Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraints on the Cosmological Parameters with Three-Parameter Correlation of Gamma-Ray Bursts

As one of the most energetic and brightest events, gamma-ray bursts (GRBs) can be treated as a promising probe of the high-redshift universe. Similar to type Ia supernovae, GRBs with the same physical origin could be treated as standard candles. We select GRB samples with the same physical origin, which are divided into two groups. The first group consists of 31 GRBs with a plateau phase feature of a constant luminosity followed by a decay index of about −2 in the X-ray afterglow light curves, and the second group has 50 GRBs with a shallow decay phase in the optical light curves. For the selected GRB samples, we confirm that there is a tight correlation between the plateau luminosity L 0, the end time of plateau t b , and the isotropic energy release E γ,iso. We also find that the L 0–t b − E γ,iso correlation is insensitive to the cosmological parameters and that no valid limitations on the cosmological parameters can be obtained using this correlation. We explore a new three-parameter correlation L 0, t b , and the spectral peak energy in the rest-frame E p,i (L 0–t b –E p,i ), and find that this correlation can be used as a standard candle to constrain the cosmological parameters. By employing the optical sample only, we find the constraints of Ωm=0.697−0.278+0.402(1σ) for a flat ΛCDM model. For the non-flat ΛCDM model, the best-fitting results are Ωm=0.713−0.278+0.346 , ΩΛ=0.981−0.580+0.379(1σ) . For the combination of the X-ray and optical samples, we find Ωm=0.313−0.125+0.179(1σ) for a flat ΛCDM model, and Ωm=0.344−0.112+0.176 , ΩΛ=0.770−0.416+0.366(1σ) for a non-flat ΛCDM model.
Feature screening for clustering analysis

In this paper, we consider feature screening for ultrahigh dimensional clustering analyses. Based on the observation that the marginal distribution of any given feature is a mixture of its conditional distributions in different clusters, we propose to screen clustering features by independently evaluating the homogeneity of each feature's mixture distribution. Important cluster-relevant features have heterogeneous components in their mixture distributions and unimportant features have homogeneous components. The well-known EM-test statistic is used to evaluate the homogeneity. Under general parametric settings, we establish the tail probability bounds of the EM-test statistic for the homogeneous and heterogeneous features, and further show that the proposed screening procedure can achieve the sure independent screening and even the consistency in selection properties. Limiting distribution of the EM-test statistic is also obtained for general parametric distributions. The proposed method is computationally efficient, can accurately screen for important cluster-relevant features and help to significantly improve clustering, as demonstrated in our extensive simulation and real data analyses.
Finite-time Lyapunov exponents of deep neural networks

We compute how small input perturbations affect the output of deep neural networks, exploring an analogy between deep feed-forward networks and dynamical systems, where the growth or decay of local perturbations is characterized by finite-time Lyapunov exponents. We show that the maximal exponent forms geometrical structures in input space, akin to coherent structures in dynamical systems. Ridges of large positive exponents divide input space into different regions that the network associates with different classes. These ridges visualize the geometry that deep networks construct in input space, shedding light on the fundamental mechanisms underlying their learning capabilities.
PEBO-SLAM: Observer design for visual inertial SLAM with convergence guarantees

This paper introduces a new linear parameterization to the problem of visual inertial simultaneous localization and mapping (VI-SLAM) -- without any approximation -- for the case only using information from a single monocular camera and an inertial measurement unit. In this problem set, the system state evolves on the nonlinear manifold $SE(3)\times \mathbb{R}^{3n}$, on which we design dynamic extensions carefully to generate invariant foliations, such that the problem can be reformulated into online \emph{constant parameter} identification, then interestingly with linear regression models obtained. It demonstrates that VI-SLAM can be translated into a linear least squares problem, in the deterministic sense, \emph{globally} and \emph{exactly}. Based on this observation, we propose a novel SLAM observer, following the recently established parameter estimation-based observer (PEBO) methodology. A notable merit is that the proposed observer enjoys almost global asymptotic stability, requiring neither persistency of excitation nor uniform complete observability, which, however, are widely adopted in most existing works with provable stability but can hardly be assured in many practical scenarios.
Vortex Shedding Characteristics on an Airfoil at Low Reynolds Numbers

Characteristics of shear layer roll-up vortices and wake vortices forming on an airfoil in low Reynolds number flows are investigated. The focus is on the effect of the Reynolds number on the shedding frequency of these coherent structures. The pertinent experiments were conducted on a NACA 0025 airfoil for a range of Reynolds numbers from 55,000 to 210,000 and three angles of attack. The shedding frequency of the shear layer roll-up vortices is found to exhibit a power law dependency on the Reynolds number; whereas, the wake vortex shedding frequency varies almost linearly with the Reynolds number. However, the results demonstrate that these correlations depend significantly on the shear layer behavior. Moreover, in contrast to flows over circular cylinders, the ratio of the two frequencies does not exhibit a power low dependency on the Reynolds number.
Comparative analysis of interacting stepped dark radiation

