18 research outputs found
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.Comment: Accepted by KDD 201
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data
There are threefold challenges in emotion recognition. First, it is difficult
to recognize human's emotional states only considering a single modality.
Second, it is expensive to manually annotate the emotional data. Third,
emotional data often suffers from missing modalities due to unforeseeable
sensor malfunction or configuration issues. In this paper, we address all these
problems under a novel multi-view deep generative framework. Specifically, we
propose to model the statistical relationships of multi-modality emotional data
using multiple modality-specific generative networks with a shared latent
space. By imposing a Gaussian mixture assumption on the posterior approximation
of the shared latent variables, our framework can learn the joint deep
representation from multiple modalities and evaluate the importance of each
modality simultaneously. To solve the labeled-data-scarcity problem, we extend
our multi-view model to semi-supervised learning scenario by casting the
semi-supervised classification problem as a specialized missing data imputation
task. To address the missing-modality problem, we further extend our
semi-supervised multi-view model to deal with incomplete data, where a missing
view is treated as a latent variable and integrated out during inference. This
way, the proposed overall framework can utilize all available (both labeled and
unlabeled, as well as both complete and incomplete) data to improve its
generalization ability. The experiments conducted on two real multi-modal
emotion datasets demonstrated the superiority of our framework.Comment: arXiv admin note: text overlap with arXiv:1704.07548, 2018 ACM
Multimedia Conference (MM'18
MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion
Query expansion is a commonly-used technique in many search systems to better
represent users' information needs with additional query terms. Existing
studies for this task usually propose to expand a query with retrieved or
generated contextual documents. However, both types of methods have clear
limitations. For retrieval-based methods, the documents retrieved with the
original query might not be accurate enough to reveal the search intent,
especially when the query is brief or ambiguous. For generation-based methods,
existing models can hardly be trained or aligned on a particular corpus, due to
the lack of corpus-specific labeled data. In this paper, we propose a novel
Large Language Model (LLM) based mutual verification framework for query
expansion, which alleviates the aforementioned limitations. Specifically, we
first design a query-query-document generation pipeline, which can effectively
leverage the contextual knowledge encoded in LLMs to generate sub-queries and
corresponding documents from multiple perspectives. Next, we employ a mutual
verification method for both generated and retrieved contextual documents,
where 1) retrieved documents are filtered with the external contextual
knowledge in generated documents, and 2) generated documents are filtered with
the corpus-specific knowledge in retrieved documents. Overall, the proposed
method allows retrieved and generated documents to complement each other to
finalize a better query expansion. We conduct extensive experiments on three
information retrieval datasets, i.e., TREC-DL-2020, TREC-COVID, and MSMARCO.
The results demonstrate that our method outperforms other baselines
significantly
Serum CIRP increases the risk of acute kidney injury after cardiac surgery
IntroductionAcute kidney injury (AKI) is a frequent perioperative complication. The underlying mechanisms of cardiac surgery-associated AKI are still not completely elucidated. Cold-induced RNA-binding protein (CIRP) has been subsequently found to be regulated by various stress conditions. During cardiac surgery and cardiopulmonary bypass (CPB), the host is subjected to hypothermia and inadequate organ perfusion, resulting in an upregulation of CIRP secretion. The aim of this study is to evaluate the role of elevated extracellular CIRP level as a contributing factor in the development of AKI.MethodsA total of 292 patients who underwent cardiac surgery were retrospectively enrolled and their serum samples were collected preoperative and postoperative. Demographic data, intraoperative data, in-hospital outcomes, and the occurrence of AKI were also collected for the patients. The correlation between CIRP and intraoperative procedures, as well as its association with postoperative outcomes were analyzed.ResultsIn multivariable analysis, higher ΔCIRP (p = 0.036) and body mass index (p = 0.015) were independent risk factors for postoperative AKI. Meanwhile, patients with postoperative AKI exhibited lower survival rate in 2-year follow-up (p = 0.008). Compared to off-pump coronary artery bypass grafting surgery, patients who underwent on-pump coronary artery bypass grafting, valve surgery, aortic dissection and other surgery showed higher ΔCIRP, measuring 1,093, 666, 914 and 258 