varunr14/newsummary huggingface dataset:
In the paper we consider three quadratic optimization problems which are frequently applied in portfolio theory i.e . the Markowitz mean variance problem as well as the problems based on the mean variance utility function and the quadratic utility . Conditions are derived under which the solutions of these three optimization procedures coincide and are lying on the efficient frontier the set of mean variance optimal portfolios . It is shown that the solutions of the Markowitz optimization problem and the quadratic utility problem are not always mean variance efficient . The conditions for the mean variance efficiency of the solutions depend on the unknown parameters of the asset returns . We deal with the problem of parameter uncertainty in detail and derive the probabilities that the estimated solutions of the Markowitz problem and the quadratic utility problem are mean variance efficient . Because these probabilities deviate from one the above mentioned quadratic optimization problems are not stochastically equivalent . The obtained results are illustrated by an empirical study . 


Summary: We consider three quadratic optimization problems applied in portfolio theory. Conditions are derived under which the solutions of these three optimization procedures are lying on the efficient frontier. We show that the three quadratic optimization problems are mathematically equivalent but not from a stochastic point of view. We derive the probabilities that the estimated solutions of the Markowitz problem and the quadratic utility problem are mean variance efficient.
In reputation management knowing what impact a tweet has on the reputation of a brand or company is crucial . The reputation polarity of a tweet is a measure of how the tweet influences the reputation of a brand or company . We consider the task of automatically determining the reputation polarity of a tweet . For this classification task we propose a feature based model based on three dimensions the source of the tweet the contents of the tweet and the reception of the tweet i.e . how the tweet is being perceived . For evaluation purposes we make use of the RepLab 2012 and 2013 datasets . We study and contrast three training scenarios . The first is independent of the entity whose reputation is being managed the second depends on the entity at stake but has over 90 fewer training samples per model on average . The third is dependent on the domain of the entities . We find that reputation polarity is different from sentiment and that having less but entity dependent training data is significantly more effective for predicting the reputation polarity of a tweet than an entity independent training scenario . Features related to the reception of a tweet perform significantly better than most other features . 


Summary: We find that reputation polarity of a post is different from sentiment. We model reputation polarity using feature classes from communication theory. We introduce new features based on the replies to a post. We propose different ways to operationalise the RepLab 2012 and 2013 tasks.
This paper focuses on detecting nuclear weapons on cargo containers using port security screening methods where the nuclear weapons would presumably be used to attack a target within the United States . This paper provides a linear programming model that simultaneously identifies optimal primary and secondary screening policies in a prescreening based paradigm where incoming cargo containers are classified according to their perceived risk . The proposed linear programming model determines how to utilize primary and secondary screening resources in a cargo container screening system given a screening budget prescreening classifications and different device costs . Structural properties of the model are examined to shed light on the optimal screening policies . The model is illustrated with a computational example . Sensitivity analysis is performed on the ability of the prescreening in correctly identifying prescreening classifications and secondary screening costs . Results reveal that there are fewer practical differences between the screening policies of the prescreening groups when prescreening is inaccurate . Moreover devices that can better detect shielded nuclear material have the potential to substantially improve the system s detection capabilities . 


Summary: We apply knapsack analysis to the problem of screening for nuclear materials. The analysis considers various levels of intelligence based pre screening . We illustrate the effectiveness of screening methods using speculative values. The analysis models a range of threats when screening for nuclear material.
This paper clarifies the relation between decisions of a risk averse decision maker based on expected utility theory on the one hand and spectral risk measures on the other . We first demonstrate that recent approaches to this problem generally do not provide strongly consistent results i.e . they fail to induce identical preference orders simultaneously with both concepts . Then we detail the relation between risk averse decisions under the dual theory of choice and spectral risk measures . This relation is identified as the fundamental reason why it is not in general possible to establish a simple one to one mapping between expected utility theory and spectral risk measures . We are nonetheless able to use spectral risk measures to model decisions obtained using expected utility theory . Interestingly this implies that a given utility function corresponds to a whole family of risk spectra . 


