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The pervasive spread of misinformation and disinformation in social media underscores the critical importance of detecting media bias. While robust Large Language Models (LLMs) have emerged as foundational tools for bias prediction, concerns about inherent biases within these models persist. In this work, we investigate the presence and nature of bias within LLMs and its consequential impact on media bias detection. Departing from conventional approaches that focus solely on bias detection in media content, we delve into biases within the LLM systems themselves. Through meticulous examination, we probe whether LLMs exhibit biases, particularly in political bias prediction and text continuation tasks. Additionally, we explore bias across diverse topics, aiming to uncover nuanced variations in bias expression within the LLM framework. Importantly, we propose debiasing strategies, including prompt engineering and model fine-tuning. Extensive analysis of bias tendencies across different LLMs sheds light on the broader landscape of bias propagation in language models. This study advances our understanding of LLM bias, offering critical insights into its implications for bias detection tasks and paving the way for more robust and equitable AI systems

In the age of information overload and polarized discourse, understanding media bias has become imperative for informed decision-making and fostering a balanced public discourse. This paper presents IndiTag, an innovative online media bias analysis and annotation system that leverages fine-grained bias indicators to dissect and annotate bias in digital content. IndiTag offers a novel approach by incorporating large language models, bias indicator, vector database to automatically detect and interpret bias. Complemented by a user-friendly interface facilitating both automated bias analysis and manual annotation, IndiTag offers a comprehensive platform for in-depth bias examination. We demonstrate the efficacy and versatility of IndiTag through experiments on four datasets encompassing news articles from diverse platforms. Furthermore, we discuss potential applications of IndiTag in fostering media literacy, facilitating fact-checking initiatives, and enhancing the transparency and accountability of digital media platforms. IndiTag stands as a valuable tool in the pursuit of fostering a more informed, discerning, and inclusive public discourse in the digital age. The demonstration video can be accessed from https://youtu.be/Gt2T4T7DYqs. We release an online system for end users and the source code is available at https://github.com/lylin0/IndiTag.

The evolution and advancement of cyberattacks pose challenges to existing security products. Recent concentrated research on provenance graph-based detection has proved its effectiveness in attack detection and investigation. However, implementing these approaches in practice encounters challenges such as high overhead, slow responsiveness, and low interpretability and extensibility. Towards practical attack detection and investigation with provenance graphs, we propose TAGS, a tag-propagation-based streaming provenance graph alignment system. Utilizing the tag-based intermediate result caching mechanism alongside carefully crafted propagation rules, we eliminate the need to store and duplicate raw data processing. This approach effectively mitigates in-memory storage requirements and minimizes data processing overhead, facilitating rapid on-stream graph alignment while significantly conserving CPU and memory resources. As a result, TAGS can detect and investigate various cyber-attacks in real-time. Moreover, TAGS allows analysts to customize attack query graphs flexibly to identify extended attacks in data streams. We conduct experimental evaluations on two large-scale public datasets containing 257.42 GB of audit logs with 12 relevant query graphs of varying sizes, covering multiple attack techniques and scenarios. The results demonstrate that TAGS is sufficiently efficient to process 176K events per second while sufficiently accurately identifying all 29 alignments in massive data. Moreover, it can effectively handle the dependency explosion problem with steady, low-level memory consumption (less than 300MB), producing only 3 false positives. Overall, the performance of TAGS significantly outperforms the state-of-the-art methods.

The aim of this study is to investigate Machine Unlearning (MU), a burgeoning field focused on addressing concerns related to neural models inadvertently retaining personal or sensitive data. Here, a novel approach is introduced to achieve precise and selective forgetting within language models. Unlike previous methodologies that adopt completely opposing training objectives, this approach aims to mitigate adverse effects on language model performance, particularly in generation tasks. Furthermore, two innovative evaluation metrics are proposed: Sensitive Information Extraction Likelihood (S-EL) and Sensitive Information Memory Accuracy (S-MA), designed to gauge the effectiveness of sensitive information elimination. To reinforce the forgetting framework, an effective method for annotating sensitive scopes is presented, involving both online and offline strategies. The online selection mechanism leverages language probability scores to ensure computational efficiency, while the offline annotation entails a robust two-stage process based on Large Language Models (LLMs).

This study focuses on media bias detection, crucial in today's era of influential social media platforms shaping individual attitudes and opinions. In contrast to prior work that primarily relies on training specific models tailored to particular datasets, resulting in limited adaptability and subpar performance on out-of-domain data, we introduce a general bias detection framework, IndiVec, built upon large language models. IndiVec begins by constructing a fine-grained media bias database, leveraging the robust instruction-following capabilities of large language models and vector database techniques. When confronted with new input for bias detection, our framework automatically selects the most relevant indicator from the vector database and employs majority voting to determine the input's bias label. IndiVec excels compared to previous methods due to its adaptability (demonstrating consistent performance across diverse datasets from various sources) and explainability (providing explicit top-k indicators to interpret bias predictions). Experimental results on four political bias datasets highlight IndiVec's significant superiority over baselines. Furthermore, additional experiments and analysis provide profound insights into the framework's effectiveness.

Endpoint detection and response (EDR) systems have emerged as a critical component of enterprise security solutions, effectively combating endpoint threats like APT attacks with extended lifecycles. In light of the growing significance of endpoint detection and response (EDR) systems, many cybersecurity providers have developed their own proprietary EDR solutions. It's crucial for users to assess the capabilities of these detection engines to make informed decisions about which products to choose. This is especially urgent given the market's size, which is expected to reach around 3.7 billion dollars by 2023 and is still expanding. MITRE is a leading organization in cyber threat analysis. In 2018, MITRE started to conduct annual APT emulations that cover major EDR vendors worldwide. Indicators include telemetry, detection and blocking capability, etc. Nevertheless, the evaluation results published by MITRE don't contain any further interpretations or suggestions. In this paper, we thoroughly analyzed MITRE evaluation results to gain further insights into real-world EDR systems under test. Specifically, we designed a whole-graph analysis method, which utilizes additional control flow and data flow information to measure the performance of EDR systems. Besides, we analyze MITRE evaluation's results over multiple years from various aspects, including detection coverage, detection confidence, detection modifier, data source, compatibility, etc. Through the above studies, we have compiled a thorough summary of our findings and gained valuable insights from the evaluation results. We believe these summaries and insights can assist researchers, practitioners, and vendors in better understanding the strengths and limitations of mainstream EDR products., Comment: 16 pages, 7 figures, to appear in AsiaCCS 2024

As cyber-attacks become increasingly sophisticated and stealthy, it becomes more imperative and challenging to detect intrusion from normal behaviors. Through fine-grained causality analysis, provenance-based intrusion detection systems (PIDS) demonstrated a promising capacity to distinguish benign and malicious behaviors, attracting widespread attention from both industry and academia. Among diverse approaches, rule-based PIDS stands out due to its lightweight overhead, real-time capabilities, and explainability. However, existing rule-based systems suffer low detection accuracy, especially the high false alarms, due to the lack of fine-grained rules and environment-specific configurations. In this paper, we propose CAPTAIN, a rule-based PIDS capable of automatically adapting to diverse environments. Specifically, we propose three adaptive parameters to adjust the detection configuration with respect to nodes, edges, and alarm generation thresholds. We build a differentiable tag propagation framework and utilize the gradient descent algorithm to optimize these adaptive parameters based on the training data. We evaluate our system based on data from DARPA Engagement and simulated environments. The evaluation results demonstrate that CAPTAIN offers better detection accuracy, less detection latency, lower runtime overhead, and more interpretable detection alarms and knowledge compared to the SOTA PIDS.