Models which address both the Hubble and $S_8$ tensions with the same mechanism generically cause a pre-recombination suppression of the small scale matter power spectrum. Here we focus on two such models. Both models introduce a self-interacting dark radiation fluid scattering with dark matter, which has a step in its abundance around some transition redshift. In one model, the interaction is weak and with all of the dark matter whereas in the other it is strong but with only a fraction of the dark matter. The weakly interacting case is able to address both tensions simultaneously and provide a good fit to a the Planck measurements of the cosmic microwave background (CMB), the Pantheon Type Ia supernovae, and a combination of low and high redshift baryon acoustic oscillation data, whereas the strongly interacting model cannot significantly ease both tensions simultaneously. The addition of high-resolution cosmic microwave background (CMB) measurements (ACT DR4 and SPT-3G) slightly limits both model's ability to address the Hubble tension. The use of the effective field theory of large-scale structures analysis of BOSS DR12 LRG and eBOSS DR16 QSO data additionally limits their ability to address the $S_8$ tension. We explore how these models respond to these data sets in detail in order to draw general conclusions about what is required for a mechanism to address both tensions. We find that in order to fit the CMB data the time dependence of the suppression of the matter power spectrum plays a central role.
Fantastic Fits with fantasy of Active Galactic Nuclei Spectra: Exploring the Fe ii Emission near the Hα Line

In this study, a refined approach for multicomponent fitting of active galactic nuclei (AGNs) spectra is presented utilizing the newly developed Python code Fully Automated pythoN Tool for AGN Spectra analYsis (fantasy). AGN spectra are modeled by simultaneously considering the underlying broken power-law continuum, predefined emission line lists, and an Fe ii model, which is here extended to cover the wavelength range 3700–11000 Å. The Fe ii model, founded solely on atomic data, effectively describes the extensive emission of the complex iron ion in the vicinity of the Hγ and Hβ lines, as well as near the Hα line, which was previously rarely studied. The proposed spectral fitting approach is tested on a sample of high-quality AGN spectra from the Sloan Digital Sky Survey Data Release 17. The results indicate that when Fe ii emission is present near Hβ, it is also detected redward from Hα, potentially contaminating the broad Hα line wings and thus affecting the measurements of its flux and width. The production of Fe ii emission is found to be strongly correlated with Eddington luminosity and appears to be controlled by a similar mechanism as the hydrogen Balmer lines. The study highlights the benefits of fitting type 1 AGN spectra with the fantasy code, pointing that it may be used as a robust tool for analyzing a large number of AGN spectra in the coming spectral surveys.
STAR-RIS-Assisted Privacy Protection in Semantic Communication System

Semantic communication (SemCom) has emerged as a promising architecture in the realm of intelligent communication paradigms. SemCom involves extracting and compressing the core information at the transmitter while enabling the receiver to interpret it based on established knowledge bases (KBs). This approach enhances communication efficiency greatly. However, the open nature of wireless transmission and the presence of homogeneous KBs among subscribers of identical data type pose a risk of privacy leakage in SemCom. To address this challenge, we propose to leverage the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to achieve privacy protection in a SemCom system. In this system, the STAR-RIS is utilized to enhance the signal transmission of the SemCom between a base station and a destination user, as well as to covert the signal to interference specifically for the eavesdropper (Eve). Simulation results demonstrate that our generated task-level disturbance outperforms other benchmarks in protecting SemCom privacy, as evidenced by the significantly lower task success rate achieved by Eve.
DreamEdit: Subject-driven Image Editing

Subject-driven image generation aims at generating images containing customized subjects, which has recently drawn enormous attention from the research community. However, the previous works cannot precisely control the background and position of the target subject. In this work, we aspire to fill the void and propose two novel subject-driven sub-tasks, i.e., Subject Replacement and Subject Addition. The new tasks are challenging in multiple aspects: replacing a subject with a customized one can change its shape, texture, and color, while adding a target subject to a designated position in a provided scene necessitates a context-aware posture. To conquer these two novel tasks, we first manually curate a new dataset DreamEditBench containing 22 different types of subjects, and 440 source images with different difficulty levels. We plan to host DreamEditBench as a platform and hire trained evaluators for standard human evaluation. We also devise an innovative method DreamEditor to resolve these tasks by performing iterative generation, which enables a smooth adaptation to the customized subject. In this project, we conduct automatic and human evaluations to understand the performance of DreamEditor and baselines on DreamEditBench. For Subject Replacement, we found that the existing models are sensitive to the shape and color of the original subject. The model failure rate will dramatically increase when the source and target subjects are highly different. For Subject Addition, we found that the existing models cannot easily blend the customized subjects into the background smoothly, leading to noticeable artifacts in the generated image. We hope DreamEditBench can become a standard platform to enable future investigations toward building more controllable subject-driven image editing. Our project homepage is https://dreameditbenchteam.github. io/.
Oscillator strengths for high-excitation Ti II from laboratory measurements and calculations

