cssi/SciSciGPT-SciSciNet huggingface dataset:
Stochastic Block Coordinate Frank-Wolfe Algorithm for Large-Scale Biological Network Alignment

With increasingly “big” data available in biomedical research, deriving accurate and reproducible biology knowledge from such big data imposes enormous computational challenges. In this paper, motivated by recently developed stochastic block coordinate algorithms, we propose a highly scalable randomized block coordinate Frank-Wolfe algorithm for convex optimization with general compact convex constraints, which has diverse applications in analyzing biomedical data for better understanding cellular and disease mechanisms. We focus on implementing the derived stochastic block coordinate algorithm to align protein-protein interaction networks for identifying conserved functional pathways based on the IsoRank framework. Our derived stochastic block coordinate Frank-Wolfe (SBCFW) algorithm has the convergence guarantee and naturally leads to the decreased computational cost (time and space) for each iteration. Our experiments for querying conserved functional protein complexes in yeast networks confirm the effectiveness of this technique for analyzing large-scale biological networks.
RAINFALL INSURANCE FOR MIDWEST CROP PRODUCTION

The paper discusses a methodology for design and pricing of index insurance contracts for crop production. The methodology heavily relies on establishing a relationship between the index and yields in order to evaluate the contract performance in hedging farmers' risk. However, analysis of yield/rainfall data series for Iowa corn and Kansas wheat fail to produce a reliable and meaningful relationship which can be used uniformly across several counties and/or crop producing districts. Further research is needed as to applicability of rainfall insurance to specific crop/region combinations.
Reflections of a Community Lawyer

In May 2002, I opened a law office in one of the most underserved communities in Los Angeles County. Many questioned the sanity of such a career path when evaluating my financial stability and the personal toll that such a career path can exact. Given that I graduated from some of the best universities in the country, my friends, family, and strangers were even more perplexed at my choice. I cannot say that my decision to build a law practice in Compton, California, has been easy. However, time and time again, I found myself rejecting more secure and prestigious job offers and continued in what some of my law school friends call “the more difficult route.” This article recounts my brief, unrefined, and continuing journey as a novice attorney. My story is not unique or new; however, the triumphs, challenges and defeats of community- based private practitioners serving individuals’ everyday legal needs are largely undocumented. By providing a personal account of my experiences as a solo practitioner, I hope to encourage others working with low-income and modest-means clients to share their experiences and demand more support from our law schools, our bar associations, and legal aid organizations to allow us to better serve our clients and sustain ourselves in the profession
Developing Bus Transfer Facilities for Maximum Transit Agency and Community Benefit

Bus transfer centers are often regarded as 'undesirable neighbors' that are difficult to site and difficult to gain support for due to noise, exhaust, traffic congestion, and the presence of unwanted passengers. In fact, there are some locations where major bus transfer activities are not only not welcome, but are probably not in the best interests of the surrounding development. However, many transit agencies are elevating the acceptance and relevance of transit in their service areas by making their transfer centers true community assets rather than nuisances. This often means finding the right location for a transit center for both the surrounding community and the passengers, replacing run down development with new facilities, incorporating exciting and inspiring architecture and design, and improving the pedestrian amenities and safety and security of the immediate area around the transfer center. Some communities have greatly expanded the concept of bus transfer centers by using them as locations for vital health and human services, as well as other conveniences that improve the quality of life for the residents of the surrounding community. In addition, others have utilized the advantage of their Federal grants to build transit centers that provide opportunities for joint development that help to generate revenues that can be used for other public improvements in the immediate area, which help to attract additional private investment and positive development where there once was blight. This report highlights how four transit agencies used their bus transfer centers to not only improve their image and community relations, but to serve as catalysts for positive development in the surrounding areas.
Heavy Elements in the Lyman Alpha Forest: Abundances and Clustering at Z=3

For the purpose of studying the properties of heavy elements associated with the Lyman α forest, we observed the gravitational lens Q1422+2309. We used the HIRES instrument on the W.M. Keck telescope to obtain a high resolution, very high signal-to-noise spectrum of this z = 3.63 quasar; the spectrum covers wavelengths from below Lyman β up to the C IV emission line. Consistent with previous estimates, we find that a moderate fraction of the Ly α forest clouds have been enriched with heavy elements at a level significantly below solar abundance. However, unlike the fairly uniform distribution of Ly α forest lines, we show that the C IV absorption lines are clustered on large velocity scales.
WSAT(CC) — A Fast Local-Search ASP Solver

