Miracle-dz/UniFAR-FTUs huggingface dataset:
Outcome-Based Conditionality : The Role and Optimal Design

The paper offers a formal model of outcomes-based conditionality from a principle-agent perspective. The results suggest that conditioning IMF finance on outcomes is a good option when opposition to reforms is relatively weak and when the IMF offers financing on market terms. With strong opposition, the benefits from outcomes-based conditionality are less clear and its role and optimal design depend on the type of opposition and terms of IMF financing. To be able to use conditionality as an incentive tool, the IMF would have to offer a loan with an element of a subsidy. The equilibrium policy choice is determined by the competitive power of the IMF versus the domestic lobby. Unobservability, however, weakens IMF’s competitive power and the only “player”, who never loses from the fact that the IMF does not monitor government decisions, is the lobby. Introduction International Monetary Fund (IMF) programs are in essence incentive schemes. The country is “rewarded” with IMF financing if it implements certain policies and achieves certain outcomes. For an IMF program to be successful, its conditionality should be designed in a way that provides the government with the “right” incentives to adopt necessary policy changes. In other words, the government should be willing to implement reforms. This willingness is often referred to as ownership of a reform program. One of the proposals to enhance program ownership through conditionality design is greater use of outcomes-based conditionality under which IMF financing is conditional on the member country meeting particular targets or objectives rather than implementing specific actions (policies). Outcome-based conditionality is viewed as “reducing the perception of micro-management of countries’ economic policies and helping foster and build on country 1 I am indebted to Charles Engel for valuable advice, support and encouragement through the whole project. Special thanks go to Akito Matsumoto for numerous enlightening discussions. I haved also benefited from comments of Robert Staiger, Scott Taylor, Peter Eso, Alex Mourmouras, Nienke Oomes and Bill Sandholm. The views expressed in this paper are those of the author and should not be attributed to the International Monetary Fund, its Executive Board, or its management. 2 This view of IMF conditionality was first suggested in Dixit (2000a). 3 A formal definition of ownership adopted by the IMF is as follows: “Ownership is a willing assumption of responsibility for an agreed upon program of policies, by officials in the borrowing country who have the responsibility to formulate and carry out those policies, based on an understanding that the program is achievable and is in the country’s own interest” (IMF (2001a)). The approach was advocated by Carlos Diaz-Alejandro (1984), and discussed in Khan and Sharma (2001), IMF, (2001e) and IMF (2002b).
Language Extensions and Compilation Techniques for Data Intensive Computations

Processing and analyzing large volumes of data plays an increasingly important role in many domains of scienti c research. Typical examples of very large scienti c datasets include long running simulations of time-dependent phenomena that periodically generate snapshots of their state, archives of raw and processed remote sensing data, and archives of medical images. High-level language and compiler support for developing applications that analyze and process such datasets has, however, been lacking so far. We are developing language extensions and a compilation framework for expressing the applications that process large multidimensional datasets in a high-level data-parallel fashion. We have chosen a dialect of Java for expressing these applications. Our dialect of Java includes data-parallel extensions for specifying collection of objects, a parallel for loop, and reduction variables. Our compiler will analyze parallel loops and optimize the processing of datasets through the use of an existing runtime system, called Active Data Repository (ADR), developed at University of Maryland. We present design of a compiler/runtime interface which allows the compiler to e ectively utilize the existing runtime system. We show how interprocedural static program slicing can be used by the compiler to extract relevant information for the runtime system. Implementation of these compiler techniques is currently underway using the Titanium infrastructure.
Electrospinning of poly(lactic acid)/polyhedral oligomeric silsesquioxane nanocomposites and their potential in chondrogenic tissue regeneration

The study was conducted to evaluate the cytocompatibility and hydrolytic degradability of the new poly(lactic acid)/polyethylene glycol-polyhedral oligomeric silsesquioxane (peg-POSS/PLLA) nanocomposite as potential material for cartilage regeneration. PLLA scaffolds containing 0 to 5% of peg-POSS were fabricated by electrospinning. Human mesenchymal stem cells (hMSC’s) were cultured in vitro to evaluate the cytocompatibility of the new nanocomposite material. Hydrolytic degradation studies were also carried out to analyze the mass loss rate of the nanocomposites through time. The addition of the peg-POSS to the PLLA did not affect the processability of the nanocomposite by electrospinning. It was also observed that peg-POSS did not show any relevant change in fibers morphology, concluding that it was well dispersed. However, addition of peg-POSS caused noticeable decrease in mean fiber diameter, which made the specific surface area of the scaffold to rise. hMSC’s were able to attach, to proliferate, and to differentiate into chondrocytes in a similar way onto the different types of electrospun peg-POSS/PLLA and pure PLLA scaffolds, showing that the peg-POSS as nano-additive does not exhibit any cytotoxicity. The hydrolytic degradation rate of the material was lower when peg-POSS was added, showing a higher durability of the nanocomposites through time. Results demonstrate that the addition of peg-POSS to the PLLA scaffolds does not affect its cytocompatibility to obtain hyaline cartilage from hMSC’s.
Capture & Access Lifelogging Assistive Technology for People with Episodic Memory Impairment

