Miracle-dz/UniFAR-FTUs huggingface dataset:
Backward Renormalization Priors and the Cortical Source Localization Problem with EEG or MEG

We study source localization from high dimensional M/EEG data by extending a multiscale method based on Entropic inference devised to increase the spatial resolution of inverse problems. This method is used to construct informative prior distributions in a manner inspired in the context of fMRI (Amaral et al 2004). We construct a set of renormalized lattices that approximate the cortex region where the source activity is located and address the related problem of defining the relevant variables in a coarser scale representation of the cortex. The priors can be used in conjunction with other Bayesian methods such as the Variational Bayes method (VB, Sato et al 2004). The central point of the algorithm is that it uses a posterior obtained at a coarse scale to induce a prior at the next finer scale stage of the problem. We present results which suggest, on simulated data, that this way of including prior information is a useful aid for the source location problem. This is judged by the rate and magnitude of errors in source localization. Better convergence times are also achieved. We also present results on public data collected during a face recognition task.
Image-based Ear Biometric Smartphone App for Patient Identification in Field Settings

We present a work in progress of a computer vision application that would directly impact the delivery of healthcare in underdeveloped countries. We describe the development of an image-based smartphone application prototype for ear biometrics. The application targets the public health problem of managing medical records at on-site medical clinics in less developed countries where many individuals do not hold IDs. The domain presents challenges for an ear biometric system, including varying scale, rotation, and illumination. It was not clear which feature descriptors would work best for the application, so a comparative study of three ear biometric extraction techniques was performed, one of which was used to develop an iOS application prototype to establish the identity of an individual using a smartphone camera image. A pilot study was then conducted on the developed application to test feasibility in naturalistic settings.
Machine Preservation of the Liver (cm Miller, Section Editor) Hypothermic Oxygenated Liver Perfusion: Basic Mechanisms and Clinical Application

Dynamic preservation strategies such as hypother-mic machine perfusion are increasingly discussed to improve liver graft quality before transplantation. This review summarizes current knowledge of this perfusion technique for liver preservation. We discuss optimization of perfusion conditions and current strategies to assess graft quality during cold per-fusion. Next, we provide an overview of possible pathways of protection from ischemia-reperfusion injury. Finally, we report on recent clinical applications of human hypothermic machine liver perfusion. Abbreviations CS cold storage NMP normothermic machine perfusion SNMP subnormothermic machine perfusion HMP hypothermic machine perfusion DCD donation after cardiac death ROS reactive oxygen species DAMPs danger-associated molecular patterns UW University of Wisconsin MP machine perfusion IGL-1 Institute George Lopez-1 HTK histidine-tryptophan-ketoglutarate AST aspartate aminotransferase ALT alanine aminotransferase LDH lactate dehydrogenase BCL-2 protein B cell lymphoma 2 protein BH-3 Bcl-2 homology-3 BID BH-3-interacting domain BAX Bcl-2-associated x protein BAK Bcl-2-antagonist/killer-1 MPT Pore mitochondria permeability transition pore IAP binding protein inhibitor of apoptosis protein DIABLO direct IAP-binding protein with low pI AIF apoptosis-inducing factor CK 18 cytokeratin-18 DISC death-inducing signaling complex miRNA micro-RNA ER endoplasmic reticulum HMGB-1 high-mobility group box protein 1 RAGE receptor for advanced glycation end products TLR-4 roll-like receptor 4 LPS lipopolysaccharides ATP adenosine triphosphate HOPE hypothermic oxygenated perfusion DBD donation after brain death
Chapter 3 Applications of Modal Logic in Model Theoretic Syntax 1

Since extending the logic of strings to capture more complex string languages than the regular languages often leads to undecidability (see e.g. Lautemann et al. (1995)), one approach to extending the coverage of logic is to describe more complex structures: move from strings to trees. Thus, the Kripke structures we will be considering are trees, and the logics will contain more complicated modalities to describe trees. One immediate advantage of this approach for linguistic purposes is that these logics will automatically be connected to strong generative capacity, since they describe sets of trees. One disadvantage is that therecognitionor parsingproblem, which in the string case just amounts to model checking, now involves satisfiability checking (see below). The extension of the descriptive approach to trees was originally also motivated by decidability questions Thatcher and Wright (1968). Even though the connections to CFLs were pointed out by Thatcher (1967), this line of research did not find applications in linguistics until the development of constraint based grammar formalisms which replaced the derivational approach to natural language syntax. The work of Rogers (1998), Kracht (2003), and others provided formal models for these constraint based grammar formalisms and established formal language theoretic results for them at the same time. As mentioned above our Kripke structures will now be trees. We will use the concept of tree domains Gorn (1967) to define such Kripke structures. A (finite, binary) tree domain, T, is a finite subset of {0,1}, such that for all u,v∈ {0,1}∗
Curriculum-Based Measurement of Reading Progress Monitoring: The Importance of Growth Magnitude and Goal Setting in Decision Making