We describe WSAT(CC), a local-search solver for computing models of theories in the language of propositional logic extended by cardinality atoms. WSAT(CC) is a processing back-end for the logic PS + , a recently proposed formalism for answer-set programming.
Introduction to Cosmology

This second edition of Introduction to Cosmology is an exciting update of an award-winning textbook. It is aimed primarily at advanced undergraduate students in physics and astronomy, but is also useful as a supplementary text at higher levels. It explains modern cosmological concepts, such as dark energy, in the context of the Big Bang theory. Its clear, lucid writing style, with a wealth of useful everyday analogies, makes it exceptionally engaging. Emphasis is placed on the links between theoretical concepts of cosmology and the observable properties of the universe, building deeper physical insights in the reader. The second edition includes recent observational results, fuller descriptions of special and general relativity, expanded discussions of dark energy, and a new chapter on baryonic matter that makes up stars and galaxies. It is an ideal textbook for the era of precision cosmology in the accelerating universe.
Peace, Prosperity and Safety Nets in Timor‐Leste: Competing Priorities or Complementary Investments?

Social protection cash transfers are a vital policy instrument in tackling poverty, and provide an affordable, broad-based foundation for inclusive growth and social stability. Yet, international experience has shown that many countries have too many programs, but with coverage gaps and fragmented benefits which leave significant proportions of the population without adequate poverty relief or improved economic security. Here, we examine the design and poverty impact of the primary cash transfer programs in the Democratic Republic of Timor-Leste using nationally representative survey data. We find that though Timor-Leste's level of social protection expenditure is high by international standards, the overall poverty impact is incommensurate with spending levels. This is explained by the proportion of expenditure devoted to transfers to veterans, the large proportion of the poor population which is not reached by the current targeting mechanisms, and the small coverage and benefit level of the only program that explicitly targets poor households.
A Guide (and Advice) for Economists on the U. S. Junior Academic Job Market

This document describes the U. S. academic job market for new Ph.D. economists and offers advice on conducting an academic job search. It reports findings from published papers, describes practical details, and provides links to internet resources. Topics addressed include: preparing to go on the market, applying for academic jobs, interviewing, campus visits, offers and negotiating, diversity, and dual job searches. This paper is no longer available on this working paper archive. The paper is available as part of the September 2002 Job Openings for Economists and can be downloaded at: http://www.eco.utexas.edu/joe/
Characterization of Volatile Organic Compounds and Odorants Associated with Swine Barn Particulate Matter Using Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry-Olfactometry

dor, volatile organic compounds (VOCs) and other gases, and particulate matter (PM). Particulate matter has been proposed to be an important pathway for carrying odor. In this research, continuous PM sampling was conducted simultaneously with three collocated TEOM (tapered element oscicllating microbalance) analyzers inside a 1000- head swine finish barn located in central Iowa. Each TEOM was fitted with total suspended particulate (TSP), PM-10, PM-2.5 and PM-1 preseparators. Used filters were stored in 40 mL vials and transported to the laboratory. Carboxen/polydimethylsiloxane (PDMS) 85 μm solid phase microextraction (SPME) fibers were used to extract VOCs. Simultaneous chemical and olfactometry analyses of VOCs and odor associated with swine PM were completed using a gas chromatography-mass spectrometer-olfactometry (GCMS-O) system. Fifty VOCs categorized into nine chemical function groups were identified and confirmed with standards. Five of them are classified as hazardous air pollutants. VOCs were characterized with a wide range of molecular weight, boiling points, vapor pressures, water solubilities, odor detection thresholds, and atmospheric reactivities. All characteristic swine VOCs and odorants were present in PM and their abundance was proportional to PM size. However, the majority of VOCs and characteristic swine odorants were preferentially bound to smaller-size PM. The findings indicate that a significant fraction of swine odor can be carried by PM.
Dark Ages Radio Explorer mission: Probing the cosmic dawn