Summary: We establish a formal link between expected utility and spectral risk measures. We give sufficient conditions for existence and uniqueness of such a link. We provide a spectral version of the Pratt Arrow risk aversion measure.
The explosion of online user generated content and the development of big data analysis provide a new opportunity and challenge to understand and respond to public opinions in the G2C e government context . To better understand semantic searching of public comments on an online platform for citizens opinions about urban affairs issues this paper proposed an approach based on the latent Dirichlet allocation a probabilistic topic modeling method and designed a practical system to provide users municipal administrators of B city with satisfying searching results and the longitudinal changing curves of related topics . The system is developed to respond to actual demand from B city s local government and the user evaluation experiment results show that a system based on the LDA method could provide information that is more helpful to relevant staff members . Municipal administrators could better understand citizens online comments based on the proposed semantic search approach and could improve their decision making process by considering public opinions . 


Summary: The explosion of online user generated content UGC and the development of big data analysis provide a new opportunity and challenge to understand and respond to public opinions in the G2C e government context. We proposed an approach based on the latent Dirichlet allocation LDA and designed a practical system to provide users with satisfying searching results and the longitudinal changing curves of related topics. Municipal administrators could better understand citizens online comments based on the proposed semantic search approach and could improve their decision making process by considering public opinions.
We propose TripBuilder an unsupervised framework for planning personalized sightseeing tours in cities . We collect categorized Points of Interests from Wikipedia and albums of geo referenced photos from Flickr . By considering the photos as traces revealing the behaviors of tourists during their sightseeing tours we extract from photo albums spatio temporal information about the itineraries made by tourists and we match these itineraries to the Points of Interest of the city . The task of recommending a personalized sightseeing tour is modeled as an instance of the Generalized Maximum Coverage problem where a measure of personal interest for the user given her preferences and visiting time budget is maximized . The set of actual trajectories resulting from the GMC solution is scheduled on the tourist s agenda by exploiting a particular instance of the Traveling Salesman Problem . Experimental results on three different cities show that our approach is effective efficient and outperforms competitive baselines . 


Summary: The formal problem of planning a tourist visit as a fully automatic two step process. An unsupervised method for mining common patterns of movements of tourists. A comprehensive evaluation of TripBuilder.
Data envelopment analysis allows us to evaluate the relative efficiency of each of a set of decision making units . However the methodology does not permit us to identify specific sources of inefficiency because DEA views the DMU as a black box that consumes a mix of inputs and produces a mix of outputs . Thus DEA does not provide a DMU manager with insight regarding the internal source of the organization s inefficiency . Recent methodological developments have extended the basic DEA methodology to allow the analyst to look inside the DMU and model the network of production processes that comprise the organization . In such models sub DMUs consume inputs from outside the DMU and intermediate products from other sub DMUs to produce outputs that flow out of the DMU and intermediate products that flow into other sub DMUs . In this paper we present an unoriented two stage DEA model to measure efficiency in situations in which analysts seek to simultaneously reduce input quantities and increase output quantities . The methodology extends previous work in which the model must be either input oriented or output oriented . The key to the methodology is an iterative algorithm that alternates between an input oriented push backward step and an output oriented push forward step that is characterized by damped oscillations in the intermediate products . We apply the methodology to Major League Baseball teams during the 2009 season to demonstrate how this approach provides a deeper understanding of each team s operations . 


Summary: We introduce an iterative approach to solve the unoriented two stage DEA model using the classical radial objective. Our approach determines optimal levels for each input and output by simultaneously reducing inputs and increasing outputs while the intermediate products oscillate and converge. Unlike the slacks based approach our model does not require arbitrary weights on inputs and outputs. We demonstrate our model by applying it to Major League Baseball teams during the 2009 season. The model has one input two intermediate products and one output.
Researchers have shown that a weighted linear combination in data fusion can produce better results than an unweighted combination . Many techniques have been used to determine the linear combination weights . In this work we have used the Genetic Algorithm for the same purpose . The GA is not new and it has been used earlier in several other applications . But to the best of our knowledge the GA has not been used for fusion of runs in information retrieval . First we use GA to learn the optimum fusion weights using the entire set of relevance assessment . Next we learn the weights from the relevance assessments of the top retrieved documents only . Finally we also learn the weights by a twofold training and testing on the queries . We test our method on the runs submitted in TREC . We see that our weight learning scheme using both full and partial sets of relevance assessment produces significant improvements over the best candidate run CombSUM CombMNZ Z Score linear combination method with performance level performance level square weighting scheme multiple linear regression based weight learning scheme mixture model result merging scheme LambdaMerge ClustFuseCombSUM and ClustFuseCombMNZ . Furthermore we study how the correlation among the scores in the runs can be used to eliminate redundant runs in a set of runs to be fused . We observe that similar runs have similar contributions in fusion . So eliminating the redundant runs in a group of similar runs does not hurt fusion performance in any significant way . 


