LLukas22/scidocs huggingface dataset:
Clothing retrieval with visual attention model

Clothing retrieval is a challenging problem in computer vision. With the advance of Convolutional Neural Networks (CNNs), the accuracy of clothing retrieval has been significantly improved. FashionNet [1], a recent study, proposes to employ a set of artificial features in the form of landmarks for clothing retrieval, which are shown to be helpful for retrieval. However, the landmark detection module is trained with strong supervision which requires considerable efforts to obtain. In this paper, we propose a self-learning Visual Attention Model (VAM) to extracts attention maps from clothing images. The VAM is further connected to a global network to form an end-to-end network structure through Impdrop connection which randomly Dropout on the feature maps with the probabilities given by the attention map. Extensive experiments on several widely used benchmark clothing retrieval data sets have demonstrated the promise of the proposed method. We also show that compared to the trivial Product connection, the Impdrop connection makes the network structure more robust when training sets of limited size are used.
Digital Libraries and Autonomous Citation Indexing

The Web is revolutionizing the way researchers access scientific literature, however scientific literature on the Web is largely disorganized. Autonomous citation indexing can help organize the literature by automating the construction of citation indices. Autonomous citation indexing aims to improve the dissemination and retrieval of scientific literature, and provides improvements in cost, availability, comprehensiveness, efficiency, and timeliness.
Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework

This paper presents an empirical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices. *
An investigation into round touchscreen wristwatch interaction

The wristwatch is a device that is quick to access, but is currently under-utilized as a platform for interaction. We investigate interaction on a circular touchscreen wristwatch, empirically determining the error rate for variously-sized buttons placed around the rim. We consider three types of inter-target movements, and derive a mathematical model for error rate given a movement type and angular and radial button widths.
Robust entropy-based endpoint detection for speech recognition in noisy environments

This paper presents an entropy-based algorithm for accurate and robust endpoint detection for speech recognition under noisy environments. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the speech segments accurately. Experimental results show that this algorithm outperforms the energy-based algorithms in both detection accuracy and recognition performance under noisy environments, with an average error rate reduction of more than 16%.
Linear Programming for Power-System Network Security Applications

A linear programming (LP) method for security dispatch and emergency control calculations on large power systems is presented. The method is reliable, fast, flexible, easy to program, and requires little computer storage. It works directly with the normal power-system variables and limits, and incorporates the usual sparse matrix techniques. An important feature of the method is that it handles multi-segment generator cost curves neatly and efficiently.
An Arabic optical character recognition system using recognition-based segmentation

Optical character recognition (OCR) systems improve human}machine interaction and are widely used in many areas. The recognition of cursive scripts is a di$cult task as their segmentation su!ers from serious problems. This paper proposes an Arabic OCR system, which uses a recognition-based segmentation technique to overcome the classical segmentation problems. A newly developed Arabic word segmentation algorithm is also introduced to separate horizontally overlapping Arabic words/subwords. There is also a feedback loop to control the combination of character fragments for recognition. The system was implemented and the results show a 90% recognition accuracy with a 20 chars/s recognition rate. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Relationship between generic strategies , competitive advantage and organizational performance : an empirical analysis

In recent years, there has been a growing intensity of competition in virtually all areas of business in both markets upstream for raw materials such as components, supplies, capital and technology and markets downstream for consumer goods and services. This paper examines the relationships among generic strategy, competitive advantage, and organizational performance. Firstly, the nature of generic strategies, competitive advantage, and organizational performance is examined. Secondly, the relationship between generic strategies and competitive advantage is analyzed. Finally, the implications of generic strategies, organizational performance, performance measures and competitive advantage are studied. This study focuses on: (i) the relationship of generic strategy and organisational performance in Australian manufacturing companies participating in the “Best Practice Program in Australia”, (ii) the relationship between generic strategies and competitive advantage, and (iii) the relationship among generic strategies, competitive advantage and organisational performance.  1999 Elsevier Science Ltd. All rights reserved.
Social vulnerability and survival across levels of frailty in the Honolulu-Asia Aging Study.

PURPOSE
we evaluated mortality risk in relation to social vulnerability across levels of frailty among a cohort of older Japanese-American men.


METHODS
in secondary analysis of the Honolulu-Asia Aging Study (HAAS), participants (n = 3,271) were aged 72-93 years at baseline. A frailty index (FI) created using 58 potential health deficits to quantify participants' frailty level at baseline, with four frailty strata: 0.0 < FI ≤ 0.1 (n = 1,074); 0.1 < FI ≤ 0.20 (n = 1,549); 0.2 < FI ≤ 0.30 (n = 472); FI > 0.3 (n = 176). Similarly, a social vulnerability index was created using 19 self-reported social deficits. Cox proportional hazard modelling was employed to estimate the impact of social vulnerability across the four levels of frailty, accounting for age, smoking, alcohol use and variation in health deficits within each frailty level.


