cssi/SciSciGPT-SciSciNet huggingface dataset:
Quantitative, Image-Based Phenotyping Methods Provide Insight into Spatial and Temporal Dimensions of Plant Disease.

Plant disease symptoms exhibit complex spatial and temporal patterns that are challenging to quantify. Image-based phenotyping approaches enable multidimensional characterization of host-microbe interactions and are well suited to capture spatial and temporal data that are key to understanding disease progression. We applied image-based methods to investigate cassava bacterial blight, which is caused by the pathogen Xanthomonas axonopodis pv. manihotis (Xam). We generated Xam strains in which individual predicted type III effector (T3E) genes were mutated and applied multiple imaging approaches to investigate the role of these proteins in bacterial virulence. Specifically, we quantified bacterial populations, water-soaking disease symptoms, and pathogen spread from the site of inoculation over time for strains with mutations in avrBs2, xopX, and xopK as compared to wild-type Xam. ∆avrBs2 and ∆xopX both showed reduced growth in planta and delayed spread through the vasculature system of cassava. ∆avrBs2 exhibited reduced water-soaking symptoms at the site of inoculation. In contrast, ∆xopK exhibited enhanced induction of disease symptoms at the site of inoculation but reduced spread through the vasculature. Our results highlight the importance of adopting a multipronged approach to plant disease phenotyping to more fully understand the roles of T3Es in virulence. Finally, we demonstrate that the approaches used in this study can be extended to many host-microbe systems and increase the dimensions of phenotype that can be explored.
Distributed Job Allocation for Large-Scale Manycores

Contemporary operating systems heavily rely on single system images with shared memory constructs that may not scale well to large core counts. We consider the challenge of distributed job allocation, where each job is comprised of a set of tasks to be mapped to disjoint cores. A naive solution performing fragmented allocations may quickly escalate to deadlocks, where jobs hold and wait for cores in circular dependencies. To tackle these challenges, we propose a deadlock free distributed job allocation protocol. We have devised two policies for avoiding deadlocks, namely active cancellation and sequencer-based atomic broadcast. The protocol and the two policies have been implemented and evaluated on a Tilera TilePro64 processor with 64 cores on a single socket. Results show sparse job allocations to incur lower overhead for active cancellation while sequencer-based atomic broadcast has less overhead for denser allocations.
The varying income effects of weather variation : initial insights from rural Vietnam

To estimate the impact of weather on rural income changes over time, this study combines data from the panel subsample of the latest Vietnam Household Living Standard Surveys 2010, 2012, and 2014 and gridded weather data from the Climate Research Unit. The analyses show: (i) crop cultivation, livestock management, forestry and fishing activities, and agricultural wages remain important income sources in rural Vietnam—especially for poorer households; (ii) rural communes are exposed to substantial inter- and intra-annual weather variation, as measured by annual, seasonal, abnormal, and extreme weather conditions and weather events; and (iii) these types of weather variation are indeed related to income variation. In particular, warmer temperatures and heat extremes can have negative income effects in all climate contexts and for all socioeconomic groups and most income activities. Only staple crops, forestry, and fishing seem to be less sensitive to hotter conditions. The effects of rainfall are more difficult to generalize. Some findings indicate that more rainfall is beneficial in drier places but harmful in wetter places. Interestingly, the incomes of poorer households seem to be negatively affected by wetter conditions, while those of wealthier households are more impacted by drier conditions. An increase in rainfall levels and flood conditions between 2012 and 2014, which were relatively wet years, is related to reduced income growth between these two years. Altogether these findings suggests that greater attention has to be paid to making rural livelihoods more resilient to weather variation which, is very likely to increase because of climate change.
Composite Icosahedron/Cube Endohedral Clusters in Rh2Cd15

We present Rh2Cd15, the first binary compound in the Rh–Cd system. It is based on transition-metal-embedded (Rh@Cd11 and Rh@Cd12) endohedral Cd clusters that are single- and double-capped IC10 composite icosahedra/cubes. We demonstrate the structural connections between the clusters. On the basis of the analysis of atomic interactions and electron counting, 50 electrons per Rh2Cd15 is postulated to be the boundary between bonding and antibonding interactions. This understanding is supported by electronic structure calculations showing that the electron count for Rh2Cd15 (48 electrons per Rh2Cd15) is located close to a deep pseudogap in the electronic density of states at 50 electrons per formula unit, which we postulate is an important factor in determining the new compound’s stability.
Blind equalization and estimation of digital communication FIR channels using cumulant matching

