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Cosmological simulations indicate that nearly half of the baryons in the nearby Universe are in the warm-hot intergalactic medium (WHIM) phase, with about half of which residing in cosmic filaments. Recent observational studies using stacked survey data and deep exposures of galaxy cluster outskirts have detected soft X-ray excess associated with optically identified filaments. However, the physical characteristics of WHIM in filaments remain largely undetermined due to the lack of direct spectral diagnostics. In this work, we aim to select appropriate targets for WHIM characterization through pointing observations with the future Hot Universe Baryon Surveyor (HUBS) mission, which is designed with eV level energy resolution in the 0.1-2.0 keV band and a 1 square degree field-of-view. We built a sample of 1577 inter-cluster filaments based on the first eROSITA All-Sky Survey (eRASS1) supercluster catalog and estimated their soft X-ray emission, and used their modeled geometrical properties and oxygen line intensities to select four most appropriate candidate targets for HUBS observations. By simulating and analyzing their mock observations, we demonstrated that with 200 ks HUBS exposure for each candidate, the gas properties of individual filaments can be accurately determined, with the temperature constrained to +-0.01 keV, metallicity constrained to < +-0.03 solar, and density constrained to < +-10%. Elemental abundance of O, Ne, Mg, and Fe can be measured separately, providing unprecedented insights into the chemical history of the filament gas. We also showed that direct mapping of the WHIM distribution is promising with narrow-band imaging of the O viii line. Our work forecasts that next-generation X-ray missions such as HUBS will provide substantial improvement in our understanding of the physical status and evolution history of the diffuse WHIM gas in cosmic large-scale structure., Comment: 13+4 pages, 6+2 figures, submitted to A&A

Twenty-one-centimetre signals from the Epoch of Reionization (EoR) are expected to be detected in the low-frequency radio window by the next-generation interferometers, particularly the Square Kilometre Array (SKA). However, precision data analysis pipelines are required to minimize the systematics within an infinitesimal error budget. Consequently, there is a growing need to characterize the sources of errors in EoR analysis. In this study, we identify one such error origin, namely source blending, which is introduced by the overlap of objects in the densely populated observing sky under SKA1-Low's unprecedented sensitivity and resolution, and evaluate its two-fold impact in both the spatial and frequency domains using a novel hybrid evaluation (HEVAL) pipeline combining end-to-end simulation with an analytic method to mimic EoR analysis pipelines. Sky models corrupted by source blending induce small but severe frequency-dependent calibration errors when coupled with astronomical foregrounds, impeding EoR parameter inference with strong additive residuals in the two-dimensional power spectrum space. We report that additive residuals from poor calibration against sky models with blending ratios of 5 and 0.5 per cent significantly contaminate the EoR window. In contrast, the sky model with a 0.05 per cent blending ratio leaves little residual imprint within the EoR window, therefore identifying a blending tolerance at approximately 0.05 per cent. Given that the SKA observing sky is estimated to suffer from an extended level of blending, strategies involving de-blending, frequency-dependent error mitigation, or a combination of both, are required to effectively attenuate the calibration impact of source-blending defects., Comment: 25 pages, 15 figures, 5 tables, accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS), Fg21Sim+ github repository: https://github.com/Fg21Sim/Fg21SimPlus

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices; Matches published version

Lowering the memory requirement in full-parameter training on large models has become a hot research area. MeZO fine-tunes the large language models (LLMs) by just forward passes in a zeroth-order SGD optimizer (ZO-SGD), demonstrating excellent performance with the same GPU memory usage as inference. However, the simulated perturbation stochastic approximation for gradient estimate in MeZO leads to severe oscillations and incurs a substantial time overhead. Moreover, without momentum regularization, MeZO shows severe over-fitting problems. Lastly, the perturbation-irrelevant momentum on ZO-SGD does not improve the convergence rate. This study proposes ZO-AdaMU to resolve the above problems by adapting the simulated perturbation with momentum in its stochastic approximation. Unlike existing adaptive momentum methods, we relocate momentum on simulated perturbation in stochastic gradient approximation. Our convergence analysis and experiments prove this is a better way to improve convergence stability and rate in ZO-SGD. Extensive experiments demonstrate that ZO-AdaMU yields better generalization for LLMs fine-tuning across various NLP tasks than MeZO and its momentum variants.

The diversity of tables makes table detection a great challenge, leading to existing models becoming more tedious and complex. Despite achieving high performance, they often overfit to the table style in training set, and suffer from significant performance degradation when encountering out-of-distribution tables in other domains. To tackle this problem, we start from the essence of the table, which is a set of text arranged in rows and columns. Based on this, we propose a novel, light-weighted and robust Table Detection method based on Learning Text Arrangement, namely TDeLTA. TDeLTA takes the text blocks as input, and then models the arrangement of them with a sequential encoder and an attention module. To locate the tables precisely, we design a text-classification task, classifying the text blocks into 4 categories according to their semantic roles in the tables. Experiments are conducted on both the text blocks parsed from PDF and extracted by open-source OCR tools, respectively. Compared to several state-of-the-art methods, TDeLTA achieves competitive results with only 3.1M model parameters on the large-scale public datasets. Moreover, when faced with the cross-domain data under the 0-shot setting, TDeLTA outperforms baselines by a large margin of nearly 7%, which shows the strong robustness and transferability of the proposed model., Comment: AAAI 2024

The excellent generalization, contextual learning, and emergence abilities in the pre-trained large models (PLMs) handle specific tasks without direct training data, making them the better foundation models in the adversarial domain adaptation (ADA) methods to transfer knowledge learned from the source domain to target domains. However, existing ADA methods fail to account for the confounder properly, which is the root cause of the source data distribution that differs from the target domains. This study proposes an adversarial domain adaptation with confounder balancing for PLMs fine-tuning (ADA-CBF). The ADA-CBF includes a PLM as the foundation model for a feature extractor, a domain classifier and a confounder classifier, and they are jointly trained with an adversarial loss. This loss is designed to improve the domain-invariant representation learning by diluting the discrimination in the domain classifier. At the same time, the adversarial loss also balances the confounder distribution among source and unmeasured domains in training. Compared to existing ADA methods, ADA-CBF can correctly identify confounders in domain-invariant features, thereby eliminating the confounder biases in the extracted features from PLMs. The confounder classifier in ADA-CBF is designed as a plug-and-play and can be applied in the confounder measurable, unmeasurable, or partially measurable environments. Empirical results on natural language processing and computer vision downstream tasks show that ADA-CBF outperforms the newest GPT-4, LLaMA2, ViT and ADA methods.

The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses large surface arrays of autonomous radio-detection units to look for the radio emission from extensive air showers that are triggered by the interaction of ultra-high-energy cosmic rays, gamma rays, and neutrinos in the atmosphere or underground. In particular, for ultra-high-energy neutrinos, the future final phase of GRAND aims to be sensitive enough to discover them in spite of their plausibly tiny flux. Presently, three prototype GRAND radio arrays are in operation: GRANDProto300, in China, GRAND@Auger, in Argentina, and GRAND@Nancay, in France. Their goals are to field-test the design of the radio-detection units, understand the radio background to which they are exposed, and develop tools for diagnostic, data gathering, and data analysis. This list of contributions to the 38th International Cosmic Ray Conference (ICRC 2023) presents an overview of GRAND, in its present and future incarnations, and a look at the first data collected by GRANDProto13, the first phase of GRANDProto300., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page (v2: fixed name spelling of one author)

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has discovered more than 650 new pulsars, which account for 20% of our known Galactic pulsar population. In this paper, we estimate the prospect of a pulsar survey with a radio telescope array to be planned -- the FAST Array (FASTA), consists of six "FAST-type" telescopes. Such a sensitive radio telescope array would be a powerful instrument in probing the pulsar population deep into our Galaxy as well as in nearby galaxies. We simulate the FASTA pulsar discovery prospects with different Galactic pulsar population models and instrumental parameter combinations. We find that FASTA could detect tens of thousands of canonical pulsars and well-over thousands of millisecond pulsars. We also estimate the potential yield if the FASTA is used to search for pulsars from the nearby spiral galaxy M31, and find that it would probably discover around a hundred new radio pulsars.

Observing and timing a group of millisecond pulsars (MSPs) with high rotational stability enables the direct detection of gravitational waves (GWs). The GW signals can be identified from the spatial correlations encoded in the times-of-arrival of widely spaced pulsar-pairs. The Chinese Pulsar Timing Array (CPTA) is a collaboration aiming at the direct GW detection with observations carried out using Chinese radio telescopes. This short article serves as a `table of contents' for a forthcoming series of papers related to the CPTA Data Release 1 (CPTA DR1) which uses observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Here, after summarizing the time span and accuracy of CPTA DR1, we report the key results of our statistical inference finding a correlated signal with amplitude $\log A_{\rm c}= -14.4 \,^{+1.0}_{-2.8}$ for spectral index in the range of $\alpha\in [-1.8, 1.5]$ assuming a GW background (GWB) induced quadrupolar correlation. The search for the Hellings-Downs (HD) correlation curve is also presented, where some evidence for the HD correlation has been found that a 4.6-$\sigma$ statistical significance is achieved using the discrete frequency method around the frequency of 14 nHz. We expect that the future International Pulsar Timing Array data analysis and the next CPTA data release will be more sensitive to the nHz GWB, which could verify the current results., Comment: 18 pages, 6 figures, submitted to "Research in astronomy and astrophysics" 22nd March 2022

