Among the already proposed intelligent PT methods, reinforcement learning (RL) based PT has achieved state-of-the-art (SOTA) performance. Unfortunately, RL-based PT is still challenged in real exploitation scenarios because the agent’s action space is usually high-dimensional discrete, thus leading to algorithm convergence difﬁculty. Besides, most PT methods still rely on the decisions of security experts. Addressing the challenges, for the ﬁrst time, we introduce expert knowledge to guide the agent to make better decisions in RL-based PT, and propose a Generative Adversarial Imitation Learning based generic intelligent Penetration Testing framework , denoted as GAIL-PT, to solve the problems of higher labor costs due to the involvement of security experts and high-dimensional discrete action space. Speciﬁcally, ﬁrst, we manually collect the state-action pairs to construct an expert knowledge base when the pre-trained RL / DRL model executes successful penetration testings. Second, we input the expert knowledge and the state-action pairs generated online by the different RL / DRL models into the discriminator of GAIL for training. At last, we apply the output reward of the discriminator to guide the agent to perform the action with a higher penetration success rate to improve PT’s performance. Extensive experiments conducted on the real target host and simulated network scenarios show that GAIL-PT achieves SOTA penetration performance against DeepExploit in exploiting actual target Metasploitable2 and Q-learning in optimizing penetration path, not only in small-scale with or without honey-pot network environments, but also in the large-scale simulated network environment. The code of GAIL-PT is open-sourced at https://github.com/Shulong98/GAIL-PT// .
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games

We propose a multi-agent distributed reinforcement learning algorithm that balances between potentially conflicting short-term reward and sparse, delayed long-term reward, and learns with partial information in a dynamic environment. We compare different long-term rewards to incentivize the algorithm to maximize individual payoff and overall social welfare. We test the algorithm in two simulated auction games, and demonstrate that 1) our algorithm outperforms two benchmark algorithms in a direct competition, with cost to social welfare, and 2) our algorithm's aggressive competitive behavior can be guided with the long-term reward signal to maximize both individual payoff and overall social welfare.
Forward Signal Propagation Learning

—We propose a new learning algorithm for propagating a learning signal and updating neural network parameters via a forward pass, as an alternative to backpropagation. In forward signal propagation learning (sigprop), there is only the forward path for learning and inference, so there are no additional structural or computational constraints on learning, such as feedback connectivity, weight transport, or a backward pass, which exist under backpropagation. Sigprop enables global supervised learning with only a forward path. This is ideal for parallel training of layers or modules. In biology, this explains how neurons without feedback connections can still receive a global learning signal. In hardware, this provides an approach for global supervised learning without backward connectivity. Sigprop by design has better compatibility with models of learning in the brain and in hardware than backpropagation and alternative approaches to relaxing learning constraints. We also demonstrate that sigprop is more efﬁcient in time and memory than they are. To further explain the behavior of sigprop, we provide evidence that sigprop provides useful learning signals in context to backpropagation. To further support relevance to biological and hardware learning, we use sigprop to train continuous time neural networks with Hebbian updates and train spiking neural networks without surrogate functions.
Acute Sensitivity of Global Ocean Circulation and Heat Content to Eddy Energy Dissipation Timescale

The global ocean overturning circulation, critically dependent on the global density stratification, plays a central role in regulating climate evolution. While it is well known that the global stratification profile exhibits a strong dependence to Southern Ocean dynamics and in particular to wind and buoyancy forcing, we demonstrate here that the stratification is also acutely sensitive to the mesoscale eddy energy dissipation timescale. Within the context of a global ocean circulation model with an energy constrained mesoscale eddy parameterization, it is shown that modest variations in the eddy energy dissipation timescale lead to significant variations in key metrics relating to ocean circulation, namely the Antarctic Circumpolar Current transport, Atlantic Meridional Overturning Circulation strength, and global ocean heat content, over long timescales. The results highlight a need to constrain uncertainties associated with eddy energy dissipation for climate model projections over centennial timescales and also for paleoclimate simulations over millennial timescales.
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning

As automatic speech recognition (ASR) systems are now being widely deployed in the wild, the increasing threat of adversarial attacks raises serious questions about the security and reliability of using such systems. On the other hand, multi-task learning (MTL) has shown success in training models that can resist adversarial attacks in the computer vision domain. In this work, we investigate the impact of performing such multi-task learning on the adversarial robustness of ASR models in the speech domain. We conduct extensive MTL experimentation by combining semantically diverse tasks such as accent classification and ASR, and evaluate a wide range of adversarial settings. Our thorough analysis reveals that performing MTL with semantically diverse tasks consistently makes it harder for an adversarial attack to succeed. We also discuss in detail the serious pitfalls and their related remedies that have a significant impact on the robustness of MTL models. Our proposed MTL approach shows considerable absolute improvements in adversarially targeted WER ranging from 17.25 up to 59.90 compared to single-task learning baselines (attention decoder and CTC respectively). Ours is the first in-depth study that uncovers adversarial robustness gains from multi-task learning for ASR.
Anisotropic tomography of heavy quark dissociation by using the general propagator structure in a finite magnetic field

