Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model-free Deep Reinforcement Learning Approach To Maneuver A Quadrotor Despite Single Rotor Failure

Ability to recover from faults and continue mission is desirable for many quadrotor applications. The quadrotor's rotor may fail while performing a mission and it is essential to develop recovery strategies so that the vehicle is not damaged. In this paper, we develop a model-free deep reinforcement learning approach for a quadrotor to recover from a single rotor failure. The approach is based on Soft-actor-critic that enables the vehicle to hover, land, and perform complex maneuvers. Simulation results are presented to validate the proposed approach using a custom simulator. The results show that the proposed approach achieves hover, landing, and path following in 2D and 3D. We also show that the proposed approach is robust to wind disturbances.
VIA: Analyzing Device Interfaces of Protected Virtual Machines

Both AMD and Intel have presented technologies for confidential computing in cloud environments. The proposed solutions — AMD SEV (-ES, -SNP) and Intel TDX — protect VMs (VMs) against attacks from higher privileged layers through memory encryption and integrity protection. This model of computation draws a new trust boundary between virtual devices and the VM, which in so far lacks thorough examination. In this paper, we therefore present an analysis of the virtual device interface and discuss several attack vectors against a protected VM. Further, we develop and evaluate VIA, an automated analysis tool to detect cases of improper sanitization of input recieved via the virtual device interface. VIA improves upon existing approaches for the automated analysis of device interfaces in the following aspects: (i) support for virtualization relevant buses, (ii) efficient Direct Memory Access (DMA) support and (iii) performance. VIA builds upon the Linux Kernel Library and clang’s libfuzzer to fuzz the communication between the driver and the device via MMIO, PIO, and DMA. An evaluation of VIA shows that it performs 570 executions per second on average and improves performance compared to existing approaches by an average factor of 2706. Using VIA, we analyzed 22 drivers in Linux 5.10. 0-rc6, thereby uncovering 50 bugs and initiating multiple patches to the virtual device driver interface of Linux. To prove our findings’ criticality under the threat model of AMD SEV and Intel TDX, we showcase three exemplary attacks based on the bugs found. The attacks enable a malicious hypervisor to corrupt the memory and gain code execution in protected VMs with SEV-ES and are theoretically applicable to SEV-SNP and TDX.
Generalized T-Product Tensor Bernstein Bounds

Since Kilmer et al. introduced the new multiplication method between two third-order tensors around 2008 and third-order tensors with such multiplication structure are also called as T-product tensors, T-product tensors have been applied to many fields in science and engineering, such as low-rank tensor approximation, signal processing, image feature extraction, machine learning, computer vision, and the multi-view clustering problem, etc. However, there are very few works dedicated to exploring the behavior of random T-product tensors. This work considers the problem about the tail behavior of the unitarily invariant norm for the summation of random symmetric T-product tensors. Majorization and antisymmetric Kronecker product tools are main techniques utilized to establish inequalities for unitarily norms of multivariate T-product tensors. The Laplace transform method is integrated with these inequalities for unitarily norms of multivariate T-product tensors to provide us with Bernstein Bounds estimation of Ky Fan $k$-norm for functions of the symmetric random T-product tensors summation.
Selecting Datasets for Evaluating an Enhanced Deep Learning Framework

A framework was developed to address limitations associated with existing techniques for analysing sequences. This work deals with the steps followed to select suitable datasets characterised by discrete irregular sequential patterns. To identify, select, explore and evaluate which datasets from various sources extracted from more than 400 research articles, an interquartile range method for outlier calculation and a qualitative Billauer's algorithm was adapted to provide periodical peak detection in such datasets. The developed framework was then tested using the most appropriate datasets. The research concluded that the financial market-daily currency exchange domain is the most suitable kind of data set for the evaluation of the designed deep learning framework, as it provides high levels of discrete irregular patterns.
Onset of universality in the dynamical mixing of a pure state

We study the time dynamics of random density matrices generated by evolving the same pure state using a Gaussian orthogonal ensemble (GOE) of Hamiltonians. We show that the spectral statistics of the resulting mixed state is well described by random matrix theory (RMT) and undergoes a crossover from the GOE to the Gaussian unitary ensemble (GUE) for short and large times respectively. Using a semi-analytical treatment relying on a power series of the density matrix as a function of time, we find that the crossover occurs in a characteristic time that scales as the inverse of the Hilbert space dimension. The RMT results are contrasted with a paradigmatic model of many-body localization in the chaotic regime, where the GUE statistics is reached at large times, while for short times the statistics strongly depends on the peculiarity of the considered subspace.
Formation and evolution of young massive clusters in galaxy mergers: the SMUGGLE view