We consider the problem of blind estimation and equalization of digital communication FIR (finite impulse response) channels. The channel parameters are estimated by nonlinear batch optimization of a quadratic cumulant matching criterion involving second and fourth order cumulants of the received data. A new algorithm is proposed for (asymptotically) globally convergent, linear estimation of the FIR channel parameters where the FIR model order is not necessarily known. The nonlinear cumulant matching algorithm is initialized by the linear parameter estimator. The estimated channel impulse response is then used to construct a linear equalizer. Two illustrative simulation examples using 4, 16 and 64-QAM signals are presented. >
Intelligent biology and medicine in 2015: Advancing interdisciplinary education, collaboration, and data science

We summarize the 2015 International Conference on Intelligent Biology and Medicine (ICIBM 2015) and the editorial report of the supplement to BMC Genomics. The supplement includes 20 research articles selected from the manuscripts submitted to ICIBM 2015. The conference was held on November 13–15, 2015 at Indianapolis, Indiana, USA. It included eight scientific sessions, three tutorials, four keynote presentations, three highlight talks, and a poster session that covered current research in bioinformatics, systems biology, computational biology, biotechnologies, and computational medicine.
The capabilities driving participation in global value chains

Global value chains have altered the nature of global trade and offer significant opportunities for developing countries to expand exports, access technology, and raise productivity. Policy makers rightly seek to understand what it takes to participate in global value chains. In practice, this means understanding what it takes to attract lead firms and upgrade to higher value-added activities. Recent literature has pointed to a range of underlying characteristics that may drive participation in global value chains. Using a modified factor-content methodology, this paper shows that proximity to markets, efficient logistics, and strength of institutions are among the most important capabilities. However, the paper also shows that each sector has a unique mix of capability requirements. Fixed structural characteristics limit the range of sectoral possibilities for a given country, but, by reducing policy-related gaps, a country may be able to increase its competitiveness for participating in global value chains. The paper applies the methodology to Southern African Customs Union countries, and demonstrates that, by filling gaps in underlying capabilities, these countries could increase participation in certain global value chain sectors.
Role of SNARE proteins in HIV-1 assembly and release

August 20-22, 2012 R assembly is a complex process that requires the orchestrated participation of viral components and host-cell factors. The concerted movement of different viral proteins to specific sites in the plasma membrane allows for virus particle assembly and ultimately budding and maturation of infectious virions. The soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins constitute the minimal machinery that catalyzes the fusion of intracellular vesicles with the plasma membrane, thus regulating protein trafficking. Using siRNA and dominant negative approaches we demonstrate here that generalized disruption of the host SNARE machinery results in a significant reduction in human immunodeficiency virus type 1 (HIV-1) and equine infectious anemia virus particle production. Further analysis of the mechanism involved revealed a defect at the level of HIV-1 Gag localization to the plasma membrane. Our findings demonstrate for the first time a role of SNARE proteins in HIV-1 assembly and release, likely by affecting cellular trafficking pathways required for Gag transport and association with the plasma membrane.
ACETABULAR OPTIONS: FROM MONOBLOC TO MODULAR

Concerns with long-term fixation of cemented all-poly sockets have led to the near-universal acceptance of cementless fixation of the acetabulum. The sockets most often utilised today are bone-ingrowth porous metal, made of titanium, cobalt-chrome, or hyper-porous materials such as tantalum trabecular metal. Porous ingrowth sockets are extraordinarily reliable, with reports of 99–100% achieving stable fixation. The problem with sockets is not the fixation, but the bearing. Modular polyethylene liners are most commonly used, but wear on the primary bearing surface as well as backside wear can lead to osteolysis and late failure. Holes in the socket designed for supplemental screws act as a conduit for particles to enter the ace tabular bone where a cascade of inflammatory responses activate osteoclastic activity and bone destruction. One alternative is a socket without holes, using spikes or fins for additional stability. The concern here is that the socket may not be completely seated and the surgeon is unaware. Another alternative is the use of a monoblock acetabular component with the polyethylene molded to the metal shell. While excellent results are reported, the downside is an inability to exchange the polyethylene. Resurfacing sockets are also monoblocks, and some offer peripheral supplemental screws. Here again, incomplete seating may occur, leaving a gap behind the dome. Serial x-rays almost always reveal that this gap gradually fills in. Improvements in polyethylene, better locking mechanisms and alternative bearing materials such as ceramic and metal are the final steps to ensure acetabular durability.
Latent Sequence Decompositions

We present the Latent Sequence Decompositions (LSD) framework. LSD decomposes sequences with variable lengthed output units as a function of both the input sequence and the output sequence. We present a training algorithm which samples valid extensions and an approximate decoding algorithm. We experiment with the Wall Street Journal speech recognition task. Our LSD model achieves 12.9% WER compared to a character baseline of 14.8% WER. When combined with a convolutional network on the encoder, we achieve 9.6% WER.
Super-Fast MST Algorithms in the Congested Clique Using o(m) Messages