Capture and access lifelogging technologies use a combination of wearable and environmental sensors to record people’s experiences and then make the data available for subsequent review. These types of technologies can help people with episodic memory impairment (such as Alzheimer’s disease) regain an awareness of the important experiences of their life and reduce the burden they impose on their caregivers. However, lifelogging technologies can produce an overwhelmingly large amount of information (in the form of photos, audio, videos, and sensor logs) that is too difficult for people with cognitive impairment to review. Our approach focuses designing systems that select and present only lifelog data that are the most effective memory cues for the person with memory impairment. To design such systems, we conducted two field studies to investigate the strategies people use to compensate for their episodic memory impairment and also what kinds of information are the most effective memory cues. Based on the findings from these studies, we discuss the design of a capture and access lifelogging system that passively records people experiences, selects out the most effective cues, and presents these cues to the person with memory impairment in an engaging cueing interaction that allows them to exercise their memory abilities.
Estimation of Parameters in Multiple Regression with Missing X-observations Using Modiied First Order Regression Procedure

This paper considers the estimation of coeecients in a linear regression model with missing observations in the independent variables and introduces a modiication of the standard rst order regression method for imputation of missing values. The modiication provides stochastic values for imputation. Asymptotic properties of the estimators for the regression coeecients arising from the proposed modiication are derived when either both the number of complete observations and the number of missing values grow large or only the number of complete observations grows large and the number of missing observations stays xed. Using these results, the proposed procedure is compared with two popular procedures|one which utilizes only the complete observations and the other which employs the standard rst order regression imputation method for missing values. It is suggested that an elaborate simulation experiment will be helpful to evaluate the gain in eeciency especially in case of discrete regressor variables and to examine some other interesting issues like the impact of varying degree of multicollinearity in explanatory variables. Applications to some concrete data sets may also shed some light on these aspects. Some work on these lines is in progress and will be reported in a future article to follow.
A business-driven framework for evaluating cloud computing

Cloud computing carries the promise of powerful models and abstractions that could transform the way IT services are delivered today. While the cloud's economic model has been argued to provide cost benefits compared to traditional in-house IT delivery, it remains to be seen how this argument changes for different business applications and environments. In this paper, we develop a framework for exploring the factors that influence IT delivery cost within an organization compared to the cost of using the cloud. We then apply the framework to develop insight on the economics of migrating to the cloud from the perspective of users in emerging markets. We find that emerging markets have different characteristics from established economies that result in different barriers and benefits realized from using the cloud.
General Perturbations in Compressed Sensing

We analyze the Basis Pursuit recovery of signals when observing K-sparse data with general perturbations (i.e., additive, as well as multiplicative noise). This completely perturbed model extends the previous work of Candès, Romberg and Tao on stable signal recovery from incomplete and inaccurate measurements. Our results show that, under suitable conditions, the stability of the recovered signal is limited by the noise level in the observation. Moreover, this accuracy is within a constant multiple of the best-case reconstruction using the technique of least squares.
Research on Reliability of Complex Coal Mine Ventilation Networks

Ventilation networks is the main part of coal mine ventilation system, it takes the responsibility to transfer fresh air to the underground laneways to keep the whole safety of coal mine. In this paper, we discuss about the reliability of the ventilation networks by using Boolean calculation and Shannon formula, and expatiate the application process with a real case, the result shows the highly efficiency and maneuverability.
A Survey of Recommendation Algorithms

Recommender system can be defined as the subclass of information filtering system which attempts to give the guidance to the users regarding the useful services based on their personalized preferences, past behavior or based on their similar likings with other users. The various approaches of recommendation systems, like content-based, collaborative filtering, hybrid, etc, can further be classified according to their algorithmic technique as memory-based (heuristic) or model-based recommendation algorithms. Service recommender systems provides appropriate recommendations of services like movies, hotels, gadgets, etc, leading to an increase in the amount of data on the web, known as Big Data. It is becoming difficult to capture, store, manage and analyze such big data that affects the service recommender systems with issues like scalability and inefficiency. Also many existing service recommender system provides the same recommendations to different users based on ratings and rankings only, without considering the taste and preference of an individual user. This paper presents a survey on various recommendation algorithms, elaborating all its types along with its drawbacks. The paper also focuses on the solutions to overcome these drawbacks and provide apt recommendations to the users. It also deals with the solution to provide apt recommendations of the services to the users in big data environment. The issues of scalability and inefficiency while managing big data can be solved by using a distributed computing platform known as Hadoop.
Longitudinal assessment of anti-PGL-I serology in contacts of leprosy patients in Bangladesh