pg/mL, respectively (p < 0.001). The levels of ΔCIRP were significantly higher in patients who underwent CPB compared to those who did not (793.0 ± 648.7 vs. 149.5 ± 289.1 pg/mL, p < 0.001). Correlation analysis revealed a positive correlation between ΔCIRP levels and the duration of CPB (r = 0.502, p < 0.001). Patients with higher CIRP levels are at greater risk of postoperative AKI (OR: 1.67, p = 0.032), especially the stage 2–3 AKI (OR: 2.11, p = 0.037).ConclusionCIRP secretion increases with prolonged CPB time after cardiac surgery, and CIRP secretion is positively correlated with the duration of CPB. Cardiac surgeries with CPB exhibited significantly higher levels of CIRP compared to non-CPB surgeries. Elevation of CIRP level is an independent risk factor for the incidence of AKI, especially the severe AKI, and were associated with adverse in-hospital outcomes
Whole-genome sequencing of cultivated and wild peppers provides insights into Capsicum domestication and specialization
As an economic crop, pepper satisfies people's spicy taste and has medicinal uses worldwide. To gain a better understanding of Capsicum evolution, domestication, and specialization, we present here the genome sequence of the cultivated pepper Zunla-1 (C. annuum L.) and its wild progenitor Chiltepin (C. annuum var. glabriusculum). We estimate that the pepper genome expanded similar to 0.3 Mya (with respect to the genome of other Solanaceae) by a rapid amplification of retrotransposons elements, resulting in a genome comprised of similar to 81% repetitive sequences. Approximately 79% of 3.48-Gb scaffolds containing 34,476 protein-coding genes were anchored to chromosomes by a high-density genetic map. Comparison of cultivated and wild pepper genomes with 20 resequencing accessions revealed molecular footprints of artificial selection, providing us with a list of candidate domestication genes. We also found that dosage compensation effect of tandem duplication genes probably contributed to the pungent diversification in pepper. The Capsicum reference genome provides crucial information for the study of not only the evolution of the pepper genome but also, the Solanaceae family, and it will facilitate the establishment of more effective pepper breeding programs
Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial
Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials.
Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure.
Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen.
Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049
Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): a double-blind, randomised, placebo-controlled trial
Background: Short-term treatment for people with type 2 diabetes using a low dose of the selective endothelin A receptor antagonist atrasentan reduces albuminuria without causing significant sodium retention. We report the long-term effects of treatment with atrasentan on major renal outcomes. Methods: We did this double-blind, randomised, placebo-controlled trial at 689 sites in 41 countries. We enrolled adults aged 18–85 years with type 2 diabetes, estimated glomerular filtration rate (eGFR)25–75 mL/min per 1·73 m 2 of body surface area, and a urine albumin-to-creatinine ratio (UACR)of 300–5000 mg/g who had received maximum labelled or tolerated renin–angiotensin system inhibition for at least 4 weeks. Participants were given atrasentan 0·75 mg orally daily during an enrichment period before random group assignment. Those with a UACR decrease of at least 30% with no substantial fluid retention during the enrichment period (responders)were included in the double-blind treatment period. Responders were randomly assigned to receive either atrasentan 0·75 mg orally daily or placebo. All patients and investigators were masked to treatment assignment. The primary endpoint was a composite of doubling of serum creatinine (sustained for ≥30 days)or end-stage kidney disease (eGFR <15 mL/min per 1·73 m 2 sustained for ≥90 days, chronic dialysis for ≥90 days, kidney transplantation, or death from kidney failure)in the intention-to-treat population of all responders. Safety was assessed in all patients who received at least one dose of their assigned study treatment. The study is registered with ClinicalTrials.gov, number NCT01858532. Findings: Between May 17, 2013, and July 13, 2017, 11 087 patients were screened; 5117 entered the enrichment period, and 4711 completed the enrichment period. Of these, 2648 patients were responders and were randomly assigned to the atrasentan group (n=1325)or placebo group (n=1323). Median follow-up was 2·2 years (IQR 1·4–2·9). 79 (6·0%)of 1325 patients in the atrasentan group and 105 (7·9%)of 1323 in the placebo group had a primary composite renal endpoint event (hazard ratio [HR]0·65 [95% CI 0·49–0·88]; p=0·0047). Fluid retention and anaemia adverse events, which have been previously attributed to endothelin receptor antagonists, were more frequent in the atrasentan group than in the placebo group. Hospital admission for heart failure occurred in 47 (3·5%)of 1325 patients in the atrasentan group and 34 (2·6%)of 1323 patients in the placebo group (HR 1·33 [95% CI 0·85–2·07]; p=0·208). 58 (4·4%)patients in the atrasentan group and 52 (3·9%)in the placebo group died (HR 1·09 [95% CI 0·75–1·59]; p=0·65). Interpretation: Atrasentan reduced the risk of renal events in patients with diabetes and chronic kidney disease who were selected to optimise efficacy and safety. These data support a potential role for selective endothelin receptor antagonists in protecting renal function in patients with type 2 diabetes at high risk of developing end-stage kidney disease. Funding: AbbVie