In a sequence of recent results (PODC 2015 and PODC 2016), the running time of the fastest algorithm for the minimum spanning tree (MST) problem in the Congested Clique model was first improved to O(log(log(log(n)))) from O(log(log(n))) (Hegeman et al., PODC 2015) and then to O(log^*(n)) (Ghaffari and Parter, PODC 2016). All of these algorithms use Theta(n^2) messages independent of the number of edges in the input graph. This paper positively answers a question raised in Hegeman et al., and presents the first "super-fast" MST algorithm with o(m) message complexity for input graphs with m edges. Specifically, we present an algorithm running in O(log^*(n)) rounds, with message complexity ~O(sqrt{m * n}) and then build on this algorithm to derive a family of algorithms, containing for any epsilon, 0 < epsilon <= 1, an algorithm running in O(log^*(n)/epsilon) rounds, using ~O(n^{1 + epsilon}/epsilon) messages. Setting epsilon = log(log(n))/log(n) leads to the first sub-logarithmic round Congested Clique MST algorithm that uses only ~O(n) messages. Our primary tools in achieving these results are (i) a component-wise bound on the number of candidates for MST edges, extending the sampling lemma of Karger, Klein, and Tarjan (Karger, Klein, and Tarjan, JACM 1995) and (ii) Theta(log(n))-wise-independent linear graph sketches (Cormode and Firmani, Dist. Par. Databases, 2014) for generating MST candidate edges.
Crowdsourcing the Discovery of Software Repositories in an Educational Environment

In software repository mining, it’s important to have a broad representation of projects. In particular, it may be of interest to know what proportion of projects are public. Discovering public projects can be easily parallelized but not so easy to automate due to a variety of data sources. We evaluate the research and educational potential of crowdsourcing such research activity in an educational setting. Students were instructed on three ways of discovering the projects and assigned a task to discover the list of public projects from top 45 forges with each student assigned to one forge. Students had to discover as many of the projects as they could using the method of their choice and provide a market-research report for a ctional customer based on the attribute they selected. A subset of the results was sampled and veried for accuracy. We found that many of the public forges do not host public projects, that a substantial fraction of forges do not provide APIs and the APIs vary dramatically among the remaining forges. Some forges have been discontinued and others renamed, making the discovery task into an archaeological exercise. The students’ ndings raise a number of new research questions and demonstrate the teaching potential of the approach. The accuracy of the results obtained, however, was low, suggesting that crowdsourcing would require at least two or more likely a larger number of investigators per forge or a better way to gauge investigator skill. We expect that these lessons will be helpful in creating education-sourcing eorts in software data discovery.
OPTIMAL GLENOID FIXATION REQUIRES CEMENT – AFFIRMS

Total shoulder arthroplasty with a cemented polyethylene glenoid is a proven technology with excellent survivorship, even in young, active patients followed for over 20 years. Despite numerous efforts to avoid cement fixation, uncemented technology has failed to improve survival even in the short and middle term, and in fact has usually yielded worse results at early follow-up – hardly promising that further follow will reveal an advantage. There have been two approaches – metal-backed designs and all-poly “magic peg” designs. There are two major problems with metal backing: there can be technical issues and fretting failure with snap-fit mechanisms, and the stiff metal backing induces early polyethylene wear. Although some early studies report cautious optimism, larger series with careful, long follow-up have reported poor results. Some have attempted to use less-stiff metal but the value is still unclear. While early results from all-poly uncemented designs have less early failure, the wisdom of having long-term close apposition of polyethylene with bone, the large bone-removal for most large-peg designs, and the unknown long-term mechanical failure mechanisms suggest caution. Further, early work on eliminating cement was prompted by a desire to avoid “cement disease”, while more recent studies across multiple joints suggest that loosening and bone resorption are more likely due to polyethylene particles than any effect of the cement. Since most uncemented designs stiffen the basic polyethylene surface with larger pegs or metal-backing, the wisdom of the entire effort is debatable. In 2016 optimal glenoid fixation requires cement.
The Hawaii Infrared Parallax Program. II. Young Ultracool Field Dwarfs