The period between the creation of the cosmic microwave background at a redshift of ∼1000 and the formation of the first stars and black holes that re-ionize the intergalactic medium at redshifts of 10–20 is currently unobservable. The baryonic component of the universe during this period is almost entirely neutral hydrogen, which falls into local regions of higher dark matter density. This seeds the formation of large-scale structures including the cosmic web that we see today in the filamentary distribution of galaxies and clusters of galaxies. The only detectable signal from these dark ages is the 21-cm spectral line of hydrogen, redshifted down to frequencies of approximately 10–100 MHz. Space-based observations of this signal will allow us to determine the formation epoch and physics of the first sources of ionizing radiation, and potentially detect evidence for the decay of dark matter particles. JPL is developing deployable low frequency antenna and receiver prototypes and calibration techniques to enable both all-sky spectral measurements of neutral hydrogen and ultimately to map the spatial distribution of the signal as a function of redshift. Such observations must be done from space because of Earth's ionosphere and ubiquitous radio interference. Both lunar orbiting and lunar surface based instruments are under development. A specific application of these technologies is the Dark Ages Radio Explorer (DARE) mission. This small Explorer class mission is designed to measure the sky-averaged hydrogen signal from the shielded region above the far side of the Moon. Observations of neutral hydrogen from the dark ages and the cosmic dawn (when light from the first generation of stars appeared) will provide unique and critical information on the early evolution of the universe. These data will complement ground-based radio observations of the final stages of intergalactic re-ionization at higher frequencies. DARE will also serve as a scientific percursor for space-based interferometry missions to image the distirbution of hydrogen during the cosmic dark ages.
A Dictionary-Based Approach for Estimating Shape and Spatially-Varying Reflectance

We present a technique for estimating the shape and reflectance of an object in terms of its surface normals and spatially-varying BRDF. We assume that multiple images of the object are obtained under fixed view-point and varying illumination, i.e, the setting of photometric stereo. Assuming that the BRDF at each pixel lies in the non-negative span of a known BRDF dictionary, we derive a per-pixel surface normal and BRDF estimation framework that requires neither iterative optimization techniques nor careful initialization, both of which are endemic to most state-of the-art techniques. We showcase the performance of our technique on a wide range of simulated and real scenes where we outperform competing methods.
Industrial policy after the East Asian crisis: From "outward-orientation" to new internal capabilities?

Before East Asia's financial meltdown in 1997, many believed that the entrepreneurial state contributed to the East Asian miracle by accelerating industrial development in a market-friendly way. The now-evolving international consensus on industrial policy emphasizes a hands-off approach, with activist government playing a reduced role and competition policy playing an important role. But implementing such a policy will require sophisticated new skills in public administration. Before East Asia's financial meltdown in the second half of 1997, there appeared to be prospects for an uneasy consensus on the East Asian miracle, a consensus that recognized the role of the entrepreneurial state in accelerating industrial development but emphasized the market-friendly nature of the state's interventions. After the financial crisis, East Asian policies and institutions are once again under scrutiny - for their failures rather than for their miracles. Mody finds that the prospects for a consensus that incorporated the East Asian experience were ill founded. East Asian policymakers emphasized growth through quantitative targets; price signals played a significant but secondary role. Mody illustrates these propositions by examining trade policy, industrial conglomerates, and the provision of physical infrastructure. The evolving international consensus on industrial policy, which predates the Asian crisis, emphasizes a hands-off approach in which an activist government plays a reduced role and competition policy plays an important role. But policies emphasizing greater competition and a level playing field - implicitly thought to require less government action - may require more government expertise, not less. If implementing a 10 percent export subsidy is difficult, consider the difficulty of determining whether a firm is exercising market power or restraining trade. So the prospect of governments stepping back may be unrealistic. The new consensus also proposes deep integration, or the adoption of uniform standards in such areas as competition policy and labor and environmental standards. For East Asia, the shift to the international consensus may be appropriate because government-driven growth has declined in intellectual respectability. Also, it may be time to consolidate the gains from the rapid trade-led growth by focusing on creating a stronger incentive structure for efficiently using resources. The current consensus is based on strong priors rather than on solid empirical evidence, however, and the dangers of international uniformity in policy are evident. This paper - a product of the Development Prospects Group - is part of a larger effort in the group to analyze trends in the global economy.
Inflation driven by unification energy

We examine the hypothesis that inflation is primarily driven by vacuum energy at a scale indicated by gauge coupling unification. Concretely, we consider a class of hybrid inflation models wherein the vacuum energy associated with a grand unified theory condensate provides the dominant energy during inflation, while a second ``inflaton'' scalar slow rolls. We show that it is possible to obtain significant tensor-to-scalar ratios while fitting the observed spectral index.
Secure and efficient protocols for iris and fingerprint identification

Recent advances in biometric recognition and the increasing use of biometric data prompt significant privacy challenges associated with the possible misuse, loss, or theft of biometric data. Biometric matching is often performed by two mutually distrustful parties, one of which holds one biometric image while the other owns a possibly large biometric collection. Due to privacy and liability considerations, neither party is willing to share its data. This gives rise to the need to develop secure computation techniques over biometric data where no information is revealed to the parties except the outcome of the comparison or search. To address the problem, in this work we develop and implement the first privacy-preserving identification protocol for iris codes. We also design and implement a secure protocol for fingerprint identification based on FingerCodes with a substantial improvement in the performance compared to existing solutions. We show that new techniques and optimizations employed in this work allow us to achieve particularly efficient protocols suitable for large data sets and obtain notable performance gain compared to the state-of-the-art prior work.
Information Theoretic Learning

