Miracle-dz/UniFAR-FTUs huggingface dataset:
Serving Ads to "Yahoo Answers" Occasional Visitors

Modern ad serving systems can benefit when allowed to accumulate user information and use it as part of the serving algorithm. However, this often does not coincide with how the web is used. Many domains will see users for only brief interactions, as users enter a domain through a search result or social media link and then leave. Having access to little or no user information and no ability to assemble a user profile over a prolonged period of use, we would still like to leverage the information we have to the best of our ability. In this paper we attempt several methods of improving ad serving for occasional users, including leveraging user information that is still available, content analysis of the page, information about the page's content generators and historical breakdown of visits to the page. We compare and combine these methods in a framework of a collaborative filtering algorithm, test them on real data collected from Yahoo Answers, and achieve significant improvements over baseline algorithms.
Aspiration under anaesthesia: risk assessment and decision-making

Pulmonary aspiration complicates between 1 in 900 to 1 in 10 000 general anaesthetics, dependent on risk factors. All novice anaesthetists in the UK are taught to consider the risk of aspiration and to modify their anaesthetic technique accordingly. The prevention of aspiration remains a cornerstone of anaesthetic practice. The recent Royal College of Anaesthetists 4th National Audit Project (NAP4) collected data on the incidence and causes of major airway complications in the UK. Over 50% of airway-related deaths in anaesthesia were as a consequence of aspiration, outweighing the much feared can’t intubate can’t ventilate (CICV) scenario. In addition, 23% of all cases reported to NAP4 involved aspiration as either the primary or secondary event. Cases not resulting in death commonly resulted in significant morbidity and prolonged stay on intensive care. Despite the awareness among anaesthetists of the need to minimize the risks of aspiration and advances in anaesthetic practices, NAP4 provided evidence that aspiration often occurred as a consequence of incomplete assessment of aspiration risk or a failure to modify anaesthetic technique. This review aims to highlight the key findings from NAP4 with regard to aspiration and evaluates the literature on aspiration risk assessment and decision-making.
New State-Space Relaxations for Solving the Traveling Salesman Problem with Time Windows

The traveling salesman problem with time windows (TSPTW) is the problem of finding in a weighted digraph a least-cost tour starting from a selected vertex, visiting each vertex of the graph exactly once according to a given time window, and returning to the starting vertex. This nP-hard problem arises in routing and scheduling applications. This paper introduces a new tour relaxation, called ngL-tour, to compute a valid lower bound on the TSPTW obtained as the cost of a near-optimal dual solution of a problem that seeks a minimum-weight convex combination of nonnecessarily elementary tours. This problem is solved by column generation. The optimal integer TSPTW solution is computed with a dynamic programming algorithm that uses bounding functions based on different tour relaxations and the dual solution obtained. An extensive computational analysis on basically all TSPTW instances (involving up to 233 vertices) from the literature is reported. The results show that the proposed algorithm solves all but one instance and outperforms all exact methods published in the literature so far.
Base Model Combination Algorithm for Resolving Tied Predictions for K-Nearest Neighbor OVA Ensemble Models

Model aggregation is the process of constructing several base models that are then combined into a single model for prediction. Ensemble classification has been studied by many researchers and found to provide significant performance improvements over single models. This paper presents a new base model combination algorithm for K-nearest neighbor KNN ensemble models based on One-Versus-All OVA classification. The proposed algorithm uses two decision functions to determine the best prediction among the many predictions provided by the base models. It is demonstrated in this paper that tied or conflicting predictions can be effectively resolved when a probabilistic function and a distance function are used by a combination algorithm for OVA KNN base model predictions. The resolution of tied predictions leads to improvements in predictive performance.
Copyright 2009 by Human Factors and Ergonomics Society, Inc. All rights reserved. 10.1518/107118109X12524442636427

Image-guided navigation (IGN) systems support the surgeon in navigating through the patients’ anatomy. Previous research on IGN has focused on technical feasibility and clinical applications. Yet, as the introduction of IGN corresponds to a partial automation of the surgeon’s task, well known issues of humanautomation interaction might play a crucial role for the success of IGN as well. The present study represents a first attempt to assess the impact of IGN on four key issues of human automation-interaction, i.e., workload, situation awareness, trust, and skill degradation, from the surgeons’ perspective. A nation-wide survey among 213 German surgeons from 94 different hospitals was conducted. Results revealed (1) a workload-shift due to IGN rather than a reduction of workload, (2) benefits of IGN with respect to situation awareness, (3) comparatively high levels of perceived reliability, trust and reliance, and (4) skill degradation as a possible risk, albeit only for inexperienced surgeons.
The MESS: is it must?