(Abridged) We present a large, uniform analysis of young (~10-150 Myr) ultracool dwarfs, based on new high-precision IR parallaxes for 68 objects. We find that low-gravity (VL-G) late-M and L dwarfs form a continuous sequence in IR color-magnitude diagrams, separate from field objects and current theoretical models. VL-G objects also appear distinct from young substellar (brown dwarf and exoplanet) companions, suggesting the two populations have a different range of physical properties. In contrast, at the L/T transition, young, old, and peculiar objects all span a narrow range in near-IR absolute magnitudes. At a given spectral type, the IR absolute magnitudes of young objects can be offset from ordinary field dwarfs, with the largest offsets occurring in the Y and J bands for late-M dwarfs (brighter than the field) and mid/late-L dwarfs (fainter than the field). Overall, low-gravity (VL-G) objects have the most uniform photometric behavior while intermediate-gravity (INT-G) objects are more diverse, suggesting a third governing parameter beyond spectral type and gravity class. We examine the moving group memberships for all young ultracool dwarfs with parallaxes, changing/refuting the status of 23 objects and fortifying the status of another 28 objects. We use our resulting age-calibrated sample to establish empirical young isochrones and find a declining frequency of VL-G objects relative to INT-G objects with increasing age. Notable objects in our sample include high-velocity INT-G objects; very red, late-L dwarfs with high surface gravities; candidate disk-bearing members of the MBM20 cloud and beta Pic moving group; and very young distant interlopers. Finally, we provide a comprehensive summary of the absolute magnitudes and spectral classifications of 102 young ultracool dwarfs, found in the field and as substellar companions to young stars.
Electrical double layer properties of spherical oxide nanoparticles

The accurate characterization of the electrical double layer properties of nanoparticles is of fundamental importance for optimizing their physicochemical properties for specific biotechnological and biomedical applications. In this article, we use classical solvation density functional theory and a surface complexation model to investigate the effects of the pH and the nanoparticle size on the structural and electrostatic properties of an electrolyte solution surrounding a spherical silica oxide nanoparticle. The formulation has been particularly useful for identifying dominant interactions governing the ionic driving force at a variety of pH levels and nanoparticle sizes. As a result of the energetic interplay displayed between electrostatic potential, ion-ion correlation and particle crowding effects on the nanoparticle surface titration, rich, non-trivial ion density profiles and mean electrostatic potential behavior have been found.
Comparison of Ramsauer and Optical Model Neutron Angular Distributions

In a recent paper it has been shown that the nuclear Ramsauer model does not do well in representing details of the angular distribution of neutron elastic scattering for incident energies of less than 60 MeV for {sup 208}Pb. We show that the default angular bin dispersion most widely used in Monte Carlo transport codes is such that the observed differences in angular shapes are on too fine a scale to affect transport calculations. The effect of increasing the number of Monte Carlo angle bins is studied to determine the dispersion necessary for calculations to be sensitive to the observed discrepancies in angular distributions. We also show that transport calculations are sensitive to differences in the elastic scattering cross section given by recent fits of {sup 208}Pb data compared with older fits.
Generating derivative superstructures for systems with high configurational freedom

Abstract Modeling alloys requires the exploration of all possible configurations of atoms. Additionally, modeling the thermal properties of materials requires knowledge of the possible ways of displacing the atoms. One solution to finding all symmetrically unique configurations and displacements is to generate the complete list of possible configurations and remove those that are symmetrically equivalent. This approach, however, suffers from a combinatorial explosion when the supercell size is large, when there are more than two atom types, or when there are many displaced atoms. This problem persists even when there are only a relatively small number of unique arrangements that survive the elimination process. Here, we extend an existing algorithm to include the extra configurational degrees of freedom from the inclusion of displacement directions. The algorithm uses group theory and a tree-like data structure to eliminate large classes of configurations, avoiding the typical combinatoric explosion. With this approach we can now enumerate previously inaccessible cases, including atomic displacements.
Adaptive Inverse Control

Abstract Adaptive control is seen as a two part problem, control of plant dynamics and control of plant noise. The parts are treated separately. An unknown plant will track an input command signal if the plant is driven by a controller whose transfer function approximates the inverse of the plant transfer function. An adaptive inverse identification process can be used to obtain a stable controller, even if the plant is nonminimum phase. A model-reference version of this idea allows system dynamics to closely approximate desired reference model dynamics. No direct feedback is used, except that the plant output is monitored and utilized in order to adjust the parameters of the controller. Control of internal plant noise is accomplished with an optimal adaptive noise canceller. The canceller does not affect plant dynamics, but feeds back plant noise in a way that minimizes plant output noise power.
Hydroxylation of collagen by lungs of rats administered bleomycin.

Collagen synthesized by tissue minces from lungs of rats administered 1 unit of bleomycin by intratracheal instillation 1 or 2 wk earlier contained relatively more hydroxylysine than did collagen made by lungs from saline-instilled control animals. Most, if not all, of the relative increase in lysine hydroxylation could be localized to the α1 (I) chain of type I collagen. Lung homogenates from bleomycin-treated rats showed increased activity of lysyl hydroxylase (EC 1.14.11.4), the enzyme catalyzing the conversion of collagen-bound lysine to hydroxylysine. Thus, the increased hydroxylation of lysine and of lysine-derived crosslinks previously observed in collagen of diseased human lungs and in animal models of lung fibrosis is reflected in an in vitro system.
Scope Patterns for Projects Modeled as Sociotechnical Systems