Galaxy mergers are known to host abundant young massive cluster (YMC) populations, whose formation mechanism is still not well-understood. Here, we present a high-resolution galaxy merger simulation with explicit star formation and stellar feedback prescriptions to investigate how mergers aﬀect the properties of the interstellar medium and YMCs. Compared with a controlled simulation of an isolated galaxy, the mass fraction of dense and high-pressure gas is much higher in mergers. Consequently, the mass function of both molecular clouds and YMCs becomes shallower and extends to higher masses. Moreover, cluster formation eﬃciency is signiﬁcantly enhanced and correlates positively with the star formation rate surface density and gas pressure. We track the orbits of YMCs and investigate the time evolution of tidal ﬁelds during the course of the merger. At an early stage of the merger, the tidal ﬁeld strength correlates positively with YMC mass, 𝜆 tid ∝ 𝑀 0 . 71 , which systematically aﬀects the shape of the mass function and age distribution of the YMCs. At later times, most YMCs closely follow the orbits of their host galaxies, gradually sinking into the center of the merger remnant due to dynamical friction, and are quickly dissolved via eﬃcient tidal disruption. Interestingly, YMCs formed during the ﬁrst passage, mostly in tidal tails and bridges, are distributed over a wide range of galactocentric radii, greatly increasing their survivability because of the much weaker tidal ﬁeld in the outskirts of the merger system. These YMCs are promising candidates for globular clusters that survive to the present day.
Incorporating Data Uncertainty in Object Tracking Algorithms

Methodologies for incorporating the uncertainties characteristic of data-driven object detectors into object tracking algorithms are explored. Object tracking methods rely on measurement error models, typically in the form of measurement noise, false positive rates, and missed detection rates. Each of these quantities, in general, can be dependent on object or measurement location. However, for detections generated from neural-network processed camera inputs, these measurement error statistics are not sufficient to represent the primary source of errors, namely a dissimilarity between run-time sensor input and the training data upon which the detector was trained. To this end, we investigate incorporating data uncertainty into object tracking methods such as to improve the ability to track objects, and particularly those which out-of-distribution w.r. t. training data. The proposed methodologies are validated on an object tracking benchmark as well on experiments with a real autonomous aircraft.
Towards Automatic Bias Detection in Knowledge Graphs

With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident. Previous works have demonstrated that KGs are prone to various social biases, and have proposed multiple methods for debiasing them. However, in such studies, the focus has been on debiasing techniques, while the relations to be debiased are specified manually by the user. As manual specification is itself susceptible to human cognitive bias, there is a need for a system capable of quantifying and exposing biases, that can support more informed decisions on what to debias. To address this gap in the literature, we describe a framework for identifying biases present in knowledge graph embeddings, based on numerical bias metrics. We illustrate the framework with three different bias measures on the task of profession prediction, and it can be flexibly extended to further bias definitions and applications. The relations flagged as biased can then be handed to decision makers for judgement upon subsequent debiasing.
COVR: A Test-Bed for Visually Grounded Compositional Generalization with Real Images

While interest in models that generalize at test time to new compositions has risen in recent years, benchmarks in the visually-grounded domain have thus far been restricted to synthetic images. In this work, we propose COVR, a new test-bed for visually-grounded compositional generalization with real images. To create COVR, we use real images annotated with scene graphs, and propose an almost fully automatic procedure for generating question-answer pairs along with a set of context images. COVR focuses on questions that require complex reasoning, including higher-order operations such as quantification and aggregation. Due to the automatic generation process, COVR facilitates the creation of compositional splits, where models at test time need to generalize to new concepts and compositions in a zero- or few-shot setting. We construct compositional splits using COVR and demonstrate a myriad of cases where state-of-the-art pre-trained language-and-vision models struggle to compositionally generalize.
Optimal Control for Linear Networked Control Systems with Information Transmission Constraints

This paper addresses the problem of robust control of a linear discrete-time system subject to bounded disturbances and to measurement and control budget constraints.Using Q-parameterization and a polytope containment method, we prove that the co-design of an affine feedback controller, a measurement schedule and a control schedule can be exactly formulated as a mixed integer linear program with 2 binary variables per time step. As a consequence, this problem can be solved efficiently, even when an exhaustive search for measurement and control times would have been impossible in a reasonable amount of time.
Self-consistent population spectral synthesis with FADO: mean stellar metallicity of galaxies in spectral synthesis methods

