Deep Context Resolution

Conversations depend on information from the context. To go beyond one-round conversation, a chatbot must resolve contextual information such as: 1) co-reference resolution, 2) ellipsis resolution, and 3) conjunctive relationship resolution. There are simply not enough data to avoid these problems by trying to train a sequence-to-sequence model for multi-round conversation similar to that of one-round conversation. The contributions of this paper are: 1) We formulate the problem of context resolution for conversation; 2) We present deep learning models, including an end-to-end network for context resolution; 3) We propose a way of creating a huge amount o

HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds

3D object detection in point clouds is important for autonomous driving systems. A primary challenge in 3D object detection stems from the sparse distribution of points within the 3D scene. Existing high-performance methods typically employ 3D sparse convolutional neural networks with small kernels to extract features. To reduce computational costs, these methods resort to submanifold sparse convolutions, which prevent the information exchange among spatially disconnected features. Some recent approaches have attempted to address this problem by introducing large-kernel convolutions or self-attention mechanisms, but they either achieve limited accuracy improvements or incur excessive computational costs. We propose HEDNet, a hierarchical encoder-decoder network for 3D object detection, which leverages encoder-decoder blocks to capture long-range dependencies among features in the spatial space, particularly for large and distant objects. We conducted extensive experiments on the Waymo Open and nuScenes datasets. HEDNet achieved superior detection accuracy on both datasets than previous state-of-the-art methods with competitive efficiency. The code is available at https://github.com/zhanggang001/HEDNet.Comment: Accepted by NeurIPS 202

Public Awareness of Dengue Fever and Willingness-to-Pay for vaccine of Dengue Fever : Case Study of south part of Taiwan

University of Tokyo(東京大学

Co-movement Pattern Mining from Videos

Co-movement pattern mining from GPS trajectories has been an intriguing subject in spatial-temporal data mining. In this paper, we extend this research line by migrating the data source from GPS sensors to surveillance cameras, and presenting the first investigation into co-movement pattern mining from videos. We formulate the new problem, re-define the spatial-temporal proximity constraints from cameras deployed in a road network, and theoretically prove its hardness. Due to the lack of readily applicable solutions, we adapt existing techniques and propose two competitive baselines using Apriori-based enumerator and CMC algorithm, respectively. As the principal technical contributions, we introduce a novel index called temporal-cluster suffix tree (TCS-tree), which performs two-level temporal clustering within each camera and constructs a suffix tree from the resulting clusters. Moreover, we present a sequence-ahead pruning framework based on TCS-tree, which allows for the simultaneous leverage of all pattern constraints to filter candidate paths. Finally, to reduce verification cost on the candidate paths, we propose a sliding-window based co-movement pattern enumeration strategy and a hashing-based dominance eliminator, both of which are effective in avoiding redundant operations. We conduct extensive experiments for scalability and effectiveness analysis. Our results validate the efficiency of the proposed index and mining algorithm, which runs remarkably faster than the two baseline methods. Additionally, we construct a video database with 1169 cameras and perform an end-to-end pipeline analysis to study the performance gap between GPS-driven and video-driven methods. Our results demonstrate that the derived patterns from the video-driven approach are similar to those derived from groundtruth trajectories, providing evidence of its effectiveness

Open Vocabulary Object Detection with Pseudo Bounding-Box Labels

Despite great progress in object detection, most existing methods work only on a limited set of object categories, due to the tremendous human effort needed for bounding-box annotations of training data. To alleviate the problem, recent open vocabulary and zero-shot detection methods attempt to detect novel object categories beyond those seen during training. They achieve this goal by training on a pre-defined base categories to induce generalization to novel objects. However, their potential is still constrained by the small set of base categories available for training. To enlarge the set of base classes, we propose a method to automatically generate pseudo bounding-box annotations of diverse objects from large-scale image-caption pairs. Our method leverages the localization ability of pre-trained vision-language models to generate pseudo bounding-box labels and then directly uses them for training object detectors. Experimental results show that our method outperforms the state-of-the-art open vocabulary detector by 8% AP on COCO novel categories, by 6.3% AP on PASCAL VOC, by 2.3% AP on Objects365 and by 2.8% AP on LVIS. Code is available at https://github.com/salesforce/PB-OVD.Comment: ECCV 202