This work reports new experimental radiative lifetimes of six 3d2(3F)5s levels in singly ionized titanium, with an energy around 63 000 cm-1 and four 3d2(3F)4p odd parity levels where we confirm previous investigations. Combining the new 5s lifetimes with branching fractions measured previously by Pickering et al., we report 57 experimental log gf values for transitions from the 5s levels. The lifetime measurements are performed using time-resolved laser-induced fluorescence on ions produced by laser ablation. One- and two-step photon excitation is employed to reach the 4p and 5s levels, respectively. Theoretical calculations of the radiative lifetimes of the measured levels as well as of oscillator strengths for 3336 transitions from these levels are reported. The calculations are carried out by a pseudorelativistic Hartree-Fock method taking into account core-polarization effects. The theoretical results are in a good agreement with the experiments and are needed for accurate abundance determinations in astronomical objects. (Less)
Natural Language Generation for Advertising: A Survey

Natural language generation methods have emerged as effective tools to help advertisers increase the number of online advertisements they produce. This survey entails a review of the research trends on this topic over the past decade, from template-based to extractive and abstractive approaches using neural networks. Additionally, key challenges and directions revealed through the survey, including metric optimization, faithfulness, diversity, multimodality, and the development of benchmark datasets, are discussed.
XACML Extension for Graphs: Flexible Authorization Policy Specification and Datastore-independent Enforcement

The increasing use of graph-structured data for business- and privacy-critical applications requires sophisticated, flexible and fine-grained authorization and access control. Currently, role-based access control is supported in graph databases, where access to objects is restricted via roles. This does not take special properties of graphs into account such as vertices and edges along the path between a given subject and resource. In previous iterations of our research, we started to design an authorization policy language and access control model, which considers the specification of graph paths and enforces them in the multi-model database ArangoDB. Since this approach is promising to consider graph characteristics in data protection, we improve the language in this work to provide flexible path definitions and specifying edges as protected resources. Furthermore, we introduce a method for a datastore-independent policy enforcement. Besides discussing the latest work in our XACML4G model, which is an extension to the Extensible Access Control Markup Language (XACML), we demonstrate our prototypical implementation with a real case and give an outlook on performance.
Off the Radar: Uncertainty-Aware Radar Place Recognition with Introspective Querying and Map Maintenance

Localisation with Frequency-Modulated Continuous-Wave (FMCW) radar has gained increasing interest due to its inherent resistance to challenging environments. However, complex artefacts of the radar measurement process require appropriate uncertainty estimation - to ensure the safe and reliable application of this promising sensor modality. In this work, we propose a multi-session map management system which constructs the “best” maps for further localisation based on learned variance properties in an embedding space. Using the same variance properties, we also propose a new way to introspectively reject localisation queries that are likely to be incorrect. For this, we apply robust noise-aware metric learning, which both leverages the short-timescale variability of radar data along a driven path (for data augmentation) and predicts the downstream uncertainty in metric-space-based place recognition. We prove the effectiveness of our method over extensive cross-validated tests of the Oxford Radar RobotCar and MulRan dataset. In this, we outperform the current state-of-the-art in radar place recognition and other uncertainty-aware methods when using only single nearest-neighbour queries. We also show consistent performance increases when rejecting queries based on uncertainty over a difficult test environment, which we did not observe for a competing uncertainty-aware place recognition system.
Watt-Class CMOS-Compatible Power Amplifier

Watt-level optical amplifiers have various applications such as laser detection and ranging, modelocked lasers, frequency combs and laser surgery. However, power amplifiers are usually limited to solid-state and fibre-based technologies, as in integrated photonics the very property of tight mode confinement becomes an impediment to high power applications due to small optical mode cross-section, and thus limiting powers to a few 10s of milliwatt range at best [1]. Semiconductor based slab coupled waveguide amplifiers [2] can allow for large mode area thus high power; the integration, however, has proven to be challenging. Rare earth doped chip-scale amplifier recently reached signal level > 100 mW, however, that is at the expense of a complex fabrication process [3]. The situation is even worse beyond telecom wavelength window, in the mid-infrared, where only a few mWs of amplified signal has so far been achieved [4]. Here, we demonstrate for the first time a CMOS compatible watt-scale power amplifier with signal amplification up to >0.9 W in a compact footprint of 3.6mm2 in the eye safe window. The signal power reaches a level enjoyed by commercial bench top fiber amplifiers [5].
A Prior Regularized Full Waveform Inversion Using Generative Diffusion Models

