Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions

In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time. While this issue has been partially addressed for systems with known dynamics, it remains largely unaddressed for systems with unknown dynamics. In this paper, we propose a Gaussian process-based time-varying control method that leverages backstepping and control barrier functions to achieve safety requirements within prescribed time windows. It can be used to keep a system within a safe region or to make it return to a safe region within a limited time window. These properties are cemented by rigorous theoretical results. The effectiveness of the proposed controller is demonstrated in a simulation of a robotic manipulator.
IG-FIQA: Improving Face Image Quality Assessment through Intra-class Variance Guidance robust to Inaccurate Pseudo-Labels

In the realm of face image quality assesment (FIQA), method based on sample relative classification have shown impressive performance. However, the quality scores used as pseudo-labels assigned from images of classes with low intra-class variance could be unrelated to the actual quality in this method. To address this issue, we present IG-FIQA, a novel approach to guide FIQA training, introducing a weight parameter to alleviate the adverse impact of these classes. This method involves estimating sample intra-class variance at each iteration during training, ensuring minimal computational overhead and straightforward implementation. Furthermore, this paper proposes an on-the-fly data augmentation methodology for improved generalization performance in FIQA. On various benchmark datasets, our proposed method, IG-FIQA, achieved novel state-of-the-art (SOTA) performance.
Asynchronous classiﬁcation of error-related potentials in human-robot interaction (cid:63)

The use of implicit evaluations of humans such as electroencephalogram (EEG)-based human feedback is relevant for robot applications, e.g., robot learning or corrections of robot’s actions. In the presented study, we implemented a scenario, in which a simulated robot communicates with its human partner through speech and gestures. The robot announces its intention verbally and selects the appropriate action using pointing gestures. The human partner in turn implicitly evaluates whether the robot’s verbal announcement matches the robot’s action choice. Error-related potentials (ErrPs) are expressions of this implicit evaluation, which are triggered in case of discrepancies between the robot’s verbal announcement and the corresponding actions (pointing gestures) chosen by the robot. In our scenario, the task takes a long time. Therefore, asynchronous EEG classiﬁcations that continuously segment EEGs are advantageous or even necessary. However, asynchronous EEG classiﬁcations are challenging due to the large number of false positives during the long task time of the robot. In this work, we propose an approach to improve asynchronous classiﬁcation performance by selecting and extracting features that are only relevant for EEG classiﬁcations in the long time series that the robot needs to perform tasks. We achieved a high classiﬁcation performance, i
On the number of prime numbers between $n^2$ and ${(n+1)}^2$

Let $p_{r+1}-1>n \geq p_r-1$, based on a sequence $\{1,2,3\cdots\ M_r(M_r=p_1p_2\cdots p_r)\}$, we compare the density of coprime numbers and establish a correlation between the proportions of coprime numbers in the ranges from 1 to consecutive square numbers. Then, we derive the relationship between the number of coprimes in the interval of $n^2 \sim {(n+1)}^2$ and the proportion of coprimes in the interval of $1 \sim n^2$, proving that there is at least one prime number between any $n^2$ and ${(n+1)}^2$. By extending our research to the range of $1 \sim M_r^2$, we establish the relationship between the proportions of backwards coprime numbers in the ranges from ${M_r}^2$ to consecutive square numbers; furthermore, we establish a relationship between the proportions of coprimes in small interval and the whole interval. Then, in conclusion, the number of coprimes between $n^2$ and ${(n+1)}^2$ is greater than $n\prod_{i=1}^{r}{(1-\frac{1}{p_i}})$, thus proving that there are at least 2 prime numbers between $n^2$ and ${(n+1)}^2$.
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques

Recent advancements in Intrusion Detection Systems (IDS), integrating Explainable AI (XAI) methodologies, have led to notable improvements in system performance via precise feature selection. However, a thorough understanding of cyber-attacks requires inherently explainable decision-making processes within IDS. In this paper, we present the Interpretable Generalization Mechanism (IG), poised to revolutionize IDS capabilities. IG discerns coherent patterns, making it interpretable in distinguishing between normal and anomalous network traffic. Further, the synthesis of coherent patterns sheds light on intricate intrusion pathways, providing essential insights for cybersecurity forensics. By experiments with real-world datasets NSL-KDD, UNSW-NB15, and UKM-IDS20, IG is accurate even at a low ratio of training-to-test. With 10%-to-90%, IG achieves Precision (PRE)=0.93, Recall (REC)=0.94, and Area Under Curve (AUC)=0.94 in NSL-KDD; PRE=0.98, REC=0.99, and AUC=0.99 in UNSW-NB15; and PRE=0.98, REC=0.98, and AUC=0.99 in UKM-IDS20. Notably, in UNSW-NB15, IG achieves REC=1.0 and at least PRE=0.98 since 40%-to-60%; in UKM-IDS20, IG achieves REC=1.0 and at least PRE=0.88 since 20%-to-80%. Importantly, in UKM-IDS20, IG successfully identifies all three anomalous instances without prior exposure, demonstrating its generalization capabilities. These results and inferences are reproducible. In sum, IG showcases superior generalization by consistently performing well across diverse datasets and training-to-test ratios (from 10%-to-90% to 90%-to-10%), and excels in identifying novel anomalies without prior exposure. Its interpretability is enhanced by coherent evidence that accurately distinguishes both normal and anomalous activities, significantly improving detection accuracy and reducing false alarms, thereby strengthening IDS reliability and trustworthiness.
Do Agents Dream of Electric Sheep?: Improving Generalization in Reinforcement Learning through Generative Learning

The Overfitted Brain hypothesis suggests dreams happen to allow generalization in the human brain. Here, we ask if the same is true for reinforcement learning agents as well. Given limited experience in a real environment, we use imagination-based reinforcement learning to train a policy on dream-like episodes, where non-imaginative, predicted trajectories are modified through generative augmentations. Experiments on four ProcGen environments show that, compared to classic imagination and offline training on collected experience, our method can reach a higher level of generalization when dealing with sparsely rewarded environments.
Reactivation history of the North Anatolian fault zone based on calcite age-strain analyses

Perach Nuriel1*, John Craddock2, Andrew R.C. Kylander-Clark3, I. Tonguç Uysal4, Volkan Karabacak5, Ramazan Kadir Dirik6, Bradley R. Hacker3, and Ram Weinberger1,7 1Geological Survey of Israel, 32 Yeshayahu Leibowitz St. Jerusalem, 9371234, Israel 2Department of Geology, Macalester College, St. Paul, Minnesota 55105, USA 3Department of Earth Science, University of California, Santa Barbara, California 93106, USA 4CSIRO Energy, 26 Dick Perry Avenue, Kensington, Western Australia 6151, Australia 5Department of Geological Engineering, Eskisehir Osmangazi University, Eskisehir 26040, Turkey 6Department of Geological Engineering, Hacettepe University, Ankara 06640, Turkey 7Department of Geological and Environmental Sciences, Ben-Gurion University, Beer-Sheva 84105, Israel
Height-bounded Lempel-Ziv encodings

We introduce height-bounded LZ encodings (LZHB), a new family of compressed representations that is a variant of Lempel-Ziv parsings with a focus on allowing fast access to arbitrary positions of the text directly via the compressed representation. Any LZHB encoding whose referencing height is bounded by $h$ allows access to an arbitrary position of the underlying text using $O(h)$ predecessor queries. We show that there exists a constant $c$ such that the size $\hat{z}_{\mathit{HB}(c\log n)}$ of the optimal (smallest) LZHB encoding whose height is bounded by $c\log n$ for any string of length $n$ is $O(\hat{g}_{\mathrm{rl}})$, where $\hat{g}_{\mathrm{rl}}$ is the size of the smallest run-length grammar. Furthermore, we show that there exists a family of strings such that $\hat{z}_{\mathit{HB}(c\log n)} = o(\hat{g}_{\mathrm{rl}})$, thus making $\hat{z}_{\mathit{HB}(c\log n)}$ one of the smallest known repetitiveness measures for which $O(\mathit{polylog} n)$ time access is possible using $O(\hat{z}_{\mathit{HB}(c\log n)})$ space. While computing the optimal LZHB representation for any given height seems difficult, we propose linear and near linear time greedy algorithms which we show experimentally can efficiently find small LZHB representations in practice.
Recurrent Attention Walk for Semi-supervised Classification

