The ionization rates of galactic nuclei and disks from Herschel/HIFI observations of water and its associated ions

(Abridged) We present Herschel/HIFI spectra of the H2O 1113 GHz and H2O+ 1115 GHz lines toward five nearby prototypical starburst/AGN systems, and OH+ 971 GHz spectra toward three of these. The beam size of 20" corresponds to resolutions between 0.35 and 7 kpc. The observed line profiles range from pure absorption (NGC 4945, M82) to P-Cygni indicating outflow (NGC 253, Arp 220) and inverse P-Cygni indicating infall (Cen A). The similarity of the H2O, OH+, and H2O+ profiles to each other and to HI indicates that diffuse and dense gas phases are well mixed. We estimate column densities assuming negligible excitation (for absorption features) and using a non-LTE model (for emission features), adopting calculated collision data for H2O and OH+, and rough estimates for H2O+. Column densities range from ~10^13 to ~10^15 cm^-2 for each species, and are similar between absorption and emission components, indicating that the nuclear region does not contribute much to the emission in these ground-state lines. The N(H2O)/N(H2O+) ratios of 1.4-5.6 indicate an origin of the lines in diffuse gas, and the N(OH+)/N(H2O+) ratios of 1.6-3.1 indicate a low H2 fraction (~11%) in the gas. Adopting recent Galactic values for the average gas density and the ionization efficiency, we find ionization rates for our sample galaxies of ~3x10^-16 s^-1 which are similar to the value for the Galactic disk, but ~10x below that of the Galactic Center and ~100x below estimates for AGN from excited-state H3O+ lines. We conclude that the ground-state lines of water and its associated ions probe primarily non-nuclear gas in the disks of these centrally active galaxies. Our data thus provide evidence for a decrease in ionization rate by a factor of ~10 from the nuclei to the disks of galaxies, as found before for the Milky Way.Comment: Accepted for publication in Astronomy & Astrophysics; 8 pages, 8 figure

Research of Coordinated Control Strategy for Multi-UHVDC in AC/DC Hybrid Power Grid

AbstractThe control strategy and modulation scheme of DC system have great effect on transient stability and dynamical stability in AC/DC hybrid power grid. In order to decrease the effect of UHVDC blocks and AC lines faults, a coordinated control strategy of emergency power modulation and small signal modulation is put forward by making use of the fast controllability and the overload capability of HVDC system. Simulation results show that the coordinated control strategy may decrease power loss and improve the dynamical stability of the AC/DC hybrid system

Simulation of coastal resource and environmental carrying capacity in the Yangtze River delta coastal zone based on shared socioeconomic pathways

Study of resource and environmental carrying capacity is an important research content of sustainable development science and the theoretical support for land space optimization. Existing research theories need to be deepened, and spatial simulation studies are relatively lacking. This study aimed to assess the current and future resource and environmental carrying capacity in the Yangtze River Delta region’s coastal zone and enhance sustainable development by exploring the application of shared socioeconomic pathway (SSPs) scenarios at the spatial pattern scale in regional resource and environmental carrying capacity simulation studies. Based on the FLUS and InVEST models, this study introduced the Coastal Resource and Environmental Carrying Capacity Index (CRECC) from the dimensions of “pressure” and “support” using land use remote sensing monitoring data and SSPs scenario data. A CRECC evaluation index system and quantitative evaluation method for the Yangtze River Delta were constructed. The results showed that from 2000 to 2020, the CRECC of the Yangtze River Delta coastal zone increased, the carrying capacity decreased, and the spatial distribution was low in the north and high in the south. The carrying capacity under the five SSP scenarios did not improve. The mismatch between natural ecological conditions and the intensity of human activities in the shoreline area was more prominent than in the study area, with the SSP1 and SSP5 scenarios being the most obvious. The supporting indicators have a more significant influence on improving CRECC than the pressure indicators, among which the supply capacity of water resources, land resources, and atmospheric environmental quality are the main limiting factors in the process of future sustainable economic-ecological development. This study provides ideas and examples for exploring spatial and temporal predictions of resources and environmental carrying capacity in coastal zones

SEAT: Stable and Explainable Attention

Currently, attention mechanism becomes a standard fixture in most state-of-the-art natural language processing (NLP) models, not only due to outstanding performance it could gain, but also due to plausible innate explanation for the behaviors of neural architectures it provides, which is notoriously difficult to analyze. However, recent studies show that attention is unstable against randomness and perturbations during training or testing, such as random seeds and slight perturbation of embedding vectors, which impedes it from becoming a faithful explanation tool. Thus, a natural question is whether we can find some substitute of the current attention which is more stable and could keep the most important characteristics on explanation and prediction of attention. In this paper, to resolve the problem, we provide a first rigorous definition of such alternate namely SEAT (Stable and Explainable Attention). Specifically, a SEAT should has the following three properties: (1) Its prediction distribution is enforced to be close to the distribution based on the vanilla attention; (2) Its top-k indices have large overlaps with those of the vanilla attention; (3) It is robust w.r.t perturbations, i.e., any slight perturbation on SEAT will not change the prediction distribution too much, which implicitly indicates that it is stable to randomness and perturbations. Finally, through intensive experiments on various datasets, we compare our SEAT with other baseline methods using RNN, BiLSTM and BERT architectures via six different evaluation metrics for model interpretation, stability and accuracy. Results show that SEAT is more stable against different perturbations and randomness while also keeps the explainability of attention, which indicates it is a more faithful explanation. Moreover, compared with vanilla attention, there is almost no utility (accuracy) degradation for SEAT.Comment: To be appeared in AAAI 202