Word sense disambiguation tries to learn the appropriate sense of an ambiguous word in a given context. The existing pre-trained language methods and the methods based on multi-embeddings of word did not explore the power of the unsupervised word embedding sufficiently. In this paper, we discuss a capsule network-based approach, taking advantage of capsule's potential for recognizing highly overlapping features and dealing with segmentation. We propose a Capsule network-based method to Decompose the unsupervised word Embedding of an ambiguous word into context specific Sense embedding, called CapsDecE2S. In this approach, the unsupervised ambiguous embedding is fed into capsule network to produce its multiple morpheme-like vectors, which are defined as the basic semantic language units of meaning. With attention operations, CapsDecE2S integrates the word context to reconstruct the multiple morpheme-like vectors into the context-specific sense embedding. To train CapsDecE2S, we propose a sense matching training method. In this method, we convert the sense learning into a binary classification that explicitly learns the relation between senses by the label of matching and non-matching. The CapsDecE2S was experimentally evaluated on two sense learning tasks, i.e., word in context and word sense disambiguation. Results on two public corpora Word-in-Context and English all-words Word Sense Disambiguation show that, the CapsDecE2S model achieves the new state-of-the-art for the word in context and word sense disambiguation tasks.

We present a morphological classification of 14,245 radio active galactic nuclei (AGNs) into six types, i.e., typical Fanaroff--Riley Class I / II (FRI/II), FRI/II-like bent-tailed, X-shaped radio galaxy, and ringlike radio galaxy, by designing a convolutional neural network (CNN) based autoencoder, namely MCRGNet, and applying it to a labeled radio galaxy (LRG) sample containing 1442 AGNs and an unlabeled radio galaxy (unLRG) sample containing 14,245 unlabeled AGNs selected from the Best--Heckman sample. We train MCRGNet and implement the classification task by a three-step strategy, i.e., pre-training, fine-tuning, and classification, which combines both unsupervised and supervised learnings. A four-layer dichotomous tree is designed to classify the radio AGNs, which leads to a significantly better performance than the direct six-type classification. On the LRG sample, our MCRGNet achieves a total precision of $\sim 93\%$ and an averaged sensitivity of $\sim 87\%$, which are better than those obtained in previous works. On the unLRG sample, whose labels have been human-inspected, the neural network achieves a total precision of $\sim 80\%$. Also, using the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) to calculate the $r$-band absolute magnitude ($M_\mathrm{opt}$) and using the flux densities to calculate the radio luminosity ($L_\mathrm{radio}$), we find that the distributions of the unLRG sources on the $L_\mathrm{radio}$--$M_\mathrm{opt}$ plane do not show an apparent redshift evolution and could confirm with a sufficiently large sample that there could not exist an abrupt separation between FRIs and FRIIs as reported in some previous works., Comment: Accepted by ApJS. The full table of the catalog and code for our network can be downloaded from https://github.com/myinxd/mcrgnet

The Giant Radio Array for Neutrino Detection (GRAND) is a planned large-scale observatory of ultra-high-energy (UHE) cosmic particles, with energies exceeding 10^8 GeV. Its goal is to solve the long-standing mystery of the origin of UHE cosmic rays. To do this, GRAND will detect an unprecedented number of UHE cosmic rays and search for the undiscovered UHE neutrinos and gamma rays associated to them with unmatched sensitivity. GRAND will use large arrays of antennas to detect the radio emission coming from extensive air showers initiated by UHE particles in the atmosphere. Its design is modular: 20 separate, independent sub-arrays, each of 10 000 radio antennas deployed over 10 000 km^2. A staged construction plan will validate key detection techniques while achieving important science goals early. Here we present the science goals, detection strategy, preliminary design, performance goals, and construction plans for GRAND., Comment: 45 pages, 34 figures. Matches published version

TREND50 is a radio detection setup of 50 self-triggered antennas working in the 50-100MHz frequency range and deployed in a radio-quiet valley of the Tianshan mountains (China). TREND50 achieved its goal: the autonomous radiodetection and identification of air showers. Thanks to a dedicated offine selection algorithm, 564 air shower candidates were indeed selected out of $7\cdot10^8$ transient radio signals recorded during the 314 live days of data taken during the first two years of operation of this setup (2011 and 2012). This event rate, as well as the distribution of the candidate directions of arrival, is consistent with what is expected from cosmic-ray-induced air showers according to simulations, assuming an additional $\sim$20% contamination of the final sample by background events. This result is obtained at the cost of a reduced air shower detection efficiency, estimated to be $\sim$3%. This low efficiency is mostly due to the large amount of dead time of the setup. This result paves the way for the GRANDProto35 experiment, the first stage of the GRAND project., Comment: 41 pages, 19 figures, published in Astropart. Phys

