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In this paper, we distinguish two guessing algorithms for decoding binary linear codes. One is the guessing noise decoding (GND) algorithm, and the other is the guessing codeword decoding (GCD) algorithm. We prove that the GCD is a maximum likelihood (ML) decoding algorithm and that the GCD is more efficient than GND for most practical applications. We also introduce several variants of ordered statistic decoding (OSD) to trade off the complexity of the Gaussian elimination (GE) and that of the guessing, which may find applications in decoding short block codes in the high signal-to-noise ratio (SNR) region., Comment: 6 pages, 4 figures

This paper is concerned with the ordered statistic decoding with local constraints (LC-OSD) of binary linear block codes, which is a near maximum-likelihood decoding algorithm. Compared with the conventional OSD, the LC-OSD significantly reduces both the maximum and the average number of searches. The former is achieved by performing the serial list Viterbi algorithm (SLVA) or a two-way flipping pattern tree (FPT) algorithm with local constraints on the test error patterns, while the latter is achieved by incorporating tailored early termination criteria. The main objective of this paper is to explore the relationship between the performance of the LC-OSD and decoding parameters, such as the constraint degree and the maximum list size. To this end, we approximate the local parity-check matrix as a totally random matrix and then estimate the performance of the LC-OSD by analyzing with a saddlepoint approach the performance of random codes over the channels associated with the most reliable bits (MRBs). The random coding approach enables us to derive an upper bound on the performance and predict the average rank of the transmitted codeword in the list delivered by the LC-OSD. This allows us to balance the constraint degree and the maximum list size for the average (or maximum) time complexity reduction. Simulation results show that the approximation by random coding approach is numerically effective and powerful. Simulation results also show that the RS codes decoded by the LC-OSD can approach the random coding union (RCU) bounds, verifying the efficiency and universality of the LC-OSD., Comment: This paper is a revision of https://doi.org/10.36227/techrxiv.22771085.v1

The Tsinghua University-Ma Huateng Telescope for Survey (TMTS) has been constantly monitoring the northern sky since 2020 in search of rapidly variable stars. To find variable white dwarfs (WDs), the TMTS catalog is cross-matched with the WD catalog of Gaia EDR3, resulting in over 3000 light curves of WD candidates. The WD TMTS J17184064+2524314 (hereafter J1718) is the second ZZ~Ceti star discovered among these common sources. Based on the light curves from TMTS, follow-up photometric observations, and TESS, 10 periods and 3 combination periods are detected. A rotation period of $25.12\pm0.18$ hr is derived, according to the identified rotational splitting. Our spectroscopic observation indicates that this WD belongs to DA type with $T_{\rm eff}=11,670\pm604$ K, log $g=8.16\pm0.36$, $M = 0.70\pm0.23$ M$_{\odot}$, and age=$0.51\pm0.34$ Gyr. Based on core-parameterized asteroseismological model grids ($\geqslant$ 14 million), we derive a best-fit solution of $T_{\rm eff}=11,640\pm20$ K, log $g=8.267\pm0.008$, and $M = 0.750\pm0.005$ M$_{\odot}$ for J1718, consistent with the spectral fitting results. For this WD, the corresponding carbon and oxygen abundances in the core are 0.43 and 0.57, respectively. The distance derived from the intrinsic luminosity given by asteroseismology is $64\pm15$ pc, in accord with the distance of $70.1\pm0.2$ pc from Gaia DR3 within the uncertainties., Comment: 11 pages, 8 figures, Accepted for publication in MNRAS. arXiv admin note: text overlap with arXiv:2305.11585

With the rapid proliferation of large Low Earth Orbit (LEO) satellite constellations, a huge amount of in-orbit data is generated and needs to be transmitted to the ground for processing. However, traditional LEO satellite constellations, which downlink raw data to the ground, are significantly restricted in transmission capability. Orbital edge computing (OEC), which exploits the computation capacities of LEO satellites and processes the raw data in orbit, is envisioned as a promising solution to relieve the downlink burden. Yet, with OEC, the bottleneck is shifted to the inelastic computation capacities. The computational bottleneck arises from two primary challenges that existing satellite systems have not adequately addressed: the inability to process all captured images and the limited energy supply available for satellite operations. In this work, we seek to fully exploit the scarce satellite computation and communication resources to achieve satellite-ground collaboration and present a satellite-ground collaborative system named TargetFuse for onboard object detection. TargetFuse incorporates a combination of techniques to minimize detection errors under energy and bandwidth constraints. Extensive experiments show that TargetFuse can reduce detection errors by 3.4 times on average, compared to onboard computing. TargetFuse achieves a 9.6 times improvement in bandwidth efficiency compared to the vanilla baseline under the limited bandwidth budget constraint., Comment: Accepted by IEEE INFOCOM 2024-IEEE Conference on Computer Communications