Background: Massive lower extremity trauma presents an immediate and complex decision making challenge. Significant advancements have made in the field of reconstructive surgery over the past 30 years. Severely traumatised limbs which would have been treated by primary amputation 20 years ago are beginning to be saved. On the contrary, such extensive reconstructive procedures may not always produce the best of results in terms of functional outcomes. Thus the management of a severely crushed extremity presents a therapeutic dilemma as whether to amputate or to attempt salvage. Methods: All Prospective study of 40 patients with severe crush injury to the lower extremities treated in Sri Ramachandra Medical College, Chennai between June 2012 and June 2014. The inclusion criteria were grade IIIb and grade IIIc open fractures of the lower extremity with a Mangled extremity severity score (MESS) of 7 and above with minimum of 12 months follow up. All the fractures were classified according to the Gustilo and Anderson classification system and Mangled extremity severity score. Out of the 40 patients 18 of them had their limbs salvaged and 22 underwent primary amputation. Results: The lowest MESS in this study was 7 and highest MESS was 12. The mean score in the limb salvage group was 8 and in the amputated group was 9.7. Complication rates in salvage group were higher. The mean SF score for amputated group for physical component summary was 40.15 and mental component summary was 44.30 while for limb salvage group score for physical component summary was 30.91 and mental component summary was 36.90. Conclusions: The MESS scheme provides excellent guidelines to the treating surgeon when faced with a dilemma of whether to attempt salvage or amputate a severely injured limb. The decision of whether to amputate or salvage an injured limb must be made very early in the course of treatment. This is because immediate amputation is most often viewed by the patient as a result of injury; whereas, delayed amputation is often considered as a failure of treatment.
Ultra-wideband bias-tee design using distributed network synthesis

This paper presents a design methodology for ultrawideband bias tees, using a distributed network synthesis considering both RF and DC performance. For the design of bias tees, transfer functions of distributed circuits are offered using equal ripple approximation, and DC current-handling capacity is incorporated into the network synthesis by calculating capacity in terms of the characteristic impedance of a transmission line. A bias-tee circuit with the desired characteristics can be synthesized by properly adjusting the minimum insertion loss (MIL) and ripple of the transfer function with reference to the required performance. As an example, a distributed network synthesis is applied to design a bias tee for ultra-wideband applications.
Providing Consistent Opinions from Online Reviews: A Heuristic Stepwise Optimization Approach

The consistency between review summaries and review ranking lists is important for consumers so they can utilize online reviews effectively and efficiently in their purchase decisions. This paper examines this consistency issue and formulates it as an optimization problem. Based on consumers’ reading behaviors, all possible sets of reviews that consumers would read from ranking lists are considered; the objective is to maximize the expected consistency. Because of the NP-hardness of the problem, exact methods that search for the optimal ranking lists are generally not acceptable in practice. Hence, a heuristic approach (the enhanced stepwise optimization procedure) is proposed. This approach is an effective and efficient approximation that selects reviews iteratively to add to the ranking lists in light of expected consistency value, superiority, and execution time. Intensive experiments on both synthetic and real data are conducted, with various environments and settings, along with a relevant user study.
Propagation path loss characteristics of vertical dipole above perfect ground

In this letter, we calculated the propagation path loss because of the vertical electric dipole on perfect electric conductor ground in time domain. We derived the electric field in time domain and calculated the transient response and the received signal strength. The results show that the changes in the height of the base station caused the changes in the transient response in time domain and that such changes also affect the changes in received signal strength and the location of the near field because of the effects of ground surface. It is expected that such results can be applied in mobile communication network design and in military communication such as ground surface radars.
Active Learning with Multiple Localized Regression Models

Oftentimes businesses face the challenge of requiring costly information to improve the accuracy of prediction tasks. One notable example is obtaining informative customer feedback (e.g., customer-product ratings via costly incentives) to improve the effectiveness of recommender systems. In this paper, we develop a novel active learning approach, which aims to intelligently select informative training instances to be labeled so as to maximally improve the prediction accuracy of a real-valued prediction model. We focus on large, heterogeneous, and dyadic data, and on localized modeling techniques, which have been shown to model such data particularly well, as compared to a single, “global” model. Importantly, dyadic data with covariates is pervasive in contemporary big data applications such as large-scale recommender systems and search advertising. A key benefit from incorporating dyadic information is their simple, meaningful representation of heterogeneous data, in contrast to alternative local modeling.
A Pruning Procedure for Exact Graph Coloring