INTRODUCTION Learning systems depend on three interrelated components: topologies, cost/performance functions, and learning algorithms. Topologies provide the constraints for the mapping, and the learning algorithms offer the means to find an optimal solution; but the solution is optimal with respect to what? Optimality is characterized by the criterion and in neural network literature, this is the least addressed component, yet it has a decisive influence in generalization performance. Certainly, the assumptions behind the selection of a criterion should be better understood and investigated. Traditionally, least squares has been the benchmark criterion for regression problems; considering classification as a regression problem towards estimating class posterior probabilities, least squares has been employed to train neural network and other classifier topologies to approximate correct labels. The main motivation to utilize least squares in regression simply comes from the intellectual comfort this criterion provides due to its success in traditional linear least squares regression applications – which can be reduced to solving a system of linear equations. For nonlinear regression, the assumption of Gaussianity for the measurement error combined with the maximum likelihood principle could be emphasized to promote this criterion. In nonparametric regression, least squares principle leads to the conditional expectation solution, which is intuitively appealing. Although these are good reasons to use the mean squared error as the cost, it is inherently linked to the assumptions and habits stated above. Consequently, there is information in the error signal that is not captured during the training of nonlinear adaptive systems under non-Gaussian distribution conditions when one insists on secondorder statistical criteria. This argument extends to other linear-second-order techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and canonical correlation analysis (CCA). Recent work tries to generalize these techniques to nonlinear scenarios by utilizing kernel techniques or other heuristics. This begs the question: what other alternative cost functions could be used to train adaptive systems and how could we establish rigorous techniques for extending useful concepts from linear and second-order statistical techniques to nonlinear and higher-order statistical learning methodologies?
A large-scale relativistic configuration-interaction calculation for the 4s–4p transition energies of copperlike heavy ions

The 4s-4p and 4p-4d transition energies for high-Z copperlike ions are calculated using the relativistic configuration-interaction (RCI) method. Mass polarization (MP) and quantum electrodynamic (QED) corrections are also evaluated. For the 4s-4p transitions, the present RCI energies agree very well with results from the relativistic many-body perturbation theory. With QED and MP corrections included, our total transition energies are in very good agreement with recent high precision measurements.
Generalization of Boltzmann Equilibration Dynamics

We propose a novel approach in the study of transport phenomena in dense systems or systems with long range interactions where multiple particle interactions must be taken into consideration. Within Boltzmann’s kinetic formalism, we study the influence of other interacting particles in terms of a random distortion of energy and momentum conservation occurring when multi-particle interactions are considered as binary collisions. Energy and momentum conservation still holds exactly but not in each model binary collision. We show how this new system differs from the Boltzmann system and we note that our approach naturally explains the emergence of Tsallis-like equilibrium statistics in physically relevant systems in terms of the long since neglected physics of interacting and dense systems.
The Mesozoic ERA of Relativistic Heavy Ion Physics and Beyond

In order to understand how matter 15 billion years ago in the form of quarks, gluons and leptons at a temperature of 2 × 1012 °K evolved to become today’s Universe, the goal of relativistic and ultra-relativistic heavy ion physics is to understand the equation of state of nuclear, hadronic and partonic matter. This quest is of cross-disciplinary interest. The phase transition from partonic matter to hadronic matter tens of micro-seconds after the beginning of the universe is of interest to cosmology. Fluctuations during this phase transition would influence nucleosynthesis and the understanding of baryonic inhomogeneities in the universe. The nuclear matter equation of state, which describes the incompressibility of nuclear matter, governs neutron star stability. It determines the possible existence of strange quark matter stars and the dynamics of supernova expansion in astrophysics. The existence of collective nuclear phenomena in nuclear physics is also determined by the nuclear equation of state. In relativistic heavy ion collisions collective nuclear flow has been observed and is being studied extensively to obtain a better understanding of the incompressibility of nuclear matter. In high energy nuclear and particle physics, production and excitations of hadronic final states have been studied in detail and are important to an overall understanding of the equation of state of nuclear matter at finite temperature. The possibility in ultra-relativistic heavy ion collisions to create and study highly excited hadronic and partonic degrees of freedom provides a unique opportunity for understanding the behavior of nuclear, hadronic and partonic matter. Study of the QCD vacuum, of particular interest in particle physics, would provide a better understanding of symmetry-breaking mechanisms and the origins of the masses of the various quarks and particles. Creation and study of the quark-gluon plasma, an excitation of the QCD vacuum, is the goal of physicists in the new field of ultra-relativistic heavy ion collisions.
Avoiding dark energy with 1/r modifications of gravity