Effective field theory (EFT) provides a model-independent framework for interpreting the results of dark matter (DM) direct detection experiments. In this study, we demonstrate that the two fermionic DM-quark tensor operators, $(\bar{\chi} i\sigma^{\mu\nu} \gamma^5 \chi) (\bar{q} \sigma_{\mu\nu}q)$ and $(\bar{\chi} \sigma^{\mu\nu} \chi) (\bar{q} \sigma_{\mu\nu} q)$, can contribute to the DM electric and magnetic dipole moments via nonperturbative QCD effects, in addition to the well-studied contact DM-nucleon operators. We then investigate the constraints on these two operators by considering both the contact and the dipole contributions using the XENON1T nuclear recoil and Migdal effect data. We also recast other existing bounds on the DM dipole operators, derived from electron and nuclear recoil measurements in various direct detection experiments, as constraints on the two tensor operators. For $m_\chi \lesssim 1\,\rm GeV$, our results significantly extend the reach of constraints on the DM-quark tensor operators to masses as low as $5\,\rm MeV$, with the bound exceeding that obtained by the Migdal effect with only contact interactions by an order of magnitude or so. In particular, for the operator $(\bar{\chi} \sigma^{\mu\nu}i\gamma_5 \chi) (\bar{q} \sigma_{\mu\nu}q)$ with DM mass $m_\chi \gtrsim 10\,\rm GeV$, the latest PandaX constraint on the DM electric dipole moment puts more stringent bounds than the previous direct detection limit., Comment: v1: 19 pages, 4 figures; v2: We have updated discussion on nucleon form factors by using more recent numbers, without modifying our final numerical results; v3: Add comparisons with other constraints and provide a UV model for the generation of tensor operators from tree-level matching; To be published in CPC

Generating animation of physics-based characters with intuitive control has long been a desirable task with numerous applications. However, generating physically simulated animations that reflect high-level human instructions remains a difficult problem due to the complexity of physical environments and the richness of human language. In this paper, we present InsActor, a principled generative framework that leverages recent advancements in diffusion-based human motion models to produce instruction-driven animations of physics-based characters. Our framework empowers InsActor to capture complex relationships between high-level human instructions and character motions by employing diffusion policies for flexibly conditioned motion planning. To overcome invalid states and infeasible state transitions in planned motions, InsActor discovers low-level skills and maps plans to latent skill sequences in a compact latent space. Extensive experiments demonstrate that InsActor achieves state-of-the-art results on various tasks, including instruction-driven motion generation and instruction-driven waypoint heading. Notably, the ability of InsActor to generate physically simulated animations using high-level human instructions makes it a valuable tool, particularly in executing long-horizon tasks with a rich set of instructions., Comment: NeurIPS 2023. Project page is at https://jiawei-ren.github.io/projects/insactor

Video Correlation Learning (VCL), which aims to analyze the relationships between videos, has been widely studied and applied in various general video tasks. However, applying VCL to instructional videos is still quite challenging due to their intrinsic procedural temporal structure. Specifically, procedural knowledge is critical for accurate correlation analyses on instructional videos. Nevertheless, current procedure-learning methods heavily rely on step-level annotations, which are costly and not scalable. To address this problem, we introduce a weakly supervised framework called Collaborative Procedure Alignment (CPA) for procedure-aware correlation learning on instructional videos. Our framework comprises two core modules: collaborative step mining and frame-to-step alignment. The collaborative step mining module enables simultaneous and consistent step segmentation for paired videos, leveraging the semantic and temporal similarity between frames. Based on the identified steps, the frame-to-step alignment module performs alignment between the frames and steps across videos. The alignment result serves as a measurement of the correlation distance between two videos. We instantiate our framework in two distinct instructional video tasks: sequence verification and action quality assessment. Extensive experiments validate the effectiveness of our approach in providing accurate and interpretable correlation analyses for instructional videos., Comment: has been accepted by AAAI 24