This paper examines the treatment of scope as project attribute, arguing that an improved representation will allow increased fidelity of project plan trade space enumeration and preferred plan selection. Cost, schedule, and scope are emergent characteristics of a project’s integrated architecture, activities, and resources uniquely for a project at hand. System engineering as commonly practiced places strong, early emphasis on product architecture and requirements, including enumeration of system options prior to interplay with aspects of project implementation. As such, system options are often framed and pruned prior to effective examination of project feasibility. Characteristics of scope are presented suitable for model-based design of projects. Scope is defined as the tangible outcomes of project tasks. Scope items should be useful in the evolution of project knowledge and interplay with requirements and resources. Target-neutral, resource-nominal, and exception-realistic patterns of scope are described.
Learning the structure of generative models without labeled data

Curating labeled training data has become the primary bottleneck in machine learning. Recent frameworks address this bottleneck with generative models to synthesize labels at scale from weak supervision sources. The generative model's dependency structure directly affects the quality of the estimated labels, but selecting a structure automatically without any labeled data is a distinct challenge. We propose a structure estimation method that maximizes the l1-regularized marginal pseudolikelihood of the observed data. Our analysis shows that the amount of unlabeled data required to identify the true structure scales sublinearly in the number of possible dependencies for a broad class of models. Simulations show that our method is 100x faster than a maximum likelihood approach and selects 1/4 as many extraneous dependencies. We also show that our method provides an average of 1.5 F1 points of improvement over existing, user-developed information extraction applications on real-world data such as PubMed journal abstracts.
Marginal reductions in vehicle emissions under a dual-blend ethanol mandate: Evidence from a natural experiment

Among the many reasons policy makers across the world have sought to supplement fuel supplies with ethanol-blended fuels are the cited environmental benefits that come with replacing a fossil-fuel with a cleaner burning alternative. Dual-blend ethanol mandates, in which multiple ethanol blends are simultaneously available, are one way policy markers can move forward with more aggressive mandates more quickly. The recent ethanol mandate in the state of New South Wales, Australia offers a unique natural experiment to quantify the potential environmental benefits and costs of a dual blend ethanol policy. This paper estimates the impact on carbon dioxide (CO2) emissions from road-activity that are attributable to the implementation of the New South Wales ethanol requirements. We find that there was a decrease in emissions due to the policy, but that the decrease is relatively minor given the size of the market and that it comes at a high cost. The cost was over $1200 per ton of carbon to reduce gasoline emissions by just 1.2%.
Amortized analysis of smooth quadtrees in all dimensions

Quadtrees are a well-known data structure for representing geometric data in the plane, and naturally generalize to higher dimensions. A basic operation is to expand the tree by splitting a given leaf. A quadtree is smooth if adjacent leaf boxes differ by at most one in height.
Seasonality of Rural Finance

Simultaneity of borrowing, withdrawal of savings, and loan defaults due to the pronounced seasonality of ag-riculture often leads to investment failure of rural financial institutions. Lack of borrowing leads to lack of in-come- and consumption-smoothing, and in turn, causes inefficient resource allocation by rural households. Financial institutions that are active in rural areas take different measures to address the covariate risks in in-termediation. For example, microfinance institutions have sought various measures such as supporting non-farm activities to diversify income, introducing seasonal loans, and bringing flexibility in loan repayments to reduce non-payments in lean seasons. This paper examines whether the financial inclusion policies of micro-finance institutions have successfully helped reduce the adverse effects of covariate risks. Analysis of house-hold and program level data from Bangladesh suggests that despite the innovative measures taken by the MFIs to cope with the covariate risks, seasonality of income still affects seasonality of borrowing and invest-ment decisions of both the households and MFIs beyond and above what is caused normally by agricultural seasonality. Innovation is needed to promote, among other things, sectoral diversification of financial inter-mediation and to avert the extreme seasonality of rural income. Rural labor markets should be diversified enough to address the seasonality of income and consumption. Public policies guiding rural financial inter-mediation must reflect such realities of rural economies.
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity

Learning the directed acyclic graph (DAG) structure of a Bayesian network from observational data is a notoriously difficult problem for which many non-identifiability and hardness results are known. In this paper we propose a provably polynomial-time algorithm for learning sparse Gaussian Bayesian networks with equal noise variance --- a class of Bayesian networks for which the DAG structure can be uniquely identified from observational data --- under high-dimensional settings. We show that $O(k^4 \log p)$ number of samples suffices for our method to recover the true DAG structure with high probability, where $p$ is the number of variables and $k$ is the maximum Markov blanket size. We obtain our theoretical guarantees under a condition called \emph{restricted strong adjacency faithfulness} (RSAF), which is strictly weaker than strong faithfulness --- a condition that other methods based on conditional independence testing need for their success. The sample complexity of our method matches the information-theoretic limits in terms of the dependence on $p$. We validate our theoretical findings through synthetic experiments.
An adaptivity hierarchy theorem for property testing