In this work we have explored the imaginary part of the Heavy Quark (HQ) potential and subsequently the dissociation of heavy quarkonia at ﬁnite temperature and magnetic ﬁeld. With respect to earlier investigations on this topic, present work contain three new ingredients. First one is considering all Landau level summation, for which present work can be applicable in entire magnetic ﬁeld domain - from weak to strong. Second one is the general structure of the gauge boson propagator in a hot magnetized medium, which is used here in heavy quark potential problem ﬁrst time. Third one is a rich anisotropic structure of the complex heavy quark potential, which explicitly depends on the longitudinal and transverse distance. By comparing with earlier references, we have attempted to display our new contributions by plotting heavy quark potential tomography and dissociation probability at ﬁnite temperature and magnetic ﬁeld.
The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19 Misinformation

During the COVID-19 pandemic, large amounts of COVID-19 misinformation are spreading on social media. We are interested in the stance of Twitter users towards COVID-19 misinformation. However, due to the relative recent nature of the pandemic, only a few stance detection datasets fit our task. We have constructed a new stance dataset consisting of 2631 tweets annotated with the stance towards COVID-19 misinformation. In contexts with limited labeled data, we fine-tune our models by leveraging the MNLI dataset and two existing stance detection datasets (RumourEval and COVIDLies), and evaluate the model performance on our dataset. Our experimental results show that the model performs the best when fine-tuned sequentially on the MNLI dataset and the combination of the undersampled RumourEval and COVIDLies datasets. Our code and dataset are publicly available at https://github.com/yanfangh/covid-rumor-stance
Probabilistic Embeddings with Laplacian Graph Priors

We introduce probabilistic embeddings using Laplacian priors (PELP). The proposed model enables incorporating graph side-information into static word embeddings. We theoretically show that the model unifies several previously proposed embedding methods under one umbrella. PELP generalises graph-enhanced, group, dynamic, and cross-lingual static word embeddings. PELP also enables any combination of these previous models in a straightforward fashion. Furthermore, we empirically show that our model matches the performance of previous models as special cases. In addition, we demonstrate its flexibility by applying it to the comparison of political sociolects over time. Finally, we provide code as a TensorFlow implementation enabling flexible estimation in different settings.
The Probabilistic Normal Epipolar Constraint for Frame- To-Frame Rotation Optimization under Uncertain Feature Positions

The estimation of the relative pose of two camera views is a fundamental problem in computer vision. Kneip et al. proposed to solve this problem by introducing the normal epipolar constraint (NEC). However, their approach does not take into account uncertainties, so that the accuracy of the estimated relative pose is highly dependent on accurate feature positions in the target frame. In this work, we introduce the probabilistic normal epipolar constraint (PNEC) that overcomes this limitation by accounting for anisotropic and inhomogeneous uncertainties in the feature positions. To this end, we propose a novel objective function, along with an efficient optimization scheme that effectively minimizes our objective while maintaining real-time performance. In experiments on synthetic data, we demonstrate that the novel PNEC yields more accurate rotation estimates than the original NEC and several popular relative rotation estimation algorithms. Furthermore, we integrate the proposed method into a state-of-the-art monocular rotation-only odometry system and achieve consistently improved results for the real-world KITTI dataset.
Leveraging Speech Separation for Conversational Telephone Speaker Diarization

Speech separation and speaker diarization have strong simi-larities. In particular with respect to end-to-end neural diarization (EEND) methods. Separation aims at extracting each speaker from overlapped speech, while diarization identiﬁes time boundaries of speech segments produced by the same speaker. In this paper, we carry out an analysis of the use of speech separation guided diarization (SSGD) where diarization is performed simply by separating the speakers signals and applying voice activity detection. In particular we compare two speech separation (SSep) models, both in ofﬂine and online set-tings. In the online setting we consider both the use of continuous source separation (CSS) and causal SSep models ar-chitectures. As an additional contribution, we show a simple post-processing algorithm which reduces signiﬁcantly the false alarm errors of a SSGD pipeline. We perform our experiments on Fisher Corpus Part 1 and CALLHOME datasets evaluating both separation and diarization metrics. Notably, without ﬁne-tuning, our SSGD DPRNN-based online model achieves 12 . 7 % DER on CALLHOME, comparable with state-of-the-art EEND models despite having considerably lower latency, i.e., 50 ms vs 1 s.
Spatial-temporal distortions and calibration of ultrashort pulses in complex optical systems

