Miracle-dz/UniFAR-FTUs huggingface dataset:
GRB 221009A with an Unconventional Precursor: A Typical Two-stage Collapsar Scenario?

As the brightest gamma-ray burst (GRB) ever detected, GRB 221009A may offer a chance that reveals some interesting features that are hidden in those bursts that are not so bright. There seems to be a very weak emission with a flux of 10−8 ∼ 10−7 erg cm−2 s−1 between the first pulse (T 0 ∼ T 0 + 50 s; T 0 is the trigger time) and the main burst (which appears from T 0 + 180 s). Thus, the gap time between them is not really quiescent, and the first pulse could be taken as an unconventional precursor, which may provide a peculiar case study for GRB precursor phenomena. A two-stage collapsar scenario is proposed as the most likely origin for this burst. In this model, the jet for the precursor is produced during the initial core-collapse phase, and should be weak enough not to disrupt the star when it breaks out of the envelope, so that the fallback accretion process and the forming of the disk could continue. We present an approach in which the duration and flux both provide constraints on the luminosity (L j) and the Lorentz factor at the breakout time (Γb) of this weak jet. The estimated L j ≲ 1049 erg s−1 and Γb has an order of 10, which are well consistent with the theoretical prediction. Besides, the weak emission in the gap time could be interpreted as an MHD outflow due to a magnetically driven wind during the period from the proto-neutron-star phase to the forming of the accretion disk in this scenario.
Implicit regularization in AI meets generalized hardness of approximation in optimization - Sharp results for diagonal linear networks

Understanding the implicit regularization imposed by neural network architectures and gradient based optimization methods is a key challenge in deep learning and AI. In this work we provide sharp results for the implicit regularization imposed by the gradient flow of Diagonal Linear Networks (DLNs) in the over-parameterized regression setting and, potentially surprisingly, link this to the phenomenon of phase transitions in generalized hardness of approximation (GHA). GHA generalizes the phenomenon of hardness of approximation from computer science to, among others, continuous and robust optimization. It is well-known that the $\ell^1$-norm of the gradient flow of DLNs with tiny initialization converges to the objective function of basis pursuit. We improve upon these results by showing that the gradient flow of DLNs with tiny initialization approximates minimizers of the basis pursuit optimization problem (as opposed to just the objective function), and we obtain new and sharp convergence bounds w.r. t. \ the initialization size. Non-sharpness of our results would imply that the GHA phenomenon would not occur for the basis pursuit optimization problem -- which is a contradiction -- thus implying sharpness. Moreover, we characterize $\textit{which}$ $\ell_1$ minimizer of the basis pursuit problem is chosen by the gradient flow whenever the minimizer is not unique. Interestingly, this depends on the depth of the DLN.
Coherent Electric-Field Control of Orbital state in a Neutral Nitrogen-Vacancy Center

The coherent control of the orbital state is crucial for color centers in diamonds for realizing extremely low-power manipulation. Here, we propose the neutrally charged nitrogen-vacancy center, NV$^0$, as an ideal system for orbital control through electric fields. We estimate electric susceptibility in the ground state of NV$^0$ to be comparable to that in the excited state of NV$^-$. Also, we demonstrate coherent control of the orbital states of NV$^0$. The required power for orbital control is three orders of magnitude smaller than that for spin control, highlighting the potential for interfacing a superconducting qubit operated in a dilution refrigerator.
Near-infrared Spectra of a Sample of Galactic Unclassified B[e] Stars