Adaptivity is known to play a crucial role in property testing. In particular, there exist properties for which there is an exponential gap between the power of adaptive testing algorithms, wherein each query may be determined by the answers received to prior queries, and their non-adaptive counterparts, in which all queries are independent of answers obtained from previous queries. In this work, we investigate the role of adaptivity in property testing at a finer level. We first quantify the degree of adaptivity of a testing algorithm by considering the number of “rounds of adaptivity” it uses. More accurately, we say that a tester is k-(round) adaptive if it makes queries in $${k+1}$$ rounds, where the queries in the $${i}$$ ’th round may depend on the answers obtained in the previous $${i-1}$$ rounds. Then, we ask the following question: We provide a positive answer to the foregoing question by proving an adaptivity hierarchy theorem for property testing. Specifically, our main result shows that for every $${n \in \mathbb{N}}$$ and $${0 \le k \le n^{0.33}}$$ there exists a property $${\mathcal{P}_{n,k}}$$ of functions for which (1) there exists a $${k}$$ -adaptive tester for $${\mathcal{P}_{n,k}}$$ with query complexity $${{\tilde O}{(k)}}$$ , yet (2) any $${(k-1)}$$ -adaptive tester for $${\mathcal{P}_{n,k}}$$ must make $${{\tilde \Omega}{(n/k^2)}}$$ queries. In addition, we show that such a qualitative adaptivity hierarchy can be witnessed for testing natural properties of graphs.
Legislative coalitions with incomplete information.

In most parliamentary democracies, proportional representation electoral rules mean that no single party controls a majority of seats in the legislature. This in turn means that the formation of majority legislative coalitions in such settings is of critical political importance. Conventional approaches to modeling the formation of such legislative coalitions typically make the “common knowledge” assumption that the preferences of all politicians are public information. In this paper, we develop a theoretical framework to investigate which legislative coalitions form when politicians’ policy preferences are private information, not known with certainty by the other politicians with whom they are negotiating over what policies to implement. The model we develop has distinctive implications. It suggests that legislative coalitions should typically be either of the center left or the center right. In other words our model, distinctively, predicts only center-left or center-right policy coalitions, not coalitions comprising the median party plus parties both to its left and to its right.
Reevaluating Agricultural Productivity Gaps with Longitudinal Microdata

Recent research has pointed to large gaps in labor productivity between the agricultural and nonagricultural sectors in low-income countries, as well as between workers in rural and urban areas. Most estimates are based on national accounts or repeated cross-sections of micro-survey data, and as a result typically struggle to account for individual selection between sectors. This paper contributes to this literature using long-run individual-level panel data from two low-income countries (Indonesiaand Kenya). Accounting for individual fixed effects leads to much smaller estimated productivity gains from moving into the non-agricultural sector (or urban areas), reducing estimated gaps by over80 percent. Per capita consumption gaps between non-agricultural and agricultural sectors, as well as between urban and rural areas, are also close to zero once individual fixed effects are included. Estimated productivity gaps do not emerge up to five years after a move between sectors, nor are they larger in big cities. We evaluate whether these findings imply a re-assessment of the currentconventional wisdom regarding sectoral gaps, discuss how to reconcile them with existing crosssectional estimates, and consider implications for the desirability of sectoral reallocation of labor.
Why Do Firms Use Insurance to Fund Worker Health Benefits? The Role of Corporate Finance

When a firm offers health benefits to workers, it exposes the firm to the risk of making payments when workers get sick. A firm can either pay health expenses out of its general assets, keeping the risk inside the firm, or it can purchase insurance, shifting the risk outside the firm. We analyze the firm’s decision to manage this risk. Using data on the insurance decisions of publicly-traded firms, we find that smaller firms, firms with more investment opportunities, and firms that face a convex tax schedule are more likely to hedge the risk of health benefit payments. Health risk is common to all firms, making this application an important contribution to understanding firms’ hedging decisions. Additionally, we reveal new and important determinants of the hedging decision relative to regulatory regimes. We also show that hedging health risk mitigates investment-cash flow sensitivities.
Computing Linear Transformations With Unreliable Components