Scalar quintessence seems epicyclic because one can choose the potential to reproduce any cosmology (I review the construction) and because the properties of this scalar seem to raise more questions than they answer. This is why there has been so much recent interest in modified gravity. I review the powerful theorem of Ostrogradski which demonstrates that the only potentially stable, local modification of general relativity is to make the Lagrangian an arbitrary function of the Ricci scalar. Such a theory can certainly reproduce the current phase of cosmic acceleration without Dark Energy. However, this explanation again seems epicyclic in that one can construct a function of the Ricci scalar to support any cosmology (I give the technique). Models of this form are also liable to problems in the way they couple to matter, both in terms of matter’s impact upon them and in terms of the long range gravitational force they predict. Because of these problems my own preference for avoiding Dark Energy is to bypass Ostrogradski’s theorem by considering the fully nonlocal effective action built up by quantum gravitational processes during the epoch of primordial inflation.
On quotients of Banach spaces having shrinking unconditional bases

It is proved that if a Banach space $Y$ is a quotient of a Banach space having a shrinking unconditional basis, then every normalized weakly null sequence in $Y$ has an unconditional subsequence. The proof yields the corollary that every quotient of Schreier's space is $c_o$-saturated.
Managing Query Compilation Memory Consumption to Improve DBMS Throughput.

While there are known performance trade-offs between database page buffer pool and query execution memory allocation policies, little has been written on the impact of query compilation memory use on overall throughput of the database management system (DBMS). We present a new aspect of the query optimization problem and discuss a solution implemented in Microsoft SQL Server 2005. The solution provides stable throughput for a range of workloads even when memory requests outstrip the ability of the hardware to service those requests.
The magnetic topology of the plasmoid flux rope in a MHD simulation of magnetotail reconnection

On the basis of a 3D MHD simulation, the magnetic topology of a plasmoid that forms by a localized reconnection process in a magnetotail configuration (including a net dawn-dusk magnetic field component B sub y N is discussed. As a consequence of B sub y N not equalling 0, the plasmoid assumes a helical flux rope structure rather than an isolated island or bubble structure. Initially all field lines of the plasmoid flux rope remain connected with the earth, while at later times a gradually increasing amount of flux tubes becomes separated, connecting to either the distant boundary or to the flank boundaries. In this stage, topologically different flux tubes become tangled and wrapped around each other, consistent with predictions on the basis of an ad hoc plasmoid model.
Quantum Causal Networks.

Intervention theories of causality define a relationship as causal if appropriately specified interventions to manipulate a putative cause tend to produce changes in the putative effect. Interventionist causal theories are commonly formalized by using directed graphs to represent causal relationships, local probability models to quantify the relationship between cause and effect, and a special kind of conditioning operator to represent the effects of interventions. Such a formal model represents a family of joint probability distributions, one for each allowable intervention policy. This paper interprets the von Neumann formalization of quantum theory as an interventionist theory of causality, describes its relationship to interventionist theories popular in the artificial intelligence literature, and presents a new family of graphical models that extends causal Bayesian networks to quantum systems.
Socially Enhanced Indexing: Applying Enhanced Indexing Techniques to Socially Responsible Investment

While socially responsible investing (SRI) is large and growing, current SRI offerings have shortcomings—including style biases, excessive expenses, tracking error and an incomplete menu of screens. We propose using factor-based models in SRI-screened investment universes to create “socially enhanced indexing” or SRI enhanced indexing. Using commercial software and readily available portfolio screens, socially enhanced indexing offers mass customization of values-based investment programs on the cheap.
Socially-Optimal Design of Service Exchange Platforms with Imperfect Monitoring