The graph coloring problem can be stated: “ Given an undirected graph, using a minimal number of colors, assign each vertex a color so that if two vertices are connected by an edge then they are not assigned the same color . ” Graph coloring can be used to solve scheduling problems with constraints of the form: events e and e ' cannot be scheduled together. Graph coloring is an NP -hard problem. Generally, large problems are solved heuristically, although some of the better heuristic algorithms use an exact graph coloring algorithm to finish coloring a graph after first reducing it heuristically to manageable size. Exact graph coloring is important both in its own right and as a component of heuristic coloring. We present an improved exact graph coloring algorithm which runs, in the mean, over 20% faster than the DSATUR algorithm on some classes of random graphs. The improvement over DSATUR stems from the ability to prune the search tree by detecting, in certain instances, the existence of a complete subgraph of cardinality equal to the number of colors used in the best coloring found so far. INFORMS Journal on Computing , ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.
TSPLIB - A Traveling Salesman Problem Library

This paper contains the description of a traveling salesman problem library (TSPLIB) which is meant to provide researchers with a broad set of test problems from various sources and with various properties. For every problem a short description is given along with known lower and upper bounds. Several references to computational tests on some of the problems are given. INFORMS Journal on Computing , ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.
An Implementation of the Generalized Basis Reduction Algorithm for Integer Programming

In recent years many advances have been made in solution techniques for specially structured 0-1 integer programming problems. In contrast, very little progress has been made on solving general (mixed integer) problems. This, of course, is not true when viewed from the theoretical side: Lenstra (1981) made a major breakthrough, obtaining a polynomial-time algorithm when the number of integer variables is fixed. We discuss a practical implementation of a Lenstra-like algorithm, based on the generalized basis reduction method of Lovasz and Scarf (1988). This method allows us to avoid the ellipsoidal approximations required in Lenstra's algorithm. We report on the solution of a number of small (but difficult) examples, up to 100 integer variables. Our computer code uses the linear programming optimizer CPlex as a subroutine to solve the linear programming problems that arise.
Network Reoptimization Algorithms: A Statistically Designed Comparison

Statistical design and analysis of experiments is a cornerstone methodology for scientific exploration and verification that is rarely employed in reporting mathematical software testing. This approach is used to study the relative effectiveness of three key algorithms for the reoptimization of network flow problems primal simplex, dual method, and out-of-kilter. Carefully designed experimentation with state-of-the-art codes accompanied by a rigorous statistical analysis isolates the most efficient method(s) for commonly encountered reoptimization problems, and identifies the effect on solution time of problem class, problem size, type of change (bounds, costs, or node requirements), degree and number of changes in parameters, and number of problems in the reoptimization series. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.
The hardware design of a new ionospheric sounding system

The ionospheric sounding system is a type of high-frequency over-the-horizon skywave radar developed for ionospheric research and HF channel management. The hardware system of a portable low-power multifunctional ionospheric sounding system is presented in this paper. The device can be used for typical ionospheric sounding and spectrum monitoring at monostatic and bistatic station. The system structure and the primary module are depicted in detail. Typical experimental results demonstrate that the design of this device is successful for ionospheric researching.
CloudDrops: Stamp-sized Pervasive Displays for Situated Awareness of Web-based Information

CloudDrops is a pervasive awareness platform that integrates virtual information from the Web more closely with the contextually rich physical spaces in which we live and work. CloudDrops consists of many interactive stamp-sized displays, each showing a tiny bit of digital information. The large number of displays and their small size allows the user to flexibly instrument, orchestrate and reconfigure her personal information environment. We show different form factors for stamp-sized displays, provide a device concept and a first implementation. We propose lightweight visualizations and interaction techniques that are tailored to the tiny device form factor. Moreover, we contribute techniques for associating these small displays with content that the user wants to stay aware of, including Web pages, contacts, and places. To demonstrate the capabilities of the platform, we present several application examples. A user study provides first insights into how CloudDrops enable users to create a personalized information environment by distributing stamp-sized displays over the entire architectural space.
The pyramid-technique: towards breaking the curse of dimensionality

