A fast response and recovery H2S gas sensor based on free-standing TiO2 nanotube array films prepared by one-step anodization method

International audienceThe free-standing TiO 2 nanotube (TiNT) array film was firstly synthesized by a one-step anodization method. The characterization results with SEM, TEM, XRD and EDX indicated that the main compound on the TiNT array film was titania with anatase phase, and the average inner diameter of the nanotube was around 110 nm with a wall thickness of 16 nm and a layer thickness of 3.8 µm. Subsequently, the TiNT-based gas sensor was fabricated and its sensing properties toward H 2 S were investigated. The results showed that, operating under the optimum temperature of 300 °C, the TiNT-based gas sensor not only had excellent reversibility, selectivity and stability, but also attained the response values 4.5-26.2 to the detected H 2 S gas at 1-50 ppm, and good linearity between the sensor response and H 2 S concentration could be observed. Meanwhile, the response and recovery time of the sensor to 50 ppm H 2 S gas were as low as 22 s and 6 s, respectively. In addition, the growth mechanism of the free-standing TiNT array film and the gas sensing mechanism of the TiNT-based gas sensor towards H 2 S were also given in the article. Lastly, the outstanding gas sensing properties and easy fabrication of the TiNT-based gas sensor presented the potential industrial applications in the future

Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering

A common thread of open-domain question answering (QA) models employs a retriever-reader pipeline that first retrieves a handful of relevant passages from Wikipedia and then peruses the passages to produce an answer. However, even state-of-the-art readers fail to capture the complex relationships between entities appearing in questions and retrieved passages, leading to answers that contradict the facts. In light of this, we propose a novel knowledge Graph enhanced passage reader, namely Grape, to improve the reader performance for open-domain QA. Specifically, for each pair of question and retrieved passage, we first construct a localized bipartite graph, attributed to entity embeddings extracted from the intermediate layer of the reader model. Then, a graph neural network learns relational knowledge while fusing graph and contextual representations into the hidden states of the reader model. Experiments on three open-domain QA benchmarks show Grape can improve the state-of-the-art performance by up to 2.2 exact match score with a negligible overhead increase, with the same retriever and retrieved passages. Our code is publicly available at https://github.com/jumxglhf/GRAPE.Comment: Findings of EMNLP202

Solving Coupled Differential Equation Groups Using PINO-CDE

As a fundamental mathmatical tool in many engineering disciplines, coupled differential equation groups are being widely used to model complex structures containing multiple physical quantities. Engineers constantly adjust structural parameters at the design stage, which requires a highly efficient solver. The rise of deep learning technologies has offered new perspectives on this task. Unfortunately, existing black-box models suffer from poor accuracy and robustness, while the advanced methodologies of single-output operator regression cannot deal with multiple quantities simultaneously. To address these challenges, we propose PINO-CDE, a deep learning framework for solving coupled differential equation groups (CDEs) along with an equation normalization algorithm for performance enhancing. Based on the theory of physics-informed neural operator (PINO), PINO-CDE uses a single network for all quantities in a CDEs, instead of training dozens, or even hundreds of networks as in the existing literature. We demonstrate the flexibility and feasibility of PINO-CDE for one toy example and two engineering applications: vehicle-track coupled dynamics (VTCD) and reliability assessment for a four-storey building (uncertainty propagation). The performance of VTCD indicates that PINO-CDE outperforms existing software and deep learning-based methods in terms of efficiency and precision, respectively. For the uncertainty propagation task, PINO-CDE provides higher-resolution results in less than a quarter of the time incurred when using the probability density evolution method (PDEM). This framework integrates engineering dynamics and deep learning technologies and may reveal a new concept for CDEs solving and uncertainty propagation

Dense X Retrieval: What Retrieval Granularity Should We Use?

Dense retrieval has become a prominent method to obtain relevant context or world knowledge in open-domain NLP tasks. When we use a learned dense retriever on a retrieval corpus at inference time, an often-overlooked design choice is the retrieval unit in which the corpus is indexed, e.g. document, passage, or sentence. We discover that the retrieval unit choice significantly impacts the performance of both retrieval and downstream tasks. Distinct from the typical approach of using passages or sentences, we introduce a novel retrieval unit, proposition, for dense retrieval. Propositions are defined as atomic expressions within text, each encapsulating a distinct factoid and presented in a concise, self-contained natural language format. We conduct an empirical comparison of different retrieval granularity. Our results reveal that proposition-based retrieval significantly outperforms traditional passage or sentence-based methods in dense retrieval. Moreover, retrieval by proposition also enhances the performance of downstream QA tasks, since the retrieved texts are more condensed with question-relevant information, reducing the need for lengthy input tokens and minimizing the inclusion of extraneous, irrelevant information