Guangyou Duan,1,* Haibo Deng,2,* Hong Fu,3 Lingzhi Wang,4 Hanyu Yang5 1Department of Anesthesiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing City, 400000, People’s Republic of China; 2Department of Anesthesiology, Huizhou Third People’s Hospital, Guangzhou Medical University, Guangzhou, 516002, People’s Republic of China; 3Department of Anesthesiology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 400014, People’s Republic of China; 4Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 051026, People’s Republic of China; 5Department of Anesthesiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guangyou Duan, Department of Anesthesiology, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Road, Chongqing City, 400000, People’s Republic of China, Email duangy@hospital.cqmu.edu.cnPurpose: This study aimed to compare the electrolyte balance efficacies of two Gelatin-Balanced Crystalloid in clinical applications.Methods: A multi-center, prospective, randomized, single-blind, parallel controlled study was conducted among non-cardiac surgery patients, with clinical registration number ChiCTR2200062999. They were randomized into Succinylated Gelatin, Multiple Electrolytes and Sodium Acetate Injection (SG-MESAI) group (experimental group) and Succinylated Gelatin Injection (SGI) infusion group (control group). The same anesthetic induction technique, anesthetic method, and calculation method for the volume of colloid infusion were used in the two groups. Between-group differences in the changes in base excess (BE), Chloride ion (Cl−), bicarbonate radical (HCO3⁻) and other parameters were recorded at 15 min, 30 min

Stars with initial masses in the range of 8-25 solar masses are thought to end their lives as hydrogen-rich supernovae (SNe II). Based on the pre-explosion images of Hubble Space Telescope ($HST$) and $Spitzer$ Space Telescope, we place tight constraints on the progenitor candidate of type IIP SN 2023ixf in Messier 101. Fitting of the spectral energy distribution (SED) of its progenitor with dusty stellar spectral models results in an estimation of the effective temperature as 3091$^{+422}_{-258}$ K. The luminosity is estimated as log($L/$L$_{\odot}$)$\sim4.83$, consistent with a red supergiant (RSG) star with an initial mass of 12$^{+2}_{-1}$ M$_{\odot}$. The derived mass loss rate (6-9$\times10^{-6}$ M$_{\odot}$ yr$^{-1}$) is much lower than that inferred from the flash spectroscopy of the SN, suggesting that the progenitor experienced a sudden increase in mass loss when approaching the final explosion. In the infrared bands, significant deviation from the range of regular RSGs in the color-magnitude diagram and period-luminosity space of the progenitor star indicates enhanced mass loss and dust formation. Combining with new evidence of polarization at the early phases of SN 2023ixf, such a violent mass loss is likely a result of binary interaction., Comment: 8 figures, 13 pages; accepted for publication in Sci. China-Phys. Mech. Astron

Type Ia supernovae (SNe Ia) arise from the thermonuclear explosion in binary systems involving carbon-oxygen white dwarfs (WDs). The pathway of WDs acquiring mass may produce circumstellar material (CSM). Observing SNe Ia within a few hours to a few days after the explosion can provide insight into the nature of CSM relating to the progenitor systems. In this paper, we propose a CSM model to investigate the effect of ejecta-CSM interaction on the early-time multi-band light curves of SNe Ia. By varying the mass-loss history of the progenitor system, we apply the ejecta-CSM interaction model to fit the optical and ultraviolet (UV) photometric data of eight SNe Ia with early excess. The photometric data of SNe Ia in our sample can be well-matched by our CSM model except for the UV-band light curve of iPTF14atg, indicating its early excess may not be due to the ejecta-CSM interaction. Meanwhile, the CSM interaction can generate synchrotron radiation from relativistic electrons in the shocked gas, making radio observations a distinctive probe of CSM. The radio luminosity based on our models suggests that positive detection of the radio signal is only possible within a few days after the explosion at higher radio frequencies (e.g., ~250 GHz); at lower frequencies (e.g., ~1.5 GHz) the detection is difficult. These models lead us to conclude that a multi-messenger approach that involves UV, optical, and radio observations of SNe Ia a few days past explosion is needed to address many of the outstanding questions concerning the progenitor systems of SNe Ia., Comment: Published to MNRAS

Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog. CTSs have become a key pillar in educational data mining research. A key challenge in CTSs is to engage the student in the conversation while exposing them to a diverse set of teaching strategies, akin to a human teacher, thereby, helping them learn in the process. Different from previous work that generates responses given the strategies as input, we propose to jointly predict teaching strategies and generate tutor responses accordingly, which fits a more realistic application scenario. We benchmark several competitive models on three dialog tutoring datasets and propose a unified framework that combines teaching response generation and pedagogical strategy prediction, where a self-distillation mechanism is adopted to guide the teaching strategy learning and facilitate tutor response generation. Our experiments and analyses shed light on how teaching strategies affect dialog tutoring., Comment: Accepted by EACL 2023 Findings

Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors. Although there have been significant recent advances in neural conversational systems using large language models (LLMs) and growth in available dialog corpora, dialog tutoring has largely remained unaffected by these advances. In this paper, we rigorously analyze various generative language models on two dialog tutoring datasets for language learning using automatic and human evaluations to understand the new opportunities brought by these advances as well as the challenges we must overcome to build models that would be usable in real educational settings. We find that although current approaches can model tutoring in constrained learning scenarios when the number of concepts to be taught and possible teacher strategies are small, they perform poorly in less constrained scenarios. Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring, which measures learning opportunities for students and how engaging the dialog is. To understand the behavior of our models in a real tutoring setting, we conduct a user study using expert annotators and find a significantly large number of model reasoning errors in 45% of conversations. Finally, we connect our findings to outline future work., Comment: EACL 2023 (main conference, camera-ready)

Relation extraction (RE) involves identifying the relations between entities from unstructured texts. RE serves as the foundation for many natural language processing (NLP) applications, such as knowledge graph completion, question answering, and information retrieval. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art of RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including RE datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works from three perspectives (text representation, context encoding, and triplet prediction). Third, we discuss several important challenges faced by RE and summarize potential techniques to tackle these challenges. Finally, we outline some promising future directions and prospects in this field. This survey is expected to facilitate researchers' collaborative efforts to tackle the challenges of real-life RE systems.

Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set. Previous work mainly focuses on computer vision scenarios and largely ignores the essentials of unlearning in NLP field, where text data contains more explicit and sensitive personal information than images. In this paper, we propose a general unlearning framework called KGA to induce forgetfulness. Different from previous work that tries to recover gradients or forces models to perform close to one specific distribution, KGA maintains distribution differences (i.e., knowledge gap). This relaxes the distribution assumption. Furthermore, we first apply the unlearning method to various NLP tasks (i.e., classification, translation, response generation) and propose several unlearning evaluation metrics with pertinence. Experiments on large-scale datasets show that KGA yields comprehensive improvements over baselines, where extensive analyses further validate the effectiveness of KGA and provide insight into unlearning for NLP tasks., Comment: Accepted to ACL 2023

Shock breakout emission is light that arises when a shockwave, generated by core-collapse explosion of a massive star, passes through its outer envelope. Hitherto, the earliest detection of such a signal was at several hours after the explosion, though a few others had been reported. The temporal evolution of early light curves should reveal insights into the shock propagation, including explosion asymmetry and environment in the vicinity, but this has been hampered by the lack of multiwavelength observations. Here we report the instant multiband observations of a type II supernova (SN 2023ixf) in the galaxy M101 (at a distance of 6.85+/-0.15 Mpc), beginning at about 1.4 hours after the explosion. The exploding star was a red supergiant with a radius of about 440 solar radii. The light curves evolved rapidly, on timescales of 1-2 hours, and appeared unusually fainter and redder than predicted by models within the first few hours, which we attribute to an optically thick dust shell before it was disrupted by the shockwave. We infer that the breakout and perhaps the distribution of the surrounding dust were not spherically symmetric., Comment: 32 pages, 9 figures, and 3 tables. Online publication in Nature (Dec. 13th 2023)

Dust associated with various stellar sources in galaxies at all cosmic epochs remains a controversial topic, particularly whether supernovae (SNe) play an important role in dust production. We report evidence of dust formation in the cold, dense shell behind the ejecta-circumstellar medium (CSM) interaction in the Type Ia-CSM SN 2018evt three years after the explosion, characterized by a rise in the mid-infrared (MIR) emission accompanied by an accelerated decline in the optical radiation of the SN. Such a dust-formation picture is also corroborated by the concurrent evolution of the profiles of the Ha emission line. Our model suggests enhanced CSM dust concentration at increasing distances from the SN as compared to what can be expected from the density profile of the mass loss from a steady stellar wind. By the time of the last MIR observations at day +1041, a total amount of 1.2+-0.2x10^{-2} Msun of new dust has been formed by SN 2018evt, making SN 2018evt one of the most prolific dust factories among SNe with evidence of dust formation. The unprecedented witness of the intense production procedure of dust may shed light on the perceptions of dust formation in cosmic history., Comment: Accepted by Nature Astronomy, 6 main figures, 7 extended figures, and 2 extended tables

Dialogue systems, including task-oriented_dialogue_system (TOD) and open-domain_dialogue_system (ODD), have undergone significant transformations, with language_models (LM) playing a central role. This survey delves into the historical trajectory of dialogue systems, elucidating their intricate relationship with advancements in language models by categorizing this evolution into four distinct stages, each marked by pivotal LM breakthroughs: 1) Early_Stage: characterized by statistical LMs, resulting in rule-based or machine-learning-driven dialogue_systems; 2) Independent development of TOD and ODD based on neural_language_models (NLM; e.g., LSTM and GRU), since NLMs lack intrinsic knowledge in their parameters; 3) fusion between different types of dialogue systems with the advert of pre-trained_language_models (PLMs), starting from the fusion between four_sub-tasks_within_TOD, and then TOD_with_ODD; and 4) current LLM-based_dialogue_system, wherein LLMs can be used to conduct TOD and ODD seamlessly. Thus, our survey provides a chronological perspective aligned with LM breakthroughs, offering a comprehensive review of state-of-the-art research outcomes. What's more, we focus on emerging topics and discuss open challenges, providing valuable insights into future directions for LLM-based_dialogue_systems. Through this exploration, we pave the way for a deeper_comprehension of the evolution, guiding future developments in LM-based dialogue_systems.

Large language models (LLMs) have demonstrated exceptional performance in planning the use of various functional tools, such as calculators and retrievers, particularly in question-answering tasks. In this paper, we expand the definition of these tools, centering on conceptual tools within the context of dialogue systems. A conceptual tool specifies a cognitive concept that aids systematic or investigative thought. These conceptual tools play important roles in practice, such as multiple psychological or tutoring strategies being dynamically applied in a single turn to compose helpful responses. To further enhance the reasoning and planning capability of LLMs with these conceptual tools, we introduce a multi-persona collaboration framework: Think-Plan-Execute (TPE). This framework decouples the response generation process into three distinct roles: Thinker, Planner, and Executor. Specifically, the Thinker analyzes the internal status exhibited in the dialogue context, such as user emotions and preferences, to formulate a global guideline. The Planner then generates executable plans to call different conceptual tools (e.g., sources or strategies), while the Executor compiles all intermediate results into a coherent response. This structured approach not only enhances the explainability and controllability of responses but also reduces token redundancy. We demonstrate the effectiveness of TPE across various dialogue response generation tasks, including multi-source (FoCus) and multi-strategy interactions (CIMA and PsyQA). This reveals its potential to handle real-world dialogue interactions that require more complicated tool learning beyond just functional tools. The full code and data will be released for reproduction.

Knowledge graph completion is a task that revolves around filling in missing triples based on the information available in a knowledge graph. Among the current studies, text-based methods complete the task by utilizing textual descriptions of triples. However, this modeling approach may encounter limitations, particularly when the description fails to accurately and adequately express the intended meaning. To overcome these challenges, we propose the augmentation of data through two additional mechanisms. Firstly, we employ ChatGPT as an external knowledge base to generate coherent descriptions to bridge the semantic gap between the queries and answers. Secondly, we leverage inverse relations to create a symmetric graph, thereby creating extra labeling and providing supplementary information for link prediction. This approach offers additional insights into the relationships between entities. Through these efforts, we have observed significant improvements in knowledge graph completion, as these mechanisms enhance the richness and diversity of the available data, leading to more accurate results.