B[e] stars are surrounded by cool and dense circumstellar environments where molecules and dust are formed. These stars are rare and display complex spectra whose photospheric features are frequently veiled by their circumstellar envelopes, making it difficult to assign them spectral types and evolutionary phases. On the basis of a detailed analysis of medium-resolution spectra in the near-infrared region, together with optical observations and available data from astronomical databases, we attempt to improve their spectral classification and infer the properties of their circumstellar environments. To this aim, we have selected a group of five bright IRAS sources (IRAS 02155+6410, MWC 728, AS 119, MWC 819, and IRAS 07080+0605): one star with large infrared excess and four unclassified B[e] stars or FS CMa stars, most of which have been poorly studied. We present and describe our observations and discuss possible classifications of the sample objects. For all stars, medium-resolution spectra in the region of 2.17–2.39 μm and in the L band are shown for the first time. Infrared features, such as the absorption molecular CO bands and metallic lines, reveal the presence of a late-type companion in MWC 728, and suggest the possible binary nature in AS 119. IRAS 07080+0605 shows evidence of a surrounding cool CO molecular cloud. CS molecular emission may be present in MWC 819 L-band spectra. We suggest that this object is a protoplanetary nebulae.
Generating entanglement by quantum resetting

We consider a closed quantum system subjected to stochastic Poissonian resetting with rate $r$ to its initial state. Resetting drives the system to a nonequilibrium stationary state (NESS) with a mixed density matrix which has both classical and quantum correlations. We provide a general framework to study these NESS correlations for a closed quantum system with a general Hamiltonian $H$. We then apply this framework to a simple model of a pair of ferromagnetically coupled spins, starting from state $\mid\downarrow\downarrow \rangle$ and resetting to the same state with rate $r$. We compute exactly the NESS density matrix of the full system. This then provides access to three basic observables, namely (i) the von Neumann entropy of a subsystem (ii) the fidelity between the NESS and the initial density matrix and (iii) the concurrence in the NESS (that provides a measure of the quantum entanglement in a mixed state), as a function of the two parameters: the resetting rate and the interaction strength. One of our main conclusions is that a nonzero resetting rate and a nonzero interaction strength generates quantum entanglement in the NESS (quantified by a nonzero concurrence) and moreover this concurrence can be maximized by appropriately choosing the two parameters. Our results show that quantum resetting provides a simple and effective mechanism to enhance entanglement between two parts of an interacting quantum system.
Distributed Planning for Rigid Robot Formations Using Consensus on the Transformation of a Base Configuration

Coordinated movement among multiple robots is crucial in various multi-robot operations. This paper presents a novel approach to multi-robot formation planning. The proposed method builds upon distributed feasible rigid for-mation planning, finding a set of motions that allow robots to navigate while keeping an a priori determined formation shape. By mapping desired velocities into a parameter space, performing consensus and constraint steps, and remapping to robot velocities, the algorithm ensures that the robots maintain formation. The efficacy and runtime efficiency have been evaluated through simulations, demonstrating its promise for deployment on computation-limited robots.
Bankrupting DoS Attackers

To defend against denial-of-service (DoS) attacks, we employ a technique called resource burning (RB). RB is the verifiable expenditure of a resource, such as computational power, required from clients before receiving service from the server. To the best of our knowledge, we present the first DoS defense algorithms where the algorithmic cost -- the cost to both the server and the honest clients -- is bounded as a function of the attacker's cost. We model an omniscient, Byzantine attacker, and a server with access to an estimator that estimates the number of jobs from honest clients in any time interval. We examine two communication models: an idealized zero-latency model and a partially synchronous model. Notably, our algorithms for both models have asymptotically lower costs than the attacker's, as the attacker's costs grow large. Both algorithms use a simple rule to set required RB fees per job. We assume no prior knowledge of the number of jobs, the adversary's costs, or even the estimator's accuracy. However, these quantities parameterize the algorithms' costs. We also prove a lower bound on the cost of any randomized algorithm. This lower bound shows that our algorithms achieve asymptotically tight costs as the number of jobs grows unbounded, whenever the estimator output is accurate to within a constant factor.
Local type conditions for the $C^*$-crossed product and sufficient families of representations