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Myopia is a manifestation of visual impairment caused by an excessively elongated eyeball. Image data is critical material for studying high myopia and pathological myopia. Measurements of spherical equivalent and axial length are the gold standards for identifying high myopia, but the available image data for matching them is scarce. In addition, the criteria for defining high myopia vary from study to study, and therefore the inclusion of samples in automated screening efforts requires an appropriate assessment of interpretability. In this work, we propose a model called adjustable robust transformer (ARTran) for high myopia screening of optical coherence tomography (OCT) data. Based on vision transformer, we propose anisotropic patch embedding (APE) to capture more discriminative features of high myopia. To make the model effective under variable screening conditions, we propose an adjustable class embedding (ACE) to replace the fixed class token, which changes the output to adapt to different conditions. Considering the confusion of the data at high myopia and low myopia threshold, we introduce the label noise learning strategy and propose a shifted subspace transition matrix (SST) to enhance the robustness of the model. Besides, combining the two structures proposed above, the model can provide evidence for uncertainty evaluation. The experimental results demonstrate the effectiveness and reliability of the proposed method. Code is available at: https://github.com/maxiao0234/ARTran., Comment: 11 pages, 3 figures, MICCAI 2023 - Accepted Papers; International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023: 504-514

Large language model (LLM) powered chatbots are primarily text-based today, and impose a large interactional cognitive load, especially for exploratory or sensemaking tasks such as planning a trip or learning about a new city. Because the interaction is textual, users have little scaffolding in the way of structure, informational "scent", or ability to specify high-level preferences or goals. We introduce ExploreLLM that allows users to structure thoughts, help explore different options, navigate through the choices and recommendations, and to more easily steer models to generate more personalized responses. We conduct a user study and show that users find it helpful to use ExploreLLM for exploratory or planning tasks, because it provides a useful schema-like structure to the task, and guides users in planning. The study also suggests that users can more easily personalize responses with high-level preferences with ExploreLLM. Together, ExploreLLM points to a future where users interact with LLMs beyond the form of chatbots, and instead designed to support complex user tasks with a tighter integration between natural language and graphical user interfaces., Comment: 19 pages, 11 figures

Nonlocality is the defining feature of quantum entanglement. Entangled states with multiple particles are of crucial importance in fundamental tests of quantum physics as well as in many quantum information tasks. One of the archetypal multipartite quantum states, Greenberger-Horne-Zeilinger (GHZ) state, allows one to observe the striking conflict of quantum physics to local realism in the so-called all-versus-nothing way. This is profoundly different from Bell's theorem for two particles, which relies on statistical predictions. Here, we demonstrate an integrated photonic chip capable of generating and manipulating the four-photon GHZ state. We perform a complete characterization of the four-photon GHZ state using quantum state tomography and obtain a state fidelity of 0.729(6). We further use the all-versus-nothing test and the Mermin inequalities to witness the quantum nonlocality of GHZ entanglement. Our work paves the way to perform fundamental tests of quantum physics with complex integrated quantum devices., Comment: 10 pages, 3 figures

In recent years, Low Earth Orbit (LEO) satellites have witnessed rapid development, with inference based on Deep Neural Network (DNN) models emerging as the prevailing technology for remote sensing satellite image recognition. However, the substantial computation capability and energy demands of DNN models, coupled with the instability of the satellite-ground link, pose significant challenges, burdening satellites with limited power intake and hindering the timely completion of tasks. Existing approaches, such as transmitting all images to the ground for processing or executing DNN models on the satellite, is unable to effectively address this issue. By exploiting the internal hierarchical structure of DNNs and treating each layer as an independent subtask, we propose a satellite-ground collaborative computation partial offloading approach to address this challenge. We formulate the problem of minimizing the inference task execution time and onboard energy consumption through offloading as an integer linear programming (ILP) model. The complexity in solving the problem arises from the combinatorial explosion in the discrete solution space. To address this, we have designed an improved optimization algorithm based on branch and bound. Simulation results illustrate that, compared to the existing approaches, our algorithm improve the performance by 10%-18%, Comment: Accepted by ICNP 2023 Workshop