In this paper, we present Delta-LoRA, which is a novel parameter-efficient approach to fine-tune large language models (LLMs). In contrast to LoRA and other low-rank adaptation methods such as AdaLoRA, Delta-LoRA not only updates the low-rank matrices $\bA$ and $\bB$, but also propagate the learning to the pre-trained weights $\bW$ via updates utilizing the delta of the product of two low-rank matrices ($\bA^{(t+1)}\bB^{(t+1)} - \bA^{(t)}\bB^{(t)}$). Such a strategy effectively addresses the limitation that the incremental update of low-rank matrices is inadequate for learning representations capable for downstream tasks. Moreover, as the update of $\bW$ does not need to compute the gradients of $\bW$ and store their momentums, Delta-LoRA shares comparable memory requirements and computational costs with LoRA. Extensive experiments show that Delta-LoRA significantly outperforms existing low-rank adaptation methods. We further support these results with comprehensive analyses that underscore the effectiveness of Delta-LoRA.

Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of the external domain-specific information needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user preferences for the recommendation, where a time-aware attention is designed to emphasize the recently appeared items in entity-level representations. We further use the pre-trained BART to initialize the generation module to alleviate the data scarcity and enhance the context modeling. In addition to conducting experiments on a popular dataset (ReDial), we also include a multi-domain dataset (OpenDialKG) to show the effectiveness of our model. Experiments on both datasets show that our model achieves better performance on most evaluation metrics with less external knowledge and generalizes well to other domains. Additional analyses on the recommendation and generation tasks demonstrate the effectiveness of our model in different scenarios.

A rare class of supernovae (SNe) is characterized by strong interaction between the ejecta and several solar masses of circumstellar matter (CSM) as evidenced by strong Balmer-line emission. Within the first few weeks after the explosion, they may display spectral features similar to overluminous Type Ia SNe, while at later phase their observation properties exhibit remarkable similarities with some extreme case of Type IIn SNe that show strong Balmer lines years after the explosion. We present polarimetric observations of SN2018evt obtained by the ESO Very Large Telescope from 172 to 219 days after the estimated time of peak luminosity to study the geometry of the CSM. The nonzero continuum polarization decreases over time, suggesting that the mass loss of the progenitor star is aspherical. The prominent H$\alpha$ emission can be decomposed into a broad, time-evolving component and an intermediate-width, static component. The former shows polarized signals, and it is likely to arise from a cold dense shell (CDS) within the region between the forward and reverse shocks. The latter is significantly unpolarized, and it is likely to arise from shocked, fragmented gas clouds in the H-rich CSM. We infer that SN2018evt exploded inside a massive and aspherical circumstellar cloud. The symmetry axes of the CSM and the SN appear to be similar. SN\,2018evt shows observational properties common to events that display strong interaction between the ejecta and CSM, implying that they share similar circumstellar configurations. Our preliminary estimate also suggests that the circumstellar environment of SN2018evt has been significantly enriched at a rate of $\sim0.1$ M$_\odot$ yr$^{-1}$ over a period of $>100$ yr., Comment: 29 pages, 20 figures, 10 tables, accepted for publication in MNRAS

Early-time radiative signals from type Ia supernovae (SNe Ia) can provide important constraints on the explosion mechanism and the progenitor system. We present observations and analysis of SN 2019np, a nearby SN Ia discovered within 1-2 days after the explosion. Follow-up observations were conducted in optical, ultraviolet, and near-infrared bands, covering the phases from $\sim-$16.7 days to $\sim$+367.8 days relative to its $B-$band peak luminosity. The photometric and spectral evolutions of SN 2019np resembles the average behavior of normal SNe Ia. The absolute B-band peak magnitude and the post-peak decline rate are $M_{\rm max}(B)=-19.52 \pm 0.47$mag and $\Delta m_{\rm15}(B) =1.04 \pm 0.04$mag, respectively. No Hydrogen line has been detected in the near-infrared and nebular-phase spectra of SN 2019np. Assuming that the $^{56}$Ni powering the light curve is centrally located, we find that the bolometric light curve of SN 2019np shows a flux excess up to 5.0% in the early phase compared to the radiative diffusion model. Such an extra radiation perhaps suggests the presence of an additional energy source beyond the radioactive decay of central nickel. Comparing the observed color evolution with that predicted by different models such as interactions of SN ejecta with circumstellar matter (CSM)/companion star, a double-detonation explosion from a sub-Chandrasekhar mass white dwarf (WD), and surface $^{56}$Ni mixing, the latter one is favored., Comment: 23 pages, 16 figures, accepted for publication in MNRAS

Photocatalysis is a promising technology to treat dilute phenol–Cr(VI) mixture, where photoinduced electrons are commonly thought of as the main active species for Cr(VI) reduction. However, it generally depends on the surface adsorption to achieve efficient electron transfer. Meanwhile, the possible contribution of reductive quinone derivatives oxidized from phenol to Cr(VI) reduction has been rarely explored. The key is to explicitly understand the relation between the phenol oxidation pathway and Cr(VI) reduction. Herein, Ag/AgCl/MIL-101(Fe) prepared by directly loading Ag nanoparticles on MIL-101(Fe) was applied to the joint treatment of phenol and Cr(VI). The role of quinone derivatives in reducing Cr(VI) was revealed by excluding the surface adsorption. During the phenol oxidation, •OH radicals were more consumed in the initial stage for the ring opening of phenol. Meanwhile, 1O2 evolved from •O2– gradually caused the accumulation of reductive quinone intermediates, which dramatically accelerated the Cr(VI) reduction afterward. This study demonstrates the significance of controlling the evolution process of active oxygen species for the joint photocatalytic treatment of phenol–Cr(VI) mixture.

Photocatalysis is a promising technology to treat dilute phenol–Cr(VI) mixture, where photoinduced electrons are commonly thought of as the main active species for Cr(VI) reduction. However, it generally depends on the surface adsorption to achieve efficient electron transfer. Meanwhile, the possible contribution of reductive quinone derivatives oxidized from phenol to Cr(VI) reduction has been rarely explored. The key is to explicitly understand the relation between the phenol oxidation pathway and Cr(VI) reduction. Herein, Ag/AgCl/MIL-101(Fe) prepared by directly loading Ag nanoparticles on MIL-101(Fe) was applied to the joint treatment of phenol and Cr(VI). The role of quinone derivatives in reducing Cr(VI) was revealed by excluding the surface adsorption. During the phenol oxidation, •OH radicals were more consumed in the initial stage for the ring opening of phenol. Meanwhile, 1O2 evolved from •O2– gradually caused the accumulation of reductive quinone intermediates, which dramatically accelerated the Cr(VI) reduction afterward. This study demonstrates the significance of controlling the evolution process of active oxygen species for the joint photocatalytic treatment of phenol–Cr(VI) mixture.