We study the design of service exchange platforms in which long-lived anonymous users exchange services with each other. The users are randomly and repeatedly matched into pairs of clients and servers, and each server can choose to provide high-quality or low-quality services to the client with whom it is matched. Since the users are anonymous and incur high costs (e.g., exert high effort) in providing high-quality services, it is crucial that the platform incentivizes users to provide high-quality services. Rating mechanisms have been shown to work effectively as incentive schemes in such platforms. A rating mechanism labels each user by a rating, which summarizes the user's past behaviors, recommends a desirable behavior to each server (e.g., provide higher-quality services for clients with higher ratings), and updates each server's rating based on the recommendation and its client's report on the service quality. Based on this recommendation, a low-rating user is less likely to obtain high-quality services, thereby providing users with incentives to obtain high ratings by providing high-quality services.However, if monitoring or reporting is imperfect—clients do not perfectly assess the quality or the reports are lost—a user's rating may not be updated correctly. In the presence of such errors, existing rating mechanisms cannot achieve the social optimum. In this article, we propose the first rating mechanism that does achieve the social optimum, even in the presence of monitoring or reporting errors. On one hand, the socially-optimal rating mechanism needs to be complicated enough, because the optimal recommended behavior depends not only on the current rating distribution, but also (necessarily) on the history of past rating distributions in the platform. On the other hand, we prove that the social optimum can be achieved by “simple” rating mechanisms that use binary rating labels and a small set of (three) recommended behaviors. We provide design guidelines of socially-optimal rating mechanisms and a low-complexity online algorithm for the rating mechanism to determine the optimal recommended behavior.
Do poor countries really need more IT ? the role of relative prices and industrial composition

Conventional wisdom suggests too little information and communication technologies (ICT) in poor countries. Indeed, within 70 countries at various levels of development, there is a positive relationship between income per capita and the capital share of ICT. While this regularity is consistent with explanations based on technology adoption lags and ICT-labor substitutability, there is little empirical support for these hypotheses. Instead, the paper establishes that this regularity can be fully accounted for by (a) relatively higher ICT prices in low-income countries and (b) industrial composition.
Group Embeddings with Algorithmic Properties

We show that every countable group H with solvable word problem can be subnormally embedded into a 2-generated group G which also has solvable word problem. Moreover, the membership problem for H < G is also solvable. We also give estimates of time and space complexity of the word problem in G and of the membership problem for H < G.
Direct Search for Dark Matter Particles with Very Large Detectors

We briefly discuss the expected level of supersymmetric dark matter crosssections as a reference for dark matter detectors. We then discuss the current ZEPLIN II program as a prototype of large liquid Xenon detectors. Cryoarray is a possible cryogenic detector. Finally we discuss ZEPLIN IV and other one ton liquid Xenon detectors and the limiting backgrounds for such detectors.
Energy poverty in rural and urban India : are the energy poor also income poor ?

Energy poverty is a frequently used term among energy specialists, but unfortunately the concept is rather loosely defined. Several existing approaches measure energy poverty by defining an energy poverty line as the minimum quantity of physical energy needed to perform such basic tasks as cooking and lighting. This paper proposes an alternative measure that is based on energy demand. The energy poverty line is defined as the threshold point at which energy consumption begins to rise with increases in household income. This approach was applied to cross-sectional data from a comprehensive 2005 household survey representative of both urban and rural India. The findings suggest that in rural areas some 57 percent of households are energy poor, versus 22 percent that are income poor. For urban areas the energy poverty rate is 28 percent compared with 20 percent that are income poor. Policies to reduce energy poverty would include support for rural electrification, the promotion of more modern cooking fuels, and encouraging greater adoption of improved biomass stoves. A combination of these programs would play a significant role in reducing energy poverty in rural India.
Efficiency consideration of DC link soft-switching inverters for motor drive applications

This paper critically evaluates efficiencies of DC link soft-switching inverters suitable for motor drive applications. Three types of DC link soft-switching inverters, including actively clamped resonant DC link inverter, clamped-mode resonant pole-inverter, and auxiliary switch commutated resonant pole inverter, were studied. Loss separation methods for evaluating soft-switching inverters were developed using a simplified device model. The developed methods were applied to a 50 kW induction motor and its associated inverters for efficiency evaluation under different speed and torque conditions. The evaluation results were then compared with those of the hard-switching pulse-width-modulation (PWM) inverter. The clamped-mode soft-switching inverter, although saving the switching losses, shows poorest efficiency because its resonant circuit associated circulation losses are very substantial. The actively clamped resonant DC link and auxiliary switch commutated inverters show the same level of substantial efficiency improvement over the hard switching PWM inverter. The actual energy saving, however, can be further improved by optimization of circuit components and selection of the inverter switching algorithm in actual implementations. >
Dynamically driven star formation in models of NGC 7252