Hyperuniformity, whereby the static structure factor (or density correlator) obeys $S(q)\sim q^{\varsigma}$ with $\varsigma> 0$, emerges at criticality in systems having multiple absorbing states, such as periodically sheared suspensions. These lie in the conserved directed percolation (C-DP) universality class, for which analytic results for $\varsigma$ are lacking. Specifically, $\varsigma$ appears inaccessible within an exact `interfacial mapping' that yields other C-DP exponents via functional renormalization group (FRG). Here, using Doi-Peliti field theory for interacting particles and perturbative RG about a Gaussian model, we find $\varsigma = 0^+$ and $\varsigma= 2\epsilon/9 + O(\epsilon^2)$ in dimension $d>4$ and $d=4-\epsilon$ respectively. The latter disproves a previously conjectured scaling relation for $\varsigma$. We show how hyperuniformity emerges from anticorrelation of strongly fluctuating active and passive densities. Our calculations also yield the remaining C-DP exponents without recourse to functional RG methods.

A crucial challenge for generative large language models (LLMs) is diversity: when a user's prompt is under-specified, models may follow implicit assumptions while generating a response, which may result in homogenization of the responses, as well as certain demographic groups being under-represented or even erased from the generated responses. In this paper, we formalize diversity of representation in generative LLMs. We present evaluation datasets and propose metrics to measure diversity in generated responses along people and culture axes. We find that LLMs understand the notion of diversity, and that they can reason and critique their own responses for that goal. This finding motivated a new prompting technique called collective-critique and self-voting (CCSV) to self-improve people diversity of LLMs by tapping into its diversity reasoning capabilities, without relying on handcrafted examples or prompt tuning. Extensive empirical experiments with both human and automated evaluations show that our proposed approach is effective at improving people and culture diversity, and outperforms all baseline methods by a large margin., Comment: To appear at EMNLP 2023 main conference

As large language models (LLMs) are widely adopted, new safety issues and policies emerge, to which existing safety classifiers do not generalize well. If we have only observed a few examples of violations of a new safety rule, how can we build a classifier to detect violations? In this paper, we study the novel setting of domain-generalized few-shot learning for LLM-based text safety classifiers. Unlike prior few-shot work, these new safety issues can be hard to uncover and we do not get to choose the few examples. We demonstrate that existing few-shot techniques do not perform well in this setting, and rather we propose to do parameter-efficient fine-tuning (PEFT) combined with augmenting training data based on similar examples in prior existing rules. We empirically show that our approach of similarity-based data-augmentation + prompt-tuning (DAPT) consistently outperforms baselines that either do not rely on data augmentation or on PEFT by 7-17% F1 score in the Social Chemistry moral judgement and 9-13% AUC in the Toxicity detection tasks, even when the new rule is loosely correlated with existing ones.

Characterizing urban environments with broad coverages and high precision is more important than ever for achieving the UN's Sustainable Development Goals (SDGs) as half of the world's populations are living in cities. Urban building height as a fundamental 3D urban structural feature has far-reaching applications. However, so far, producing readily available datasets of recent urban building heights with fine spatial resolutions and global coverages remains a challenging task. Here, we provide an up-to-date global product of urban building heights based on a fine grid size of 150 m around 2020 by combining the spaceborne lidar instrument of GEDI and multi-sourced data including remotely sensed images (i.e., Landsat-8, Sentinel-2, and Sentinel-1) and topographic data. Our results revealed that the estimated method of building height samples based on the GEDI data was effective with 0.78 of Pearson's r and 3.67 m of RMSE in comparison to the reference data. The mapping product also demonstrated good performance as indicated by its strong correlation with the reference data (i.e., Pearson's r = 0.71, RMSE = 4.60 m). Compared with the currently existing products, our global urban building height map holds the ability to provide a higher spatial resolution (i.e., 150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This work will boost future urban studies across many fields including climate, environmental, ecological, and social sciences.

Structural investigations conducted using transmission electron microscopy (TEM) on LuH2 synthesized under atmospheric pressure (AP-LuH2) and nitrogen-doped LuH2 synthesized under high pressure (HP-LuH2) have revealed numerous microstructural phenomena. Both materials show a clear superstructure modulation with wave vector, q^* = 1/4 (2-20), and this modulation can be well interpreted by the displacements of Lu atoms. Further investigations on the nitrogen-doped HP-LuH2 materials reveal the appearance of high-density antiphase boundaries, in particular, domain walls of a few atomic layer thickness without structural modulation can be observed, suggesting possible interface properties could be detected in this system. In-situ TEM observations of AP-LuH2 suggest that no evident structural phase transition occurs between 94 K and 673 K., Comment: 8 pages, 7 figures