In this work, we investigate the reliability of spectral synthesis methods in the estimation of the mean stellar age and metallicity, addressing the question of which signal-to-noise ratios (S/N) are needed to determine these quantities and how these depend on the tool used to model the spectra. To address this problem we used realistic simulated spectra containing stellar and nebular emission, reproducing the evolution of a galaxy for a constant and exponentially declining star formation law. The spectra have been degraded to different S/N and analysed with three different spectral synthesis codes: Fado, Steckmap, and Starlight assuming similar fitting set-ups and the same spectral bases. For S/N ≤ 5 all tools considered show a large diversity in the results. Fado and Starlight find median differences in light-weighted mean stellar ages of ∼0.1 dex, while Steckmap shows a higher value of ∼0.2 dex. For S/N > 50 the median differences in Fado are ∼0.03 dex (∼ 7%), a factor 3 and 4 lower than the 0.08 dex (∼20%) and 0.11 dex (∼30%) obtained from Starlight and Steckmap, respectively. Our results indicate that phases of high specific star formation rate (sSFR) in galaxies require analysis tools that do not neglect the nebular continuum emission in the fitting process, since purely stellar models would have strong problems in the estimation of star formation history, even in presence of high S/N spectra. The median value of these differences are of the order of 7% (Fado), 20% (Starlight), and 30% (Steckmap) for light-weighted quantities, and 20% (Fado), 60% (Starlight), and 20% (Steckmap) for mass-weighted quantities. That implies a severe overestimation of the mass-to-light ratio and stellar mass, even in the presence of a mild contribution from the nebular continuum. Our work underlines once more the importance of a self-consistent treatment of nebular emission, which is the only route towards a reliable determination of the assembly of any high-sSFR galaxy at high and low redshift.
The banking transactions dataset and its comparative analysis with scale-free networks

We construct a network of 1.6 million nodes from banking transactions of users of Rabobank. We assign two weights on each edge, which are the aggregate transferred amount and the total number of transactions between the users from the year 2010 to 2020. We present a detailed analysis of the unweighted and both weighted networks by examining their degree, strength, and weight distributions, as well as the topological assortativity and weighted assortativity, clustering, and weighted clustering, together with correlations between these quantities. We further study the meso-scale properties of the networks and compare them to a randomized reference system. This will be the first publicly shared dataset of intra-bank transactions, and this work highlights the unique characteristics of banking transaction networks with other scale-free networks.
Monopole-like configurations in the O(3) spin model at the upper critical dimension

We present a high-precision Monte Carlo study of the O (3) spin theory on the lattice in D = 4 dimensions. This model exhibits interesting dynamical features, in particular in the brokensymmetry phase, where suitable boundary conditions can be used to enforce monopole-like topological excitations. We investigate the Euclidean time propagation and the features of these excitations close to the critical point, where our numerical results show an excellent quantitative agreement with analytic predictions derived from purely quantum-field-theoretical tools by G. Delfino. We conclude by commenting on the implications of our findings for a conjectured violation of Derrick’s theorem at the quantum level and on the consequences in various areas of physics, ranging from condensed matter to astro-particle physics.
A Survey on Reinforcement Learning for Recommender Systems

Recommender systems have been widely applied in different real-life scenarios to help us find useful information. In particular, reinforcement learning (RL)-based recommender systems have become an emerging research topic in recent years, owing to the interactive nature and autonomous learning ability. Empirical results show that RL-based recommendation methods often surpass supervised learning methods. Nevertheless, there are various challenges in applying RL in recommender systems. To understand the challenges and relevant solutions, there should be a reference for researchers and practitioners working on RL-based recommender systems. To this end, we first provide a thorough overview, comparisons, and summarization of RL approaches applied in four typical recommendation scenarios, including interactive recommendation, conversational recommendation, sequential recommendation, and explainable recommendation. Furthermore, we systematically analyze the challenges and relevant solutions on the basis of existing literature. Finally, under discussion for open issues of RL and its limitations of recommender systems, we highlight some potential research directions in this field.
Role of the chiral anomaly in polarized deeply inelastic scattering. II. Topological screening and transitions from emergent axionlike dynamics

In [1], we demonstrated that the structure function g1(xB , Q ) measured in polarized deeply inelastic scattering (DIS) is dominated by the triangle anomaly in both the Bjorken limit of large Q and the Regge limit of small xB . In the worldline formulation of quantum field theory, the triangle anomaly arises from the imaginary part of the worldline effective action. We show explicitly how a Wess-Zumino-Witten term coupling the topological charge density to a primordial isosinglet η̄ arises in this framework. We demonstrate the fundamental role played by this contribution both in topological mass generation of the η′ and in the cancellation of the off-forward pole arising from the triangle anomaly in the proton’s helicity Σ(Q). We recover the striking result by Shore and Veneziano that Σ ∝ √ χ′(0), where χ′ is the slope of the QCD topological susceptibility in the forward limit. We construct an axion-like effective action for g1 at small xB that describes the interplay between gluon saturation and the topology of the QCD vacuum. In particular, we outline the role of “over-the-barrier” sphaleron-like transitions in spin diffusion at small xB . Such topological transitions can be measured in polarized DIS at a future Electron-Ion Collider.
Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results

