cssi/SciSciGPT-SciSciNet huggingface dataset:
Evolution of multifunctionality through a pleiotropic substitution in the innate immune protein S100A9.

A single protein sometimes does multiple jobs. For instance, our immune system uses a small number of multipurpose proteins to respond quickly to a large number of threats. One example is the protein S100A9. It acts as an antimicrobial by preventing microbes from getting the nutrients they need, while also stimulating inflammation by inducing the release of molecules that recruit white blood cells. S100A9, like all proteins, is made up of a chain of small building blocks. These building blocks interact with each other and with other molecules in the environment. The sequence of the building blocks thus determines what jobs the protein can do. Therefore, a single change to the sequence of building blocks can have a dramatic effect: one change might render the protein faulty, while another change might allow it to do a new job. Proteins face similar challenges humans do when trying to do several things at once. A person driving a car while using their phone will not do either task well. Likewise, a protein that does two jobs faces challenges a single-purpose protein does not. Harman et al. were interested in how S100A9 was able to evolve and maintain its dual functionality, despite this potential problem. They started by asking when S100A9 acquired its two purposes. They measured the antimicrobial and inflammatory activity of S100A9 proteins from humans, mice and opossums. The activities of S100A9 in these species was similar, suggesting that S100A9 acquired its different jobs in the ancestor of mammals, some 160 million years ago. Next, Harman et al. computationally reconstructed ancestral forms of S100A9 by comparing hundreds of similar proteins and building an evolutionary tree. They then measured the antimicrobial and inflammatory activity of these ancestral proteins. By comparing the last ancestor that did not have these activities to the first ancestor that did, they identified the sequence changes that gave S100A9 its dual activity. Importantly, these changes are located in separate regions of the protein, meaning they could occur independently, without affecting each other. Further, the same sequence change that converted S100A9 into an inflammatory signal also introduced a mechanism to regulate this activity. The results suggest that a small number of sequence changes – or even a single change – can make a protein more versatile. This means that evolving multipurpose proteins may not be as difficult as is often thought.
Convex Programs for Minimal-Area Problems

The minimal-area problem that defines string diagrams in closed string field theory asks for the metric of least area on a Riemann surface with the condition that all non-contractible closed curves have length at least $$2\pi $$ . This is an extremal length problem in conformal geometry as well as a problem in systolic geometry. We consider the analogous minimal-area problem for homology classes of curves and, with the aid of calibrations and the max flow-min cut theorem, formulate it as a local convex program. We derive an equivalent dual program involving maximization of a concave functional. These two programs give new insights into the form of the minimal-area metric and are amenable to numerical solution. We explain how the homology problem can be modified to provide the solution to the original homotopy problem.
Some Results on Multithreshold Graphs

Jamison and Sprague defined a graph G to be a k-threshold graph with thresholds $$\theta _1 , \ldots , \theta _k$$ (strictly increasing) if one can assign real numbers $$(r_v)_{v \in V(G)}$$, called ranks, such that for every pair of vertices v, w, we have $$vw \in E(G)$$ if and only if the inequality $$\theta _i \le r_v + r_w$$ holds for an odd number of indices i. When $$k=1$$ or $$k=2$$, the precise choice of thresholds $$\theta _1, \ldots , \theta _k$$ does not matter, as a suitable transformation of the ranks transforms a representation with one choice of thresholds into a representation with any other choice of thresholds. Jamison asked whether this remained true for $$k \ge 3$$ or whether different thresholds define different classes of graphs for such k, offering $50 for a solution of the problem. Letting C[t] for $$t > 1$$ denote the class of 3-threshold graphs with thresholds $$-1, 1, t$$, we prove that there are infinitely many distinct classes C[t], answering Jamison’s question. We also consider some other problems on multithreshold graphs, some of which remain open.
Spherical Regression Under Mismatch Corruption With Application to Automated Knowledge Translation

Motivated by a series of applications in data integration, language translation, bioinformatics, and computer vision, we consider spherical regression with two sets of unit-length vectors when the ...
Enumeration and extensions of word-representants

Given a finite word w over a finite alphabet V, we may construct a graph with vertex set V and an edge between to elements of V if and only if they alternate in the word w. This is the notion of word-representability of graphs. In this paper, we first study minimal length words which represent graphs, giving an explicit formula for both the length and the number of such words in the case of trees and cycles. Then we extend this notion to study the graphs representable with other patterns in words, proving in all cases aside from one (still unknown to us), all graphs are representable by all other patterns. Finally, we pose a few open problems for further work.
Concepts in CMB anisotropy formation