Belle II has recently reported the new measurement ${\cal B}(B^+\to K^+\nu\bar\nu)=(2.3\pm0.7)\times 10^{-5}$ \cite{Belle-II:2023esi} which is two times larger than their previous result (although consistent within errors) and about $2.7\,\sigma$ higher than the SM prediction. We re-examine new physics scenarios we have discussed previously which can enhance this rate to determine if they can accommodate the higher value reported in the new measurement. We use consistency with existing bounds on $B\to K^*\nu\bar\nu$, $b\to s \ell^+\ell^-$, $B\to D^{(*)}\ell\bar\nu$ and $B_s$ mixing to limit possible explanations for the excess. For the case of LFV neutrino couplings, we find that only two leptoquarks remain viable requiring a large $C_{9^\prime}^{\tau\tau}=-C_{10^\prime}^{\tau\tau}$. For models with different types of light dark matter particle pairs (scalar, fermion, or vector), the preliminary $q^2$ distribution from Belle II, which shows that the excess appears mostly for bins with $3\leq q^2\leq7$ GeV$^2$ \cite{Belle-II:2023esi}, implies only the vector current operators with scalar or vector dark matter particles with masses in the hundreds of MeV can match the anomaly., Comment: Correct two typos

We introduce the effective field theory of two different light dark particles interacting with the standard model (SM) light states in a single vertex, termed dark sector effective field theory (DSEFT). We focus on the new light particles with spin up to 1 and being real in essence, namely, new real scalars $\phi$ and $S$, Majorana fermions $\chi$ and $\psi$, and real vectors $X_\mu$ and $V_\mu$. In the framework of low energy effective field theory with QED and QCD symmetry, the DSEFT can be classified into six categories, including the scalar-scalar-SM ($\phi S$-SM), fermion-fermion-SM ($\chi\psi$-SM), vector-vector-SM ($X V$-SM), scalar-fermion-SM ($\phi \chi$-SM), scalar-vector-SM ($\phi X$-SM), and fermion-vector-SM ($\chi X$-SM) cases. For each case, we construct the effective operator basis up to canonical dimension 7, which will cover most interesting phenomenology at low energy. As a phenomenological example, we investigate the longstanding neutron lifetime anomaly through the neutron dark decay modes $n \to \chi \phi \text{ or } \chi X$ from the effective interactions in the fermion-scalar-SM or fermion-vector-SM case. When treating the light fermion as a dark matter candidate, we also explore the constraints from DM-neutron annihilation signal at Super-Kamiokande. We find the neutron dark decay in each scenario can accommodate the anomaly, at the same time, without contradicting with the Super-Kamiokande limit., Comment: 33 pages, 4 figures, typos are corrected and several new references are included. To appear in JHEP

K2 photometry is suitable for the exploitation of mode variability on short timescales in hot B subdwarf stars, which is important to constrain nonlinear quantities addressed by the stellar theory of high-order perturbation in the future. We analyze the $\sim80$~d high-quality K2 data collected on PG~0101+039 and extract the frequency content of oscillation. We then determine its rotational and orbital properties, as well as characterize the dynamics of amplitude and frequency. The frequencies are extracted from light curves via a standard prewhitening technique. The binary information is obtained from variations both in brightness and radial velocities. Amplitude and frequency modulation of oscillation modes are measured by piece-wise light curves and characterized by EMCMC method. We have extracted 137 independent frequencies in PG~0101+039 and derived period spacing of ~252s and 144s for the dipole and quadruple modes, respectively. We derive a rotation rate of 8.81+-0.06d and ~8.60+-0.16d based on g- and p-mode multiplets, implying a marginally differential rotation with a probability of ~ 60%. We find that the rotation period is much shorter than the orbital period of ~0.57d, indicating that this system is not synchronized. Amplitude and frequency modulation are measurable for 44 frequencies with high enough amplitude, including 12 rotational components. We characterize their modulating patterns and find a clear correlation between amplitude and frequency variation, which is linked to nonlinear resonant couplings. In general, the modulating scale and timescale are on an order of a few dozen of nano hertz and a few tens of days, respectively, whose values are important constraints to future calculations of nonlinear amplitude equations., Comment: 16 pages, 9 figures, accepted