The local trajectories method establishes invertibility in an algebra $\mathcal{B}= \operatorname{alg}(\mathcal{A}, U_G)$, for a unital $C^*$-algebra $\mathcal{A}$ and a unitary group action $U_g$, $g\in G$, of a discrete amenable group $G$ on $\mathcal{A}$. We introduce here a local type condition that allows to establish an isomorphism between $\mathcal{B}$ and a $C^*$-crossed product, which is fundamental for the method to work. The influence of the structure of the fixed points of the group action is analysed and a new equivalent condition is introduced that applies when the action is not topologically free. If $\mathcal{A}$ is commutative, the referred conditions are related to the subalgebra $\operatorname{alg}(U_G)$ yielding, in particular, a sufficient condition that depends only on the action. It is shown that in $\pi(\mathcal{B})$, with $\pi$ the local trajectories representation, the local type condition is verified, which allows establishing the isomorphism on the local trajectories method. It is analysed whether the family of representations of the local trajectories method is sufficient using the notions of strictly norming and exhaustive families. It is shown that if $\mathcal{A}$ is a commutative algebra or a matrix algebra of continuous functions the family is sufficient.
An Analysis of Dialogue Repair in Virtual Voice Assistants

Language speakers often use what are known as repair initiators to mend fundamental disconnects that occur between them during verbal communication. Previous research in this field has mainly focused on the human-to-human use of repair initiator. We proposed an examination of dialogue repair structure wherein the dialogue initiator is human and the party that initiates or responds to the repair is a virtual assistant. This study examined the use of repair initiators in both English and Spanish with two popular assistants, Google Assistant and Apple's Siri. Our aim was to codify the differences, if any, in responses by voice assistants to dialogues in need of repair as compared to human-human dialogues also in need of repair. Ultimately the data demonstrated that not only were there differences between human-assistant and human-human dialogue repair strategies, but that there were likewise differences among the assistants and the languages studied.
High Efficiency Raman Lasers Based on Zr-Doped Silica Hybrid Microcavities

Raman lasers form a particularly unique and versatile class of optically pumped laser. Because they rely on stimulated Raman scattering (SRS), which is governed by the vibrational frequency of the gain material and not electronic transitions, the emission wavelength is determined by the pump wavelength, enabling broad tunability. Ultrahigh quality factor (Q) silica optical resonators are an ideal platform for Raman lasers because the long photon lifetime results in large circulating optical intensities. Previous work has demonstrated extremely low thresholds using these devices. However, only moderate Raman lasing efficiencies have been achieved in silica devices. In the present work, we demonstrate that a zirconium (Zr)-doped silica sol–gel coating can improve the performance of a silica microcavity Raman laser. Several concentrations of Zr-doped sol–gel are synthesized. The intrinsic Raman gain of the Zr-doped silica is measured using Raman spectroscopy, and the values show a clear dependence on Zr dopa.
Combining multitemporal optical and SAR data for LAI imputation with BiLSTM network

The Leaf Area Index (LAI) is vital for predicting winter wheat yield. Acquisition of crop conditions via Sentinel-2 remote sensing images can be hindered by persistent clouds, affecting yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery, and the ratio between its cross- and co-polarized channels (C-band) shows a high correlation with time series LAI over winter wheat regions. This study evaluates the use of time series Sentinel-1 VH/VV for LAI imputation, aiming to increase spatial-temporal density. We utilize a bidirectional LSTM (BiLSTM) network to impute time series LAI and use half mean squared error for each time step as the loss function. We trained models on data from southern Germany and the North China Plain using only LAI data generated by Sentinel-1 VH/VV and Sentinel-2. Experimental results show BiLSTM outperforms traditional regression methods, capturing nonlinear dynamics between multiple time series. It proves robust in various growing conditions and is effective even with limited Sentinel-2 images. BiLSTM's performance surpasses that of LSTM, particularly over the senescence period. Therefore, BiLSTM can be used to impute LAI with time-series Sentinel-1 VH/VV and Sentinel-2 data, and this method could be applied to other time-series imputation issues.
Graph Search Trees and Their Leaves

Graph searches and their respective search trees are widely used in algorithmic graph theory. The problem whether a given spanning tree can be a graph search tree has been considered for different searches, graph classes and search tree paradigms. Similarly, the question whether a particular vertex can be visited last by some search has been studied extensively in recent years. We combine these two problems by considering the question whether a vertex can be a leaf of a graph search tree. We show that for particular search trees, including DFS trees, this problem is easy if we allow the leaf to be the first vertex of the search ordering. We contrast this result by showing that the problem becomes hard for many searches, including DFS and BFS, if we forbid the leaf to be the first vertex. Additionally, we present several structural and algorithmic results for search tree leaves of chordal graphs.
SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes

In this work, we present SynTable, a unified and flexible Python-based dataset generator built using NVIDIA's Isaac Sim Replicator Composer for generating high-quality synthetic datasets for unseen object amodal instance segmentation of cluttered tabletop scenes. Our dataset generation tool can render a complex 3D scene containing object meshes, materials, textures, lighting, and backgrounds. Metadata, such as modal and amodal instance segmentation masks, occlusion masks, depth maps, bounding boxes, and material properties, can be generated to automatically annotate the scene according to the users' requirements. Our tool eliminates the need for manual labeling in the dataset generation process while ensuring the quality and accuracy of the dataset. In this work, we discuss our design goals, framework architecture, and the performance of our tool. We demonstrate the use of a sample dataset generated using SynTable by ray tracing for training a state-of-the-art model, UOAIS-Net. The results show significantly improved performance in Sim-to-Real transfer when evaluated on the OSD-Amodal dataset. We offer this tool as an open-source, easy-to-use, photorealistic dataset generator for advancing research in deep learning and synthetic data generation.
Energization of Charged Test Particles in Magnetohydrodynamic Fields: Waves versus Turbulence Picture

Direct numerical simulations of three-dimensional compressible magnetohydrodynamic (MHD) turbulence have been performed in order to study the relation between wave modes and coherent structures and the consequent energization of test particles. Moreover, the question of which is the main mechanism of this particle energization is rigorously discussed. In particular, using the same initial conditions, we analyzed the nonlinear and linear evolution of a turbulent state along with the case of randomized phases. Then, the behaviors of the linear and nonlinear simulations were compared through the study of the time evolution of particle kinetic energy and preferential concentration. Also, spatiotemporal spectra were used to identify the presence of wave modes and quantify the fraction of energy around the MHD modes in linear and nonlinear simulations. Finally, the variation of the correlation time of the external forcing is studied in detail along with the effect on the particle energization (and clustering) and the presence of wave modes. More specifically, particle energization tends to decrease when the fraction of linear energy increases, supporting the idea that energization by structures is the dominant mechanism for particle energization instead of resonance with wave modes as suggested by Fermi energization theory.
SLSSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion

Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest due to their low power features, high biological plausibility, and strong spatiotemporal information processing capability. Although adopting a surrogate gradient (SG) makes the non-differentiability SNN trainable, achieving comparable accuracy for ANNs and keeping low-power features simultaneously is still tricky. In this paper, we proposed an energy-efficient spike-train level spiking neural network (SLSSNN) with low computational cost and high accuracy. In the SLSSNN, spatio-temporal conversion blocks (STCBs) are applied to replace the convolutional and ReLU layers to keep the low power features of SNNs and improve accuracy. However, SLSSNN cannot adopt backpropagation algorithms directly due to the non-differentiability nature of spike trains. We proposed a suitable learning rule for SLSSNNs by deducing the equivalent gradient of STCB. We evaluate the proposed SLSSNN on static and neuromorphic datasets, including Fashion-Mnist, Cifar10, Cifar100, TinyImageNet, and DVS-Cifar10. The experiment results show that our proposed SLSSNN outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time steps and being highly energy-efficient.
Flexible Beamforming in B5G for Improving Tethered UAV Coverage over Smart Environments