These lecture notes form a primer on the theory of cosmic microwave background (CMB) anisotropy formation. With emphasis on conceptual aspects rather than technical issues, we examine the physical foundations of anisotropy evolution in relativistic kinetic and perturbation theory as well as the manifestation of these principles in primary and secondary anisotropies. We discuss gauge choice and gauge invariance and their use in understanding the CMB. Acoustic, gravitational redshift and ionization effects have robust signatures in the CMB spectrum and may allow determination of classical cosmological parameters as well as reveal general distinctions between models for structure formation. We develop the tight and weak coupling approximations as analytic tools to help understand these effects and the robustness of their signatures.
The Role of Neighborhood Characteristics in Mortgage Default Risk: Evidence from New York City

Using a rich database of non-prime mortgages from New York City, we find that census tract level neighborhood characteristics are important predictors of default behavior, even after controlling for an extensive set of controls for loan and borrower characteristics. First, default rates increase with the rate of foreclosure notices and the number of lender-owned properties (REOs) in the tract. Second, default rates on home purchase mortgages are higher in census tracts with larger shares of black residents, regardless of the borrower’s own race. We explore possible explanations for this second finding and conclude that it likely reflects differential treatment of black neighborhoods by the mortgage industry in ways that are unobserved in our data.
Labor Income and Predictable Stock Returns

We propose and test a novel economic mechanism that generates stock return predictability on both the time series and the cross section. In our model, investors’ income has two sources, wages and dividends, that grow stochastically over time. As a consequence, the fraction of total income produced by wages changes over time depending on economic conditions. We show that as this fraction fluctuates, the risk premium that investors require to hold stocks varies as well. We test the main implications of the model and find substantial support for it. A regression of stock returns on lagged values of the labor income to consumption ratio produces statistically significant coecients and adjusted R 2 ’s that are larger than those generated when using the dividend price ratio. Tests of the cross sectional implication find considerable improvements on the performance of both the conditional CAPM and CCAPM when compared to their unconditional counterparts.
The Quantum Illumination Story

Superposition and entanglement, the quintessential characteristics of quantum physics, have been shown to provide communication, computation, and sensing capabilities that go beyond what classical physics will permit. It is natural, therefore, to explore their application to radar, despite the fact that decoherence—caused by the loss and noise encountered in radar sensing—destroys these fragile quantum properties. This article tells the story of “quantum illumination,” an entanglement-based approach to quantum radar, from its inception to its current understanding. Remarkably, despite loss and noise that destroy its initial entanglement, quantum illumination does offer a target-detection performance improvement over a classical radar of the same transmitted energy. A realistic assessment of that improvement's utility, however, shows that its value is severely limited. Nevertheless, the fact that entanglement can be of value on an entanglement-breaking channel—the meta-lesson of the quantum illumination story—should spur continued research on quantum radar.
Prediction Markets for Economic Forecasting

Abstract Prediction markets – markets used to forecast future events – have been used to accurately forecast the outcome of political contests, sporting events, and, occasionally, economic outcomes. This chapter summarizes the latest research on prediction markets in order to further their utilization by economic forecasters. We show that prediction markets have a number of attractive features: they quickly incorporate new information, are largely efficient, and are impervious to manipulation. Moreover, markets generally exhibit lower statistical errors than professional forecasters and polls. Finally, we show how markets can be used to both uncover the economic model behind forecasts, as well as test existing economic models.
Chapter 6 – Pericyclic Reactions

Publisher Summary This chapter describes a variety of pericyclic reactions that include electrocyclic transformations, cycloadditions, sigmatropic rearrangements, and the ene reaction. Electrocyclic transformations involve intramolecular formation of a ring by bond-formation at the ends of a conjugated π system. The product has one more σ bond and one less π bond than the starting material. The reverse reaction, ring opening of a cyclic polyene, is also an electrocyclic process. Cycloadditions involve bonding between the termini of two π systems to produce a new ring. The product has two more σ bonds and two less π bonds than the reactants. Common examples are the Diels–Alder reaction and many 1,3-dipolar cycloadditions. In Sigmatropic reactions, an allylic σ bond at one end of a π system appears to migrate to the other end of the π system. The π bonds change position, but the total number of σ and π bonds is the same, as in the Cope and Claison rearrangements. Ene reactions combine aspects of cycloadditions and sigmatropic reactions. They may be inter- or intramolecular. Pericyclic reactions can be initiated either thermally or photochemically, but in either case, they show great stereospecificity. The conditions under which pericyclic reactions occur, and the stereochemistry of the products are formed, are dependent on the symmetry characteristics of the molecular orbitals involved. On this basis, pericyclic processes are classified as either symmetry-allowed or symmetry-forbidden.
Ramanujan graphs and the spectral gap of supercomputing topologies

