Miracle-dz/UniFAR-FTUs huggingface dataset:
Timeloops: Automatic System Call Policy Learning for Containerized Microservices

In this paper we introduce Timeloops a novel technique for automatically learning system call filtering policies for containerized microservices applications. At run-time, Timeloops automatically learns which system calls a program should be allowed to invoke while rejecting attempts to call spurious system calls. Further, Timeloops addresses many of the shortcomings of state-of-the-art static analysis-based techniques, such as the ability to generate tight filters for programs written in interpreted languages such as PHP, Python, and JavaScript. Timeloops has a simple and robust implementation because it is mainly built out of commodity, and proven, technologies such as seccomp-BPF, systemd, and Podman containers, with fewer than 500 lines of code. We demonstrate the utility of Timeloops by learning system calls for individual services and two microservices benchmark applications, which utilize popular technologies like Python Flask, Nginx (with PHP and Lua modules), Apache Thrift, Memcached, Redis, and MongoDB. Further, the amortized performance of Timeloops is similar to that of an unhardened system while producing a smaller system call filter than state-of-the-art static analysis-based techniques.
Fingering Convection in the Stably Stratified Layers of Planetary Cores

Stably stratified layers may be present at the top of the electrically conducting fluid layers of many planets either because the temperature gradient is locally subadiabatic or because a stable composition gradient is maintained by the segregation of chemical elements. Here we study the double‐diffusive processes taking place in such a stable layer, considering the case of Mercury's core where the temperature gradient is stable but the composition gradient is unstable over a 800 km‐thick layer. The large difference in the molecular diffusivities leads to the development of buoyancy‐driven instabilities that drive radial flows known as fingering convection. We model fingering convection using hydrodynamical simulations in a rotating spherical shell and varying the rotation rate and the stratification strength. For small Rayleigh numbers (i.e., weak background temperature and composition gradients), fingering convection takes the form of columnar flows aligned with the rotation axis and with an azimuthal size comparable with the layer thickness. For larger Rayleigh numbers, the flows retain a columnar structure but the azimuthal size is drastically reduced leading to thin sheet‐like structures that are elongated in the meridional direction. The azimuthal size decreases when the thermal stratification increases, following closely the scaling law expected from the linear planar theory (Stern, 1960, https://doi.org/10.1111/j.2153-3490.1960.tb01295.x). We find that the radial flows always remain laminar with local Reynolds number of order 1–10. Equatorially symmetric zonal flows form due to latitudinal variations of the axisymmetric composition. The zonal velocity exceeds the non‐axisymmetric velocities at the largest Rayleigh numbers. We discuss plausible implications for planetary magnetic fields.
EqVIO: An Equivariant Filter for Visual-Inertial Odometry

Visual-inertial odometry (VIO) is the problem of estimating a robot's trajectory by combining information from an inertial measurement unit (IMU) and a camera and is of great interest to the robotics community. This article develops a novel Lie group symmetry for the VIO problem and applies the recently proposed equivariant filter. The proposed symmetry is compatible with the invariance of the VIO reference frame, leading to improved filter consistency. The bias-free IMU dynamics are group-affine, ensuring that filter linearization errors depend only on the bias estimation error and measurement noise. Furthermore, visual measurements are equivariant with respect to the symmetry, enabling the application of the higher order equivariant output approximation to reduce the approximation error in the filter update equation. As a result, the equivariant filter based on this Lie group is a consistent estimator for VIO with lower linearization error in the propagation of state dynamics and a higher order equivariant output approximation than standard formulations. Experimental results on the popular EuRoC and UZH FPV datasets demonstrate that the proposed system outperforms other state-of-the-art VIO algorithms in terms of both speed and accuracy.
fairlib: A Unified Framework for Assessing and Improving Classification Fairness

This paper presents fairlib, an open-source framework for assessing and improving classification fairness. It provides a systematic framework for quickly reproducing existing baseline models, developing new methods, evaluating models with different metrics, and visualizing their results. Its modularity and extensibility enable the framework to be used for diverse types of inputs, including natural language, images, and audio. In detail, we implement 14 debiasing methods, including pre-processing, at-training-time, and post-processing approaches. The built-in metrics cover the most commonly used fairness criterion and can be further generalized and customized for fairness evaluation.
Putting the micro into the macro: a molecularly augmented hydrodynamic model of dynamic wetting applied to flow instabilities during forced dewetting