Unmanned Aerial Vehicles (UAVs) are being used for wireless communications in smart environments. However, the need for mobility, scalability of data transmission over wide areas, and the required coverage area make UAV beamforming essential for better coverage and user experience. To this end, we propose a flexible beamforming approach to improve tethered UAV coverage quality and maximize the number of users experiencing the minimum required rate in any target environment. Our solution demonstrates a significant achievement in flexible beamforming in smart environments, including urban, suburban, dense, and high-rise urban. Furthermore, the beamforming gain is mainly concentrated in the target to improve the coverage area based on various scenarios. Simulation results show that the proposed approach can achieve a significantly received flexible power beam that focuses the transmitted signal towards the receiver and improves received power by reducing signal power spread. In the case of no beamforming, signal power spreads out as distance increases, reducing the signal strength. Furthermore, our proposed solution is suitable for improving UAV coverage and reliability in smart and harsh environments.
Scalable Resampling in Massive Generalized Linear Models via Subsampled Residual Bootstrap

Residual bootstrap is a classical method for statistical inference in regression settings. With massive data sets becoming increasingly common, there is a demand for computationally efficient alternatives to residual bootstrap. We propose a simple and versatile scalable algorithm called subsampled residual bootstrap (SRB) for generalized linear models (GLMs), a large class of regression models that includes the classical linear regression model as well as other widely used models such as logistic, Poisson and probit regression. We prove consistency and distributional results that establish that the SRB has the same theoretical guarantees under the GLM framework as the classical residual bootstrap, while being computationally much faster. We demonstrate the empirical performance of SRB via simulation studies and a real data analysis of the Forest Covertype data from the UCI Machine Learning Repository.
Passenger flow anomaly detection in urban rail transit networks with graph convolution network–informer and Gaussian Bayes models

Passenger flow anomaly detection in urban rail transit networks (URTNs) is critical in managing surging demand and informing effective operations planning and controls in the network. Existing studies have primarily focused on identifying the source of anomalies at a single station by analysing the time-series characteristics of passenger flow. However, they ignored the high-dimensional and complex spatial features of passenger flow and the dynamic behaviours of passengers in URTNs during anomaly detection. This article proposes a novel anomaly detection methodology based on a deep learning framework consisting of a graph convolution network (GCN)–informer model and a Gaussian naive Bayes model. The GCN–informer model is used to capture the spatial and temporal features of inbound and outbound passenger flows, and it is trained on normal datasets. The Gaussian naive Bayes model is used to construct a binary classifier for anomaly detection, and its parameters are estimated by feeding the normal and abnormal test data into the trained GCN–informer model. Experiments are conducted on a real-world URTN passenger flow dataset from Beijing. The results show that the proposed framework has superior performance compared to existing anomaly detection algorithms in detecting network-level passenger flow anomalies. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Machine learning-based rail corrugation recognition: a metro vehicle response and noise perspective

Rail corrugation is a common problem in metro lines, and its efficient recognition is always an issue worth studying. To recognize the wavelength and amplitude of rail corrugation, a particle probabilistic neural network (PPNN) algorithm is developed. The PPNN is incorporated with the particle swarm optimization algorithm and the probabilistic neural network. On the basis of the above, the in-vehicle noise characteristics measured in the field are used to recognize normal rail wavelengths of 30 and 50 mm. A stepwise moving window search algorithm suitable for selecting features with a fixed order was developed to select in-vehicle noise features. Sound pressure levels at 400, 500, 630 and 800 Hz of in-vehicle noise are fed into the PPNN, and the average accuracy can reach 96.43%. The bogie acceleration characteristics calculated by the multi-body dynamics simulation model are used to recognize normal rail amplitudes of 0.1 and 0.2 mm. The bogie acceleration is decomposed by the complete ensemble empirical mode decomposition with adaptive noise, and a reconstructional signal is obtained. The energy entropy of the reconstructional signal is fed into the PPNN, and the average accuracy can reach 95.40%. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.