Wireless sensor network system for indoor air quality supervision

U okviru diplomskog rada razvijen je sustav nadzora kvalitete zraka u zatvorenim prostorima. Sustav je razvijen koristeći koncept bežične senzorske mreže. Sustav prikuplja podatke s terena čiji su izvori senzorski čvorovi, a ponori distribuirane instance korisničke aplikacije. Senzorska mreže u konačnici prikuplja podatke o: • zadimljenosti prostorije, • količini lebdećih čestica u zraku, • grubu detekciju količine štetnog plina u prostoriji, • mjerenje temperature i • iznos relativne vlažnosti zraka. Senzorski čvorovi raspoređeni su u promatranom objektu, a prikupljeni podaci su proslijeđeni centralnom poslužitelju. Centralni poslužitelj je realiziran Internet aplikacijom. U okviru rada obrađene su osnove kakvoće zraka, zatim je opisan osnovni princip definiranja sustava (bežične senzorske mreže) s predloškom za izvedbu zadanog sustava. Obrađene su potrebne jedinice, među kojima su senzori za mjerenje potrebnih fizikalnih veličina, bežični komunikacijski moduli i mikroupravljački uređaji. Opisana je priprema okoline i konfiguracije, primjerice na usmjerniku za definiranu funkcionalnost prenošenja podataka iz senzorske mreže do korisničke aplikacije. Opisana je izrada korisničke aplikacije čime je dovršena izrada funkcionalnosti sustava. U konačnici izvedena su mjerenja prijema paketa kako bi se doznala ograničenja sustava, vezana uz domet i strategiju postavljanja komunikacijskih čvorova. U zaključku su predložena moguća unapređenja postojećeg rješenja ili mogućnosti prenamjene sustava.This thesis goes through a study and a practical development of a system of indoor air quality management. Indoor air quality management system is been developed with the wireless sensor network concept in its focus. This system is capable of collecting data from a distributed network of sensor nodes. Visualization of data is made by a service of user applications which are distributed to network clients. Sensor network collects data about: • amount of smoke in an object, • amount of particle matter in the air of an object, • approximation on the amount of harmful gas or gases in the object, • current temperature, • and amount of relative humidity. Sensor nodes are deployed to a selected complex, collected data is been forwarded to a central server. Server is implemented as a web application. Thesis elaborates the basic principle of defining the system (of wireless sensor networks) with its model for the performance measurement. Study on all necessary components is been presented, including sensors, wireless modules and microcontrollers. Basic settings and preparation of the environment is been defined, e.g. for the functionality of relaying data from sensor network to the user application. Afterwards, one can find notations on a development of the user application which completes the functionality of the proposed system. Finally, measurement are been presented on the packet reception rate, which can indicate on the limitations of the system doe to the propagation of packets in indoor environment. Measurement can be used as a indication for strategies on setting up network configuration and node distribution. In the conclusion there are proposed improvements for the selected solution or possibilities of functionality conversion

What contributes to medical debt? Evidence from patients in rural China.

BACKGROUND: Rural households in developing countries usually have severe medical debt due to high out-of-pocket (OOP) payments, which contributes to bankruptcy. China implemented the critical illness insurance (CII) in 2012 to decrease patients' medical expenditure. This paper aimed to explore the medical debt of rural Chinese patients and its influencing factors. METHODS: A questionnaire survey of health expenditures and medical debt was conducted in two counties of Central and Western China in 2017. Patients who received CII were used as the sample on the basis of multi-stage stratified cluster sampling. Descriptive statistics and multivariate analysis of variance were used in all data. A two-part model was used to evaluate the occurrence and extent of medical debt. RESULTS: A total of 826 rural patients with CII were surveyed. The percentages of patients incurring medical debt exceeded 50% and the median debt load was 20,000 Chinese yuan (CNY, 650 CNY = US$100). Financial assistance from kin (P < 0.001) decreased the likelihood of medical debt. High inpatient expenses (IEs, P < 0.01), CII reimbursement ratio (P < 0.001), and non-direct medical costs (P < 0.001) resulted in increased medical debt load. CONCLUSIONS: Medical debt is still one of the biggest problems in rural China. High IEs, CII reimbursement ratio, municipal or high-level hospitals were the risk determinants of medical debt load. Financial assistance from kin and household income were the protective factors. Increasing service capability of hospitals in counties could leave more patiemts in county-level and township hospitals. Improving CII with increased reimbursement rate may also be issues of concern

Effect of critical illness insurance on the medical expenditures of rural patients in China: an interrupted time series study for universal health insurance coverage.