Summary: Genetic Algorithm is an effective scheme in determining data fusion weights. Tuning Genetic Algorithm increases time efficiency. Weight learning from only top ranked documents is useful. Redundant runs can be removed based on correlation between scores.
A main challenge in Cross Language Information Retrieval is to estimate a proper translation model from available translation resources since translation quality directly affects the retrieval performance . Among different translation resources we focus on obtaining translation models from comparable corpora because they provide appropriate translations for both languages and domains with limited linguistic resources . In this paper we employ a two step approach to build an effective translation model from comparable corpora without requiring any additional linguistic resources for the CLIR task . In the first step translations are extracted by deriving correlations between source target word pairs . These correlations are used to estimate word translation probabilities in the second step . We propose a language modeling approach for the first step where modeling based on probability distribution provides two key advantages . First our approach can be tuned easier in comparison with heuristically adjusted previous work . Second it provides a principled basis for integrating additional lexical and translational relations to improve the accuracy of translations from comparable corpora . As an indication we integrate monolingual relations of word co occurrences into the process of translation extraction which helps to extract more reliable translations for low frequency words in a comparable corpus . Experimental results on an English Persian comparable corpus show that our method outperforms the previous approaches in terms of both translation quality and the performance of CLIR . Indeed the proposed method is naturally applicable to any comparable corpus regardless of its languages . In addition we demonstrate the significant impact of word translation probabilities estimated in the second step of our approach on the performance of CLIR . 


Summary: Proposing a language modeling method to extract translations from comparable corpora. Comparing two similarity functions for deriving bilingual word correlations. Improving translation quality by integrating co occurrence relations into word models. Comparing different estimations of translation probabilities from word correlations. Showing the significant impact of probability estimation methods on CLIR performance.
A popular assumption in the current literature on remanufacturing is that the whole new product is produced by an integrated manufacturer which is inconsistent with most industries . In this paper we model a decentralised closed loop supply chain consisting of a key component supplier and a non integrated manufacturer and demonstrate that the interaction between these players significantly impacts the economic and environmental implications of remanufacturing . In our model the non integrated manufacturer can purchase new components from the supplier to produce new products and remanufacture used components to produce remanufactured products . Thus the non integrated manufacturer is not only a buyer but also a rival to the supplier . In a steady state period we analyse the performances of an integrated manufacturer and the decentralised supply chain . We find that although the integrated manufacturer always benefits from remanufacturing the remanufacturing opportunity may constitute a lose lose situation to the supplier and the non integrated manufacturer making their profits be lower than in an identical supply chain without remanufacturing . In addition the non integrated manufacturer may be worse off with a lower remanufacturing cost or a larger return rate of used products due to the interaction with the supplier . We further demonstrate that the government subsidised remanufacturing in the non integrated manufacturer is detrimental to the environment . 


Summary: We model a closed loop supply chain in which the manufacturer can remanufacture used products. The interaction with the supplier significantly impacts the performance of remanufacturing. The remanufacturing opportunity can form a lose lose situation making the players profits shrunk. The manufacturer may be worse off with a lower remanufacturing cost or a larger return rate. The remanufacturing may be detrimental to the environment.
This practical study aims to enrich the current literature by providing new practical evidence of the positive and negative influence factors of the Internet on generations Y and Z in Australia and Portugal . The Internet has become a powerful force among these Gens in relation to communication cooperation collaboration and connection but numerous problems from cognitive social and physical developments perspective are developed throughout the Internet usage . A quantitative approach was used to collect new practical evidence from 180 Australian and 85 Portuguese respondents with a total of 265 respondents completing an online survey . This study identifies new positive factors to the Internet usage as problem solving skills proactive study information gathering and awareness globally and locally communication and collaboration with their peers and family were improved and enhanced . Alternatively this study identifies new negative factors as physical contact and physical activities were prevented thinking concentrating and memory skills were reduced depressed and isolated laziness having increased nevertheless the Internet encourages Gens Y and Z to play physical and virtual games . Finally this study concluded that the Internet is becoming an essential part of the everyday routines and practices of Gens Y and Z . 