Stock assistant: a stock AI assistant for reliability modeling of stock comments
Stock comments from analysts contain important consulting information for investors to foresee stock volatility and market trends. Existing studies on stock comments usually focused on capturing coarse-grained opinion polarities or understanding market fundamentals. However, investors are often overwhelmed and confused by massive comments with huge noises and ambiguous opinions. Therefore, it is an emerging need to have a fine-grained stock comment analysis tool to identify more reliable stock comments. To this end, this paper provides a solution called StockAssIstant for modeling the reliability of stock comments by considering multiple factors, such as stock price trends, comment content, and the performances of analysts, in a holistic manner. Specifically, we first analyze the pattern of analysts' opinion dynamics from historical comments. Then, we extract key features from the time-series constructed by using the semantic information in comment text, stock prices and the historical behaviors of analysts. Based on these features, we propose an ensemble learning based approach for measuring the reliability of comments. Finally, we conduct extensive experiments and provide a trading simulation on real-world stock data. The experimental results and the profit achieved by the simulated trading in 12-month period clearly validate the effectiveness of our approach for modeling the reliability of stock comments
Sketch and Customize: A Counterfactual Story Generator
Recent text generation models are easy to generate relevant and fluent text for the given text, while lack of causal reasoning ability when we change some parts of the given text.
Counterfactual story rewriting is a recently proposed task to test the causal reasoning ability for text generation models, which requires a model to predict the corresponding story ending when the condition is modified to a counterfactual one. Previous works have shown that the traditional sequence-to-sequence model cannot well handle this problem, as it often captures some spurious correlations between the original and counterfactual endings, instead of the causal relations between conditions and endings.
To address this issue, we propose a sketch-and-customize generation model guided by the causality implicated in the conditions and endings. In the sketch stage, a skeleton is extracted by removing words which are conflict to the counterfactual condition, from the original ending. In the customize stage, a generation model is used to fill proper words in the skeleton under the guidance of the counterfactual condition. In this way, the obtained counterfactual ending is both relevant to the original ending and consistent with the counterfactual condition. Experimental results show that the proposed model generates much better endings, as compared with the traditional sequence-to-sequence model
Stability analysis of riverbanks with a dual structure under water–root–soil coupling
The collapse mechanism of dual-structure vegetation riverbanks at different water levels is unclear. A method for calculating the critical collapse width of a dual-structure vegetation bank under different failure modes that consider the variations in river and groundwater levels and the influence of vegetation roots is proposed. Combined with the influence of flow lateral erosion and slope toe accumulation, a calculation model of riverbank stability was established. The results show that shear failure is the main failure mode when the cohesive soil layer on a dual-structure bank is thick, and the critical collapse width of the bank with root soil is higher than that of the soil bank. The critical collapse width of the bank varied with the water level during different water level periods. Compared with a soil riverbank, a rooted soil riverbank can significantly prolong the bank collapse time. The collapse width of a soil bank without vegetation roots is smaller than that of a rooted soil bank, and the cumulative collapse width is related to calculation time. The greater the thickness of rooted soil, the slower the decay rate of bank stability under water flow erosion.
HIGHLIGHTS
The collapse mechanism of riverbanks with a dual structure under the influence of water level and vegetation was revealed.;
The collapse process analysis and stability calculation model of dual-structure bank with vegetation were established.;
The stability analysis and prediction of the dual-structure bank with vegetation in the lower reaches of the Yangtze River were carried out.