In this paper, we study the graph-based semi-supervised learning for classifying nodes in attributed networks, where the nodes and edges possess content information. Recent approaches like graph convolution networks and attention mechanisms have been proposed to ensemble the first-order neighbors and incorporate the relevant neighbors. However, it is costly (especially in memory) to consider all neighbors without a prior differentiation. We propose to explore the neighborhood in a reinforcement learning setting and find a walk path well-tuned for classifying the unlabelled target nodes. We let an agent (of node classification task) walk over the graph and decide where to direct to maximize classification accuracy. We define the graph walk as a partially observable Markov decision process (POMDP). The proposed method is flexible for working in both transductive and inductive setting. Extensive experiments on four datasets demonstrate that our proposed method outperforms several state-of-the-art methods. Several case studies also illustrate the meaningful movement trajectory made by the agent.
V-PRISM: Probabilistic Mapping of Unknown Tabletop Scenes

The ability to construct concise scene representations from sensor input is central to the field of robotics. This paper addresses the problem of robustly creating a 3D representation of a tabletop scene from a segmented RGB-D image. These representations are then critical for a range of downstream manipulation tasks. Many previous attempts to tackle this problem do not capture accurate uncertainty, which is required to subsequently produce safe motion plans. In this paper, we cast the representation of 3D tabletop scenes as a multi-class classification problem. To tackle this, we introduce V-PRISM, a framework and method for robustly creating probabilistic 3D segmentation maps of tabletop scenes. Our maps contain both occupancy estimates, segmentation information, and principled uncertainty measures. We evaluate the robustness of our method in (1) procedurally generated scenes using open-source object datasets, and (2) real-world tabletop data collected from a depth camera. Our experiments show that our approach outperforms alternative continuous reconstruction approaches that do not explicitly reason about objects in a multi-class formulation.
Anomalous Shiba spectrum and superconductivity induced magnetic interactions in materials with topological band inversion

We study the Shiba states in materials with bulk band inversion such as iron-based topological superconductors or doped topological insulators. We show that the structure of the Shiba state spectrum depends on the doping level relative to the chemical potential at which the band-inversion occurs. Moreover, we demonstrate that the transition from ferro-magnetic to antiferromagnetic coupling and vice versa, which is caused by the coupling of magnetic impurities through the overlap of Shiba states, is highly dependent on the doping level. Additionally, topological edge states may have a substantial impact on the Shiba states, leading to a decrease in Shiba state energies and the creation of new states when the magnetic impurity approaches the boundary.
Remote UGV Control via Practical Wireless Channels: A Model Predictive Control Approach

In addressing wireless networked control systems (WNCS) subject to unexpected packet loss and uncertainties, this paper presents a practical Model Predictive Control (MPC) based control scheme with considerations of of packet dropouts, latency, process noise and measurement noise. A discussion of the quasi-static Rayleigh fading channel is presented herein to enhance the realism of the underlying assumption in a real-world context. To achieve a desirable performance, the proposed control scheme leverages the predictive capabilities of a direct multiple shooting MPC, employs a compensation strategy to mitigate the impact of wireless channel imperfections. Instead of feeding noisy measurements into the MPC, we employ an Extended Kalman Filter (EKF) to mitigate the influence of measurement noise and process disturbances. Finally, we implement the proposed MPC algorithm on a simulated Unmanned Ground Vehicle (UGV) and conduct a series of experiments to evaluate the performance of our control scheme across various scenarios. Through our simulation results and comparative analyses, we have substantiated the effectiveness and improvements brought about by our approach through the utilization of multiple metrics.
Adaptive morphing of wing and tail for stable, resilient, and energy-efficient flight of avian-informed drones