Photocatalysis is a promising technology to treat dilute phenol–Cr(VI) mixture, where photoinduced electrons are commonly thought of as the main active species for Cr(VI) reduction. However, it generally depends on the surface adsorption to achieve efficient electron transfer. Meanwhile, the possible contribution of reductive quinone derivatives oxidized from phenol to Cr(VI) reduction has been rarely explored. The key is to explicitly understand the relation between the phenol oxidation pathway and Cr(VI) reduction. Herein, Ag/AgCl/MIL-101(Fe) prepared by directly loading Ag nanoparticles on MIL-101(Fe) was applied to the joint treatment of phenol and Cr(VI). The role of quinone derivatives in reducing Cr(VI) was revealed by excluding the surface adsorption. During the phenol oxidation, •OH radicals were more consumed in the initial stage for the ring opening of phenol. Meanwhile, 1O2 evolved from •O2– gradually caused the accumulation of reductive quinone intermediates, which dramatically accelerated the Cr(VI) reduction afterward. This study demonstrates the significance of controlling the evolution process of active oxygen species for the joint photocatalytic treatment of phenol–Cr(VI) mixture.

Photocatalysis is a promising technology to treat dilute phenol–Cr(VI) mixture, where photoinduced electrons are commonly thought of as the main active species for Cr(VI) reduction. However, it generally depends on the surface adsorption to achieve efficient electron transfer. Meanwhile, the possible contribution of reductive quinone derivatives oxidized from phenol to Cr(VI) reduction has been rarely explored. The key is to explicitly understand the relation between the phenol oxidation pathway and Cr(VI) reduction. Herein, Ag/AgCl/MIL-101(Fe) prepared by directly loading Ag nanoparticles on MIL-101(Fe) was applied to the joint treatment of phenol and Cr(VI). The role of quinone derivatives in reducing Cr(VI) was revealed by excluding the surface adsorption. During the phenol oxidation, •OH radicals were more consumed in the initial stage for the ring opening of phenol. Meanwhile, 1O2 evolved from •O2– gradually caused the accumulation of reductive quinone intermediates, which dramatically accelerated the Cr(VI) reduction afterward. This study demonstrates the significance of controlling the evolution process of active oxygen species for the joint photocatalytic treatment of phenol–Cr(VI) mixture.

Photocatalysis is a promising technology to treat dilute phenol–Cr(VI) mixture, where photoinduced electrons are commonly thought of as the main active species for Cr(VI) reduction. However, it generally depends on the surface adsorption to achieve efficient electron transfer. Meanwhile, the possible contribution of reductive quinone derivatives oxidized from phenol to Cr(VI) reduction has been rarely explored. The key is to explicitly understand the relation between the phenol oxidation pathway and Cr(VI) reduction. Herein, Ag/AgCl/MIL-101(Fe) prepared by directly loading Ag nanoparticles on MIL-101(Fe) was applied to the joint treatment of phenol and Cr(VI). The role of quinone derivatives in reducing Cr(VI) was revealed by excluding the surface adsorption. During the phenol oxidation, •OH radicals were more consumed in the initial stage for the ring opening of phenol. Meanwhile, 1O2 evolved from •O2– gradually caused the accumulation of reductive quinone intermediates, which dramatically accelerated the Cr(VI) reduction afterward. This study demonstrates the significance of controlling the evolution process of active oxygen species for the joint photocatalytic treatment of phenol–Cr(VI) mixture.

Salt and pepper noise removal is a common inverse problem in image processing. Traditional denoising methods have two limitations. First, noise characteristics are often not described accurately. For example, the noise location information is often ignored and the sparsity of the salt and pepper noise is often described by L1 norm, which cannot illustrate the sparse variables clearly. Second, conventional methods separate the contaminated image into a recovered image and a noise part, thus resulting in recovering an image with unsatisfied smooth parts and detail parts. In this study, we introduce a noise detection strategy to determine the position of the noise, and a non-convex sparsity regularization depicted by Lp quasi-norm is employed to describe the sparsity of the noise, thereby addressing the first limitation. The morphological component analysis framework with stationary Framelet transform is adopted to decompose the processed image into cartoon, texture, and noise parts to resolve the second limitation. Then, the alternating direction method of multipliers (ADMM) is employed to solve the proposed model. Finally, experiments are conducted to verify the proposed method and compare it with some current state-of-the-art denoising methods. The experimental results show that the proposed method can remove salt and pepper noise while preserving the details of the processed image.

Conversational Recommender System (CRS), which aims to recommend high-quality items to users through interactive conversations, has gained great research interest recently. A CRS is usually composed of a recommendation module and a generation module. In the previous work, these two modules are loosely connected in the model training and are shallowly integrated during inference, where a simple switching or copy mechanism is adopted to incorporate recommended items into generated responses. Moreover, the current end-to-end neural models trained on small crowd-sourcing datasets (e.g., 10K dialogs in the ReDial dataset) tend to overfit and have poor chit-chat ability. In this work, we propose a novel unified framework that integrates recommendation into the dialog (RecInDial) generation by introducing a vocabulary pointer. To tackle the low-resource issue in CRS, we finetune the large-scale pretrained language models to generate fluent and diverse responses, and introduce a knowledge-aware bias learned from an entity-oriented knowledge graph to enhance the recommendation performance. Furthermore, we propose to evaluate the CRS models in an end-to-end manner, which can reflect the overall performance of the entire system rather than the performance of individual modules, compared to the separate evaluations of the two modules used in previous work. Experiments on the benchmark dataset ReDial show our RecInDial model significantly surpasses the state-of-the-art methods. More extensive analyses show the effectiveness of our model., Comment: Accepted by AACL 2022

MAXI J1820+070 is a low-mass black hole X-ray binary system with high luminosity in both optical and X-ray bands during the outburst periods. We present extensive photometry in X-ray, ultraviolet, and optical bands, as well as densely-cadenced optical spectra, covering the phase from the beginning of optical outburst to $\sim$550 days. During the rebrightening process, the optical emission preceded the X-ray by 20.80 $\pm$ 2.85 days. The spectra are characterized by blue continua and emission features of Balmer series, He I, He II lines and broad Bowen blend. The pseudo equivalent width (pEW) of emission lines are found to show anticorrelations with the X-ray flux measured at comparable phases, which is due to the increased suppression by the optical continuum. At around the X-ray peak, the full width at half maximums (FWHMs) of H$_{\beta}$ and He II $\lambda$4686 tend to stabilize at 19.4 Angstrom and 21.8 Angstrom, which corresponds to the line forming region at a radius of 1.7 and 1.3 R_sun within the disk. We further analyzed the absolute fluxes of the lines and found that the fluxes of H$_{\beta}$ and He II $\lambda$4686 show positive correlations with the X-ray flux, favoring that the irradiation model is responsible for the optical emission. However, the fact that X-ray emission experiences a dramatic flux drop at t$\sim$200 days after the outburst, while the optical flux only shows little variations suggests that additional energy such as viscous energy may contribute to the optical radiation in addition to the X-ray irradiation., Comment: Accepted for publication in MNRAS

