LLukas22/scidocs huggingface dataset:
Using machine learning techniques to enhance the performance of an automatic backup and recovery system

A typical disaster recovery system will have mirrored storage at a site that is geographically separate from the main operational site. In many cases, communication between the local site and the backup repository site is performed over a network which is inherently slow, such as a WAN, or is highly strained, for example due to a whole-site disaster recovery operation.
 The goal of this work is to alleviate the performance impact of the network in such a scenario, and to do so using machine learning techniques. We focus on two main areas, prefetching and read-ahead size determination. In both cases we significantly improve the performance of the system.
 Our main contributions are as follows: We introduce a theoretical model of the system and the problem we are trying to solve and bound the gain from prefetching techniques. We construct two frequent pattern mining algorithms and use them for prefetching. A framework for controlling and combining multiple prefetch algorithms is presented as well. These algorithms, as well as various simple prefetch algorithms, are compared on a simulation environment. We introduce a novel algorithm for determining the amount of read ahead on such a system that is based on intuition from online competitive analysis and on regression techniques. The significant positive impact of this algorithm is demonstrated on IBM's FastBack system.
 Much of our improvements have been applied with little or no modification of the current implementation's internals. We therefore feel confident in stating that the techniques are general and are likely to have applications elsewhere.
Instructional quality of Massive Open Online Courses (MOOCs)

We present an analysis of instructional design quality of 76 randomly selected Massive Open Online Courses (MOOCs). The quality of MOOCs was determined from first principles of instruction, using a course survey instrument. Two types of MOOCs (xMOOCs and cMOOCs) were analysed and their instructional design quality was assessed and compared. We found that the majority of MOOCs scored poorly on most instructional design principles. However, most MOOCs scored highly on organisation and presentation of course material. The results indicate that although most MOOCs are well-packaged, their instructional design quality is low. We outline implications for practice and ideas for future research. © 2014 Elsevier Ltd. All rights reserved.
The geometry of nesting problems: A tutorial

Cutting and packing problems involving irregular shapes is an important problem variant with a wide variety of industrial applications. Despite its relevance to industry, research publications are relatively low when compared to other cutting and packing problems. One explanation offered is the perceived difficulty and substantial time investment of developing a geometric tool box to assess computer generated solutions. In this paper we set out to provide a tutorial covering the core geometric methodologies currently employed by researchers in cutting and packing of irregular shapes. The paper is not designed to be an exhaustive survey of the literature but instead will draw on the literature to illustrate the theory and implementation of the approaches. We aim to provide a sufficiently instructive description to equip new and current researchers in the area to select the most appropriate methodology for their needs. 2007 Elsevier B.V. All rights reserved.
Testing for tree‐ring divergence in the European Alps

Evidence for reduced sensitivity of tree growth to temperature has been reported from multiple forests along the high northern latitudes. This alleged circumpolar phenomenon described the apparent inability of temperature-sensitive tree-ring width and density chronologies to parallel increasing instrumental temperature measurements since the mid-20th century. In addition to such low-frequency trend offset, the inability of formerly temperature-sensitive tree growth to reflect high-frequency temperature signals in a warming world is indicated at some boreal sites, mainly in Alaska, the Yukon and Siberia. Here, we refer to both of these findings as the ‘divergence problem’ (DP), with their causes and scale being debated. If DP is widespread and the result of climatic forcing, the overall reliability of tree-ring-based temperature reconstructions should be questioned. Testing for DP benefits from well-replicated tree-ring and instrumental data spanning from the 19th to the 21st century. Here, we present a network of 124 larch and spruce sites across the European Alpine arc. Tree-ring width chronologies from 40 larch and 24 spruce sites were selected based on their correlation with early (1864–1933) instrumental temperatures to assess their ability of tracking recent (1934–2003) temperature variations. After the tree-ring series of both species were detrended in a manner that allows low-frequency variations to be preserved and scaled against summer temperatures, no unusual late 20th century DP is found. Independent tree-ring width and density evidence for unprecedented late 20th century temperatures with respect to the past millennium further reinforces our results.
Robust real-time face detection with skin color detection and the modified census transform

We present the combination of an illumination invariant approach to face detection with skin color detection and the false positive suppression used for improving speed and accuracy of the system. First, the non-skin color pixels of the input image are removed based on the mixed skin color model in the YCbCr and RGB chrominance spaces, from which we extract candidate human face regions. Then, face detection with the modified census transform is carried out in the candidate skin color regions where ratio of pixels of skin colour to non-skin color in the sub-windows is above a threshold. To further reduce the computational cost, ratios of pixels of skin color to non-skin color are computed by use of the integral image. Finally, false detection regions are eliminated by using knowledge-based merging and deleting process of homogeneous skin color regions. A face detection system that integrates the skin color detection and false positive suppression method under the proposed framework is built and tested on the Bao image database and two real videos. Experimental results show that our proposed method reduces significantly the false alarms and the processing time while achieves detection rates of more than 99%.
Microstrip Patch Antennas on Tunable Electromagnetic Band-Gap Substrates

