38 research outputs found
21 cm foreground removal using AI and frequency-difference technique
The deep learning technique has been employed in removing foreground
contaminants from 21 cm intensity mapping, but its effectiveness is limited by
the large dynamic range of the foreground amplitude. In this study, we develop
a novel foreground removal technique grounded in U-Net networks. The essence of
this technique lies in introducing an innovative data preprocessing step
specifically, utilizing the temperature difference between neighboring
frequency bands as input, which can substantially reduce the dynamic range of
foreground amplitudes by approximately two orders of magnitude. This reduction
proves to be highly advantageous for the U-Net foreground removal. We observe
that the HI signal can be reliably recovered, as indicated by the
cross-correlation power spectra showing unity agreement at the scale of Mpc in the absence of instrumental effects. Moreover, accounting for
the systematic beam effects, our reconstruction displays consistent
auto-correlation and cross-correlation power spectrum ratios at the
level across scales Mpc, with only a 10% reduction
observed in the cross-correlation power spectrum at Mpc. The
effects of redshift-space distortion are also reconstructed successfully, as
evidenced by the quadrupole power spectra matching. In comparison, our method
outperforms the traditional Principal Component Analysis method, which derived
cross-correlation ratios are underestimated by around 75%. We simulated various
white noise levels in the map and found that the mean cross-correlation ratio
when the level of the thermal noise is
smaller than or equal to that of the HI signal. We conclude that the proposed
frequency-difference technique can significantly enhance network performance by
reducing the amplitude range of foregrounds and aiding in the prevention of HI
loss.Comment: 18 pages, 16 figure
Is ChatGPT a Good NLG Evaluator? A Preliminary Study
Recently, the emergence of ChatGPT has attracted wide attention from the
computational linguistics community. Many prior studies have shown that ChatGPT
achieves remarkable performance on various NLP tasks in terms of automatic
evaluation metrics. However, the ability of ChatGPT to serve as an evaluation
metric is still underexplored. Considering assessing the quality of natural
language generation (NLG) models is an arduous task and NLG metrics notoriously
show their poor correlation with human judgments, we wonder whether ChatGPT is
a good NLG evaluation metric. In this report, we provide a preliminary
meta-evaluation on ChatGPT to show its reliability as an NLG metric. In detail,
we regard ChatGPT as a human evaluator and give task-specific (e.g.,
summarization) and aspect-specific (e.g., relevance) instruction to prompt
ChatGPT to evaluate the generated results of NLG models. We conduct experiments
on five NLG meta-evaluation datasets (including summarization, story generation
and data-to-text tasks). Experimental results show that compared with previous
automatic metrics, ChatGPT achieves state-of-the-art or competitive correlation
with human judgments in most cases. In addition, we find that the effectiveness
of the ChatGPT evaluator might be influenced by the creation method of the
meta-evaluation datasets. For the meta-evaluation datasets which are created
greatly depending on the reference and thus are biased, the ChatGPT evaluator
might lose its effectiveness. We hope our preliminary study could prompt the
emergence of a general-purposed reliable NLG metric.Comment: Both first authors contributed equally. Technical Report, 11 pages.
Accepted to the 4th New Frontiers in Summarization Workshop (NewSumm@EMNLP
2023
Any-Precision Deep Neural Networks
We present any-precision deep neural networks (DNNs), which are trained with
a new method that allows the learned DNNs to be flexible in numerical precision
during inference. The same model in runtime can be flexibly and directly set to
different bit-widths, by truncating the least significant bits, to support
dynamic speed and accuracy trade-off. When all layers are set to low-bits, we
show that the model achieved accuracy comparable to dedicated models trained at
the same precision. This nice property facilitates flexible deployment of deep
learning models in real-world applications, where in practice trade-offs
between model accuracy and runtime efficiency are often sought. Previous
literature presents solutions to train models at each individual fixed
efficiency/accuracy trade-off point. But how to produce a model flexible in
runtime precision is largely unexplored. When the demand of efficiency/accuracy
trade-off varies from time to time or even dynamically changes in runtime, it
is infeasible to re-train models accordingly, and the storage budget may forbid
keeping multiple models. Our proposed framework achieves this flexibility
without performance degradation. More importantly, we demonstrate that this
achievement is agnostic to model architectures and applicable to multiple
vision tasks. Our code is released at
https://github.com/SHI-Labs/Any-Precision-DNNs.Comment: AAAI 202
Relieving Effect of Rosa roxburghii Tratt. Juice Fermented by Lactobacillus paracasei SR10-1 on Dextran Sulfate Sodium-Induced Ulcerative Colitis in Mice