Photocatalysis is a promising technology to treat dilute phenol–Cr(VI) mixture, where photoinduced electrons are commonly thought of as the main active species for Cr(VI) reduction. However, it generally depends on the surface adsorption to achieve efficient electron transfer. Meanwhile, the possible contribution of reductive quinone derivatives oxidized from phenol to Cr(VI) reduction has been rarely explored. The key is to explicitly understand the relation between the phenol oxidation pathway and Cr(VI) reduction. Herein, Ag/AgCl/MIL-101(Fe) prepared by directly loading Ag nanoparticles on MIL-101(Fe) was applied to the joint treatment of phenol and Cr(VI). The role of quinone derivatives in reducing Cr(VI) was revealed by excluding the surface adsorption. During the phenol oxidation, •OH radicals were more consumed in the initial stage for the ring opening of phenol. Meanwhile, 1O2 evolved from •O2– gradually caused the accumulation of reductive quinone intermediates, which dramatically accelerated the Cr(VI) reduction afterward. This study demonstrates the significance of controlling the evolution process of active oxygen species for the joint photocatalytic treatment of phenol–Cr(VI) mixture.

In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the discussions which they wish to continue. Nevertheless, existing works only focus on exploiting chatting history and context information, and ignore the potential useful learning signals underlying conversation data, such as conversation thread patterns and repeated engagement of target users, which help better understand the behavior of target users in conversations. In this paper, we propose three interesting and well-founded auxiliary tasks, namely, Spread Pattern, Repeated Target user, and Turn Authorship, as the self-supervised signals for re-entry prediction. These auxiliary tasks are trained together with the main task in a multi-task manner. Experimental results on two datasets newly collected from Twitter and Reddit show that our method outperforms the previous state-of-the-arts with fewer parameters and faster convergence. Extensive experiments and analysis show the effectiveness of our proposed models and also point out some key ideas in designing self-supervised tasks., Comment: Accepted by EMNLP 2021 Findings

With the increasing popularity of social media, online interpersonal communication now plays an essential role in people's everyday information exchange. Whether and how a newcomer can better engage in the community has attracted great interest due to its application in many scenarios. Although some prior works that explore early socialization have obtained salient achievements, they are focusing on sociological surveys based on the small group. To help individuals get through the early socialization period and engage well in online conversations, we study a novel task to foresee whether a newcomer's message will be responded to by other participants in a multi-party conversation (henceforth Successful New-entry Prediction). The task would be an important part of the research in online assistants and social media. To further investigate the key factors indicating such engagement success, we employ an unsupervised neural network, Variational Auto-Encoder (VAE), to examine the topic content and discourse behavior from newcomer's chatting history and conversation's ongoing context. Furthermore, two large-scale datasets, from Reddit and Twitter, are collected to support further research on new-entries. Extensive experiments on both Twitter and Reddit datasets show that our model significantly outperforms all the baselines and popular neural models. Additional explainable and visual analyses on new-entry behavior shed light on how to better join in others' discussions., Comment: Accepted by WWW 2022

Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for both humans and machines. This work studies automatic quotation generation in an online conversation and explores how language consistency affects whether a quotation fits the given context. Here, we capture the contextual consistency of a quotation in terms of latent topics, interactions with the dialogue history, and coherence to the query turn's existing content. Further, an encoder-decoder neural framework is employed to continue the context with a quotation via language generation. Experiment results on two large-scale datasets in English and Chinese demonstrate that our quotation generation model outperforms the state-of-the-art models. Further analysis shows that topic, interaction, and query consistency are all helpful to learn how to quote in online conversations., Comment: Accepted by EMNLP 2020, updated with dataset link

To help individuals express themselves better, quotation recommendation is receiving growing attention. Nevertheless, most prior efforts focus on modeling quotations and queries separately and ignore the relationship between the quotations and the queries. In this work, we introduce a transformation matrix that directly maps the query representations to quotation representations. To better learn the mapping relationship, we employ a mapping loss that minimizes the distance of two semantic spaces (one for quotation and another for mapped-query). Furthermore, we explore using the words in history queries to interpret the figurative language of quotations, where quotation-aware attention is applied on top of history queries to highlight the indicator words. Experiments on two datasets in English and Chinese show that our model outperforms previous state-of-the-art models., Comment: ACL 2021

The role of high-valent Fe has rarely been explored in persulfate-based heterogeneous reaction. Herein, the existence of Fe(IV) is verified in a visible light-assisted FeTiO3/persulfate system using methyl phenyl sulfoxide as the probe. The FeTiO3/persulfate/light system is capable of selectively degrading aromatic compounds with a higher ionization potential including tetracycline and bisphenol A by photo-generated high-valent Fe(IV). The contributions from SO· 4, ·OH and 1O2 are excluded. The comparable efficiency in the dark requires higher dosages and suffers from a rapid deactivation. Based on XPS, Raman and EPR analyses, the poor dark activity is caused by the formation of a complex between in situ formed Fe(III) and SO2 4on the FeTiO3 surface; this complex is, however, the key intermediate for Fe(IV) production under the light irradiation. This study reveals the long-ignored role of SO2 4 as an abundant species in iron-based persulfate systems. We also call for re-evaluating the real oxidation mechanism in other persulfate-based reactions considering the different oxidation mechanisms of radicals and high-valent iron.

The role of high-valent Fe has rarely been explored in persulfate-based heterogeneous reaction. Herein, the existence of Fe(IV) is verified in a visible light-assisted FeTiO3/persulfate system using methyl phenyl sulfoxide as the probe. The FeTiO3/persulfate/light system is capable of selectively degrading aromatic compounds with a higher ionization potential including tetracycline and bisphenol A by photo-generated high-valent Fe(IV). The contributions from SO· 4, ·OH and 1O2 are excluded. The comparable efficiency in the dark requires higher dosages and suffers from a rapid deactivation. Based on XPS, Raman and EPR analyses, the poor dark activity is caused by the formation of a complex between in situ formed Fe(III) and SO2 4on the FeTiO3 surface; this complex is, however, the key intermediate for Fe(IV) production under the light irradiation. This study reveals the long-ignored role of SO2 4 as an abundant species in iron-based persulfate systems. We also call for re-evaluating the real oxidation mechanism in other persulfate-based reactions considering the different oxidation mechanisms of radicals and high-valent iron.