Abstract Research regarding the technical adequacy of growth estimates from curriculum-based measurement of reading progress monitoring data suggests that current decision-making frameworks are likely to yield inaccurate recommendations unless data are collected for extensive periods of time. Instances where data may not need to be collected for long periods to make defensible decisions are presented. Recommendations to collect data for upwards of 3 months may be appropriate for students whose rate of improvement (ROI) approximates the criterion to which their performance is being compared. A framework is presented to help evaluate whether a student's ROI is substantially discrepant from an expected rate of growth (i.e., goal line). A spreadsheet program was created that used user-specified parameters for goal line magnitude, dataset variability, and data collection duration, in order to identify critical ROIs to determine whether students were making adequate progress with different levels of certainty. Analyses suggest that decisions may be feasible sooner than previously thought, particularly when growth is highly discrepant from the goal line and variability in the data is limited. Implications, limitations, and directions for future research are discussed.
Using web information for author name disambiguation

In digital libraries, ambiguous author names may occur due to the existence of multiple authors with the same name (polysemes) or different name variations for the same author (synonyms). We proposed here a new method that uses information available on the Web to deal with both problems at the same time. Our idea consists of gathering information from input citations and submitting queries to a Web search engine, aiming at finding curricula vitae and Web pages containing publications of the ambiguous authors. From the content of documents in the answer sets returned by the Web search engine, useful information that can help in the disambiguation process is extracted. Using this information, author names are disambiguated by leveraging a hierarchical clustering method that groups citations in the same document together in a bottom-up fashion. Experimental results show that the our method yields results that outperform those of two state-of-the-art unsupervised methods and are statistically comparable with those of a supervised one, but requiring no training. We observe gains of up to 65.2% in the pairwise F1 metric when compared with our best unsupervised baseline method.
THE IMPACT OF GOVERNANCE MECHANISMS ON INSIDERS ’ GUIDANCE OF ANALYSTS AND MANAGEMENT OF EARNINGS SURPRISES 1

We investigate the relation between governance mechanisms and insiders’ guidance of analysts and management of earnings surprises. Corporate insiders have incentives to manipulate short-term stock prices by creating positive earnings surprises (Levitt 1998). Insiders have two ways to create positive earnings surprises: (1) manage reported earnings upwards or (2) guide analyst forecasts downwards (Matsumoto 2002). Recent studies have shown that stronger governance suppresses insiders’ management of reported earnings (Leuz et al. 2002; Bhattacharya et al. 2002), suggesting that it is likely to mitigate management of earnings surprises. However, nothing is known about the impact of governance mechanisms either on insiders’ guidance of analysts or on their management of earnings surprises. Using data from 21countries, we show that managers of firms facing stronger governance are relatively more likely to both guide analysts’ forecasts downward and create positive earnings surprises. We highlight an important unaddressed agency problem in the financial reporting process by showing that institutional governance does not monitor insiders in either their guidance of analysts or their management of earnings surprises.
An Adaptation of the Fast Fourier Transform for Parallel Processing

A modified version of the Fast Fourier Transform is developed and described. This version is well adapted for use in a special-purpose computer designed for the purpose. It is shown that only three operators are needed. One operator replaces successive pairs of data points by their sums and differences. The second operator performs a fixed permutation which is an ideal shuffle of the data. The third operator permits the multiplication of a selected subset of the data by a common complex multiplier. If, as seems reasonable, the slowest operation is the complex multiplications required, then, for reasonably sized date sets—e.g. 512 complex numbers—parallelization by the method developed should allow an increase of speed over the serial use of the Fast Fourier Transform by about two orders of magnitude. It is suggested that a machine to realize the speed improvement indicated is quite feasible. The analysis is based on the use of the Kronecker product of matrices. It is suggested that this form is of general use in the development and classification of various modifications and extensions of the algorithm.
A Hybrid Semi-supervised Learning Approach to Identifying Protected Health Information in Electronic Medical Records