Summary: Examine the influence factors of the Internet on Gens Y and Z. Empirical evidence was collected via an online survey in Australia and Portugal. This study composed new positive and negative factors of the Internet usage. Set of recommendations are raised to reduce the negativities of the Internet usage.
We consider the optimal ship navigation problem wherein the goal is to find the shortest path between two given coordinates in the presence of obstacles subject to safety distance and turn radius constraints . These obstacles can be debris rock formations small islands ice blocks other ships or even an entire coastline . We present a graph theoretic solution on an appropriately weighted directed graph representation of the navigation area obtained via 8 adjacency integer lattice discretization and utilization of the A algorithm . We explicitly account for the following three conditions as part of the turn radius constraints the ship s left and right turn radii are different ship s speed reduces while turning and the ship needs to navigate a certain minimum number of lattice edges along a straight line before making any turns . The last constraint ensures that the navigation area can be discretized at any desired resolution . Once the optimal path is determined we smoothen it to emulate the actual navigation of the ship . We illustrate our methodology on an ice navigation example involving a 100 000 DWT merchant ship and present a proof of concept by simulating the ship s path in a full mission ship handling simulator . 


Summary: We consider the optimal ship navigation problem in the presence of obstacles. We explicitly account for safety distance and realistic ship turn radius constraints. We present a graph theoretic solution on an appropriately weighted directed graph. Our methodology is illustrated on a merchant ship ice navigation example.
Competitive location problems can be characterized by the fact that the decisions made by others will affect our own payoffs . In this paper we address a discrete competitive location game in which two decision makers have to decide simultaneously where to locate their services without knowing the decisions of one another . This problem arises in a franchising environment in which the decision makers are the franchisees and the franchiser defines the potential sites for locating services and the rules of the game . At most one service can be located at each site and one of the franchisees has preferential rights over the other . This means that if both franchisees are interested in opening the service in the same site only the one that has preferential rights will open it . We consider that both franchisees have budget constraints but the franchisee without preferential rights is allowed to show interest in more sites than the ones she can afford . We are interested in studying the influence of the existence of preferential rights and overbidding on the outcomes for both franchisees and franchiser . A model is presented and an algorithmic approach is developed for the calculation of Nash equilibria . Several computational experiments are defined and their results are analysed showing that preferential rights give its holder a relative advantage over the other competitor . The possibility of overbidding seems to be advantageous for the franchiser as well as the inclusion of some level of asymmetry between the two decision makers . 


Summary: We develop a model where two players simultaneously choose their facilities sites. One of the players has preferential rights over the other. Nash equilibria are calculated by an algorithmic approach using linear programming. Including some level of asymmetry between players can benefit the franchiser.
Robust optimization problems which have uncertain data are considered . We prove surrogate duality theorems for robust quasiconvex optimization problems and surrogate min max duality theorems for robust convex optimization problems . We give necessary and sufficient constraint qualifications for surrogate duality and surrogate min max duality and show some examples at which such duality results are used effectively . Moreover we obtain a surrogate duality theorem and a surrogate min max duality theorem for semi definite optimization problems in the face of data uncertainty . 


Summary: We show a set containment characterization with data uncertainty. We investigate surrogate strong duality theorem for robust quasiconvex programming with its constraint qualification. We investigate surrogate min max duality theorem for robust quasiconvex programming with its constraint qualification. We obtain a surrogate duality theorems for semi definite optimization problems in the face of data uncertainty.
A series of events generates multiple types of time series data such as numeric and text data over time and the variations of the data types capture the events from different angles . This paper aims to integrate the analyses on such numerical and text time series data influenced by common events with a single model to better understand the events . Specifically we present a topic model called an associative topic model which finds the soft cluster of time series text data guided by time series numerical value . The identified clusters are represented as word distributions per clusters and these word distributions indicate what the corresponding events were . We applied ATM to financial indexes and president approval rates . First ATM identifies topics associated with the characteristics of time series data from the multiple types of data . Second ATM predicts numerical time series data with a higher level of accuracy than does the iterative model which is supported by lower mean squared errors . 


Summary: Introduce a probabilistic graphical model extracting topics with numerical guidance. Enhance the regression performance with unified PGM of text and numbers. Tightly links the analysis on numeric and text data over time.