The role of high-valent Fe has rarely been explored in persulfate-based heterogeneous reaction. Herein, the existence of Fe(IV) is verified in a visible light-assisted FeTiO3/persulfate system using methyl phenyl sulfoxide as the probe. The FeTiO3/persulfate/light system is capable of selectively degrading aromatic compounds with a higher ionization potential including tetracycline and bisphenol A by photo-generated high-valent Fe(IV). The contributions from SO· 4, ·OH and 1O2 are excluded. The comparable efficiency in the dark requires higher dosages and suffers from a rapid deactivation. Based on XPS, Raman and EPR analyses, the poor dark activity is caused by the formation of a complex between in situ formed Fe(III) and SO2 4on the FeTiO3 surface; this complex is, however, the key intermediate for Fe(IV) production under the light irradiation. This study reveals the long-ignored role of SO2 4 as an abundant species in iron-based persulfate systems. We also call for re-evaluating the real oxidation mechanism in other persulfate-based reactions considering the different oxidation mechanisms of radicals and high-valent iron.

The role of high-valent Fe has rarely been explored in persulfate-based heterogeneous reaction. Herein, the existence of Fe(IV) is verified in a visible light-assisted FeTiO3/persulfate system using methyl phenyl sulfoxide as the probe. The FeTiO3/persulfate/light system is capable of selectively degrading aromatic compounds with a higher ionization potential including tetracycline and bisphenol A by photo-generated high-valent Fe(IV). The contributions from SO· 4, ·OH and 1O2 are excluded. The comparable efficiency in the dark requires higher dosages and suffers from a rapid deactivation. Based on XPS, Raman and EPR analyses, the poor dark activity is caused by the formation of a complex between in situ formed Fe(III) and SO2 4on the FeTiO3 surface; this complex is, however, the key intermediate for Fe(IV) production under the light irradiation. This study reveals the long-ignored role of SO2 4 as an abundant species in iron-based persulfate systems. We also call for re-evaluating the real oxidation mechanism in other persulfate-based reactions considering the different oxidation mechanisms of radicals and high-valent iron.

We present multi-band photometry and spectroscopy of SN 2018cuf, a Type IIP ("P" for plateau) supernova (SN) discovered by the Distance Less Than 40 Mpc survey (DLT40) within 24 hours of explosion. SN 2018cuf appears to be a typical Type IIP SN, with an absolute $V$-band magnitude of $-$16.73 $\pm$ 0.32 at maximum and a decline rate of 0.21 $\pm$ 0.05 mag/50d during the plateau phase. The distance of the object is constrained to be 41.8 $\pm$ 5.7 Mpc by using the expanding photosphere method. We use spectroscopic and photometric observations from the first year after the explosion to constrain the progenitor of SN 2018cuf using both hydrodynamic light curve modelling and late-time spectroscopic modelling. The progenitor of SN 2018cuf was most likely a red supergiant of about 14.5 $\rm M_{\odot}$ that produced 0.04 $\pm$ 0.01 $\rm M_{\odot}$ $\rm ^{56}Ni$ during the explosion. We also found $\sim$ 0.07 $\rm M_{\odot}$ of circumstellar material (CSM) around the progenitor is needed to fit the early light curves, where the CSM may originate from pre-supernova outbursts. During the plateau phase, high velocity features at $\rm \sim 11000\ km~s^{-1}$ are detected both in the optical and near-infrared spectra, supporting the possibility that the ejecta were interacting with some CSM. A very shallow slope during the post-plateau phase is also observed and it is likely due to a low degree of nickel mixing or the relatively high nickel mass in the SN., Comment: 22 pages, 15 figures, submitted to ApJ

Supernova (SN) 2017cbv in NGC 5643 is one of a handful of type Ia supernovae (SNe~Ia) reported to have excess blue emission at early times. This paper presents extensive $BVRIYJHK_s$-band light curves of SN 2017cbv, covering the phase from $-16$ to $+125$ days relative to $B$-band maximum light. SN 2017cbv reached a $B$-band maximum of 11.710$\pm$0.006~mag, with a post-maximum magnitude decline $\Delta m_{15}(B)$=0.990$\pm$0.013 mag. The supernova suffered no host reddening based on Phillips intrinsic color, Lira-Phillips relation, and the CMAGIC diagram. By employing the CMAGIC distance modulus $\mu=30.58\pm0.05$~mag and assuming $H_0$=72~$\rm km \ s^{-1} \ Mpc^{-1}$, we found that 0.73~\msun $^{56}$Ni was synthesized during the explosion of SN 2017cbv, which is consistent with estimates using reddening-free and distance-free methods via the phases of the secondary maximum of the NIR-band light curves. We also present 14 near-infrared spectra from $-18$ to $+49$~days relative to the $B$-band maximum light, providing constraints on the amount of swept-up hydrogen from the companion star in the context of the single degenerate progenitor scenario. No $Pa{\beta}$ emission feature was detected from our post-maximum NIR spectra, placing a hydrogen mass upper limit of 0.1 $M_{\odot}$. The overall optical/NIR photometric and NIR spectral evolution of SN 2017cbv is similar to that of a normal SN~Ia, even though its early evolution is marked by a flux excess no seen in most other well-observed normal SNe~Ia. We also compare the exquisite light curves of SN 2017cbv with some $M_{ch}$ DDT models and sub-$M_{ch}$ double detonation models., Comment: 20 figures, Accepted for publication in The Astrophysical Journal

The role of high-valent Fe has rarely been explored in persulfate-based heterogeneous reaction. Herein, the existence of Fe(IV) is verified in a visible light-assisted FeTiO3/persulfate system using methyl phenyl sulfoxide as the probe. The FeTiO3/persulfate/light system is capable of selectively degrading aromatic compounds with a higher ionization potential including tetracycline and bisphenol A by photo-generated high-valent Fe(IV). The contributions from SO· 4, ·OH and 1O2 are excluded. The comparable efficiency in the dark requires higher dosages and suffers from a rapid deactivation. Based on XPS, Raman and EPR analyses, the poor dark activity is caused by the formation of a complex between in situ formed Fe(III) and SO2 4on the FeTiO3 surface; this complex is, however, the key intermediate for Fe(IV) production under the light irradiation. This study reveals the long-ignored role of SO2 4 as an abundant species in iron-based persulfate systems. We also call for re-evaluating the real oxidation mechanism in other persulfate-based reactions considering the different oxidation mechanisms of radicals and high-valent iron.

We present high-cadence UV, optical, and near-infrared data on the luminous Type II-P supernova SN;2017gmr from hours after discovery through the first 180 days. SN;2017gmr does not show signs of narrow, high-ionization emission lines in the early optical spectra, yet the optical light-curve evolution suggests that an extra energy source from circumstellar medium (CSM) interaction must be present for at least 2 days after explosion. Modeling of the early light curve indicates a ?500 R progenitor radius, consistent with a rather compact red supergiant, and late-time luminosities indicate that up to 0.130;;0.026 M of Ni-56 are present, if the light curve is solely powered by radioactive decay, although the Ni-56 mass may be lower if CSM interaction contributes to the post-plateau luminosity. Prominent multipeaked emission lines of H? and [O i] emerge after day 154, as a result of either an asymmetric explosion or asymmetries in the CSM. The lack of narrow lines within the first 2 days of explosion in the likely presence of CSM interaction may be an example of close, dense, asymmetric CSM that is quickly enveloped by the spherical supernova ejecta.