Objective: To investigate the protective effect of Rosa roxburghii Tratt. juice (RRTJ) fermented by Lactobacillus paracasei SR10-1 against ulcerative colitis (UC) in mice. Methods: SR10-1 fermented Rosa roxburghii Tratt. juice was prepared in the laboratory. A mouse model of UC induced by dextran sulfate sodium (DSS) was created. The experiments were designed using five groups, i.e., blank control, DSS-induced model, positive control (mesalazine), lactic acid bacteria fermented RRTJ (LAB-RRTJ) and RRTJ. Disease activity index (DAI) score, visceral organ indices, colon length, colon pathological changes, the levels of inflammatory factors including interleukin (IL)-1ÎČ, IL-6, IL-10, IL-17A, tumor necrosis factor-α (TNF-α) and interferon-Îł (IFN-Îł), the levels of oxidative stress indicators including malondialdehyde (MDA), superoxide dismutase (SOD) and glutathione (GSH), the activity of myeloperoxidase (MPO), and the expression levels of gut barrier-related genes (claudin-3, ZO-1 and MUC2) were analyzed in UC mice. Results: Compared with the DSS-induced model group, LAB-RRTJ significantly reduced the DAI score (P < 0.05), and relieved diarrhea, bloody stools, colonic atrophy and pathological changes of mice. In addition, the colon length was significantly increased (P < 0.001), and the spleen and liver indices were significantly decreased (P < 0.001 and P < 0.05, respectively). The levels of IL-1ÎČ, IL-6, IL-17A, TNF-α, and IFN-Îł were significantly decreased (P < 0.05), while the level of IL-10 was significantly increased (P < 0.05). The levels of MDA and MPO were significantly decreased (P < 0.05), the activities of SOD and GSH were significantly increased (P < 0.001 and P < 0.05), and the expression levels of claudin-3, ZO-1 and MUC2 were significantly increased (P < 0.01). Conclusion: Fermented Rosa roxburghii Tratt. juice with Lactobacillus paracasei SR10-1 could reduce intestinal damage in UC mice by improving inflammatory responses and regulating the level of oxidative stress and intestinal barrier function
Testing Electron-phonon Coupling for the Superconductivity in Kagome Metal
In crystalline materials, electron-phonon coupling (EPC) is a ubiquitous
many-body interaction that drives conventional Bardeen-Cooper-Schrieffer
superconductivity. Recently, in a new kagome metal ,
superconductivity that possibly intertwines with time-reversal and spatial
symmetry-breaking orders is observed. Density functional theory calculations
predicted weak EPC strength,, supporting an unconventional pairing
mechanism in . However, experimental determination of
is still missing, hindering a microscopic understanding of the intertwined
ground state of . Here, using 7-eV laser-based angle-resolved
photoemission spectroscopy and Eliashberg function analysis, we determine an
intermediate =0.45~0.6 at T=6 K for both Sb 5p and V 3d electronic
bands, which can support a conventional superconducting transition temperature
on the same magnitude of experimental value in . Remarkably,
the EPC on the V 3d-band enhances to ~0.75 as the superconducting
transition temperature elevated to 4.4 K in .
Our results provide an important clue to understand the pairing mechanism in
the Kagome superconductor .Comment: To appear in Nature Communication
Robotstyrning med VÀgenintegrerad PolitikförbÀttring och Djupa Dynamik Modeller
Robotics is an interdisciplinary field that integrates computer science, electrical engineering, mechanical engineering, control engineering and other related fields. As the quick development of these fields, people have been building more complex robots with more advanced control strategies in order to solve more challenging tasks. In addition, it is always a target for researchers to achieve autonomous operation of robots so that the manpower can be saved and the robot can work in harsh environment like on Mars. In this project, I focus on the trajectory planning problem of a unicycle model running in 2D environment. I choose Path Integral Policy Improvement (PI2) control algorithm in this project as the main study object. And Model Predictive Control (MPC) is chosen as a reference in order to be compared with PI2 to evaluate the performance of PI2. In order to simulate the tasks that the robot needs to handle in practice, I use obstacles to represent the complex environment and I use Signal Temporal Logic (STL) to represent the complex tasks. Furthermore, I also incorporate the deep dynamics model in the project so that the the method put forward in this project is able to handle complex robot models and complex working environments. To evaluate the performances of PI2 and MPC, five criteria are put forward in this project. Finally, based on the evaluation results, possible improvement and future research are proposed. Robotics Àr ett tvÀrvetenskapligt omrÄde som integrerar datavetenskap, elektroteknik, maskinteknik, styrteknik och andra relaterade omrÄden. Som den snabba utvecklingen av dessa fÀlt har mÀnniskor byggt mer komplexa robotar med mer avancerade kontrollstrategier för att lösa mer utmanande uppgifter. Dessutom Àr det alltid ett mÄl för forskare att uppnÄ autonom drift av robotar sÄ att arbetskraften kan sparas och roboten kan arbeta i tuffa miljöer som pÄ Mars. I det hÀr projektet fokuserar jag pÄ banplaneringsproblemet för en enhjulingsmodell som körs i 2D-miljö. Jag vÀljer Path Integral Policy Improvement (PI2) kontrollalgoritm i detta projekt som huvudstudieobjekt. Och Model Predictive Control (MPC) vÀljs som referens för att kunna jÀmföras med PI2 för att utvÀrdera prestandan för PI2. För att simulera de uppgifter som roboten behöver hantera i praktiken anvÀnder jag hinder för att representera den komplexa miljön och jag anvÀnder Signal Temporal Logic (STL) för att representera de komplexa uppgifterna. Dessutom införlivar jag ocksÄ den djupa dynamikmodellen i projektet sÄ att metoden som lÀggs fram i detta projekt kan hantera komplexa robotmodeller och komplexa arbetsmiljöer. För att utvÀrdera prestanda för PI2 och MPC presenteras fem kriterier i detta projekt. Slutligen, baserat pÄ utvÀrderingsresultaten, föreslÄs möjliga förbÀttringar och framtida forskning