De-identification of electronic medical records is one of the main tasks to make clinical data sharable for more researchers outside the associated institutions. Indeed, this de-identification task has been considered very much with positive research outcomes worldwide, especially those from the i2b2 (Informatics for Integrating Biology and the Bedside) shared tasks in 2006 and 2014. However, it has not yet been a solved problem and still needs more investigation realistically. In this paper, we propose a hybrid semi-supervised learning approach to identifying protected health information (PHI) in electronic medical records. The proposed approach combines a machine learning-based method with a conditional random fields (CRF) model and a rule-based method in a post-processing phase to handle 8 PHI types with disambiguity. The CRF-based classification phase and the rule-based post-processing phase are then conducted in a semi-supervised learning manner. As compared to the existing works, our work has the merits of PHI identification such as: (1). Effectiveness with comparable precision and recall values from the experiments on the 2006 i2b2 data set; (2). A more practical solution as enhancing the training data set over time for a more accurate classifier in a semi-supervised learning mechanism; (3). A portable approach for clinical text in non-English languages.
Mapping kinematic interactions between objects for robot motion planning

Knowledge about the possible interactions between objects is needed by a robot if it is to plan tasks employing those objects. In this paper, we focus on methods to represent the ways in which the degrees of freedom of a rigid body interlocked with another change as the relative pose of the two objects changes, in such a way so as to allow efficient planning queries about the objects. Narrow passages and occlusions make this a difficult problem for usual approaches in robotics involving sample based planners and 3D shape reconstruction. Instead, we propose a data structure (called a DoF map) that can be constructed from tactile information alone, and which, once constructed for a pair of rigid bodies, enables fast planning queries for that particular pair. The data structure is also sufficiently abstract and allows reuse even for objects with different geometry, as long as the new pair of objects is such that, if its DoF map were constructed, it would be isomorphic to the DoF map of the original pair. The DoF map then provides a possible criterion for object pair classification that is more general than exact geometric shape but more informative than simple topology. We describe a method to construct DoF maps, as well as a method to reuse a known one. We test these ideas in simulation.
Kenya's Grand Coalition Government -Another Obstacle to Urgent Constitutional Reform?

Kenya's Grand Coalition Government was formed in the aftermath of a serious post-electoral crisis including widespread violence. No political progress has been recorded; in striking contrast to all expectations for more moderation and consensual preparation of a constitutional reform. Based on secondary data from textbooks, newspapers, journals, and documents from both governmental and non-governmental organisations, the author finds that the continuing wrangles within the Grand Coalition Government, the fear of the debilitating effects of a constitutional referendum and the hard-line positions on the contentious issues pose a big threat to the achievement of a new constitution before the 2012 Elections.
A review of fertilizer policy issues in Nigeria

Low fertilizer use is professed to be among the many reasons for low agricultural productivity in Nigeria. Fertilizer application, estimated at 13 kg/ha in 2009 by the Federal Ministry of Agriculture and Rural Development, is far lower than the 200 kg/ha recommended by the United Nations Food and Agriculture Organization (FAO). This report reviews the status of the fertilizer sector in Nigeria on the basis of a thorough overview of existing literature on Nigeria, reports on recent survey results, and analytic work conducted by IFPRI in Nigeria under the Global Food Security Response (GFSR) initiative of the USAID. It synthesizes the findings on key fertilizer issues in Nigeria, focusing on the demand and supply environment, the role played by subsidies, the regulatory environment, and the use of the vouchers system to aid fertilizer distribution. A key finding is that the heavy emphasis on price subsidization to the detriment of other approaches, such as complementary actions to improve farmersi? ½ fertilizer-use techniques, has hampered market development. Three policy recommendations arise from this report. First, the initial step the government should take should be to eliminate the existence of dual fertilizer markets by establishing the primal role of the private sector in fertilizer production, procurement and distribution. Second, the government should seek policy stability by reducing the frequency of government intervention in preference to building capacity in the private sector to handle all levels of the fertilizer value chain activities. Finally, it should provide a clear assignment of monitoring and regulatory roles, which are needed at every stage of fertilizer production (including blending) and distribution with a broader reach of regulatory activities at peri-urban and rural markets.
Mining Publication Records on Personal Publication Web Pages Based on Conditional Random Fields

A publication record denotes a list of semi-structured citation string of publications of a research institute or an individual researcher. Publication records are integrated into a digital library to become an important knowledge base which in turn enables a variety of applications. A publication record is usually found among other information on a publication Web page (or publication page for short). It is thus an interesting problem to extract publication record from these Web pages. The problem is difficult due to several reasons including the flexibility in formatting the metadata of a publication into a semi-structured citation string and expressing the citation string into its visual presentation in HTML. Furthermore, two citation strings with similar visual presentation on the same Web page may have different HTML constructs. In this paper, we present a content analysis approach based on Conditional Random Fields and data region boundary analysis to automatically extract citation record on a publication page. Experimental results show that our method performs well on a benchmark containing manually crafted publication Web pages. The precision, recall, and F-measure are 82.5%, 87.6%, and 85.0% respectively. This is an improvement over previous results.
Performance of Generalized Diversity Combining System for Coded Transmissions