Ultrashort laser pulse has been widely used in various applications. Its parameters, such as the pulse duration and the spectral bandwidth, should be controlled accurately in order to achieve high spatial and temporal resolution, as well as high local field intensity. In this paper, we have proposed a method to trace the propagation of ultrashort pulses through optical systems, especially the complex optics. The approach, in which both the material’s dispersion and optical aberrations are taken into consideration, is developed based on the geometrical ray-tracing combined with wave theories. This method is validated by simulating the propagation of a femtosecond pulse through a specific practical imaging system. As the numerical result shows that the spatial-temporal performances of pulses are influenced greatly by optical elements, the calibration arrangement is employed to compensate for those undesired distortions. The negative dispersion of the optical grisms (the combination of gratings and prisms) is utilized in the calibration process to offset the positive dispersion introduced by lenses. The final result shows effectiveness of the correction.
Semi-Nonparametric Estimation of Distribution Divergence in Non-Euclidean Spaces

This paper explores methods for estimating or approximating the total variation distance and the chi-squared divergence of probability measures within topological sample spaces, using independent and identically distributed samples. Our focus is on the practical scenario where the sample space is homeomorphic to subsets of Euclidean space, with the specific homeomorphism remaining unknown. Our proposed methods rely on the integral probability metric with witness functions in universal reproducing kernel Hilbert spaces (RKHSs). The estimators we develop consist of learnable parametric functions mapping the sample space to Euclidean space, paired with universal kernels defined in Euclidean space. This approach effectively overcomes the challenge of constructing universal kernels directly on non-Euclidean spaces. Furthermore, the estimators we devise demonstrate asymptotic consistency, and we provide a detailed statistical analysis, shedding light on their practical implementation.
Modular bootstrap for D4-D2-D0 indices on compact Calabi-Yau threefolds

We investigate the modularity constraints on the generating series $h_r(\tau)$ of BPS indices counting D4-D2-D0 bound states with fixed D4-brane charge $r$ in type IIA string theory compactified on complete intersection Calabi-Yau threefolds with $b_2 = 1$. For unit D4-brane, $h_1$ transforms as a (vector-valued) modular form under the action of $SL(2,Z)$ and thus is completely determined by its polar terms. We propose an Ansatz for these terms in terms of rank 1 Donaldson-Thomas invariants, which incorporates contributions from a single D6-anti-D6 pair. Using an explicit overcomplete basis of the relevant space of weakly holomorphic modular forms (valid for any $r$), we find that for 10 of the 13 allowed threefolds, the Ansatz leads to a solution for $h_1$ with integer Fourier coefficients, thereby predicting an infinite series of DT invariants.For $r>1$, $h_r$ is mock modular and determined by its polar part together with its shadow. Restricting to $r = 2$, we use the generating series of Hurwitz class numbers to construct a series $h^{an}_2$ with exactly the same modular anomaly as $h_2$, so that the difference $h_{2}-h^{an}_2$ is an ordinary modular form fixed by its polar terms. For lack of a satisfactory Ansatz, we leave the determination of these polar terms as an open problem.
Streaming Facility Location in High Dimension via Geometric Hashing

In Euclidean Uniform Facility Location, the input is a set of clients in $\mathrm{R}^{d}$ and the goal is to place facilities to serve them, so as to minimize the total cost of opening facilities plus connecting the clients. We study the classical setting of dynamic geometric streams, where the clients are presented as a sequence of insertions and deletions of points in the grid $\{1,ldots\,\Delta \}^{d}$, and we focus on the high-dimensional regime, where the algorithm’s space complexity must be polynomial (and certainly not exponential) in $d \cdot \log \Delta$. We present a new algorithmic framework, based on importance sampling from the stream, for $O(1)$-approximation of the optimal cost using only poly $(d\cdot\log\Delta)$ space. This framework is easy to implement in two passes, one for sampling points and the other for estimating their contribution. Over random-order streams, we can extend this to a one-pass algorithm by using the two halves of the stream separately. Our main result, for arbitrary-order streams, computes $O(d^{1.5})$-approximation in one pass by using the new framework but combining the two passes differently. This improves upon previous algorithms that either need space exponential in d or only guarantee $O(d\cdot\log^{2}\Delta)$-approximation, and therefore our algorithms for high-dimensional streams are the first to avoid the $O(\log\Delta)$ factor in approximation that is inherent to the widely-used quadtree decomposition. Our improvement is achieved by employing a geometric hashing scheme that maps points in $\mathbb{R}^{d}$ into buckets of bounded diameter, with the key property that every point set of small-enough diameter is hashed into at most poly $(d)$ distinct buckets.Finally, we complement our results with a proof that every streaming 1.085-approximation algorithm requires space exponential in poly $(d \cdot log \Delta)$, even for insertion-only streams.
Optical Ultracompact Directional Antennas Based on a Dimer Nanorod Structure