In this paper, we propose the Pyramid-Technique, a new indexing method for high-dimensional data spaces. The Pyramid-Technique is highly adapted to range query processing using the maximum metric Lmax. In contrast to all other index structures, the performance of the Pyramid-Technique does not deteriorate when processing range queries on data of higher dimensionality. The Pyramid-Technique is based on a special partitioning strategy which is optimized for high-dimensional data. The basic idea is to divide the data space first into 2d pyramids sharing the center point of the space as a top. In a second step, the single pyramids are cut into slices parallel to the basis of the pyramid. These slices from the data pages. Furthermore, we show that this partition provides a mapping from the given d-dimensional space to a 1-dimensional space. Therefore, we are able to use a B+-tree to manage the transformed data. As an analytical evaluation of our technique for hypercube range queries and uniform data distribution shows, the Pyramid-Technique clearly outperforms index structures using other partitioning strategies. To demonstrate the practical relevance of our technique, we experimentally compared the Pyramid-Technique with the X-tree, the Hilbert R-tree, and the Linear Scan. The results of our experiments using both, synthetic and real data, demonstrate that the Pyramid-Technique outperforms the X-tree and the Hilbert R-tree by a factor of up to 14 (number of page accesses) and up to 2500 (total elapsed time) for range queries.
Enhanced hypertext categorization using hyperlinks

A major challenge in indexing unstructured hypertext databases is to automatically extract meta-data that enables structured search using topic taxonomies, circumvents keyword ambiguity, and improves the quality of search and profile-based routing and filtering. Therefore, an accurate classifier is an essential component of a hypertext database. Hyperlinks pose new problems not addressed in the extensive text classification literature. Links clearly contain high-quality semantic clues that are lost upon a purely term-based classifier, but exploiting link information is non-trivial because it is noisy. Naive use of terms in the link neighborhood of a document can even degrade accuracy. Our contribution is to propose robust statistical models and a relaxation labeling technique for better classification by exploiting link information in a small neighborhood around documents. Our technique also adapts gracefully to the fraction of neighboring documents having known topics. We experimented with pre-classified samples from Yahoo! 1 and the US Patent Database2. In previous work, we developed a text classifier that misclassified only 13% of the documents in the well-known Reuters benchmark; this was comparable to the best results ever obtained. This classifier misclassified 36% of the patents, indicating that classifying hypertext can be more difficult than classifying text. Naively using terms in neighboring documents increased error to 38%; our hypertext classifier reduced it to 21%. Results with the Yahoo! sample were more dramatic: the text classifier showed 68% error, whereas our hypertext classifier reduced this to only 21%.
Evaluation of the knowledge in hygiene practices: a qualitative approach

The knowledge of goat milkers on milking hygiene was investigated, by means of a qualitative approach, before and after a Good Manufacturing Practices (GMP) training course. Milkers from three goat dairy farms in the state of Sao Paulo, Brazil, were interviewed using a semi-structured questionnaire. Responses to the questions were recorded, transcribed and analyzed by means of the Discourse of the Collective Subject. Milkers were then trained in a one-hour content and dialogue-based lecture on "GPP principles" and "recommended hygienic procedures for milking". Two months after the training course, milkers were interviewed again with the same questionnaire used before the training course. No difference was found between discourses obtained before and after the training course. Individual analysis of the discourses showed that milkers had previous experience milking of animals, liked their jobs and were willing to learn more about it. However, they had no clear suggestions on how to improve their work routine.
GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations

Recommending users with their preferred points-of-interest (POIs), e.g., museums and restaurants, has become an important feature for location-based social networks (LBSNs), which benefits people to explore new places and businesses to discover potential customers. However, because users only check in a few POIs in an LBSN, the user-POI check-in interaction is highly sparse, which renders a big challenge for POI recommendations. To tackle this challenge, in this study we propose a new POI recommendation approach called GeoSoCa through exploiting geographical correlations, social correlations and categorical correlations among users and POIs. The geographical, social and categorical correlations can be learned from the historical check-in data of users on POIs and utilized to predict the relevance score of a user to an unvisited POI so as to make recommendations for users. First, in GeoSoCa we propose a kernel estimation method with an adaptive bandwidth to determine a personalized check-in distribution of POIs for each user that naturally models the geographical correlations between POIs. Then, GeoSoCa aggregates the check-in frequency or rating of a user's friends on a POI and models the social check-in frequency or rating as a power-law distribution to employ the social correlations between users. Further, GeoSoCa applies the bias of a user on a POI category to weigh the popularity of a POI in the corresponding category and models the weighed popularity as a power-law distribution to leverage the categorical correlations between POIs. Finally, we conduct a comprehensive performance evaluation for GeoSoCa using two large-scale real-world check-in data sets collected from Foursquare and Yelp. Experimental results show that GeoSoCa achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.