OBJECTIVE: The objective of this study is to determine if critical illness insurance (CII) promotes the universal health coverage to reduce out-of-pocket (OOP) medical expenditures and improve the effective reimbursement rate (ERR) in rural China. STUDY DESIGN: The 5-year monthly hospitalisation data, starting 2 years before the CII (ie, the 'intervention') began, were collected. Interrupted time series analysis models were used to evaluate the immediate and gradual effects of CII on OOP payment and ERR. SETTING: The study was conducted in Xiantao County, Hubei Province, China. PARTICIPANTS: A total of 511 221 inpatients within 5 years were included in the analysis. RESULTS: In 2016, 100 288 patients received in-patient services, among which 4137 benefited from CII. After the implementation of CII, OOP expenses increased 32.2% (95% CI 24.8% to 39.5%, p<0.001). Compared with the preintervention periods, the trend changes decline at a rate of 0.7% per month after the implementation of CII. Similarly, a significant decrease was observed in log ERR after the intervention started. The rate of level change is 16% change (95% CI -20.0% to -12.1%, p<0.001). CONCLUSION: CII did not decrease the OOP payments of rural inpatients in 2011-2016 periods. The limited extents of population coverage and financing resources can be attributed to these results. Therefore, the Chinese government must urgently raise the funds of CII and improve the CII policy reimbursement rate

RUNDC3A regulates SNAP25-mediated chemotherapy resistance by binding AKT in gastric neuroendocrine carcinoma (GNEC)

Gastric neuroendocrine carcinoma (GNEC) is a common type of neuroendocrine carcinoma (NEC) with a poor prognosis and limited therapeutic options. The underlying mechanisms of chemoresistance in patients with GNEC and those with NEC are largely unknown, and thus, reliable biomarkers and therapeutic targets that could improve treatment outcomes in patients with NECs are lacking. The aim of this study was to identify specific targets and investigate their roles in GNEC progression and treatment resistance. Differentially expressed genes (DEGs) were identified in GNEC specimens and were further analysed by focusing on their roles in chemoresistance. Gene Ontology (GO) and pathway enrichment analyses of GNEC DEGs revealed that synapse-related function was the most prominent cellular function perturbed in GNEC. SNAP25 was identified as the target gene involved in most of the enriched pathways. In vitro and in vivo experiments showed that SNAP25 plays a role in proliferation and chemoresistance in GNEC cell lines. AKT has been identified as a downstream target, and SNAP25 binds to AKT protein and promotes AKT protein half-life. Further analysis of other types of NEC as well as small cell lung cancer, which resembles NEC on a molecular level, has identified RUNDC3A as an upstream molecule that regulates SNAP25 expression and the associated phenotypes that could enhance chemoresistance in NECs. Our results show that SNAP25 expression in GNEC is mediated by RUNDC3A and promotes GNEC progression and chemoresistance via posttranslational modification of AKT. Thus, our results suggest that the RUNDC3A/SNAP25/Akt axis could be a potential therapeutic target in GNEC

Ultrathin Few-Layer GeP Nanosheets via Lithiation-Assisted Chemical Exfoliation and Their Application in Sodium Storage

2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Ultrathin few-layer materials have attracted intensive research attention because of their distinctive and unique properties. Few-layer GeP (FL-GP) is potentially interesting for application in electronics and optoelectronics because of its appropriate band gap and good stability under ambient conditions. Nevertheless, it is a challenge to achieve ultrathin few-layer or single layer GeP from exfoliation of bulk crystals. Here, a lithiation-assisted chemical exfoliation technique is employed to achieve FL-GP, in which the interlayer spacing can be efficiently enlarged after a preliminary lithium ion intercalation, allowing the bulk crystal to be readily exfoliated in a following ultrasonication. As a result, ultrathin FL-GP is obtained. In a demonstration, the FL-GP/reduced graphene oxide (rGO) demonstrates remarkable sodium storage performance. The FL-GP with a two-dimensional structure shortens the ion transport pathways and alleviates the volume variation during sodiation. Meanwhile, the rGO in the composite improves the conductivity of the whole electrode. The as-prepared FL-GP/rGO electrode exhibits a high capacity of 504.2 mAh g−1 at 100 mA g−1, remarkable rate performance, and superior cycling stability in the half cells. FL-GP/rGO//Na3V2(PO4)3 full cells are also assembled and demonstrated satisfactory electrochemical performance, indicating potential application of the as-prepared anode materials