A tunable metamaterial in integrated circuit structures is investigated through an example of a microstrip patch antenna on a mushroom-type electromagnetic band-gap (EBG) structure. The patch antenna is designed as a half-wavelength resonator of an EBG loaded microstrip transmission line. The operating frequency of a patch antenna can be switched and controlled dynamically by loading diode switches in between vias and the ground plane. When the switches are on, the EBG surface is short to the ground and is at its on-state; while when the switches are turned off, the EBG surface is dc open and is at its off-state. It is found that the resulting patch antenna in-band resonant frequencies are very different at these two states. Antenna dual-band frequencies, gain, efficiency, and radiation patterns are characterized. The designed switchable EBG-patch antenna is fabricated and tested in these two states. The measurement is found in good agreement with simulation. An example is also given for the case of selected switches within the EBG elements to tune dynamically the resonant frequency.
Semi-automatic semantic annotation of PubMed queries: A study on quality, efficiency, satisfaction

Information processing algorithms require significant amounts of annotated data for training and testing. The availability of such data is often hindered by the complexity and high cost of production. In this paper, we investigate the benefits of a state-of-the-art tool to help with the semantic annotation of a large set of biomedical queries. Seven annotators were recruited to annotate a set of 10,000 PubMed® queries with 16 biomedical and bibliographic categories. About half of the queries were annotated from scratch, while the other half were automatically pre-annotated and manually corrected. The impact of the automatic pre-annotations was assessed on several aspects of the task: time, number of actions, annotator satisfaction, inter-annotator agreement, quality and number of the resulting annotations. The analysis of annotation results showed that the number of required hand annotations is 28.9% less when using pre-annotated results from automatic tools. As a result, the overall annotation time was substantially lower when pre-annotations were used, while inter-annotator agreement was significantly higher. In addition, there was no statistically significant difference in the semantic distribution or number of annotations produced when pre-annotations were used. The annotated query corpus is freely available to the research community. This study shows that automatic pre-annotations are found helpful by most annotators. Our experience suggests using an automatic tool to assist large-scale manual annotation projects. This helps speed-up the annotation time and improve annotation consistency while maintaining high quality of the final annotations.
A non-pharmacologic approach to address challenging behaviors of Veterans with dementia: description of the tailored activity program-VA randomized trial

BACKGROUND
Behavioral symptoms accompanying dementia are associated with increased health care costs, reduced quality of life and daily functioning, heightened family caregiver burden, and nursing home placement. Standard care typically involves pharmacologic agents, but these are, at best, modestly effective, carry serious risks, including mortality, and do not address behavioral symptoms families consider most distressful and which may prompt nursing home placement. Given dementia's devastating effects and the absence of an imminent cure, the Veterans Administration has supported the development and testing of new approaches to manage challenging behaviors at home.


METHODS/DESIGN
The Tailored Activity Program - Veterans Administration is a Phase III efficacy trial designed to reduce behavioral symptoms in Veterans with dementia living with their caregivers in the community. The study uses a randomized two-group parallel design with 160 diverse Veterans and caregivers. The experimental group receives a transformative patient-centric intervention designed to reduce the burden of behavioral symptoms in Veterans with dementia. An occupational therapist conducts an assessment to identify a Veteran's preserved capabilities, deficit areas, previous roles, habits, and interests to develop activities tailored to the Veteran. Family caregivers are then trained to incorporate activities into daily care. The attention-control group receives bi-monthly telephone contact where education on topics relevant to dementia is provided to caregivers. Key outcomes include reduced frequency and severity of behavioral symptoms using the 12-item Neuropsychiatric Inventory (primary endpoint), reduced caregiver burden, enhanced skill acquisition, efficacy using activities, and time spent providing care at 4 months; and long-term effects (8 months) on the Veteran's quality of life and frequency and severity of behavioral symptoms, and caregiver use of activities. The programs' impact of Veterans Administration cost is also examined. Study precision will be increased through face-to-face research team trainings with procedural manuals and review of audio-taped interviews and intervention sessions.