Graph eigenvalues play a fundamental role in controlling structural properties which are critical considerations in the design of supercomputing interconnection networks, such as bisection bandwidth, diameter, and fault tolerance. This motivates considering graphs with optimal spectral expansion, called Ramanujan graphs, as potential candidates for interconnection networks. In this work, we explore this possibility by comparing Ramanujan graph properties against those of a wide swath of current and proposed supercomputing topologies. We derive analytic expressions for the spectral gap, bisection bandwidth, and diameter of these topologies, some of which were previously unknown. We find the spectral gap of existing topologies is well separated from the optimal achievable by Ramanujan topologies, suggesting the potential utility of adopting Ramanujan graphs as interconnection networks.
Silicon-on-insulator dynamic threshold ESD networks and active clamp circuitry

Active clamp circuits are essential to minimize electrical overshoot and undershoot and minimize reflected signals and achieve performance objectives and reliability requirements in high performance CMOS circuits. This paper discusses for the first time the electrostatic discharge (ESD) protection circuits of silicon-on-insulator (SOI) active clamp networks, dynamic threshold MOSFET SOI ESD techniques and the synthesis of DTMOS concepts, ESD protection networks and active clamp circuitry for high-pin-count high-performance semiconductor chips.
A cytochrome peroxidase from Pseudomonas fluorescens.

A CRUDE extract of Pseudomonas fluorescens was found to oxidize a reduced dye, 2,6-dichlorobenz-enoneindo-3′-chlorophenol. The activity in the crude extracts seemed similar to the ‘dye oxidase’ activity described by Smith and Stotz in plant tissues1. On partial purification, the ‘dye oxidase’ activity was always associated with a pigmented component. Spectral analysis of this pigment in the reduced and oxidized state indicated that it had an absorption similar to the respective states of cytochrome c. However, this pigment is not reduced by reduced triphosphopyridine nucleotide using purified tri-phosphopyridine cytochrome c reductase obtained from liver2.
The Great Migration of Black Americans from the US South: A Guide and Interpretation

Abstract The Great Migration from the US South is a prominent theme in economic history research not only because it was a prime example of large scale internal migration, but also because it had far-reaching ramifications for American economic, social, and political change. This article offers a concise review of the literature focused on questions of timing, selection, and migrants’ outcomes, and then offers a more speculative interpretation of how the Great Migration fostered the advancement of Civil Rights. It concludes by pointing out areas where further exploration would be valuable.
A convergent numerical method to recover the initial condition of nonlinear parabolic equations from lateral Cauchy data

We propose a new numerical method for the solution of the problem of the reconstruction of the initial condition of a quasilinear parabolic equation from the measurements of both Dirichlet and Neumann data on the boundary of a bounded domain. Although this problem is highly nonlinear, we do not require an initial guess of the true solution. The key in our method is the derivation of a boundary value problem for a system of coupled quasilinear elliptic equations whose solution is the vector function of the spatially dependent Fourier coefficients of the solution to the governing parabolic equation. We solve this problem by an iterative method. The global convergence of the system is rigorously established using a Carleman estimate. Numerical examples are presented.
Knowledge As Priors: Cross-Modal Knowledge Generalization for Datasets Without Superior Knowledge

Cross-modal knowledge distillation deals with transferring knowledge from a model trained with superior modalities (Teacher) to another model trained with weak modalities (Student). Existing approaches require paired training examples exist in both modalities. However, accessing the data from superior modalities may not always be feasible. For example, in the case of 3D hand pose estimation, depth maps, point clouds, or stereo images usually capture better hand structures than RGB images, but most of them are expensive to be collected. In this paper, we propose a novel scheme to train the Student in a Target dataset where the Teacher is unavailable. Our key idea is to generalize the distilled cross-modal knowledge learned from a Source dataset, which contains paired examples from both modalities, to the Target dataset by modeling knowledge as priors on parameters of the Student. We name our method "Cross-Modal Knowledge Generalization" and demonstrate that our scheme results in competitive performance for 3D hand pose estimation on standard benchmark datasets.
RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images