Diversity combining is an important technique for combating fading in wireless communications. Generalized diversity combining selects a subset of diversity branches and combines them coherently. It has been established that when the transmission is uncoded, the optimal generalized diversity combining in the sense of minimizing bit error rate is to select branches that offer the maximum sum log-likelihood ratio (LLR). In the coded case, such diversity combining is not optimal any more. We present in this paper an upper bound result on the bit error rate performance of coded transmissions over general memoryless channels, and apply the analysis to the diversity selection problem. The resulting semi-analytical method for evaluating performance of coded diversity combining schemes offers insight on the difference between the performances of the coded and uncoded systems. It is shown via a numerical example that a scheme that selects individually "strong" branches (in the sense of large LLR) can out-perform a scheme that selects branches that are jointly "strong"
Algorithms for Two Versions of LCS Problem for Indeterminate Strings

We study the complexity of the longest common subsequence (LCS) problem from a new perspective. By an indeterminate string (istring, in short) we mean a sequence e X = e X[1] e X[2] . e X[n], where e X[i] ⊆ Σ for each i, and Σ is a given alphabet of potentially large size. A subsequence of e X is any usual string over Σ which is an element of the finite (but usually of exponential size) language e X[i1] e X[i2] . e X[ip], where 1 ≤ i1 < i2 < i3 . < ip ≤ n, p ≥ 0. Similarly, we define a supersequence of x. Our first version of the LCS problem is Problem ILCS: for given i-strings e X and e Y , find their longest common subsequence. From the complexity point of view, new parameters of the input correspond to |Σ| and maximum size ` of the subsets in e X and e Y . There is also a third parameter R, which gives a measure of similarity between e X and e Y . The smaller the R, the lesser is the time for solving Problem ILCS. Our second version of the LCS problem is Problem CILCS (constrained ILCS): for given i-strings e X and e Y and a plain string Z, find the longest common subsequence of e X and e Y which is, at the same time, a supersequence of Z. In this paper, we present several efficient algorithms to solve both ILCS and CILCS problems. The efficiency in our algorithms are obtained in particular by using an efficient data structure for special type of range maxima queries and fast multiplication of boolean matrices. Part of this research work was carried out when Costas Iliopoulos and M. Sohel Rahman were visiting Institute of Informatics, Warsaw University. Supported by EPSRC and Royal Society grants. Supported by the Commonwealth Scholarship Commission in the UK under the Commonwealth Scholarship and Fellowship Plan (CSFP). † On Leave from Department of CSE, BUET, Dhaka-1000, Bangladesh. ‡ Supported by the grant of the Polish Ministery of Science and Higher Education N
Leading by Example? Investment Decisions in a Mixed Sequential-Simultaneous Public Bad Experiment

This paper investigates the effect of having a leader in a laboratory public bad experiment with five subjects in each group.The control treatment is a standard public bad experiment, while in the leader treatments the design is such that in each group the leader decides first on his or her investment in the public bad.After being informed about the leader s decision, the four followers in each group make their investment decision.Two treatments of the leadership game are played with each group.In the same-leader-costs treatment, all subjects are confronted with the same costs, while in the no-leader-costs treatment the leader faces no direct costs of acting socially.It is found that followers invest significantly less in the public bad when there is a leader compared with a situation when there is no leader.Comparing the two treatments, we find, moreover, that the leadership effect is somewhat stronger when leaders face the same costs as followers compared with the situation in which leaders bear no costs.Randomly chosen leaders set an example by investing less than average players in a standard public bad game, and leader investments are lowest when the costs of leading are low.
Heterogeneous features and model selection for event-based media classification

With the rapid development of social media sites, a lot of user generated content is being shared in the Web, leading to new challenges for traditional media retrieval techniques. An event describes the happening at a specific time and place in real-world, and it is one of the most important cues for people to recall past memories. The reminder value of an event makes it extremely helpful in organizing human life. Thus, organizing media by events has recently drawn much attention within the multimedia research community. In this paper, we focus on two fundamental problems related to event based social media analysis: the study of feature importance for modeling the relation between events and media, and how to deal with missing and erroneous metadata often present in social media data. These issues are studied within an event-based media classification framework. Different learning approaches are employed to train the event models on different features. We find, through experiments on a large set of events, that the best discriminant features are tags, spatial and temporal feature. We address the missing value problem by extending the feature with an extra attribute to indicate if the values are missing. Promising results are achieved demonstrating the effectiveness of the proposed method.