Background Despite elimination efforts, the number of Mycobacterium leprae (M. leprae) infected individuals who develop leprosy, is still substantial. Solid evidence exists that individuals living in close proximity to patients are at increased risk to develop leprosy. Early diagnosis of leprosy in endemic areas requires field-friendly tests that identify individuals at risk of developing the disease before clinical manifestation. Such assays will simultaneously contribute to reduction of current diagnostic delay as well as transmission. Antibody (Ab) levels directed against the M.leprae-specific phenolic glycolipid I (PGL-I) represents a surrogate marker for bacterial load. However, it is insufficiently defined whether anti-PGL-I antibodies can be utilized as prognostic biomarkers for disease in contacts. Particularly, in Bangladesh, where paucibacillary (PB) patients form the majority of leprosy cases, anti-PGL-I serology is an inadequate method for leprosy screening in contacts as a directive for prophylactic treatment. Methods Between 2002 and 2009, fingerstick blood from leprosy patients’ contacts without clinical signs of disease from a field-trial in Bangladesh was collected on filter paper at three time points covering six years of follow-up per person. Analysis of anti-PGL-I Ab levels for 25 contacts who developed leprosy during follow-up and 199 contacts who were not diagnosed with leprosy, was performed by ELISA after elution of bloodspots from filter paper. Results Anti-PGL-I Ab levels at intake did not significantly differ between contacts who developed leprosy during the study and those who remained free of disease. Moreover, anti-PGL-I serology was not prognostic in this population as no significant correlation was identified between anti-PGL-I Ab levels at intake and the onset of leprosy. Conclusion In this highly endemic population in Bangladesh, no association was observed between anti-PGL-I Ab levels and onset of disease, urging the need for an extended, more specific biomarker signature for early detection of leprosy in this area. Trial registration ClinicalTrials.gov ISRCTN61223447
Duqu: Analysis, Detection, and Lessons Learned

In September 2011, a European company sought our help to investigate a security incident that happened in their IT system. During the investigation, we discovered a new malware that was unknown to all mainstream anti-virus products, however, it showed striking similarities to the infamous Stuxnet worm. We named the new malware Duqu, and we carried out its first analysis. Our findings led to the hypothesis that Duqu was probably created by the same people who developed Stuxnet, but with a different purpose: unlike Stuxnet whose mission was to attack industrial equipment, Duqu is an information stealer rootkit. Nevertheless, both pieces of malware have a modular structure, and they can be re-configured remotely from a Command and Control server to include virtually any kind of functionality. In this paper, we present an abridged version of our initial Duqu analysis, which is available in a longer format as a technical report. We also describe the Duqu detector toolkit, a set of heuristic tools that we developed to detect Duqu and its variants. Finally, we discuss a number of issues that we learned, observed, or identified during our Duqu analysis project concerning the problems of preventing, detecting, and handling targeted malware attacks; we believe that solving these issues represents a great challenge to the system
User Profiling in the Chronobot/Virtual Classroom System

The Chronobot/Virtual Classroom (CVC) system is a novel time knowledge exchange platform where any pair of users can exchange their time and knowledge. User profile that contains user attributes, preferences, and learning patterns serves as a primary basis to identify exchange partners and determine exchange rates. In this paper, we described the methodology to acquire knowledge about users i.e. user profile from their activities. The association between user profile and user behaviors (e.g. online reading, chatting and time/knowledge exchanging) is identified by several feedback indicators extracted from browsing history, chatting session and exchange transaction. A linear learning model is constructed to fuse multiple feedback indicators to infer user preference. The methods utilizing user profile to identify the exchange partners and determine the exchange rate are also described in detail.
Tracking and classification of arbitrary objects with bottom-up/top-down detection

Recently, the introduction of dense, long-range 3D sensors has facilitated tracking of arbitrary objects. Especially in the context of autonomous driving, other traffic participants driving the streets usually stay well-segmented from each other. In contrast, pedestrians or bicyclists do not always stay on the road and they often get close to static structure of the environment, e.g. traffic lights or signs, bushes, parking cars etc. These objects are not as easy to segment, often resulting in an under-segmentation of the scene and wrong tracking results. This paper addresses the problem of tracking moving objects that are hard to segment from their static surroundings by utilizing top-down knowledge about the geometry of existing tracks during segmentation. This includes methods for discerning static from moving objects to reduce the rate of false positive tracks as well as a classification of tracks into pedestrian, bicyclist, motor bike, passenger car, van and truck classes by considering an objects appearance and motion history. The proposed tracking system is experimentally validated in challenging real-world inner-city traffic scenes.
When Clouds become Green: the Green Open Cloud Architecture