DISCUSSION
The Tailored Activity Program - Veterans Administration is designed to improve the quality of life of Veterans with dementia and lessen the burden of care on caregivers. Activities are tailored to reflect the Veteran's preserved capabilities and interests to enhance active engagement, while not taxing areas of cognition that are most impaired.
A Complete Methodology for Generating Multi-Robot Task Solutions using ASyMTRe-D and Market-Based Task Allocation

This paper presents an approach that enables heterogeneous robots to automatically form groups as needed to generate both strongly-cooperative and weakly-cooperative multi-robot task solutions in the same application. The fundamental contribution of this work is the layering of our low-level coalition formation algorithm for generating strongly-cooperative task solutions, with high-level, traditional task allocation methods for weakly-cooperative task solutions. At the low level, coalitions that generate strongly-cooperative multi-robot task solutions are formed using our ASyMTRe-D approach that maps environmental sensors and perceptual and motor schemas to the required flow of information in the robot team, automatically reconfiguring the connections of schemas within and across robots to form efficient solutions. At the high level, a traditional task allocation approach is used to enable individual robots and/or coalitions to compete for weakly-cooperative task assignments through task allocation. We introduce the site clearing task to motivate the work, and then formalize the problem. We then present the approach of layering ASyMTRe-D with task allocation. We validate the approach on a team of robots with the site clearing task. We believe the resulting approach is a flexible system that can handle a broad range of realistic multi-robot applications beyond what is possible using other existing approaches.
Deep Manifold Learning of Symmetric Positive Definite Matrices with Application to Face Recognition

In this paper, we aim to construct a deep neural network which embeds high dimensional symmetric positive definite (SPD) matrices into a more discriminative low dimensional SPD manifold. To this end, we develop two types of basic layers: a 2D fully connected layer which reduces the dimensionality of the SPD matrices, and a symmetrically clean layer which achieves non-linear mapping. Specifically, we extend the classical fully connected layer such that it is suitable for SPD matrices, and we further show that SPD matrices with symmetric pair elements setting zero operations are still symmetric positive definite. Finally, we complete the construction of the deep neural network for SPD manifold learning by stacking the two layers. Experiments on several face datasets demonstrate the effectiveness of the proposed method. Introduction Symmetric positive definite (SPD) matrices have shown powerful representation abilities of encoding image and video information. In computer vision community, the SPD matrix representation has been widely employed in many applications, such as face recognition (Pang, Yuan, and Li 2008; Huang et al. 2015; Wu et al. 2015; Li et al. 2015), object recognition (Tuzel, Porikli, and Meer 2006; Jayasumana et al. 2013; Harandi, Salzmann, and Hartley 2014; Yin et al. 2016), action recognition (Harandi et al. 2016), and visual tracking (Wu et al. 2015). The SPD matrices form a Riemannian manifold, where the Euclidean distance is no longer a suitable metric. Previous works on analyzing the SPD manifold mainly fall into two categories: the local approximation method and the kernel method, as shown in Figure 1(a). The local approximation method (Tuzel, Porikli, and Meer 2006; Sivalingam et al. 2009; Tosato et al. 2010; Carreira et al. 2012; Vemulapalli and Jacobs 2015) locally flattens the manifold and approximates the SPD matrix by a point of the tangent space. The kernel method (Harandi et al. 2012; Wang et al. 2012; Jayasumana et al. 2013; Li et al. 2013; Quang, San Biagio, and Murino 2014; Yin et al. 2016) embeds the manifold into a higher dimensional Reproducing Kernel Hilbert Space (RKHS) via kernel functions. On new ∗corresponding author Copyright c © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. SPD Manifold Tangent Space
Prevalence of left ventricular diastolic dysfunction in European populations based on cross-validated diagnostic thresholds

BACKGROUND
Different diagnostic criteria limit comparisons between populations in the prevalence of diastolic left ventricular (LV) dysfunction. We aimed to compare across populations age-specific echocardiographic criteria for diastolic LV dysfunction as well as their correlates and prevalence.


METHODS
We measured the E and A peaks of transmitral blood flow by pulsed wave Doppler and the e' and a' peaks of mitral annular velocities by tissue Doppler imaging (TDI) in 2 cohorts randomly recruited in Belgium (n = 782; 51.4% women; mean age, 51.1 years) and in Italy, Poland and Russia (n = 476; 55.7%; 44.5 years).


RESULTS
In stepwise regression, the multivariable-adjusted correlates of the transmitral and TDI diastolic indexes were similar in the 2 cohorts and included sex, age, body mass index, blood pressure and heart rate. Similarly, cut-off limits for the E/A ratio (2.5th percentile) and E/e' ratio (97.5th percentile) in 338 and 185 reference subjects free from cardiovascular risk factors respectively selected from both cohorts were consistent within 0.02 and 0.26 units (median across 5 age groups). The rounded 2.5th percentile of the E/A ratio decreased by ~0.10 per age decade in these apparently healthy subjects. The reference subsample provided age-specific cut-off limits for normal E/A and E/e' ratios. In the 2 cohorts combined, diastolic dysfunction groups 1 (impaired relaxation), 2 (possible elevated LV filling pressure) and 3 (elevated E/e' and abnormally low E/A) encompassed 114 (9.1%), 135 (10.7%), and 40 (3.2%) subjects, respectively.