Avian-informed drones feature morphing wing and tail surfaces, enhancing agility and adaptability in flight. Despite their large potential, realising their full capabilities remains challenging due to the lack of generalized control strategies accommodating their large degrees of freedom and cross-coupling effects between their control surfaces. Here we propose a new body-rate controller for avian-informed drones that uses all available actuators to control the motion of the drone. The method exhibits robustness against physical perturbations, turbulent airflow, and even loss of certain actuators mid-flight. Furthermore, wing and tail morphing is leveraged to enhance energy efficiency at 8m/s, 10m/s and 12m/s using in-flight Bayesian optimization. The resulting morphing configurations yield significant gains across all three speeds of up to 11.5% compared to non-morphing configurations and display a strong resemblance to avian flight at different speeds. This research lays the groundwork for the development of autonomous avian-informed drones that operate under diverse wind conditions, emphasizing the role of morphing in improving energy efficiency.
Efficient Fault Tolerance for Pipelined Query Engines via Write-ahead Lineage

Modern distributed pipelined query engines either do not support intra-query fault tolerance or employ high-overhead approaches such as persisting intermediate outputs or checkpointing state. In this work, we present write-ahead lineage, a novel fault recovery technique that combines Spark's lineage-based replay and write-ahead logging. Unlike Spark, where the lineage is determined before query execution, write-ahead lineage persistently logs lineage at runtime to support dynamic task dependencies in pipelined query engines. Since only KB-sized lineages are persisted instead of MB-sized intermediate outputs, the normal execution overhead is minimal compared to spooling or checkpointing based approaches. To ensure fast fault recovery times, tasks only consume intermediate outputs with persisted lineage, preventing global rollbacks upon failure. In addition, lost tasks from different stages can be recovered in a pipelined parallel manner. We implement write-ahead lineage in a distributed pipelined query engine called Quokka. We show that Quokka is around 2x faster than SparkSQL on the TPC-H benchmark with similar fault recovery performance.
RoboCertProb: Property Specification for Probabilistic RoboChart Models

RoboChart is a core notation in the RoboStar framework which brings modern modelling and formal verification technologies into software engineering for robotics. It is a timed and probabilistic domain-specific language for robotics and provides a UML-like architectural and state machine modelling. This work presents RoboCertProb for specifying quantitative properties of probabilistic robotic systems modelled in RoboChart. RoboCertProb's semantics is based on PCTL*. To interpret RoboCertProb over RoboChart models, we give a Markov semantics (DTMCs and MDPs) to RoboChart, derived from its existing transformation semantics to the PRISM language. In addition to property specification, RoboCertProb also entitles us to configure loose constants and unspecified functions and operations in RoboChart models. It allows us to set up environmental inputs to verify reactive probabilistic systems not directly supported in probabilistic model checkers like PRISM because they employ a closed-world assumption. We implement RoboCertProb in an accompanying tool of RoboChart, RoboTool, for specifying properties and automatically generating PRISM properties from them to formally verify RoboChart models using PRISM. We have used it to analyse the behaviour of software controllers for two real robots: an industrial painting robot and an agricultural robot for treating plants with UV lights.
PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare

We propose PAGE, a domain-incremental adaptation strategy with past-agnostic generative replay for smart healthcare. PAGE enables generative replay without the aid of any preserved data or information from prior domains. When adapting to a new domain, it exploits real data from the new distribution and the current model to generate synthetic data that retain the learned knowledge of previous domains. By replaying the synthetic data with the new real data during training, PAGE achieves a good balance between domain adaptation and knowledge retention. In addition, we incorporate an extended inductive conformal prediction (EICP) method into PAGE to produce a confidence score and a credibility value for each detection result. This makes the predictions interpretable and provides statistical guarantees for disease detection in smart healthcare applications. We demonstrate PAGE's effectiveness in domain-incremental disease detection with three distinct disease datasets collected from commercially available WMSs. PAGE achieves highly competitive performance against state-of-the-art with superior scalability, data privacy, and feasibility. Furthermore, PAGE can enable up to 75% reduction in clinical workload with the help of EICP.
Seismic Attenuation of the Eastern Himalayan and Indo‐Burman Plate Boundary Systems, Northeast India

We use local earthquake coda waveform to characterize the seismic attenuation across northeast India. We measure temporal decay of coda amplitude to estimate coda quality factor (Qc) at frequencies 1 to 14 Hz and abstract Q0 and its frequency dependence (η). Single‐trace measurements reveal similar average Qc between 1 and 3 Hz and significant variation at higher frequencies. We combine single‐trace Qc measurements into a backprojection algorithm to compute 2‐D Qc tomography maps. The Qc maps reveal strong correlation with tectonic settings. At low frequencies (1–5 Hz) we observe relatively lower Qc in the Sikkim and Eastern Himalaya, southern Tibetan Plateau, and Bengal Basin, while the intraplate region and Indo‐Burman subduction zone have higher Qc. At higher frequencies (>5 Hz) we observe pronounced low Qc beneath Sikkim Himalaya, relatively lower Qc in the intraplate region and relatively higher Qc in the Bengal Basin and Indo‐Burman subduction zone. We compare our Q0 map with Vs tomography to characterize the crust. The lateral variation in the structure of the Himalayan thrust sheets between Sikkim and Eastern Himalaya is highlighted by low and intermediate Q0, respectively. The southern Tibetan Plateau, known to have low Vs due to elevated crustal temperatures, is characterized by low Q0, indicating associated scattering and anelastic effects. The intraplate continental crust and the cold elastic subducting oceanic lithosphere have intermediate‐to‐high Vs and are marked by high Q0. The Bengal Basin, with thick low Vs sedimentary layers overlying a rift‐faulted high Vs transitional crust, has low‐to‐intermediate Q0 due to depth averaging of attenuation characteristics.
FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation

Conventional feature selection algorithms applied to Pseudo Time-Series (PTS) data, which consists of observations arranged in sequential order without adhering to a conventional temporal dimension, often exhibit impractical computational complexities with high dimensional data. To address this challenge, we introduce a Deep Learning (DL)-based feature selection algorithm: Feature Selection through Discrete Relaxation (FSDR), tailored for PTS data. Unlike the existing feature selection algorithms, FSDR learns the important features as model parameters using discrete relaxation, which refers to the process of approximating a discrete optimisation problem with a continuous one. FSDR is capable of accommodating a high number of feature dimensions, a capability beyond the reach of existing DL-based or traditional methods. Through testing on a hyperspectral dataset (i.e., a type of PTS data), our experimental results demonstrate that FSDR outperforms three commonly used feature selection algorithms, taking into account a balance among execution time, $R^2$, and $RMSE$.
Nodal d-wave pairing from spin fluctuations in a thermally disordered anti-ferromagnet

We consider electron pairing in a two-dimensional thermally disordered itinerant anti-ferromagnet. It is shown that transverse spin fluctuations in such a state can give rise to superconductivity with a sizeable critical temperature $T_c$. Below $T_c$ there is quasi-long-range spin-singlet and $d_{x^2-y^2}$ superconducting order, together with fluctuating triplet order at momentum $(\pi,\pi)$. The singlet pairs we find are tightly bound together, and the pair wavefunction has a purely inter-sublattice structure due to the U(1) spin rotation symmetry of the anti-ferromagnet.