Controlling directionality of optical emitters is of utmost importance for their application in communication and biosensing devices. Metallic nanoantennas have been proven to affect both excitation and emission properties of nearby emitters, including the directionality of their emission. In this regard, optical directional nanoantennas based on a Yagi–Uda design have been demonstrated in the visible range. Despite this impressive proof of concept, their overall size (~λ2/4) and considerable number of elements represent obstacles for the exploitation of these antennas in nanophotonic applications and for their incorporation onto photonic chips. In order to address these challenges, we investigate an alternative design. In particular, we numerically study the performance of a recently demonstrated “ultracompact” optical antenna based on two parallel gold nanorods arranged as a side-to-side dimer. Our results confirm that the excitation of the antiphase mode of the antenna by a nanoemitter placed in its near-field can lead to directional emission. Furthermore, in order to verify the feasibility of this design and maximize the functionality, we study the effect on the directionality of several parameters, such as the shape of the nanorods, possible defects in the dimer assembly, and different positions and orientations of the nanoemitter. We conclude that this design is robust to structural variations, making it suitable for experimental upscaling.
Data‐Driven Stochastic Lie Transport Modeling of the 2D Euler Equations

In this paper, we propose and assess several stochastic parametrizations for data‐driven modeling of the two‐dimensional Euler equations using coarse‐grid SPDEs. The framework of Stochastic Advection by Lie Transport (SALT) (Cotter et al., 2019, https://doi.org/10.1137/18m1167929) is employed to define a stochastic forcing that is decomposed in terms of a deterministic basis (empirical orthogonal functions, EOFs) multiplied by temporal traces, here regarded as stochastic processes. The EOFs are obtained from a fine‐grid data set and are defined in conjunction with corresponding deterministic time series. We construct stochastic processes that mimic properties of the measured time series. In particular, the processes are defined such that the underlying probability density functions (pdfs) or the estimated correlation time of the time series are retained. These stochastic models are compared to stochastic forcing based on Gaussian noise, which does not use any information of the time series. We perform uncertainty quantification tests and compare stochastic ensembles in terms of mean and spread. Reduced uncertainty is observed for the developed models. On short timescales, such as those used for data assimilation (Cotter et al., 2020a, https://doi.org/10.1007/s10955-020-02524-0), the stochastic models show a reduced ensemble mean error and a reduced spread. Particularly, using estimated pdfs yields stochastic ensembles which rarely fail to capture the reference solution on small time scales, whereas introducing correlation into the stochastic models improves the quality of the coarse‐grid predictions with respect to Gaussian noise.
MCMC for GLMMs

Generalized linear mixed models (GLMMs) are often used for analyzing correlated non-Gaussian data. The likelihood function in a GLMM is available only as a high dimensional integral, and thus closed-form inference and prediction are not possible for GLMMs. Since the likelihood is not available in a closed-form, the associated posterior densities in Bayesian GLMMs are also intractable. Generally, Markov chain Monte Carlo (MCMC) algorithms are used for conditional simulation in GLMMs and exploring these posterior densities. In this article, we present different state of the art MCMC algorithms for fitting GLMMs. These MCMC algorithms include efficient data augmentation strategies, as well as diffusions based and Hamiltonian dynamics based methods. The Langevin and Hamiltonian Monte Carlo methods presented here are applicable to any GLMMs, and are illustrated using three most popular GLMMs, namely, the logistic and probit GLMMs for binomial data and the Poisson-log GLMM for count data. We also present efficient data augmentation algorithms for probit and logistic GLMMs. Some of these algorithms are compared using a numerical example.
SNUG: Self-Supervised Neural Dynamic Garments

We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies. State-of-the-art data-driven approaches to model 3D garment deformations are trained using supervised strategies that require large datasets, usually obtained by expensive physics-based simulation methods or professional multi-camera capture setups. In contrast, we propose a new training scheme that removes the need for ground-truth samples, enabling self-supervised training of dynamic 3D garment deformations. Our key contribution is to realize that physics-based deformation models, traditionally solved in a frame-by-frame basis by implicit integrators, can be recasted as an optimization problem. We leverage such optimization-based scheme to formulate a set of physics-based loss terms that can be used to train neural networks without precomputing ground-truth data. This allows us to learn models for interactive garments, including dynamic deformations and fine wrinkles, with a two orders of magnitude speed up in training time compared to state-of-the-art supervised methods.