We present new dynamical models of the merger remnant NGC 7252 which include star formation simulated according to various phenomenological rules. By using interactive software to match our model with the observed morphology and gas velocity field, we obtain a consistent dynamical model for NGC 7252. In our models, this proto-elliptical galaxy formed by the merger of two similar gas-rich disc galaxies which fell together with an initial pericentric separation of ∼2 disc scalelengths approximately 620 Myr ago. Results from two different star formation rules - density-dependent and shock-induced - show significant differences in star formation during and after the first passage. Shock-induced star formation yields a prompt and wide-spread starburst at the time of first passage, while density-dependent star formation predicts a more slowly rising and centrally concentrated starburst. A comparison of the distributions and ages of observed clusters with results of our simulations favours shock-induced mechanism of star formation in NGC 7252. We also present simulated colour images of our model of NGC 7252, constructed by incorporating population synthesis with radiative transfer and dust attenuation. Overall, the predicted magnitudes and colours of the models are consistent with observations, although the simulated tails are fainter and redder than observed. We suggest that a lack of star formation in the tails, reflected by the redder colours, is due to an incomplete description of star formation in our models rather than insufficient gas in the tails.
Policy, Technology, and Efficiency of Brazilian Agriculture

The Brazilian agricultural sector has been transformed from a traditional system of production with low use of modern technologies to a world agricultural leader. That transformation occurred as the country moved away from import-substitution policies — which nurtured domestic industrial development at the expense of agriculture — toward market-oriented policy reforms. These reforms included openness to foreign trade and foreign investment and the use of new technologies, which led to a new growth pattern. To evaluate that transformation, the authors use agricultural censuses spanning 1985-2006 to characterize Brazilian total factor productivity growth, decomposing that growth into technical and efficiency changes. This report presents the findings of a study that focuses on the effect of Brazil’s science and technology investments and other public policies on farm production. The findings indicate that agricultural research benefits have been most rapidly adopted by the most efficient farms, widening the productivity gap between these farms and average farms. That gap, however, has been narrowed through other public policies, such as rural credit and infrastructure investments, that favor average producers.
Fast planar correlation clustering for image segmentation

We describe a new optimization scheme for finding high-quality clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the optimal correlation clustering that are typically fast to compute and tight in practice. We demonstrate our algorithm on the problem of image segmentation where this approach outperforms existing global optimization techniques in minimizing the objective and is competitive with the state of the art in producing high-quality segmentations.
A Tale of Two Solvers: Eager and Lazy Approaches to Bit-Vectors

The standard method for deciding bit-vector constraints is via eager reduction to propositional logic. This is usually done after first applying powerful rewrite techniques. While often efficient in practice, this method does not scale on problems for which top-level rewrites cannot reduce the problem size sufficiently. A lazy solver can target such problems by doing many satisfiability checks, each of which only reasons about a small subset of the problem. In addition, the lazy approach enables a wide range of optimization techniques that are not available to the eager approach. In this paper we describe the architecture and features of our lazy solver (LBV). We provide a comparative analysis of the eager and lazy approaches, and show how they are complementary in terms of the types of problems they can efficiently solve. For this reason, we propose a portfolio approach that runs a lazy and eager solver in parallel. Our empirical evaluation shows that the lazy solver can solve problems none of the eager solvers can and that the portfolio solver outperforms other solvers both in terms of total number of problems solved and the time taken to solve them.
Informality in Latin America and the Caribbean

This paper studies the causes and consequences of informality and applies the analysis to countries in Latin America and the Caribbean. It starts with a discussion on the definition and measures of informality, as well as on the reasons why widespread informality should be of great concern. The paper analyzes informality's main determinants, arguing that informality is not single-caused but results from the combination of poor public services, a burdensome regulatory regime, and weak monitoring and enforcement capacity by the state. This combination is especially explosive when the country suffers from low educational achievement and features demographic pressures and primary production structures. Using cross-country regression analysis, the paper evaluates the empirical relevance of each determinant of informality. It then applies the estimated relationships to most countries in Latin America and the Caribbean in order to assess the country-specific relevance of each proposed mechanism.
Situations, summaries and model objects

The overall goal of the top-level layer of IDL, a Lisp-based statistical programming environment, is to package together implementation level components into higher-level computational abstractions that better reflect the domain of interest. This is accomplished through extensive use of object-oriented programming.
Hyperelliptic Szpiro Inequality

We generalize the classical Szpiro inequality to the case of a semistable family of hyperelliptic curves. We show that for a semistable symplectic Lefschetz fibration of hyperelliptic curves of genus g, the number N of nonseparating vanishing cycles and the number D of singular fibers satisfy the inequality N ≤ (4g + 2)D.
A Data Science Course for Undergraduates: Thinking With Data