Abstract We report a molecularly augmented continuum-based computational model of dynamic wetting and apply it to the displacement of an externally driven liquid plug between two partially wetted parallel plates. The results closely follow those obtained in a recent molecular dynamics (MD) study of the same problem (Fernández-Toledano et al., J. Colloid Interface Sci., vol. 587, 2021, pp. 311–323), which we use as a benchmark. We are able to interpret the maximum speed of dewetting $U^*_{{crit}}$ as a fold bifurcation in the steady phase diagram and show that its dependence on the true contact angle $\theta _{{cl}}$ is quantitatively similar to that found using MD. A key feature of the model is that the contact angle is dependent on the speed of the contact line, with $\theta _{{cl}}$ emerging as part of the solution. The model enables us to study the formation of a thin film at dewetting speeds $U^*>U^*_{{crit}}$ across a range of length scales, including those that are computationally prohibitive to MD simulations. We show that the thickness of the film scales linearly with the channel width and is only weakly dependent on the capillary number. This work provides a link between matched asymptotic techniques (valid for larger geometries) and MD simulations (valid for smaller geometries). In addition, we find that the apparent angle, the experimentally visible contact angle at the fold bifurcation, is not zero. This is in contrast to the prediction of conventional treatments based on the lubrication model of flow near the contact line, but consistent with experiment.
The Limits of Word Level Differential Privacy

As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private mechanisms to perturb word embeddings, thus replacing individual words in a sentence. While these methods represent very important contributions, have various advantages over other techniques and do show anonymization capabilities, they have several shortcomings. In this paper, we investigate these weaknesses and demonstrate significant mathematical constraints diminishing the theoretical privacy guarantee as well as major practical shortcomings with regard to the protection against deanonymization attacks, the preservation of content of the original sentences as well as the quality of the language output. Finally, we propose a new method for text anonymization based on transformer based language models fine-tuned for paraphrasing that circumvents most of the identified weaknesses and also offers a formal privacy guarantee. We evaluate the performance of our method via thorough experimentation and demonstrate superior performance over the discussed mechanisms.
User-Centric Gender Rewriting

In this paper, we define the task of gender rewriting in contexts involving two users (I and/or You) – first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. We develop a multi-step system that combines the positive aspects of both rule-based and neural rewriting models. Our results successfully demonstrate the viability of this approach on a recently created corpus for Arabic gender rewriting, achieving 88.42 M2 F0.5 on a blind test set. Our proposed system improves over previous work on the first-person-only version of this task, by 3.05 absolute increase in M2 F0.5. We demonstrate a use case of our gender rewriting system by using it to post-edit the output of a commercial MT system to provide personalized outputs based on the users’ grammatical gender preferences. We make our code, data, and pretrained models publicly available.
DNA Pre-Alignment Filter Using Processing Near Racetrack Memory

Recent DNA pre-alignment filter designs employ DRAM for storing the reference genome and its associated meta-data. However, DRAM incurs increasingly high energy consumption of background and refresh energy as devices scale. To overcome this problem, this paper explores a design with racetrack memory (RTM)–an emerging non-volatile memory that promises higher storage density, faster access latency, and lower energy consumption. Multi-bit storage cells in RTM are inherently sequential and thus require data placement strategies to mitigate the performance and energy impacts of shifting during data accesses. We propose a near-memory pre-alignment filter with a novel data mapping and several shift reduction strategies designed explicitly for RTM. On a set of four input genomes from the 1000 Genome Project, our approach improves performance and energy efficiency by 68% and 52%, respectively, compared to the state-of-the-art DRAM-based architecture.
Quantitative results on algorithms for zeros of differences of monotone operators in Hilbert space

We provide quantitative information in the form of a rate of metastability in the sense of T. Tao and (under a metric regularity assumption) a rate of convergence for an algorithm approximating zeros of differences of maximally monotone operators due to A. Moudafi by using techniques from `proof mining', a subdiscipline of mathematical logic. For the rate of convergence, we provide an abstract and general result on the construction of rates of convergence for quasi-Fej\'er monotone sequences with metric regularity assumptions, generalizing previous results for Fej\'er monotone sequences due to U. Kohlenbach, G. L\'opez-Acedo and A. Nicolae.
Topical Review: greybody factors and quasinormal modes for black holes invarious theories - fingerprints of invisibles