Full waveform inversion (FWI) has the potential to provide high-resolution subsurface model estimations. However, due to limitations in observation, e.g., regional noise, limited aperture, and band-limited data, it is hard to obtain the desired high-resolution model with FWI. To address this challenge, we propose a new paradigm for FWI regularized by generative diffusion model. Specifically, we pretrain a diffusion model in a fully unsupervised manner on a prior velocity model distribution that represents our expectations of the subsurface and then adapt it to the seismic observations by incorporating the FWI into the sampling process of the generative diffusion models. What makes diffusion models uniquely appropriate for such an implementation is that the generative process retains the form and dimensions of the velocity model. Numerical examples demonstrate that our method can outperform the conventional FWI with only negligible additional computational cost. Even in cases of very sparse observations or observations with strong noise, the proposed method could still reconstruct a high-quality subsurface model. Thus, we can incorporate our prior expectations of the solutions in an efficient manner. We further test this approach on field data, which demonstrates the effectiveness of the proposed method.
Speech Emotion Diarization: Which Emotion Appears When?

Speech Emotion Recognition (SER) typically relies on utterance-level solutions. However, emotions conveyed through speech should be considered as discrete speech events with definite temporal boundaries, rather than attributes of the entire utterance. To reflect the fine-grained nature of speech emotions and to unify various fine-grained methods under a single objective, we propose a new task: Speech Emotion Diarization (SED). Just as Speaker Diarization answers the question of “Who speaks when? ”, Speech Emotion Diarization answers the question of “Which emotion appears when? ”. To facilitate the evaluation of the performance and establish a common benchmark, we introduce the Zaion Emotion Dataset (ZED), an openly accessible speech emotion dataset that includes non-acted emotions recorded in real-life conditions, along with manually annotated boundaries of emotion segments within the utterance. We provide competitive baselines and open-source the code and the pre-trained models.
Physics–guided neural networks for inversion–based feedforward control applied to hybrid stepper motors*

Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics. The increasing need for productivity and efficiency without increasing the manufacturing costs calls for innovative control design. Feedforward control is typically used in tracking control problems, where the desired reference is known in advance. In most applications, this is the case for HSMs, which need to track a periodic angular velocity and angular position reference. Performance achieved by feed-forward control is limited by the accuracy of the available model describing the inverse system dynamics. In this work, we develop a physics–guided neural network (PGNN) feedforward controller for HSMs, which can learn the effect of parasitic forces from data and compensate for it, resulting in improved accuracy. Indeed, experimental results on an HSM used in printing industry show that the PGNN outperforms conventional benchmarks in terms of the mean–absolute tracking error.
MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge

Cascade systems comprise a two-model sequence, a lightweight model processing all samples and a heavier model conditionally refining harder samples to improve accuracy. By placing the light model on the device side and the heavy model on a server, model cascades constitute a widely used distributed inference approach. With the rapid expansion of intelligent indoor environments, the new setting of Multi-Device Cascade is emerging where multiple and diverse devices are simultaneously using a shared heavy model on the same server, typically located close to the consumer environment. This work presents Multi-TASC, a multi-tenancy-aware scheduler that adaptively controls the forwarding decision functions of the devices in order to maximize the system throughput, while sustaining high accuracy and low latency. By explicitly considering device heterogeneity, our scheduler improves the latency service-level objective (SLO) satisfaction rate over state-of-the-art cascade methods in highly heterogeneous setups, while serving over 40 devices, showcasing its scalability.
xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages

We introduce a new proxy score for evaluating bitext mining based on similarity in a multilingual embedding space: xsim++. In comparison to xsim, this improved proxy leverages rule-based approaches to extend English sentences in any evaluation set with synthetic, hard-to-distinguish examples which more closely mirror the scenarios we encounter during large-scale mining. We validate this proxy by running a significant number of bitext mining experiments for a set of low-resource languages, and subsequently train NMT systems on the mined data. In comparison to xsim, we show that xsim++ is better correlated with the downstream BLEU scores of translation systems trained on mined bitexts, providing a reliable proxy of bitext mining performance without needing to run expensive bitext mining pipelines. xsim++ also reports performance for different error types, offering more fine-grained feedbacks for model development.