DetToolChain: a new prompting paradigm to unleash detection ability of MLLM

We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object detection ability of multimodal large language models (MLLMs), such as GPT-4V and Gemini. Our approach consists of a detection prompting toolkit inspired by high-precision detection priors and a new Chain-of-Thought to implement these prompts. Specifically, the prompts in the toolkit are designed to guide the MLLM to focus on regional information (e.g., zooming in), read coordinates according to measure standards (e.g., overlaying rulers and compasses), and infer from the contextual information (e.g., overlaying scene graphs). Building upon these tools, the new detection chain-of-thought can automatically decompose the task into simple subtasks, diagnose the predictions, and plan for progressive box refinements. The effectiveness of our framework is demonstrated across a spectrum of detection tasks, especially hard cases. Compared to existing state-of-the-art methods, GPT4V with our DetToolChain improves state-of-the-art object detectors by +21.5% AP50 on MS COCO Novel class set for open-vocabulary detection, +24.23% Acc on RefCOCO val set for zero-shot referring expression comprehension, +14.5% AP on D-cube describe object detection FULL setting

A point-feature label placement algorithm based on spatial data mining

The point-feature label placement (PFLP) refers to the process of positioning labels near point features on a map while adhering to specific rules and guidelines, finally obtaining clear, aesthetically pleasing, and conflict-free maps. While various approaches have been suggested for automated point feature placement on maps, few studies have fully considered the spatial distribution characteristics and label correlations of point datasets, resulting in poor label quality in the process of solving the label placement of dense and complex point datasets. In this paper, we propose a point-feature label placement algorithm based on spatial data mining that analyzes the local spatial distribution characteristics and label correlations of point features. The algorithm quantifies the interference among point features by designing a label frequent pattern framework (LFPF) and constructs an ascending label ordering method based on the pattern to reduce interference. Besides, three classical metaheuristic algorithms (simulated annealing algorithm, genetic algorithm, and ant colony algorithm) are applied to the PFLP in combination with the framework to verify the validity of this framework. Additionally, a bit-based grid spatial index is proposed to reduce cache memory and consumption time in conflict detection. The performance of the experiments is tested with 4000, 10000, and 20000 points of POI data obtained randomly under various label densities. The results of these experiments showed that: (1) the proposed method outperformed both the original algorithm and recent literature, with label quality improvements ranging from 3 to 6.7 and from 0.1 to 2.6, respectively. (2) The label efficiency was improved by 58.2% compared with the traditional grid index

circFBXW7 attenuates malignant progression in lung adenocarcinoma by sponging miR-942-5p

Background: As a type of non-coding RNA, circular RNAs (circRNAs) are considered to be functional molecules associated with human cancers. An increasing number of circRNAs have been verified in malignant progression in a number of cancers. The circRNA, circFBXW7, has been proven to play an important role in tumor proliferation and metastasis. However, whether circFBXW7 influences progression in lung adenocarcinoma (LUAD) remains unclear. Methods: Quantitative real-time reverse transcriptase PCR (qRT-PCR) was used to verify circFBXW7 in LUAD cell lines and LUAD tissues. Kaplan-Meier analysis was then used to compare the disease-free survival (DFS) and overall survival (OS) of these LUAD patients. The biological function of circFBXW7 was examined by overexpression and knockdown of circFBXW7 using MTT assay, EdU assay, wound-healing assay, and Transwell in vitro assays. To explore the mechanism of the circFBXW7, RNA pull-down assay, dual luciferase reporter assay, and RNA immunoprecipitation (RIP) assay were employed to examine the interaction between circFBXW7 and miR-942-5p. Western blot was used to study the fundamental proteins associated with the epithelial-mesenchymal transition (EMT) pathway. In vivo studies with BALB/c nude mice subcutaneously injected with cells stably overexpressing circFBXW7 were performed to further validate the in vitro results. Results: circFBXW7 was downregulated in LUAD cell lines and tissues, and LUAD patients with lower levels had shorter DFS and OS. The in vitro study showed that circFBXW7 overexpression inhibited proliferation and migration of A549 and HCC2279 cell lines. These results were confirmed by circFBXW7 knockdown, which showed the reverse effect. The in vivo model showed that the circRNA levels influenced the tumor growth. Finally, we determined that circFBXW7 target miRNA-942-5p which regulates the EMT gene BARX2. The modulation of circFBXW7 levels produced significant changes in EMT genes in vitro and in vivo. Conclusions: Our findings showed that circFBXW7 inhibits proliferation and migration by controlling the miR-942-5p/BARX2 axis in LUAD cell lines and its levels correlates with patient survival suggesting that regulating circFBXW7 could have therapeutic value in treating LUAD patients

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO