Miracle-dz/UniFAR-FTUs huggingface dataset:
Epidemiological Properties of Primary Open Angle Glaucoma in Nigeria

Background. Primary open angle glaucoma (POAG) is progressive chronic optic neuropathy in adults in which intraocular pressure (IOP) and other currently unknown factors contribute to damage. POAG is the second commonest cause of avoidable blindness in Nigeria. Pattern of Presentation. POAG is characterized by late presentation. Absence of pain which is a driving force for seeking medical help, inadequacy of trained eye care personnel, paucity of facilities, misdistribution of resources, lack of awareness, poor education, and poverty may all contribute to this. Medical and surgical treatment options available are challenging and tasking. Screening for Glaucoma. Screening is the presumptive identification of unrecognized disease (POAG) by applying test(s) which can be applied rapidly. Such test(s) should be of high reliability, validity, yield, acceptable, and cost effective. The test should ideally be sensitive, specific, and efficient. It is difficult to select a suitable test that meets these criteria. Intraocular pressure (IOP) appears to be the easiest option. But, high IOP is not diagnostic nor does normal value exclude the disease. Health education is a possible strategy in early case detection and management. Treatment of POAG. Glaucoma treatment can either be medical or surgical (this includes laser). Considering unavailability, potency, cost, and long-term effects of medication, surgery (trabeculectomy) could be a better option. Laser trabeculoplasty is available in a few centers. Viscocanalostomy is not routinely performed. Patient education is vital to success as management is for life. Conclusion. POAG remains a cause of avoidable blindness in Nigeria. There is need for long-term strategy to identify patients early and institute prompt management. Improvement in training of eye care personnel and provision of up to date equipment is essential in achieving this goal.
Printer-friendly Version Interactive Discussion Stratosphere–troposphere Exchange (ste) in the Vicinity of North Atlantic Cyclones Acpd Printer-friendly Version Interactive Discussion

This discussion paper is/has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP if available. Abstract It is well known that the storm tracks are a preferred region of stratosphere–troposphere exchange (STE), but a systematic and climatological investigation of the connection between cyclones and STE has not yet been performed. We use two established ERA-Interim climatologies of STE and cyclones for the years 1979–2011 to quantify the 5 amount of STE that occurs during the life cycle of North Atlantic cyclones. A Lagrangian method serves to identify individual STE events, and a sophisticated cyclone identification tool detects cyclones, their shape and size from the sea-level pressure field. Combining the two data sets reveals that roughly half of the total STE in the North Atlantic occurs in the vicinity of cyclones and that both downward and upward fluxes of 10 mass across the tropopause (STT and TST, respectively) are more intense in deeper cyclones (lower minimum pressure) compared to less intense cyclones. In summer, STT and TST in the vicinity of cyclones are almost equal; in the other seasons, STT is larger by 15–45 %. Cross-tropopause mass fluxes are enhanced by a factor of about two compared to climatology when a cyclone is present. On average, STE is strongest 15 during the mature phase of cyclones, i.e., in a 24 h time window around the time of maximum intensity. Systematic patterns of exchange locations relative to the cyclone centre are identified via composite analysis and shed light on the different characteristics of STT and TST. During cyclone intensification and in the mature stage, TST is mainly confined to the cyclone centre, whereas STT occurs mainly in a region further 20 southwest. During the decay of the cyclones, both STT and TST are most frequent close the cyclone centre, in a region with a fairly low tropopause.
Preserving elemental content in adherent mammalian cells for analysis by synchrotron‐based x‐ray fluorescence microscopy

Trace metals play important roles in biological function, and x‐ray fluorescence microscopy (XFM) provides a way to quantitatively image their distribution within cells. The faithfulness of these measurements is dependent on proper sample preparation. Using mouse embryonic fibroblast NIH/3T3 cells as an example, we compare various approaches to the preparation of adherent mammalian cells for XFM imaging under ambient temperature. Direct side‐by‐side comparison shows that plunge‐freezing‐based cryoimmobilization provides more faithful preservation than conventional chemical fixation for most biologically important elements including P, S, Cl, K, Fe, Cu, Zn and possibly Ca in adherent mammalian cells. Although cells rinsed with fresh media had a great deal of extracellular background signal for Cl and Ca, this approach maintained cells at the best possible physiological status before rapid freezing and it does not interfere with XFM analysis of other elements. If chemical fixation has to be chosen, the combination of 3% paraformaldehyde and 1.5 % glutaraldehyde preserves S, Fe, Cu and Zn better than either fixative alone. When chemically fixed cells were subjected to a variety of dehydration processes, air drying was proved to be more suitable than other drying methods such as graded ethanol dehydration and freeze drying. This first detailed comparison for x‐ray fluorescence microscopy shows how detailed quantitative conclusions can be affected by the choice of cell preparation method.
Generation of whole-body optimal dynamic multi-contact motions

We propose a method to plan optimal whole-body dynamic motion in multi-contact non-gaited transitions. Using a B-spline time parameterization for the active joints, we turn the motion-planning problem into a semi-infinite programming formulation that is solved by nonlinear optimization techniques. Our main contribution lies in producing constraint-satisfaction guaranteed motions for any time grid. Indeed, we use Taylor series expansion to approximate the dynamic and kinematic models over fixed successive time intervals, and transform the problem (constraints and cost functions) into time polynomials which coefficients are function of the optimization variables. The evaluation of the constraints turns then into computation of extrema (over each time interval) that are given to the solver. We also account for collisions and self-collisions constraints that have not a closed-form expression over the time. We address the problem of the balance within the optimization problem and demonstrate that generating whole-body multi-contact dynamic motion for complex tasks is possible and can be tractable, although still time consuming. We discuss thoroughly the planning of a sitting motion with the HRP-2 humanoid robot and assess our method with several other complex scenarios.
Reconciling Trade and Climate Policies

The outcome of the 15th conference of the Parties to the UNFCC showed a shift from a top-down approach with a collective target favoring environmental objectives to a bottom-up accord favoring political feasibility. There is no meaningful binding agreement in sight, also because the global climate regime and the global trade policy regime, represented by the WTO, appear to be on a collision course. Following a review of the challenges ahead, the paper argues that trade will have a second-order contribution to world-wide CO2 emissions. Evidence shows increasing carbon transfers through trade, but the magnitude of carbon leakage effects, likely to be induced by differences in climate mitigation policies, may be less than feared in some circles. Trade policy, however, will play a role in implementing climate mitigation policies in two areas: maintaining an open trading system and hence boosting growth and facilitating technological diffusion, and trade policy as a strategic instrument in negotiations. The paper concludes that an agreement with a few guiding principles and leeway where much initial mitigation would first take place unilaterally or in small groups, as under the early days of the GATT, is the most promising way ahead while preserving an open trading system and environmental integrity.
RAMP: Rules Abstraction for Modeling and Prediction

Generating accurate and robust models is crucial to the successful use and deployment of classifiers on a large scale. Rule induction, i.e., generating decision rule models from data, is often a preferred approach to classification modeling and prediction, due to the enhanced explanatory capability and interpretability of decision rules. The RAMP system for rules abstraction and modeling is evolving with accuracy and robustness as primary goals. The system provides the following key capabilities: 1) feature analysis and selection based upon contextual merits technique, 2) “optimal” discretization of numerical features, 3) generation of minimal DNF (Disjunctive Normal Form) rules based upon the R-MINI algorithm, 4) rule based regression 5) rule pruning, weighting, and editing, 6) alternate rule application strategies, and 7) accuracy evaluation of the model on test data. In addition, RAMP also provides a hierarchical capability for case management, which helps end-users carry out multiple experiments on a data set, and manage these experiments as a set of related cases. RAMP has been utilized in several large-scale real-life applications and some benchmark tasks which demonstrate its robustness. We describe RAMP and its principal components in this paper.
Analysis for Financial Management

Part I: Assessing the Financial Health of the Firm Chapter 1: Interpreting Financial Statements Chapter 2: Evaluating Financial Performance Appendix: International Differences in Financial Structure Part II: Planning Future Financial Performance Chapter 3: Financial Forecasting Chapter 4: Managing Growth Part III: Financing Operations Chapter 5: Financial Instruments and Markets Appendix: Using Financial Instruments to Manage Risks Chapter 6: The Financing Decision Appendix: The Irrelevance Proposition Part IV: Evaluating Investment Opportunities Chapter 7: Discounted Cash Flow Techniques Appendix: Mutually Exclusive Alternatives and Capital Rationing Chapter 8: Risk Analysis in Investment Decisions Appendix: Asset Beta and Adjusted Present Value Chapter 9: Business Valuation and Corporate Restructuring Appendix: The Venture Capital Method of Valuation Appendix A: Present Value of $1 Appendix B: Present Value of an Annuity of $1 Glossary Suggested Answers to Odd-Numbered End-of-Chapter Problems
Spatial characteristic with individual house properties and multilevel approach to hedonic models

The purpose of this research is to examine a multilevel approach to property hedonic models. Because individual properties are nested within neighborhoods within a city, a multilevel method is used to develop hedonic models. The author analyses several statistical and substantive reasons for explicitly modeling individual properties as belonging to neighborhoods. The reasons include spatial dependencies, non-stationarity and spatial heterogeneity. The conclusion is that multilevel models are not the only means of addressing issues of spatial dependencies and non-stationarity. The advantage of using a multilevel approach for property hedonic models is that they can analyze variables from different scales simultaneously, while also accounting for issues of spatial dependencies and non-stationarity.
OS Independent Run-time System Integrity Services

A majority of critical server vulnerabilities in 2003 and 2004 were memory based [1] . Today’s advanced viruses and worms attack software running in memory to circumvent operating system protections. Such attacks often disable intrusion detection systems in order to execute malicious payload. Malware can also make use of secrets stored in system memory to circumvent cryptographic security measures. An isolated execution environment contained within the system can provide substantial resiliency against such attacks in an OS independent fashion. We describe a firmware based approach that protects critical software agents from three classes of attacks, elimination, tamper and circumvention through the use of run-time integrity checking and unspoofable presence verification. We describe how this architecture can be leveraged to provide protection for secrets used by software. Finally, we describe our prototype implementation and provide the results of our experimental analysis, along with performance analysis to test the limits of this approach.
A Novel Approach to Word Sense Disambiguation Based on Topical and Semantic Association

Word sense disambiguation (WSD) is a fundamental problem in nature language processing, the objective of which is to identify the most proper sense for an ambiguous word in a given context. Although WSD has been researched over the years, the performance of existing algorithms in terms of accuracy and recall is still unsatisfactory. In this paper, we propose a novel approach to word sense disambiguation based on topical and semantic association. For a given document, supposing that its topic category is accurately discriminated, the correct sense of the ambiguous term is identified through the corresponding topic and semantic contexts. We firstly extract topic discriminative terms from document and construct topical graph based on topic span intervals to implement topic identification. We then exploit syntactic features, topic span features, and semantic features to disambiguate nouns and verbs in the context of ambiguous word. Finally, we conduct experiments on the standard data set SemCor to evaluate the performance of the proposed method, and the results indicate that our approach achieves relatively better performance than existing approaches.
A case study on key technologies of Android Trojans

With the growing Smartphone user groups, security issues on Android platform must be taken seriously. Currently, faced with the severe situation of the Internet Security, and considering inherent risks in Android OS, study on key technologies of Android Malware has become important. In this paper, we have analyzed technologies of Android Trojans on implantation, silent running, concealment, monitoring, confrontation with cracking, and then, designed a prototype of Trojan for evaluation. This work eventually should be able to provide a reference for the design of security mechanisms.
MCMC for non-Linear State Space Models Using Ensembles of Latent Sequences

inference problem that has no straightforward solution. We take a Bayesian approach to the inference of unknown parameters of a non-linear state model; this, in turn, requires the availability of ecient Markov Chain Monte Carlo (MCMC) sampling methods for the latent (hidden) variables and model parameters. Using the ensemble technique of Neal (2010) and the embedded HMM technique of Neal (2003), we introduce a new Markov Chain Monte Carlo method for non-linear state space models. The key idea is to perform parameter updates conditional on an enormously large ensemble of latent sequences, as opposed to a single sequence, as with existing methods. We look at the performance of this ensemble method when doing Bayesian inference in the Ricker model of population dynamics. We show that for this problem, the ensemble method is vastly more ecient than a simple Metropolis method, as well as 1 . 9 to 12.0 times more ecient than a single-sequence embedded HMM method, when all methods are tuned appropriately. We also introduce a way of speeding up the ensemble method by performing partial backward passes to discard poor proposals at low computational cost, resulting in a final eciency
P 2 P : Is Big Brother Watching You ?

In an effort to prosecute P2P users, RIAA and MPAA have reportedly started to create decoy users: they participate in P2P networks in order to identify illegal sharing of content. This has reportedly scared some users who are afraid of being caught. The question we attempt to answer is how prevalent is this phenomenon: how likely is it that a user will run into such a “fake user” and thus run the risk of a lawsuit? The first challenge is identifying these “fake users”. We collect this information from a number of free open source software projects which are trying to identify such IP address ranges by forming the so-called blocklists. The second challenge is running a large scale experiment in order to obtain reliable and diverse statistics. Using Planetlab, we conduct active measurements, spanning a period of 90 days, from January to March 2006, spread over 3 continents. Analyzing over a 100 GB of TCP header data, we quantify the probability of a P2P user of being contacted by such entities. We observe that 100% of our nodes run into entities in these lists. In fact, 12 to 17% of all distinct IPs contacted by any node were listed on blocklists. Interestingly, a little caution can have significant effect: the top five most prevalent blocklisted IP ranges contribute to nearly 94% of all blocklisted IPs and avoiding these can reduce the probability of encountering blocklisted IPs to about 1%. In addition, we examine other factors that affect the probability of encountering blocklisted IPs, such as the geographical location of the users. Finally, we find another surprising result: less than 0.5% of all unique blocklisted IPs contacted are owned explicitly by media companies.
Author Gender Analysis

Given an English paragraph of sufficient length, I would like to figure out the gender of the author with sufficiently high accuracy. I wrote a Naïve-Bayes classifier with the assistance of NLTK toolkit, and trained it with frequent words as the main features. The addition of frequent bigrams, trigrams and also part-of-speech tags slightly increased its accuracy. There are some obvious indicators, such as relation-related phrases like “my husband” or “my wife”, or topic-related words like “teaspoon” or “hardware”. However, my goal was building a classifier general enough not to use those. Excluding those salient but biased features, my classifier achieved a sub-optimal accuracy of 69%. This suggests that topic-finding is crucial to author gender analysis. Nevertheless, I still found several mild genders, which may shed light on future research.
The human agent: a work in progress toward human-humanoid interaction

At the Intelligent Robotics Laboratory of the Center for Intelligent Systems at Vanderbilt University, we have been developing a humanoid system called ISAC, (Intelligent Soft-Arm Control), over the past several years. As people work with a humanoid system, the interaction should be natural and robust. Our framework for human-humanoid interaction (HHI) considers various aspects of HHI and the paper presents information about the development of one key element of this system: the human agent.
Predicting Political Donations Using Twitter Hashtags and Character N-Grams

We describe a novel approach for predicting politicaldonations and performing psychographic segmentation basedon social data linked to election donation records. The role ofmicroblogs in enterprise informatics, specifically in relation tocustomer relationship systems is highlighted. Algorithms trainedon social data can be used to interpret and detect prospects' psychographicinformation. Contrasted with past approaches whichfocused exclusively on a single source of social data, the methodbeing presented allows us to use an objective gold standard bylinking Twitter and election records. Two experiments were conductedusing data collected from 438 Twitter users, half of whichare linked with donation event records collected from the UnitedStates Federal Election Commission. Probabilistic, entropy andkernel approaches were tested for predictive accuracy, while theCNG technique is explored as an alternative. The CNG algorithmwas found to predict political affiliation 17 percentage pointsabove the majority classifier, exceeding benchmarks suggestedby the literature. A NaïveBayes word n-gram approach wasfound to outperform CNG at predicting donations by predictingpolitical donations. Insufficient performance and poor reliabilityof standard word n-gram techniques in opinion detection revealskepticism about past work on political affiliation analysis fromsocial data alone. This suggests that prospecting systems maybenefit from constructing algorithms using data linked to external sources.
Observed and perceived dyadic and systemic functioning in families of preadolescents with spina bifida.

OBJECTIVE To examine dyadic and systemic family functioning across several domains (conflict, cohesion, and stress) in families of preadolescents with spina bifida in comparison to families of able-bodied preadolescents (8- and 9-year olds; n = 68 in each sample). METHODS Mother-, father-, and child-reported questionnaire data and observational ratings of family behavior were employed. RESULTS Findings revealed significant group and socioeconomic status (SES) differences, particularly for the observational family data. Compared to families of able-bodied children, families in the spina bifida sample were less cohesive and children from this sample were more passive during family interaction tasks. Additional analyses suggested that some of these significant associations between group status and family functioning were mediated by verbal IQ, indicating that a significant portion (42%-55%) of the overall group effect was due to variations in child cognitive functioning. Lower SES families demonstrated higher levels of observed mother-child conflict, less observed and perceived family cohesion, and more life events. Lower SES families from the spina bifida sample appear to be particularly at risk for lower levels of family cohesion. CONCLUSIONS Findings for the spina bifida sample support a resilience-disruption view (Costigan, Floyd, Harter, & McClintock, 1997) of systemic functioning in families of children with pediatric conditions.
Markerless Image-based 3 D Tracking for Real-time Augmented Reality Applications

In this contribution we describe a visual marker-less realtime tracking system for Augmented Reality applications. The system uses a fisheye lens mounted on a firewire camera with 10 fps for visual tracking of 3D scene points without any prior scene knowledge. All visual-geometric data is acquired online during the tracking using a structure-frommotion approach. 2D Image features in the hemispherical fisheye image are tracked using a 2D feature point tracker. Tracking may be facilitated by orientation compensation with an inertial sensor. Based on the image tracks, 3D camera egomotion and 3D features are estimated online from the image sequence. The tracking is robust even in the presence of moving objects as the large field of view of the camera stabilizes the tracking.
Fixed Channel Allocation Techniques Exploiting Cell Overlap for High Altitude Platforms

An investigation is performed into the capacity enhancements achievable from various fixed channel allocation techniques exploiting overlap between the cells formed by the antenna beams from a High Altitude Platform (HAP). It is shown that the areas served by more than one cell (overlap areas) benefit from the multiplexing gain and as a result they have lower blocking than the areas served by one cell. A novel technique derived from the Erlang-B distribution is described, which imposes certain restrictions in order to limit the proportion of the channels allocated to the overlap areas and be retained for use in areas with no overlap, in order to maintain a more uniform blocking probability over the coverage area. This technique has significantly improved the capacity of the system and the spectrum efficiency.