We give a pedagogical introduction to black holes (BHs) greybody factors (GFs) and quasinormal modes (QNMs) and share the recent developments on those subjects. To this end, we present some particular analytical and approximation techniques for the computations of the GFs and QNMs. We ﬁrst review the gravitational GFs and show how they are analytically calculated for static and spherically symmetric higher dimensional BHs, consisting the charged BHs and existence of cosmological constant (i.e., de Sitter (dS)/anti-de Sitter (AdS)AdS BHs). The computations performed involve both the low-energy (having real and small frequencies) and the asymptotic (having extremely high frequency of the scattered wave throughout the imaginary axis) cases. A generic method is discussed at low frequencies. This method can be used for all three types of spacetime asymptotics and it is unaﬀected by the BH’s features. For asymptotically dS BHs, GF varies de-pending on whether the spacetime dimension is even or odd, and is proportional to the ratio of the event and cosmic horizon areas. At asymptotic frequencies, the GFs can be computed by using a matching technique inspired by the monodromy method. In the meantime, we also make a general literature review on the matching technique in a separate section. While the GFs for charged or asymptotically dS BHs are generated by non-trivial functions, the GF for asymptotically AdS BHs is precisely one: pure black-body emission. QNMs, which are solutions to the relevant perturbation equations that satisfy the boundary conditions for purely outgoing (gravitational) waves at spatial inﬁnity and purely ingoing (gravitational) waves at the event horizon, are considered using some particular analytical (like the matching technique) and approximation methods. In this study, our primary focus will be on the bosonic and fermionic GFs and QNMs of various BH and brane geometries and reveal the ﬁngerprints of the invisibles with the radiation spectra to be obtained by the WKB approximation and bounding the Bogoliubov coeﬃcients (together with the Miller-Good transformation) methods.
Second Order Path Variationals in Non-Stationary Online Learning

We consider the problem of universal dynamic regret minimization under exp-concave and smooth losses. We show that appropriately designed Strongly Adaptive algorithms achieve a dynamic regret of $\tilde O(d^2 n^{1/5} C_n^{2/5} \vee d^2)$, where $n$ is the time horizon and $C_n$ a path variational based on second order differences of the comparator sequence. Such a path variational naturally encodes comparator sequences that are piecewise linear -- a powerful family that tracks a variety of non-stationarity patterns in practice (Kim et al, 2009). The aforementioned dynamic regret rate is shown to be optimal modulo dimension dependencies and poly-logarithmic factors of $n$. Our proof techniques rely on analysing the KKT conditions of the offline oracle and requires several non-trivial generalizations of the ideas in Baby and Wang, 2021, where the latter work only leads to a slower dynamic regret rate of $\tilde O(d^{2.5}n^{1/3}C_n^{2/3} \vee d^{2.5})$ for the current problem.
Impact of Frontline Demonstration on Productivity Enhancement in Chickpea

Chickpea, (Cicer arietinum L.), is a drought tolerant leguminous crop grown in a large area in Madhya Pradesh covering approximately 38 percent area of the country. There is a large gap between the yield achieved in various experimental trials and that of under the farmers’ fields in the state. The major constraints responsible for this untapped yield potential are adverse weather condition and inappropriate production practices. There is a considerable scope for enhancement in crop productivity by adopting region specific suitable production technologies. 733 frontline demonstrations were conducted by KVKs in the state during 2010–11 on integrated crop management, integrated disease management, integrated nutrient management, integrated pest management and on use of improved varieties at real farm situations. Prevailing farmers’ practices were compared with these recommended practices and an average yield enhancement of 38.07, 21.9, 30.52, 27.94, and 25.99 was recorded over the yield under farmers’ practices respectively with favorable economic indicators like gross and net monetary returns and benefit: cost ratio. Farmers were also found eager to adopt the demonstrated technologies.
Nonstationary Bandit Learning via Predictive Sampling

Thompson sampling has proven effective across a wide range of stationary bandit environments. However, as we demonstrate in this paper, it can perform poorly when applied to non-stationary environments. We attribute such failures to the fact that, when exploring, the algorithm does not differentiate actions based on how quickly the information acquired loses its usefulness due to non-stationarity. Building upon this insight, we propose predictive sampling, an algorithm that deprioritizes acquiring information that quickly loses usefulness. A theoretical guarantee on the performance of predictive sampling is established through a Bayesian regret bound. We provide versions of predictive sampling for which computations tractably scale to complex bandit environments of practical interest. Through numerical simulations, we demonstrate that predictive sampling outperforms Thompson sampling in all non-stationary environments examined.
Sectorial Paley–Wiener theorem

We consider the problem of restoring a two‐dimensional convex source of gravity based on values of its potential function on a union of half‐planes. With that aim we prove a Paley–Wiener type theorem for functions of exponential type in a sector. In particular, we find the maximal set of analytic continuation whose complement is convex inside a sector. Our result serves as a sectorial analog of G. Polya's indicator theorem. It corrects, simplifies, and extends a result of M. Morimoto. It also improves a related result of M. Dzhrbashyan and A. Avetisyan.
In Defense of Image Pre-Training for Spatiotemporal Recognition