We consider the problem of computing a binary linear transformation when all circuit components are unreliable. Two models of unreliable components are considered: probabilistic errors and permanent errors. We introduce the “ENCODED” technique that ensures that the error probability of the computation of the linear transformation is kept bounded below a small constant independent of the size of the linear transformation even when all logic gates in the computation are noisy. By deriving a lower bound, we show that in some cases, the computational complexity of the ENCODED technique achieves the optimal scaling in error probability. Further, we examine the gain in energy-efficiency from the use of a “voltage-scaling” scheme, where gate-energy is reduced by lowering the supply voltage. We use a gate energy-reliability model to show that tuning gate-energy appropriately at different stages of the computation (“dynamic” voltage scaling), in conjunction with ENCODED, can lead to orders of magnitude energy-savings over the classical “uncoded” approach. Finally, we also examine the problem of computing a linear transformation when noiseless decoders can be used, providing upper and lower bounds to the problem.
THE CAUSES OF TKA FAILURE: AVOIDING YOUR NEXT REVISION

In 1983 we underscored the importance of understanding the cause or mechanism of failure following total knee arthroplasty. In this article we reported that revision total knee replacement was generally unsuccessful unless the surgeon new the mechanism of failure. In the ensuing years we have collectively made improvements in instrumentation, component design and material properties such that the mechanisms of failure are now different and less common than in the earlier years. Early failure following total knee arthroplasty is generally related to technical issues. There are a myriad of such issues but many of them relate to component positioning and soft tissue balance. Post-operative wound complications are concerning as they cause an increased incidence of deep infection. Hematoma from over anticoagulation is a particular problem that leads to stiffness and increased risk for infection. Most knee systems now have multiple sizing options and instrument systems that can improve reproducibility of component implantation. Midterm failure is often due to flexion instability which has been reported in cruciate substitution and cruciate retention knees. The instability can be global, mid flexion, flexion or a combination of all 3. Issues with extension and mid flexion instability but no flexion instability are generally those with tight extensor mechanisms. Pain and stiffness are frequently due to component malalignment. One common problem is abnormal internal rotation of the tibial component. Late failure in our institution is generally seen due to wear and loosening from earlier designs with inferior polyethylene. Late hematogenous infection occurs in people with immunocompromise, severe diabetes and diagnoses that alter the patient9s ability to mount an immune response. The newest epidemic in total knee failure has been that of periprosthetic fracture. As these patients are becoming older and with worse proprioception, they are at greater risk. Generalised osteopenia and increased activity also increase the risk of fracture. Total knee arthroplasty represents a remarkable improvement in the care of the patient with knee arthritis. It is only by focusing upon and decreasing the causes of failure that we will advance use of this procedure in patient care.
Statistical Cost Sharing

We study the cost sharing problem for cooperative games in situations where the cost function C is not available via oracle queries, but must instead be learned from samples drawn from a distribution, represented as tuples (S, C(S)), for different subsets S of players. We formalize this approach, which we call statistical cost sharing, and consider the computation of the core and the Shapley value. Expanding on the work by Balcan et al, we give precise sample complexity bounds for computing cost shares that satisfy the core property with high probability for any function with a non-empty core. For the Shapley value, which has never been studied in this setting, we show that for submodular cost functions with curvature bounded curvature kappa it can be approximated from samples from the uniform distribution to a sqrt{1 - kappa} factor, and that the bound is tight. We then define statistical analogues of the Shapley axioms, and derive a notion of statistical Shapley value and that these can be approximated arbitrarily well from samples from any distribution and for any function.
Rulers, Religion, and Riches

For centuries following the spread of Islam, the Middle East was far ahead of Europe. Yet, the modern economy was born in Europe. Why was it not born in the Middle East? In this book Jared Rubin examines the role that Islam played in this reversal of fortunes. It argues that the religion itself is not to blame; the importance of religious legitimacy in Middle Eastern politics was the primary culprit. Muslim religious authorities were given an important seat at the political bargaining table, which they used to block important advancements such as the printing press and lending at interest. In Europe, however, the Church played a weaker role in legitimizing rule, especially where Protestantism spread (indeed, the Reformation was successful due to the spread of printing, which was blocked in the Middle East). It was precisely in those Protestant nations, especially England and the Dutch Republic, where the modern economy was born.
Investigating Properties of Commodity Price Responses to Real and Nominal Shocks

This paper studies dynamic adjustments of 49 world commodity prices in response to innovations in the nominal exchange rate and the world real GDP. After we estimate the dynamic elasticity of the prices with respect to these shocks, we obtain the kernel density of our estimates to establish stylized facts on the adjustment process of the commodity price toward a new equilibrium path. Our empirical findings imply, on average, that the law of one price holds in the long-run, whereas the substantial degree of short-run price rigidity was observed in response to the nominal exchange rate shock. The real GDP shock tends to generate substantial price fluctuations in the short-run because adjustments of the supply can be limited, but have much weaker effects in the long-run as the supply eventually counterbalances the increase in the demand. Overall, we report persistent long-lasting effects of the nominal exchange rate shock on commodity prices relative to those of the real GDP shock.
Ghostbusting in Detroit: Evidence on nonfilers from a controlled field experiment