RESULTS
for the fittest participants, social vulnerability was associated with mortality (hazards ratio (HR) = 1.04, 95% confidence interval (CI) = 1.01, 1.07; P value = 0.008). Similarly, for those considered at risk for frailty, each social deficit was associated with a 5% increased risk of mortality. For frail individuals, the Cox regression analyses indicated that social vulnerability was not significantly associated with mortality (0.2 < FI ≤ 0.3: HR = 1.016, 95% CI = 0.98, 1.06; P value = 0.442; FI > 0.3: HR = 0.98, 95% CI = 0.93, 1.04).


CONCLUSIONS
for the fittest and at-risk HAAS participants, the accumulation of social deficits was associated with significant increases in mortality risk. For frail individuals (FI > 0.20), the estimation of mortality risk may depend more so on intrinsic factors related to their health.
Earlobe keloids: classification according to gross morphology determines proper surgical approach.

BACKGROUND
A previous study described the outcomes of a treatment protocol using a prospective design and identified three clinical risk factors for recurrent keloids.


OBJECTIVE
To introduce a novel classification of earlobe keloids through a retrospective study and describe the appropriate surgical methods according to this new classification.


METHODS
One thousand twenty-seven earlobe keloids were treated at Kangbuk Samsung Hospital from March 2001 to February 2011. All cases were studied retrospectively and classified.


RESULTS
The earlobe keloids were classified into five groups. The frequency of earlobe keloids in descending order were a sessile type, single nodular pattern; pedunculated type; sessile type, multinodular pattern; buried type; and mixed type. Different surgical methods were used based on the Chang-Park classification according to gross morphology, including core extirpation using a penetrating technique, standard keloidectomy, radical keloidectomy, keloidectomy with core extirpation, and a combination of these. All cases were closed primarily without skin grafting or sacrifice of the surrounding tissue.


CONCLUSIONS
This novel classification for earlobe keloids can lead to a better understanding of the different types of earlobe keloids and inform decisions regarding surgical methods.
Alcoholic liver disease is a strong predictor of colorectal polyps in liver transplant recipients

BACKGROUND AND AIMS
 Colorectal cancer (CRC) is associated with a significantly reduced survival rate in transplant recipients. The prevalence and risk factors of CRC and of colorectal polyps after orthotopic liver transplant (OLT) remain unclear. The study aim was to determine the prevalence of colorectal polyps in OLT recipients. A secondary objective was to explore possible risk factors of polyps.


PATIENTS AND MATERIALS
 This was a retrospective single center study of all OLT recipients transplanted between 2007 and 2009. All patients who underwent a colonoscopy 5 ± 5 years after OLT were included. The outcome was colorectal polyps, as identified on colonoscopy. A logistic regression model was performed to identify potential predictors of polyps.


RESULTS
 Of 164 OLT recipients, 80 were included in this study. Polyps were diagnosed in 37 % of patients before transplant and in 33 % afterwards. With regard to post-transplant lesions, 22 % were advanced adenomas or cancerous. In the regression analysis, the odds of post-transplant polyps were 11 times higher in patients with alcoholic liver disease (OR 11.3, 95 %CI 3.2 - 39.4; P  < 0.001).


CONCLUSION
 Patients with end-stage liver disease may be at high risk of colorectal polyps before and after liver transplant, and screening should be continued in both contexts. Those with alcoholic liver disease are particularly at risk for post-OLT polyps and may benefit from more intensive screening.
Brief review of invariant texture analysis methods

This paper considers invariant texture analysis. Texture analysis approaches whose performances are not a,ected by translation, rotation, a.ne, and perspective transform are addressed. Existing invariant texture analysis algorithms are carefully studied and classi0ed into three categories: statistical methods, model based methods, and structural methods. The importance of invariant texture analysis is presented 0rst. Each approach is reviewed according to its classi0cation, and its merits and drawbacks are outlined. The focus of possible future work is also suggested. ? 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Structural integrity is decreased in both Achilles tendons in people with unilateral Achilles tendinopathy.

OBJECTIVES
A high proportion of Achilles tendinopathy patients develop bilateral symptoms with human and animal studies showing bilateral histological changes associated with overuse/pathology in one tendon. The current study examined changes in tendon structure, assessed semi-quantitatively using ultrasound tissue characterisation, in both the symptomatic and asymptomatic tendon in unilateral Achilles tendinopathy patients in comparison to individuals with no history of tendinopathy.


DESIGN
Cross-sectional case-control study.


