The Nature of Novelty Detection

Sentence level novelty detection aims at reducing redundant sentences from a sentence list. In the task, sentences appearing later in the list with no new meanings are eliminated. Aiming at a better accuracy for detecting redundancy, this paper reveals the nature of the novelty detection task currently overlooked by the Novelty community $-$ Novelty as a combination of the partial overlap (PO, two sentences sharing common facts) and complete overlap (CO, the first sentence covers all the facts of the second sentence) relations. By formalizing novelty detection as a combination of the two relations between sentences, new viewpoints toward techniques dealing with Novelty are proposed. Among the methods discussed, the similarity, overlap, pool and language modeling approaches are commonly used. Furthermore, a novel approach, selected pool method is provided, which is immediate following the nature of the task. Experimental results obtained on all the three currently available novelty datasets showed that selected pool is significantly better or no worse than the current methods. Knowledge about the nature of the task also affects the evaluation methodologies. We propose new evaluation measures for Novelty according to the nature of the task, as well as possible directions for future study.Comment: This paper pointed out the future direction for novelty detection research. 37 pages, double spaced versio

Clinical significance of obstructive sleep apnea in patients with acute coronary syndrome in relation to diabetes status.

Objective: The prognostic significance of obstructive sleep apnea (OSA) in patients with acute coronary syndrome (ACS) according to diabetes mellitus (DM) status remains unclear. We aimed to elucidate the association of OSA with subsequent cardiovascular events in patients with ACS with or without DM. Research design and methods: In this prospective cohort study, consecutive eligible patients with ACS underwent cardiorespiratory polygraphy between June 2015 and May 2017. OSA was defined as an Apnea Hypopnea Index ≥15 events/hour. The primary end point was major adverse cardiovascular and cerebrovascular events (MACCEs), including cardiovascular death, myocardial infarction, stroke, ischemia-driven revascularization, or hospitalization for unstable angina or heart failure. Results: Among 804 patients, 248 (30.8%) had DM and 403 (50.1%) had OSA. OSA was associated with 2.5 times the risk of 1 year MACCE in patients with DM (22.3% vs 7.1% in the non-OSA group; adjusted HR (HR)=2.49, 95% CI 1.16 to 5.35, p=0.019), but not in patients without DM (8.5% vs 7.7% in the non-OSA group, adjusted HR=0.94, 95% CI 0.51 to 1.75, p=0.85). Patients with DM without OSA had a similar 1 year MACCE rate as patients without DM. The increased risk of events was predominately isolated to patients with OSA with baseline glucose or hemoglobin A1c levels above the median. Combined OSA and longer hypoxia duration (time with arterial oxygen saturation22 min) further increased the MACCE rate to 31.0% in patients with DM. Conclusions: OSA was associated with increased risk of 1 year MACCE following ACS in patients with DM, but not in non-DM patients. Further trials exploring the efficacy of OSA treatment in high-risk patients with ACS and DM are warranted

Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling

Association of Obstructive Sleep Apnea With Cardiovascular Outcomes in Patients With Acute Coronary Syndrome.

Background The prognostic significance of obstructive sleep apnea ( OSA ) in patients with acute coronary syndrome ( ACS ) in the contemporary era is unclear. We performed a large, prospective cohort study and did a landmark analysis to delineate the association of OSA with subsequent cardiovascular events after ACS onset. Methods and Results Between June 2015 and May 2017, consecutive eligible patients admitted for ACS underwent cardiorespiratory polygraphy during hospitalization. OSA was defined as an apnea-hypopnea index ≥15 events·h-1. The primary end point was major adverse cardiovascular and cerebrovascular event ( MACCE ), including cardiovascular death, myocardial infarction, stroke, ischemia-driven revascularization, or hospitalization for unstable angina or heart failure. OSA was present in 403 of 804 (50.1%) patients. During median follow-up of 1 year, cumulative incidence of MACCE was significantly higher in the OSA group than in the non- OSA group (log-rank, P=0.041). Multivariate analysis showed that OSA was nominally associated with incidence of MACCE (adjusted hazard ratio, 1.55; 95% CI, 0.94-2.57; P=0.085). In the landmark analysis, patients with OSA had 3.9 times the risk of incurring a MACCE after 1 year (adjusted hazard ratio, 3.87; 95% CI, 1.20-12.46; P=0.023), but no increased risk was found within 1-year follow-up (adjusted hazard ratio, 1.18; 95% CI, 0.67-2.09; P=0.575). No significant differences were found in the incidence of cardiovascular death, myocardial infarction, and ischemia-driven revascularization, except for a higher rate of hospitalization for unstable angina in the OSA group than in the non- OSA group (adjusted hazard ratio, 2.10; 95% CI, 1.09-4.05; P=0.027). Conclusions There was no independent correlation between OSA and 1-year MACCE after ACS . The increased risk associated with OSA was only observed after 1-year follow-up. Efficacy of OSA treatment as secondary prevention after ACS requires further investigation