Abstract Many people who owe income tax fail to file a timely tax return. In communication with these “ghosts,” what messages from the tax authority are effective for eliciting a return? This is the first study to address message content in communication with income tax nonfilers. I assess the efficacy of messages related to penalty salience, punishment probability, compliance cost, and civic pride by evaluating the response to experimental mailings distributed by Detroit to 7142 suspected resident nonfilers. The penalty salience message was the most effective. Relative to a basic mailing that requested a return, penalty salience mailings that stated the statutory penalty for failing to file a return tripled response rates from 3% to 10%. Compliance cost mailings that enclosed a blank tax return and punishment probability mailings that stated the recipient's federal income also raised response rates relative to the basic mailing, but civic pride mailings did not. I investigate the impact of treatment mailings on the behavior of untreated neighbors and find no evidence of geographic network effects.
Deciphering the MSSM Higgs mass at future hadron colliders

Future hadron colliders will have a remarkable capacity to discover massive new particles, but their capabilities for precision measurements of couplings that can reveal underlying mechanisms have received less study. In this work we study the capability of future hadron colliders to shed light on a precise, focused question: is the higgs mass of 125 GeV explained by the MSSM? If supersymmetry is realized near the TeV scale, a future hadron collider could produce huge numbers of gluinos and electroweakinos. We explore whether precision measurements of their properties could allow inference of the scalar masses and tan β with sufficient accuracy to test whether physics beyond the MSSM is needed to explain the higgs mass. We also discuss dark matter direct detection and precision higgs physics as complementary probes of tan β. For concreteness, we focus on the mini-split regime of MSSM parameter space at a 100 TeV pp collider, with scalar masses ranging from 10s to about 1000 TeV.
Optimal Power Control and Scheduling for Real-Time and Non-Real-Time Data

We consider a joint scheduling-and-power-allocation problem of a downlink cellular system. The system consists of two groups of users: real-time (RT) and non-real-time (NRT) users. Given an average power constraint on the base station, the problem is to find an algorithm that satisfies the RT hard deadline constraint and NRT queue stability constraint. We propose two sum-rate-maximizing algorithms that satisfy these constraints as well as achieving the system's capacity region. In both algorithms, the power allocation policy has a closed-form expression for the two groups of users. However, interestingly, the power policy of the RT users, which we call the Lambert-power policy, differs in structure from the water-filling policy for the NRT users. The first algorithm is optimal for the on–off channel model with a polynomial-time scheduling complexity in the number of RT users. The second, on the other hand, works for any channel fading model, which is shown through simulations to have an average complexity that is close to linear. We also show the superiority of the proposed algorithms over existing approaches using extensive simulations.
Hitting the sweet spot: Variability in commute lengths and vehicle emissions across a diverse state

Abstract In this paper we consider travel across Virginia and identify sustainability “sweet spots” where commute lengths and vehicle emissions per mile combine to maximize green travel in terms of total CO 2 emissions associated with commuting. The analysis is conducted across local voter precincts (N = 2373 in the state) because they are a useful proxy for neighborhoods and well-sized for implementing policy designed to encourage sustainable travel behavior. Virginia is especially appropriate for an examination of variability in sustainable travel behavior and technologies because the state’s transportation, demographic, and political patterns are particularly diverse and have been changing rapidly. We identify four Virginia precinct-based sustainability clusters: Sweet Spots, Emerging Sweet Spots, Neutral and Non-sustaining. A model of demographic differences among the clusters shows that sustainability outcomes, understood in terms of both local commute behavior and vehicle emissions, are significantly associated with the diverse demography and politics of the state. We also look at changes in transportation sustainability and socio-demographic trends within the clusters over the past half-decade, showing that differences in sustainability and demographic metrics are actually accelerating within the state over time. We conclude with a discussion of the implications of the differences among the clusters for developing and implementing effective transportation sustainability policies across the state.
USM3D Simulations for Second Sonic Boom Workshop

The NASA Tetrahedral Unstructured Software System with the USM3D flow solver was used to compute the test cases for the Second AIAA Sonic Boom Prediction Workshop (SBPW2). The intent of this paper ...
Dolphin Pod Optimization

A novel nature-inspired deterministic derivative-free global optimization method, namely the dolphin pod optimization (DPO), is presented for solving simulation-based design optimization problems with costly objective functions. DPO implements, using a deterministic approach, the global search ability provided by a cetacean intelligence metaphor. The method is intended for unconstrained single-objective minimization and is based on a simplified social model of a dolphin pod in search for food. A parametric analysis is conducted to identify the most promising DPO setup, using 100 analytical benchmark functions and three performance criteria, varying the algorithm parameters. The most promising setup is compared with a deterministic particle swarm optimization and a DIviding RECTangles algorithm, and applied to two hull-form optimization problems, showing a very promising performance.