METHODS
Participants with Achilles tendinopathy (n=21), with varying severity and length of clinical symptoms, and six participants with no history of tendinopathy were recruited. Tendons were scanned using ultrasound tissue characterisation, which captures contiguous transverse ultrasound images every 0.2mm and renders a 3-dimensional image. Ultrasound tissue characterisation quantifies tendon structure by measuring the stability of echopattern over contiguous transverse images. Four echo-types were discriminated and expressed as a percentage. Antero-posterior diameter of all tendons was measured.


RESULTS
Significant differences were observed in the proportion of normal tendon structure between all three groups (p<0.01), with the symptomatic tendon containing the least amount of normal tendon structure (symptomatic - 79.5%, asymptomatic - 81.8%, control - 86.4%). The asymptomatic tendon contained significantly less normal tendon in comparison to the control tendon (p=0.008), suggesting the asymptomatic tendon is structurally compromised despite the absence of symptoms.


CONCLUSIONS
Both Achilles tendons are structurally compromised in patients with unilateral Achilles tendinopathy. Future studies need to investigate whether these changes increase the risk of developing symptoms.
High-Density Lipoprotein Cholesterol and Cause-Specific Mortality in Individuals Without Previous Cardiovascular Conditions: The CANHEART Study.

BACKGROUND
The prognostic importance of high-density lipoprotein cholesterol (HDL-C) as a specific risk factor for cardiovascular (CV) disease has been challenged by recent clinical trials and genetic studies.


OBJECTIVES
This study sought to reappraise the association of HDL-C level with CV and non-CV mortality using a "big data" approach.


METHODS
An observational cohort study was conducted using the CANHEART (Cardiovascular Health in Ambulatory Care Research Team) dataset, which was created by linking together 17 different individual-level data sources. People were included if they were between 40 and 105 years old on January 1, 2008, living in Ontario, Canada, without previous CV conditions or severe comorbidities, and had an outpatient fasting cholesterol measurement in the year prior to the inception date. The primary outcome was cause-specific mortality.


RESULTS
A total of 631,762 individuals were included. The mean age of our cohort was 57.2 years, 55.4% were women, and mean HDL-C level was 55.2 mg/dl. There were 17,952 deaths during a mean follow-up of 4.9 ± 0.4 years. The overall all-cause mortality rate was 8.1 per 1,000 person-years for men and 6.6 per 1,000 person-years for women. Individuals with lower HDL-C levels were more likely to have low incomes, unhealthy lifestyle, higher triglycerides levels, other cardiac risk factors, and medical comorbidities. Individuals with lower HDL-C levels were independently associated with higher risk of CV, cancer, and other mortality compared with individuals in the reference ranges of HDL-C levels. In addition, individuals with higher HDL levels (>70 mg/dl in men, >90 mg/dl in women) had increased hazard of non-CV mortality.


CONCLUSIONS
Complex associations exist between HDL-C levels and sociodemographic, lifestyle, comorbidity factors, and mortality. HDL-C level is unlikely to represent a CV-specific risk factor given similarities in its associations with non-CV outcomes.
Geospatial semantics and linked spatiotemporal data - Past, present, and future

The Geosciences and Geography are not just yet another application area for semantic technologies. The vast heterogeneity of the involved disciplines ranging from the natural sciences to the social sciences introduces new challenges in terms of interoperability. Moreover, the inherent spatial and temporal information components also require distinct semantic approaches. For these reasons, geospatial semantics, geo-ontologies, and semantic interoperability have been active research areas over the last 20 years. The geospatial semantics community has been among the early adopters of the Semantic Web, contributing methods, ontologies, use cases, and datasets. Today, geographic information is a crucial part of many central hubs on the Linked Data Web. In this editorial, we outline the research field of geospatial semantics, highlight major research directions and trends, and glance at future challenges. We hope that this text will be valuable for geoscientists interested in semantics research as well as knowledge engineers interested in spatiotemporal data. Introduction and Motivation While the Web has changed with the advent of the Social Web from mostly authoritative content towards increasing amounts of user generated information, it is essentially still about linked documents. These documents provide structure and context for the described data and easy their interpretation. In contrast, the evolving Data Web is about linking data, not documents. Such datasets are not bound to a specific document but can be easily combined and used outside of their original creation context. With a growth rate of millions of new facts encoded as RDF-triples per month, the Linked Data cloud allows users to answer complex queries spanning multiple, heterogeneous data sources from different scientific domains. However, this uncoupling of data from its creation context makes the interpretation of data challenging. Thus, research on semantic interoperability and ontologies is crucial to ensure consistency and meaningful results. Space and time are fundamental ordering principles to structure such data and provide an implicit context for their interpretation. Hence, it is not surprising that many linked datasets either contain spatiotemporal identifiers themselves or link out to such datasets, making them central hubs of the Linked Data cloud. Prominent examples include Geonames.org as well as the Linked Geo Data project, which provides a RDF serialization of Points Of Interest from Open Street Map [103]. Besides such Voluntary Geographic Information (VGI), governments 1570-0844/12/$27.50 c © 2012 – IOS Press and the authors. All rights reserved
Early pregnancy azathioprine use and pregnancy outcomes.