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be nontraditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students to a variety of techniques to analyze small, neat, and clean datasets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that are considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The ...
Do Large Modern Retailers Pay Premium Wages

With malls, franchise strips, and big-box retailers increasingly dotting the landscape, many are concerned that U.S. middle-class jobs in manufacturing are being replaced by minimum-wage jobs in re...
EigenGP: Gaussian process models with adaptive eigenfunctions

Gaussian processes (GPs) provide a nonparametric representation of functions. However, classical GP inference suffers from high computational cost for big data. In this paper, we propose a new Bayesian approach, EigenGP, that learns both basis dictionary elements--eigenfunctions of a GP prior--and prior precisions in a sparse finite model. It is well known that, among all orthogonal basis functions, eigenfunctions can provide the most compact representation. Unlike other sparse Bayesian finite models where the basis function has a fixed form, our eigenfunctions live in a reproducing kernel Hilbert space as a finite linear combination of kernel functions. We learn the dictionary elements-- eigenfunctions--and the prior precisions over these elements as well as all the other hyperparameters from data by maximizing the model marginal likelihood. We explore computational linear algebra to simplify the gradient computation significantly. Our experimental results demonstrate improved predictive performance of EigenGP over alternative sparse GP methods as well as relevance vector machines.
How do special economic zones and industrial clusters drive China's rapid development ?

In the past 30 years, China has achieved phenomenal economic growth, an unprecedented development miracle in human history. How did China achieve this rapid growth? What have been its key drivers? And, most important, what can be learned from China's success? Policy makers, business people, and scholars all over the world continue to debate these topics, but one thing is clear: the numerous special economic zones and industrial clusters that emerged after the country's reforms are without doubt two important engines of China's remarkable development. The special economic zones and industrial clusters have made crucial contributions to China's economic success. Foremost, the special economic zones (especially the first several) successfully tested the market economy and new institutions and became role models for the rest of the country to follow. Together with the numerous industrial clusters, the special economic zones have contributed significantly to gross domestic product, employment, exports, and attraction of foreign investment. The special economic zones have also played important roles in bringing new technologies to China and in adopting modern management practices. This study briefly summarizes the development experiences of China's special economic zones and industrial clusters (their formation, success factors, challenges, and possible areas or measures for policy intervention), based on case studies, interviews, field visits, and extensive reviews of the existing literature in an attempt to benefit other developing countries as well as the broader development community.
Chronic Obstructive Pulmonary Disease

The second edition of this text is the first revision in the well-known series, "Lung Biology in Health and Disease." In the updated version, 224 pages, eight contributors, and countless references have been added. The combined efforts of the many well-recognized authors result in a superb sum­ mary of the subject of chronic obstructive pulmo­ nary disease (COPD). This monograph provides many reference-book-level details but still re­ mains relatively easy to read. The preface (re­ printed in part from the first edition) indicates that the "message" was assembled by clinicians and is directed toward interested physicians, nurses, and therapists. The appeal to such a broad audience causes parts of the book to be much more appropriate for one of the aforementioned groups to the exclusion of the others. This criticism was also noted in a review of the first edition (see Mayo Clin Proc 53:827,1978). The first four chapters present general informa­ tion concerning definitions, risk factors, epidemiologic features, prevalence, outcome, and prognosis in patients with advanced COPD. The subsequent two chapters are new additions to the text that extensively describe proposed mechanisms and pathologic correlates of COPD. This detailed ma­ terial will likely be appreciated by only the most sophisticated readers. The second half of the book deals with various treatment modalities, complications, and epiphenomena in patients with COPD. The longest chapter, which is devoted to respiratory and pharmacologic therapy, has 44 pages of references and discusses many of the new medications including selective ys-agonists, calcium channel blockers, anticholinergics, and respiratory stimulants. Except for a short section that covers neuropsychiatric complications in patients with COPD, all subsequent material was contributed by Dr. Petty. Careful attention is given to outpatient oxygen therapy, pulmonary rehabilitation, and acute re­ spiratory failure, but at times the discussion de­ pends primarily on anecdotal information. The scope of the remaining chapters is more general and becomes more suitable for supportive-care personnel. In summary, this new edition offers something for everyone involved in the care of patients with COPD. The price seems somewhat excessive, and numerous spelling errors and an entirely mis­ placed paragraph detract from the quality of the book. Selection of specific topics from the table of contents based on personal interest and perusal of the corresponding portion of text might be the best way to determine the usefulness of this volume.