Music emotion recognition (MER) is usually regarded as a multi-label tagging task, and each segment of music can inspire specific emotion tags. Most researchers extract acoustic features from music and explore the relations between these features and their corresponding emotion tags. Considering the inconsistency of emotions inspired by the same music segment for human beings, seeking for the key acoustic features that really affect on emotions is really a challenging task. In this paper, we propose a novel MER method by using deep convolutional neural network (CNN) on the music spectrograms that contains both the original time and frequency domain information. By the proposed method, no additional effort on extracting specific features required, which is left to the training procedure of the CNN model. Experiments are conducted on the standard CAL500 and CAL500exp dataset. Results show that, for both datasets, the proposed method outperforms state-of-the-art methods., Comment: 7 pages, 4 figures

The Giant Radio Array for Neutrino Detection (GRAND) is a planned array of ~200 000 radio antennas deployed over ~200 000 km2 in a mountainous site. It aims primarly at detecting high-energy neutrinos via the observation of extensive air showers induced by the decay in the atmosphere of taus produced by the interaction of cosmic neutrinos under the Earth surface. GRAND aims at reaching a neutrino sensitivity of 5.10$^{11}$ E$^{-2}$ GeV$^{-1}$cm$^{-2}$s$^{-1}$sr$^{-1}$ above 3.10$^{16}$ eV. This ensures the detection of cosmogenic neutrinos in the most pessimistic source models, and ~50 events per year are expected for the standard models. The instrument will also detect UHECRs and possibly FRBs. Here we show how our preliminary design should enable us to reach our sensitivity goals, and discuss the steps to be taken to achieve GRAND., Comment: 4 pages, 2 figures, proceedings of the ARENA2016 conference

In this paper, we propose a novel model, named Stroke Sequence-dependent Deep Convolutional Neural Network (SSDCNN), using the stroke sequence information and eight-directional features for Online Handwritten Chinese Character Recognition (OLHCCR). On one hand, SSDCNN can learn the representation of Online Handwritten Chinese Character (OLHCC) by incorporating the natural sequence information of the strokes. On the other hand, SSDCNN can incorporate eight-directional features in a natural way. In order to train SSDCNN, we divide the process of training into two stages: 1) The training data is used to pre-train the whole architecture until the performance tends to converge. 2) Fully-connected neural network which is used to combine the stroke sequence-dependent representation with eight-directional features and softmax layer are further trained. Experiments were conducted on the OLHCCR competition tasks of ICDAR 2013. Results show that, SSDCNN can reduce the recognition error by 50\% (5.13\% vs 2.56\%) compared to the model which only use eight-directional features. The proposed SSDCNN achieves 97.44\% accuracy which reduces the recognition error by about 1.9\% compared with the best submitted system on ICDAR2013 competition. These results indicate that SSDCNN can exploit the stroke sequence information to learn high-quality representation of OLHCC. It also shows that the learnt representation and the classical eight-directional features complement each other within the SSDCNN architecture.

High-energy neutrino astronomy will probe the working of the most violent phenomena in the Universe. The Giant Radio Array for Neutrino Detection (GRAND) project consists of an array of $\sim10^5$ radio antennas deployed over $\sim$200000km$^2$ in a mountainous site. It aims at detecting high-energy neutrinos via the measurement of air showers induced by the decay in the atmosphere of $\tau$ leptons produced by the interaction of the cosmic neutrinos under the Earth surface. Our objective with GRAND is to reach a neutrino sensitivity of $3\times10^{-11}E^{-2}$GeV$^{-1}$cm$^{-2}$s$^{-1}$sr$^{-1}$ above $3 \times10^{16}$eV. This sensitivity ensures the detection of cosmogenic neutrinos in the most pessimistic source models, and about 100 events per year are expected for the standard models. GRAND would also probe the neutrino signals produced at the potential sources of UHECRs. We show how our preliminary design should enable us to reach our sensitivity goals, and present the experimental characteristics. We assess the possibility to adapt GRAND to other astrophysical radio measurements. We discuss in this token the technological options for the detector and the steps to be taken to achieve the GRAND project., Comment: 8 pages, 2 figures, 34th ICRC conference