Virtualization solutions appear as alternative approaches for companies to consolidate their operational services on a physical infrastructure, while preserving specific functionalities inside the Cloud perimeter (e.g., security, fault tolerance, reliability). These consolidation approaches are explored to propose some energy reduction while switching OFF unused computing nodes. We study the impact of virtual machines aggregation in terms of energy consumption. Some load-balancing strategies associated with the migration of virtual machines inside the Cloud infrastructures will be showed. We will present the design of a new original energy-efficient Cloud infrastructure called Green Open Cloud.
Power law relationships in the branches of loblolly pine, red maple and sugar maple trees.

: Power laws are interesting patterns that exist over wide ranges. Power laws may be used to deter-mine the organization of countless networks in nature. The purpose of this study was to test whether the distribution patterns in shoot lengths of three popular tree species follow a power law. This study not only adds to the general knowledge base for these species but also may be used to make predictions about other species. Three common tree species were included in this study: loblolly pine ( Pinus taeda ), red maple ( Acer rubrum ), and the sugar maple ( Acer saccharum ). The height and all shoot lengths of five individuals of each tree species were measured, recorded and sorted. Loblolly pines and sugar maples followed the same power law at individual and species level. Most of the red maple individuals did not follow a power law although they followed a power law at the species level. One possible reason might be that the red maple trees measured were too young and were in strong competition for resources with other tree species.
Functional Optical Hemodynamic Imaging of the Olfactory Cortex in Patients with Parkinson’s Disease

Olfactory dysfunction is a frequent non-motor symptom in Parkinson’s disease (PD). This symptom is considered to be an early manifestation of the disease. The aim of this study was to establish the cortical basis of olfactory function in patients with PD. This study was conducted on ten healthy, normosmic subjects and seven patients with PD (one with subjective olfactory dysfunction and nine without subjective olfactory dysfunction). We employed a 22-channel near-infrared spectroscopy (NIRS) device with eight light-incident fibers and seven light-detector fibers, each with an inter-optode distance of 2.5 centimeters on the frontal head. Isovaleric acid was used as the odor stimulant. We measured the change in total hemoglobin concentrations (totalHb) from pre-baseline values and compared the results obtained for healthy normosmic subjects and patients with PD. In all healthy normosmic subjects and three patients with PD, isovaleric acid caused remarkable changes in (totalHb), especially in the lower areas of the frontal cortex. However, in four patients with PD, isovaleric acid caused no changes. This Masayuki Karaki Department of Otorhinolaryngology, Japan & Health Sciences School of Nursing, Faculty of Medicine, Kagawa University, Japan
Preliminary Establishment of Sports Sense of Self-efficacy Scale

Based on theory on sense of self-efficacy and the theories on athletics movement,preliminary frame of the sports sense of self-efficacy scale was drawn up.Then the scale was established by the result of interview with the athletes.Through measuring 180 athletes,the data was analyzed with item and factor methods.The result indicates that the questionnaire has good reliability and good validity.Its structure includes two dimensions: competition of sense of self-efficacy and training of sense of self-eiffcacy.So the conclusion is: This scale is available for the sports sense of self-efficacy research.
Hands-free speech recognition using a reverberation model in the feature domain

A novel approach for robust hands-free speech recognition in highly reverberant environments is proposed. Unlike conventional HMM-based concepts, it implicitly accounts for the statistical dependence of successive feature vectors due to the reverberation. This property is attained by a combined acoustic model consisting of a conventional HMM, modeling the clean speech, and a reverberation model. Since the HMM is independent of the acoustic environment, it needs to be trained only once using the usual Baum-Welch re-estimation procedure. The training of the reverberation model is based on a set of room impulse responses for the corresponding acoustic environment and involves only a negligible computational effort. Thus, the recognizer can be adapted to new environments with moderate effort. In a simulation of an isolated digit recognition task in a highly reverberant room, the proposed method achieves a 60% reduction of the word error rate compared to a conventional HMM trained on reverberant speech, at the cost of an increased decoding complexity.
Mathematic model of key-point anti-air position ring-deployment and optimization

Anti-air weapons deployment optimization is a important researching of modern air defense combat, the paper propose method of anti-air position deployment of multi-types of anti-air weapons according to stand off attacking. Topography of position, intercept ability based on queuing theory are taken into account in the optimization model. For evolution algorithm, a memetic algorithm that hybridize genetic algorithm with hill climbing for solving this problem is put forward, proper crossover operator, and local search strategy are employed to improve performance of evolution procedure. Experiments results show that the memetic algorithm perform better and the optimization method is feasible and effective to solve the deployment optimization.