We present high-cadence ultraviolet (UV), optical, and near-infrared (NIR) data on the luminous Type II-P supernova SN 2017gmr from hours after discovery through the first 180 days. SN 2017gmr does not show signs of narrow, high-ionization emission lines in the early optical spectra, yet the optical lightcurve evolution suggests that an extra energy source from circumstellar medium (CSM) interaction must be present for at least 2 days after explosion. Modeling of the early lightcurve indicates a ~500R$_{\odot}$ progenitor radius, consistent with a rather compact red supergiant, and late-time luminosities indicate up to 0.130 $\pm$ 0.026 M$_{\odot}$ of $^{56}$Ni are present, if the lightcurve is solely powered by radioactive decay, although the $^{56}$Ni mass may be lower if CSM interaction contributes to the post-plateau luminosity. Prominent multi-peaked emission lines of H$\alpha$ and [O I] emerge after day 154, as a result of either an asymmetric explosion or asymmetries in the CSM. The lack of narrow lines within the first two days of explosion in the likely presence of CSM interaction may be an example of close, dense, asymmetric CSM that is quickly enveloped by the spherical supernova ejecta., Comment: Submitted to ApJ, 25 pages, plus Appendix

Hierarchical macro-mesoporous anatase TiO single crystal is one-pot synthesized in an EtOH-H O system using polystyrene (PS) as the single porogen both for macropore and mesopore and TiF as the titanium precursor. The key to the simultaneous growth of single crystal and the introduction of hierarchical pores is the assembly of PS and titania at the glassification temperature of PS (100 °C). During the hydrolytic polymerization of TiF , PS is encapsulated inside titania and gradually glassified. The interference from elastic PS on the oriental growth of TiO crystallite is thus minimized and the final removal of PS through calcination leaves interconnected macropore and mesopore inside the single crystal. According to XPS, EPR and fluorescence analyses, abundant oxygen vacancies are formed on the hierarchical porous single crystal, which presents extraordinary photocatalytic activity and stability in degrading organic pollutants under simulated sunlight irradiation using Rhodamine B as the model. The improved photocatalytic activity is a synergistic effect of improved separation of charge carrier and facilitated interfacial charge transfer benefitting from highly accessible porous single crystal structure. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.]

Hierarchical macro-mesoporous anatase TiO single crystal is one-pot synthesized in an EtOH-H O system using polystyrene (PS) as the single porogen both for macropore and mesopore and TiF as the titanium precursor. The key to the simultaneous growth of single crystal and the introduction of hierarchical pores is the assembly of PS and titania at the glassification temperature of PS (100 °C). During the hydrolytic polymerization of TiF , PS is encapsulated inside titania and gradually glassified. The interference from elastic PS on the oriental growth of TiO crystallite is thus minimized and the final removal of PS through calcination leaves interconnected macropore and mesopore inside the single crystal. According to XPS, EPR and fluorescence analyses, abundant oxygen vacancies are formed on the hierarchical porous single crystal, which presents extraordinary photocatalytic activity and stability in degrading organic pollutants under simulated sunlight irradiation using Rhodamine B as the model. The improved photocatalytic activity is a synergistic effect of improved separation of charge carrier and facilitated interfacial charge transfer benefitting from highly accessible porous single crystal structure. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.]

Hierarchical macro-mesoporous anatase TiO single crystal is one-pot synthesized in an EtOH-H O system using polystyrene (PS) as the single porogen both for macropore and mesopore and TiF as the titanium precursor. The key to the simultaneous growth of single crystal and the introduction of hierarchical pores is the assembly of PS and titania at the glassification temperature of PS (100 °C). During the hydrolytic polymerization of TiF , PS is encapsulated inside titania and gradually glassified. The interference from elastic PS on the oriental growth of TiO crystallite is thus minimized and the final removal of PS through calcination leaves interconnected macropore and mesopore inside the single crystal. According to XPS, EPR and fluorescence analyses, abundant oxygen vacancies are formed on the hierarchical porous single crystal, which presents extraordinary photocatalytic activity and stability in degrading organic pollutants under simulated sunlight irradiation using Rhodamine B as the model. The improved photocatalytic activity is a synergistic effect of improved separation of charge carrier and facilitated interfacial charge transfer benefitting from highly accessible porous single crystal structure. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.]

Hierarchical macro-mesoporous anatase TiO single crystal is one-pot synthesized in an EtOH-H O system using polystyrene (PS) as the single porogen both for macropore and mesopore and TiF as the titanium precursor. The key to the simultaneous growth of single crystal and the introduction of hierarchical pores is the assembly of PS and titania at the glassification temperature of PS (100 °C). During the hydrolytic polymerization of TiF , PS is encapsulated inside titania and gradually glassified. The interference from elastic PS on the oriental growth of TiO crystallite is thus minimized and the final removal of PS through calcination leaves interconnected macropore and mesopore inside the single crystal. According to XPS, EPR and fluorescence analyses, abundant oxygen vacancies are formed on the hierarchical porous single crystal, which presents extraordinary photocatalytic activity and stability in degrading organic pollutants under simulated sunlight irradiation using Rhodamine B as the model. The improved photocatalytic activity is a synergistic effect of improved separation of charge carrier and facilitated interfacial charge transfer benefitting from highly accessible porous single crystal structure. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.]

Hierarchical macro-mesoporous anatase TiO single crystal is one-pot synthesized in an EtOH-H O system using polystyrene (PS) as the single porogen both for macropore and mesopore and TiF as the titanium precursor. The key to the simultaneous growth of single crystal and the introduction of hierarchical pores is the assembly of PS and titania at the glassification temperature of PS (100 °C). During the hydrolytic polymerization of TiF , PS is encapsulated inside titania and gradually glassified. The interference from elastic PS on the oriental growth of TiO crystallite is thus minimized and the final removal of PS through calcination leaves interconnected macropore and mesopore inside the single crystal. According to XPS, EPR and fluorescence analyses, abundant oxygen vacancies are formed on the hierarchical porous single crystal, which presents extraordinary photocatalytic activity and stability in degrading organic pollutants under simulated sunlight irradiation using Rhodamine B as the model. The improved photocatalytic activity is a synergistic effect of improved separation of charge carrier and facilitated interfacial charge transfer benefitting from highly accessible porous single crystal structure. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.]