Recent transformer-based approaches demonstrate promising results on relational scientific information extraction. Existing datasets focus on high-level description of how research is carried out. Instead we focus on the subtleties of how experimental associations are presented by building SciClaim, a dataset of scientific claims drawn from Social and Behavior Science (SBS), PubMed, and CORD-19 papers. Our novel graph annotation schema incorporates not only coarse-grained entity spans as nodes and relations as edges between them, but also fine-grained attributes that modify entities and their relations, for a total of 12,738 labels in the corpus. By including more label types and more than twice the label density of previous datasets, SciClaim captures causal, comparative, predictive, statistical, and proportional associations over experimental variables along with their qualifications, subtypes, and evidence. We extend work in transformer-based joint entity and relation extraction to effectively infer our schema, showing the promise of fine-grained knowledge graphs in scientific claims and beyond.
Contraction rates for sparse variational approximations in Gaussian process regression

We study the theoretical properties of a variational Bayes method in the Gaussian Process regression model. We consider the inducing variables method introduced by Titsias (2009a) and derive sufficient conditions for obtaining contraction rates for the corresponding variational Bayes (VB) posterior. As examples we show that for three particular covariance kernels (Mat\'ern, squared exponential, random series prior) the VB approach can achieve optimal, minimax contraction rates for a sufficiently large number of appropriately chosen inducing variables. The theoretical findings are demonstrated by numerical experiments.
Noisy-to-Noisy Voice Conversion Framework with Denoising Model

In a conventional voice conversion (VC) framework, a V C model is often trained with a clean dataset consisting of speech data carefully recorded and selected by minimizing background interference. However, collecting such a high-quality dataset is expensive and time-consuming. Leveraging crowd-sourced speech data in training is more economical. Moreover, for some real-world VC scenarios such as VC in video and VC-based data augmentation for speech recognition systems, the background sounds themselves are also informative and need to be maintained. In this paper, to explore V C with the flexibility of handling background sounds, we propose a noisy-to-noisy (N2N) VC framework composed of a denoising module and a VC module. With the proposed framework, we can convert the speaker's identity while preserving the background sounds. Both objective and subjective evaluations are conducted, and the results reveal the effectiveness of the proposed framework.
SoK: Machine Learning Governance

The application of machine learning (ML) in computer systems introduces not only many benefits but also risks to society. In this paper, we develop the concept of ML governance to balance such benefits and risks, with the aim of achieving responsible applications of ML. Our approach first systematizes research towards ascertaining ownership of data and models, thus fostering a notion of identity specific to ML systems. Building on this foundation, we use identities to hold principals accountable for failures of ML systems through both attribution and auditing. To increase trust in ML systems, we then survey techniques for developing assurance, i.e., confidence that the system meets its security requirements and does not exhibit certain known failures. This leads us to highlight the need for techniques that allow a model owner to manage the life cycle of their system, e.g., to patch or retire their ML system. Put altogether, our systematization of knowledge standardizes the interactions between principals involved in the deployment of ML throughout its life cycle. We highlight opportunities for future work, e.g., to formalize the resulting game between ML principals.
Quantifying Model Predictive Uncertainty with Perturbation Theory

We propose a framework for predictive uncertainty quantiﬁcation of a neural network that replaces the conventional Bayesian notion of weight probability density function (PDF) with a physics based potential ﬁeld representation of the model weights in a Gaussian reproducing kernel Hilbert space (RKHS) embedding. This allows us to use perturbation theory from quantum physics to formulate a moment decomposition problem over the model weight-output relationship. The extracted moments reveal successive degrees of regularization of the weight PDF around the local neighborhood of the model output. Such localized moments determine with great sensitivity the local heterogeneity of the weight PDF around a model prediction thereby providing signiﬁcantly greater accuracy of model predictive uncertainty than the central moments characterized by Bayesian and ensemble meth-ods. We show that this consequently leads to a better ability to detect false model predictions of test data that has undergone a covariate shift away from the training PDF learned by the model. We evaluate our approach against baseline uncertainty quantiﬁcation methods on several benchmark datasets that are corrupted using common distortion techniques. Our approach provides fast model predictive uncertainty estimates with much greater precision and calibration.