Image pre-training, the current de-facto paradigm for a wide range of visual tasks, is generally less favored in the field of video recognition. By contrast, a common strategy is to directly train with spatiotemporal convolutional neural networks (CNNs) from scratch. Nonetheless, interestingly, by taking a closer look at these from-scratch learned CNNs, we note there exist certain 3D kernels that exhibit much stronger appearance modeling ability than others, arguably suggesting appearance information is already well disentangled in learning. Inspired by this observation, we hypothesize that the key to effectively leveraging image pre-training lies in the decomposition of learning spatial and temporal features, and revisiting image pre-training as the appearance prior to initializing 3D kernels. In addition, we propose Spatial-Temporal Separable (STS) convolution, which explicitly splits the feature channels into spatial and temporal groups, to further enable a more thorough decomposition of spatiotemporal features for fine-tuning 3D CNNs. Our experiments show that simply replacing 3D convolution with STS notably improves a wide range of 3D CNNs without increasing parameters and computation on both Kinetics-400 and Something-Something V2. Moreover, this new training pipeline consistently achieves better results on video recognition with significant speedup. For instance, we achieve +0.6% top-1 of Slowfast on Kinetics-400 over the strong 256-epoch 128-GPU baseline while fine-tuning for only 50 epochs with 4 GPUs. The code and models are available at https://github.com/UCSC-VLAA/Image-Pretraining-for-Video.
Few-Shot Document-Level Relation Extraction

We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora provide more realism, particularly regarding none-of-the-above (NOTA) distributions. Therefore, we propose a set of FSDLRE tasks and construct a benchmark based on two existing supervised learning data sets, DocRED and sciERC. We adapt the state-of-the-art sentence-level method MNAV to the document-level and develop it further for improved domain adaptation. We find FSDLRE to be a challenging setting with interesting new characteristics such as the ability to sample NOTA instances from the support set. The data, code, and trained models are available online (https://github.com/nicpopovic/FREDo).
All You May Need for VQA are Image Captions

Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation. In this paper, we propose a method that automatically derives VQA examples at volume, by leveraging the abundance of existing image-caption annotations combined with neural models for textual question generation. We show that the resulting data is of high-quality. VQA models trained on our data improve state-of-the-art zero-shot accuracy by double digits and achieve a level of robustness that lacks in the same model trained on human-annotated VQA data.
Photon surfaces, shadows, and accretion disks in gravity with minimally coupled scalar field

In this article, we conduct a sequential study of possible observable images of black hole simulators described by two recently obtained rotating geometries in Einstein gravity, minimally coupled to a scalar ﬁeld. One of them, “Kerr-like” (KL), can be seen as a legitimate alternative to the rotating Fisher-Janis-Newman-Winicour (FJNW) solution, and the other (TSL) is a scalar generalization of the Tomimatsu-Sato solution. Unlike the previous version of the rotating FJNW, these solutions do indeed satisfy the system’s equations of motion. Our study includes both analytical and numerical calculations of equatorial circular orbits, photon regions, gravitational shadows, and radiation from thin accretion disks for various values of the object’s angular momentum and scalar charge. The TSL solution was found to simulate Kerr for all valid parameter values with high accuracy. The maximum diﬀerence between the deviations of shadows from a circle for the Kerr and TSL cases does not exceed 1% and ﬁts into the experimental observational data M87 ∗ . However, near-extreme objects show two times smaller peak values of the observed outﬂow luminosity of the accretion disk than for the Kerr black hole. The KL solution cannot be ruled out by the experimental data for small values of the scalar charge either. As the scalar charge increases, the optical properties change dramatically. The shadow can become multiply connected, strongly oblate, and the photon region does not hide the singularity, so it should be classiﬁed as a strong singularity.
Experimental Investigations and Numerical Simulations of the Relationship between Tool Geometry and Cutting Chatter

This paper is armed at researching the suppression method of cutting chatter. The occurrence of chatter is strongly influenced by the tool geometry. In this paper, The numerical simulation results are in good agreement with the Experimental investigation results. The results show. Tool geometry is the first essential factor of the occurrence of cutting chatter. A large rake angle and a small clearance angle lead to a high cutting stability. A decrease in rake angle and an increase in clearance angle will greatly increase the degree of the cutting chatter and decrease the cutting stability.