Integrated microresonator facilitates the realization of quantum frequency comb (QFC), which provides a large number of discrete frequency modes with broadband spectral range and narrow linewidth. However, all previous demonstrations have focused on the generation of energy-time or time-bin entangled photons from QFC. Realizing polarization-entangled quantum frequency comb, which is the important resource for fundamental study of quantum mechanics and quantum information applications, remains challenging. Here, we demonstrate, for the first time, a broadband polarization-entangled quantum frequency comb by combining an integrated silicon nitride micro-resonator with a Sagnac interferometer. With a free spectral range of about 99 GHz and a narrow linewidth of about 190 MHz, our source provides 22 polarization entangled photons pairs with frequency covering the whole telecom C-band. The entanglement fidelities for all 22 pairs are above 81%, including 17 pairs with fidelities higher than 90%. Our demonstration paves the way for employing the polarization-entangled quantum frequency comb in quantum network using CMOS technology as well as standard dense wavelength division multiplexing technology., Comment: 11 pages, 9 figures

This paper is concerned with a search-number-reduced guessing random additive noise decoding (GRAND) algorithm for linear block codes, called partially constrained GRAND (PC-GRAND). In contrast to the original GRAND, which guesses error patterns without constraints, the PC-GRAND guesses only those error patterns satisfying partial constraints of the codes. In particular, the PC-GRAND takes partial rows of the parity-check matrix as constraints for generating candidate error patterns and the remaining rows as checks for validating the candidates. The number of searches can be reduced when the serial list Viterbi algorithm (SLVA) is implemented for searching over a trellis specified by the partial parity-check matrix. This is confirmed by numerical results. Numerical simulations are also provided for comparison with other decoding algorithms., Comment: 8 figures

An unsaturated linear magnetoresistance (LMR) has attracted widely attention because of potential applications and fundamental interest. By controlling growth temperature, we realized a metal-to-insulator transition in Al2O3/SrTiO3 heterostructures. The LMR is observed in metallic samples with electron mobility varying over three orders of magnitude. The observed LMR cannot be explained by the guiding center diffusion model even in samples with very high mobility. The slope of the observed LMR is proportional to Hall mobility, and the crossover field, indicating a transition from quadratic (at low fields) to linear (at high fields) field dependence, is proportional to the inverse Hall mobility. This signifies that the classical model is valid to explain the observed LMR. More importantly, we develop an analytical expression according to the effective-medium theory that is equivalent to the classical model. And the analytical expression describes the LMR data very well, confirming the validity of the classical model., Comment: 24 Pages, 4 figures, 1 table

We propose a distributed cooperative positioning algorithm using the extended Kalman filter (EKF) based spatio-temporal data fusion (STDF) for a wireless network composed of sparsely distributed high-mobility nodes. Our algorithm first makes a coarse estimation of the position and mobility state of the nodes by using the prediction step of EKF. Then it utilizes the coarse estimate as the prior of STDF that relies on factor graph (FG), thus facilitates inferring a posteriori distributions of the agents' positions in a distributed manner. We approximate the nonlinear terms of the messages passed on the associated FG with high precision by exploiting the second-order Taylor polynomial and obtain closed-form representations of each message in the data fusion step, where temporal measurements by imperfect hardware are considered additionally. In the third stage, refinement of position estimate is performed by invoking the update step of EKF. Simulation results and analysis show that our EKF-STDF has a lower computational complexity than the state-of-the-art EKF-based algorithms, while achieving an even superior positioning performance in harsh environment., Comment: 5 pages, 3 figures, accepted to appear on IEEE Communications Letters, Jun. 2023

Recently, the NANOGrav, PPTA, EPTA, and CPTA collaborations independently reported their evidence of the Stochastic Gravitational Waves Background (SGWB). While the inferred gravitational-wave background amplitude and spectrum are consistent with astrophysical expectations for a signal from the population of supermassive black-hole binaries (SMBHBs), the search for new physics remains plausible in this observational window. In this work, we explore the possibility of explaining such a signal by the scalar-induced gravitational waves (IGWs) in the very early universe. We use a parameterized broken power-law function as a general description of the energy spectrum of the SGWB and fit it to the new results of NANOGrav, PPTA and EPTA. We find that this approach can put constraints on the parameters of IGW energy spectrum and further yield restrictions on various inflation models that may produce primordial black holes (PBHs) in the early universe, which is also expected to be examined by the forthcoming space-based GW experiments., Comment: 7 pages, 2 figures, update some references