Meta-evaluation of online and offline web search evaluation metrics

As in most information retrieval (IR) studies, evaluation plays an essential part in Web search research. Both offline and online evaluation metrics are adopted in measuring the performance of search engines. Offline metrics are usually based on relevance judgments of query-document pairs from assessors while online metrics exploit the user behavior data, such as clicks, collected from search engines to compare search algorithms. Although both types of IR evaluation metrics have achieved success, to what extent can they predict user satisfaction still remains under-investigated. To shed light on this research question, we meta-evaluate a series of existing online and offline metrics to study how well they infer actual search user satisfaction in different search scenarios. We find that both types of evaluation metrics significantly correlate with user satisfaction while they reflect satisfaction from different perspectives for different search tasks. Offline metrics better align with user satisfaction in homogeneous search (i.e. ten blue links) whereas online metrics outperform when vertical results are federated. Finally, we also propose to incorporate mouse hover information into existing online evaluation metrics, and empirically show that they better align with search user satisfaction than click-based online metrics

Detecting collusive spamming activities in community question answering

Community Question Answering (CQA) portals provide rich sources of information on a variety of topics. However, the authenticity and quality of questions and answers (Q&As) has proven hard to control. In a troubling direction, the widespread growth of crowdsourcing websites has created a large-scale, potentially difficult-to-detect workforce to manipulate malicious contents in CQA. The crowd workers who join the same crowdsourcing task about promotion campaigns in CQA collusively manipulate deceptive Q&As for promoting a target (product or service). The collusive spamming group can fully control the sentiment of the target. How to utilize the structure and the attributes for detecting manipulated Q&As? How to detect the collusive group and leverage the group information for the detection task? To shed light on these research questions, we propose a unified framework to tackle the challenge of detecting collusive spamming activities of CQA. First, we interpret the questions and answers in CQA as two independent networks. Second, we detect collusive question groups and answer groups from these two networks respectively by measuring the similarity of the contents posted within a short duration. Third, using attributes (individual-level and group-level) and correlations (user-based and content-based), we proposed a combined factor graph model to detect deceptive Q&As simultaneously by combining two independent factor graphs. With a large-scale practical data set, we find that the proposed framework can detect deceptive contents at early stage, and outperforms a number of competitive baselines

How do chemical patterns affect equilibrium droplet shapes?

By utilizing a proposed analytical model in combination with the phase-field method, we present a comprehensive study on the effect of chemical patterns on equilibrium droplet morphologies. Here, three influencing factors, the droplet sizes, contact angles, and the ratios of the hydrophilic area to the hydrophobic area, are contemplated. In the analytical model, chemical heterogeneities are described by different non-linear functions. By tuning these functions and the related parameters, the analytical model is capable of calculating the energy landscapes of the system. The chemically patterned surfaces display complex energy landscapes with chemical-heterogeneity-induced local minima, which correspond to the equilibrium morphologies of the droplets. Phase-field (PF) simulations are accordingly conducted and compared with the predicted equilibrium morphologies. In addition, we propose a modified Cassie–Baxter (CB) model to delineate the equilibrium droplet shapes. In contrast to the classic CB model, our extension is not only restricted to the shape with a spherical cap. Both the energy landscape method and the modified CB model are demonstrated to have a good agreement with the PF simulations