A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability

This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability. Given the recent emergence of large-scale conversational language model ChatGPT and its impressive capabilities in both conversational abilities and code generation, we sought to evaluate its Text-to-SQL performance. We conducted experiments on 12 benchmark datasets with different languages, settings, or scenarios, and the results demonstrate that ChatGPT has strong text-to-SQL abilities. Although there is still a gap from the current state-of-the-art (SOTA) model performance, considering that the experiment was conducted in a zero-shot scenario, ChatGPT's performance is still impressive. Notably, in the ADVETA (RPL) scenario, the zero-shot ChatGPT even outperforms the SOTA model that requires fine-tuning on the Spider dataset by 4.1\%, demonstrating its potential for use in practical applications. To support further research in related fields, we have made the data generated by ChatGPT publicly available at https://github.com/THU-BPM/chatgpt-sql.Comment: 6 pages, 1 figure

Multiple domains are involved in the targeting of the mouse DNA methyltransferase to the DNA replication foci

It has been shown that, during the S-phase of the cell cycle, the mouse DNA methyltransferase (DNA MTase) is targeted to sites of DNA replication by an amino acid sequence (aa 207-455) lying in the N-terminal domain of the enzyme [Leonhardt, H., Page, A. W., Weier, H. U. and Bestor, T. H. (1992) Cell, 71, 865-873]. In this paper it is shown, by using enhanced green fluorescent protein (EGFP) fusions, that other peptide sequences of DNA MTase are also involved in this targeting. The work focuses on a sequence, downstream of the reported targeting sequence (TS), which is homologous to the Polybromo-1 protein. This motif (designated as PBHD) is separated from the reported targeting sequence by a zinc-binding motif [Bestor, T. H. (1992) EMBO J, 11, 2611-2617]. Primed in situ extension using centromeric-specific primers was used to show that both the host DNA MTase and EGFP fusion proteins containing the targeting sequences were localized to centromeric, but not telomeric, regions during late S-phase and mitosis. Also found was that, in ∼10% of the S-phase cells, the EGFP fusions did not co-localize with the centromeric regions. Mutants containing either, or both, of these targeting sequences could act as dominant negative mutants against the host DNA MTase. EGFP fusion proteins, containing the reported TS (aa 207-455), were targeted to centromeric regions throughout the mitotic stage which lead to the discovery of a similar behavior of the endogenous DNA MTase although the host MTase showed much less intense staining than in S-phase cells. The biological role of the centromeric localization of DNA MTase during mitosis is currently unknow

Making the Invisible Visible: Action Recognition Through Walls and Occlusions

Understanding people's actions and interactions typically depends on seeing them. Automating the process of action recognition from visual data has been the topic of much research in the computer vision community. But what if it is too dark, or if the person is occluded or behind a wall? In this paper, we introduce a neural network model that can detect human actions through walls and occlusions, and in poor lighting conditions. Our model takes radio frequency (RF) signals as input, generates 3D human skeletons as an intermediate representation, and recognizes actions and interactions of multiple people over time. By translating the input to an intermediate skeleton-based representation, our model can learn from both vision-based and RF-based datasets, and allow the two tasks to help each other. We show that our model achieves comparable accuracy to vision-based action recognition systems in visible scenarios, yet continues to work accurately when people are not visible, hence addressing scenarios that are beyond the limit of today's vision-based action recognition.Comment: ICCV 2019. The first two authors contributed equally to this pape

A Semantic Invariant Robust Watermark for Large Language Models

Watermark algorithms for large language models (LLMs) have achieved extremely high accuracy in detecting text generated by LLMs. Such algorithms typically involve adding extra watermark logits to the LLM's logits at each generation step. However, prior algorithms face a trade-off between attack robustness and security robustness. This is because the watermark logits for a token are determined by a certain number of preceding tokens; a small number leads to low security robustness, while a large number results in insufficient attack robustness. In this work, we propose a semantic invariant watermarking method for LLMs that provides both attack robustness and security robustness. The watermark logits in our work are determined by the semantics of all preceding tokens. Specifically, we utilize another embedding LLM to generate semantic embeddings for all preceding tokens, and then these semantic embeddings are transformed into the watermark logits through our trained watermark model. Subsequent analyses and experiments demonstrated the attack robustness of our method in semantically invariant settings: synonym substitution and text paraphrasing settings. Finally, we also show that our watermark possesses adequate security robustness. Our code and data are available at https://github.com/THU-BPM/Robust_Watermark.Comment: 16 pages, 9 figures, 2 table

Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing

In the context-dependent Text-to-SQL task, the generated SQL statements are refined iteratively based on the user input utterance from each interaction. The input text from each interaction can be viewed as component modifications to the previous SQL statements, which could be further extracted as the modification patterns. Since these modification patterns could also be combined with other SQL statements, the models are supposed to have the compositional generalization to these novel combinations. This work is the first exploration of compositional generalization in context-dependent Text-to-SQL scenarios. To facilitate related studies, we constructed two challenging benchmarks named \textsc{CoSQL-CG} and \textsc{SParC-CG} by recombining the modification patterns and existing SQL statements. The following experiments show that all current models struggle on our proposed benchmarks. Furthermore, we found that better aligning the previous SQL statements with the input utterance could give models better compositional generalization ability. Based on these observations, we propose a method named \texttt{p-align} to improve the compositional generalization of Text-to-SQL models. Further experiments validate the effectiveness of our method. Source code and data are available.Comment: Accepted to ACL 2023 (Findings), Long Paper, 11 page