CONCLUSIONS
The age-specific criteria for diastolic LV dysfunction were highly consistent across the study populations with an age-standardized prevalence of 22.4% vs. 25.1%.
Sonographic visualization and ultrasound-guided block of the third occipital nerve: prospective for a new method to diagnose C2-C3 zygapophysial joint pain.

BACKGROUND
Chronic neck pain after whiplash injury is caused by cervical zygapophysial joints in 50% of patients. Diagnostic blocks of nerves supplying the joints are performed using fluoroscopy. The authors' hypothesis was that the third occipital nerve can be visualized and blocked with use of an ultrasound-guided technique.


METHODS
In 14 volunteers, the authors placed a needle ultrasound-guided to the third occipital nerve on both sides of the neck. They punctured caudal and perpendicular to the 14-MHz transducer. In 11 volunteers, 0.9 ml of either local anesthetic or normal saline was applied in a randomized, double-blind, crossover manner. Anesthesia was controlled in the corresponding skin area by pinprick and cold testing. The position of the needle was controlled by fluoroscopy.


RESULTS
The third occipital nerve could be visualized in all subjects and showed a median diameter of 2.0 mm. Anesthesia was missing after local anesthetic in only one case. There was neither anesthesia nor hyposensitivity after any of the saline injections. The C2-C3 joint, in a transversal plane visualized as a convex density, was identified correctly by ultrasound in 27 of 28 cases, and 23 needles were placed correctly into the target zone.


CONCLUSIONS
The third occipital nerve can be visualized and blocked with use of an ultrasound-guided technique. The needles were positioned accurately in 82% of cases as confirmed by fluoroscopy; the nerve was blocked in 90% of cases. Because ultrasound is the only available technique today to visualize this nerve, it seems to be a promising new method for block guidance instead of fluoroscopy.
Analysis of different approaches to Sentence-Level Sentiment Classification

Sentiment classification is a way to analyze the subjective information in the text and then mine the opinion. Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease, making choices with regards to online shopping, choosing events, products, entities. The approaches of text sentiment analysis typically work at a particular level like phrase, sentence or document level. This paper aims at analyzing a solution for the sentiment classification at a fine-grained level, namely the sentence level in which polarity of the sentence can be given by three categories as positive, negative and neutral.
CAQE: A Certifying QBF Solver

We present a new CEGAR-based algorithm for QBF. The algorithm builds on a decomposition of QBFs into a sequence of propositional formulas, which we call the clausal abstraction. Each of the propositional formulas contains the variables of just one quantifier level and additional variables describing the interaction with adjacent quantifier levels. This decomposition leads to a simpler notion of refinement compared to earlier approaches. We also show how to effectively construct Skolem and Herbrand functions from true, respectively false, QBFs; allowing us to certify the solver result. We implemented the algorithm in a solver called CAQE. The experimental evaluation shows that CAQE has competitive performance compared to current QBF solvers and outperforms previous certifying solvers.
Redefining renal dysfunction in trauma: implementation of the Acute Kidney Injury Network staging system.

BACKGROUND
Acute renal failure (ARF) in trauma patients is associated with high mortality rates. There is currently no consensus definition for renal failure, however, the American College of Surgeons' Committee on Trauma (ACSCOT) defines ARF as a serum creatinine > or =3.5, blood urea nitrogen > 100, or renal replacement therapy. We hypothesize that by using the Acute Kidney Injury Network (AKIN) staging system we would identify smaller changes in renal function that may impact outcome, and may serve as a marker for mortality and other organ dysfunction.


METHODS
We retrospectively identified all trauma patients admitted to the surgical intensive care unit (SICU) for >48 hours during a 3-year period ending December 2007. Hourly urine output, serum creatinine, demographic data, trauma scores, admission vital signs, ICU and hospital length of stay, need for renal replacement therapy, organ failure, and death were collected and were stratified according to AKIN and ACSCOT renal dysfunction criteria. Trauma patients admitted to the SICU who did not develop renal dysfunction were used as controls.


RESULTS
A total of 571 patients were studied. Of those, only 17 patients (3.0%) were classified as having ARF by the ACSCOT criteria, whereas 170 (29.8%) had kidney injury using the AKIN criteria (146, stage 1; 15, stage 2; 9, stage 3). Compared with patients admitted to the ICU for > or =48 hours with normal renal function, patients meeting AKIN criteria had longer hospital and ICU length of stay (p < 0.001). Patients meeting AKIN criteria also had an increased incidence of multiple organ failure and death (p < 0.03).


CONCLUSIONS
Stratification using the AKIN criteria for acute kidney injury identifies an increased number of patients with renal dysfunction compared with the current ACSCOT criteria. Importantly, these patients have an increased risk of multiple organ failure and death. Inclusion into the AKIN criteria may be a marker for later morbidity and mortality.