BACKGROUND
Azathioprine (AZA) is used during pregnancy by women with inflammatory bowel disease (IBD), other autoimmune disorders, malignancy, and organ transplantation. Previous studies have demonstrated potential risks.


METHODS
The Swedish Medical Birth Register was used to identify 476 women who reported the use of AZA in early pregnancy. The effect of AZA exposure on pregnancy outcomes was studied after adjustment for maternal characteristics that could act as confounders.


RESULTS
The most common indication for AZA use was IBD. The rate of congenital malformations was 6.2% in the AZA group and 4.7% among all infants born (adjusted OR: 1.41, 95% CI: 0.98-2.04). An association between early pregnancy AZA exposure and ventricular/atrial septal defects was found (adjusted OR: 3.18, 95% CI: 1.45-6.04). Exposed infants were also more likely to be preterm, to weigh <2500 gm, and to be small for gestational age compared to all infants born. This effect remained for preterm birth and low birth weight when infants of women with IBD but without AZA exposure were used as a comparison group. A trend toward an increased risk of congenital malformations was found among infants of women with IBD using AZA compared to women with IBD not using AZA (adjusted OR: 1.42, 95% CI: 0.93-2.18).


CONCLUSIONS
Infants exposed to AZA in early pregnancy may be at a moderately increased risk of congenital malformations, specifically ventricular/atrial septal defects. There is also an increased risk of growth restriction and preterm delivery. These associations may be confounded by the severity of maternal illness.
Multilevel minimum cross entropy threshold selection based on particle swarm optimization

Thresholding is one of the popular and fundamental techniques for conducting image segmentation. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) have been widely adopted. Although the MCET method is effective in the bilevel thresholding case, it could be very time-consuming in the multilevel thresholding scenario for more complex image analysis. This paper first presents a recursive programming technique which reduces an order of magnitude for computing the MCET objective function. Then, a particle swarm optimization (PSO) algorithm is proposed for searching the near-optimal MCET thresholds. The experimental results manifest that the proposed PSO-based algorithm can derive multiple MCET thresholds which are very close to the optimal ones examined by the exhaustive search method. The convergence of the proposed method is analyzed mathematically and the results validate that the proposed method is efficient and is suited for real-time applications. 2006 Elsevier Inc. All rights reserved.
From Discrete- to Continuous-Time Finance: Weak Convergence of the Financial Gain Process

Conditions, suitable for applications in finance, are given for the weak convergence (or convergence in probability) of stochastic integrals. For example, consider a sequence S of security price processes converging in distribution to S and a sequence θ of trading strategies converging in distribution to θ. We survey conditions under which the financial gain process ∫ θ dS converges in distribution to ∫ θ dS. Examples include convergence from discrete to continuous time settings, and in particular, generalizations of the convergence of binomial option replication models to the Black-Scholes model. Counterexamples are also provided. Duffie is with the Graduate School of Business, Stanford University, Stanford, CA 94305, and acknowledges the financial support of Batterymarch Financial Management. Protter is with the Departments of Mathematics and Statistics, Purdue University, West Lafeyette, IN 47907, and is supported in part by NSF grant #DMS8805595. We thank Jaime San Martin, Jean Jacod, Tom Kurtz, Ernst Eberlein, and an unusually attentive referee for helpful comments.
Ontology extraction from relational database: Concept hierarchy as background knowledge

0950-7051/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.knosys.2010.11.003 ⇑ Corresponding author. Tel.: +60176893765. E-mail address: herezadi@yahoo.com (H.A. Santos Relational Database (RDB) has been widely used as the back-end database of information system. Contains a wealth of high-quality information, RDB provides conceptual model and metadata needed in the ontology construction. However, most of the existing ontology building approaches convert RDB schema without considering the knowledge resided in the database. This paper proposed the approach for ontology extraction on top of RDB by incorporating concept hierarchy as background knowledge. Incorporating the background knowledge in the building process of Web Ontology Language (OWL) ontology gives two main advantages: (1) accelerate the building process, thereby minimizing the conversion cost; (2) background knowledge guides the extraction of knowledge resided in database. The experimental simulation using a gold standard shows that the Taxonomic F-measure (TF) evaluation reaches 90% while Relation Overlap (RO) is 83.33%. In term of processing time, this approach is more efficient than the current approaches. In addition, our approach can be applied in any of the fields such as eGoverment, eCommerce and so on. 2010 Elsevier B.V. All rights reserved.