Recent methods for people detection in overhead, fisheye images either use radially-aligned bounding boxes to represent people, assuming people always appear along image radius or require significant pre-/post-processing which radically increases computational complexity. In this work, we develop an end-to-end rotation-aware people detection method, named RAPiD, that detects people using arbitrarily-oriented bounding boxes. Our fully-convolutional neural network directly regresses the angle of each bounding box using a periodic loss function, which accounts for angle periodicities. We have also created a new dataset 1 with spatio-temporal annotations of rotated bounding boxes, for people detection as well as other vision tasks in overhead fisheye videos. We show that our simple, yet effective method outperforms state-of-the-art results on three fisheye-image datasets. The source code for RAPiD is publicly available 2 .
On the resolution of misspecified convex optimization and monotone variational inequality problems

We consider a misspecified optimization problem, requiring the minimization of a function $$f(\cdot;\theta ^*)$$ over a closed and convex set X where $$\theta ^*$$ is an unknown vector of parameters that may be learnt by a parallel learning process. Here, we develop coupled schemes that generate iterates $$(x_k,\theta _k)$$ as $$k \rightarrow \infty$$ , then $$x_k \rightarrow x^*$$ , a minimizer of $$f(\cdot;\theta ^*)$$ over X and $$\theta _k \rightarrow \theta ^*$$ . In the first part of the paper, we consider the solution of problems where f is either smooth or nonsmooth. In smooth strongly convex regimes, we demonstrate that such schemes still display a linear rate of convergence, albeit with larger constants. When strong convexity assumptions are weakened, it can be shown that the convergence in function value sees a modification in the canonical convergence rate of $${{{\mathcal {O}}}}(1/K)$$ by an additive factor proportional to $$\Vert \theta _0-\theta ^*\Vert$$ where $$\Vert \theta _0-\theta ^*\Vert$$ represents the initial misspecification in $$\theta ^*$$ . In addition, when the learning problem is assumed to be merely convex but admits a suitable weak-sharpness property, then the convergence rate deteriorates to $${\mathcal {O}}(1/\sqrt{K})$$ . In both convex and strongly convex regimes, diminishing steplength schemes are also provided and are less reliant on the knowledge of problem parameters. Finally, we present an averaging-based subgradient scheme that displays a rate of $${\mathcal {O}}(1/\sqrt{K})+ \mathcal{O}(\|\theta_0-\theta^*\|(1/K))$$ , implying no effect on the canonical rate of $${{{\mathcal {O}}}}(1/\sqrt{K})$$ . In the second part of the paper, we consider the solution of misspecified monotone variational inequality problems, motivated by the need to contend with more general equilibrium problems as well as the possibility of misspecification in the constraints. In this context, we first present a constant steplength misspecified extragradient scheme and prove its asymptotic convergence. This scheme is reliant on problem parameters (such as Lipschitz constants) and leads to a misspecified variant of iterative Tikhonov regularization, an avenue that does not necessitate the knowledge of such constants.
Sum-Set Inequalities From Aligned Image Sets: Instruments for Robust GDoF Bounds

We present sum-set inequalities specialized to the generalized degrees of freedom (GDoF) framework. These are information theoretic lower bounds on the entropy of bounded density linear combinations of discrete, power-limited dependent random variables in terms of the joint entropies of arbitrary linear combinations of new random variables that are obtained by power level partitioning of the original random variables. These bounds generalize the aligned image sets approach, and are useful instruments to obtain GDoF characterizations for wireless networks, especially with multiple antenna nodes, subject to arbitrary channel strength and channel uncertainty levels. To demonstrate the utility of these bounds, we consider various examples of interference and broadcast channels for which we obtain tight GDoF characterizations with the aid of sum-set inequalities.
The role of Rif1 in telomere length regulation is separable from its role in origin firing.

To examine the established link between DNA replication and telomere length, we tested whether firing of telomeric origins would cause telomere lengthening. We found that RIF1 mutants that block Protein Phosphatase 1 (PP1) binding activated telomeric origins but did not elongate telomeres. In a second approach, we found overexpression of ∆N-Dbf4 and Cdc7 increased DDK activity and activated telomeric origins, yet telomere length was unchanged. We tested a third mechanism to activate origins using the sld3-A mcm5-bob1 mutant that de-regulates the pre-replication complex, and again saw no change in telomere length. Finally, we tested whether mutations in RIF1 that cause telomere elongation would affect origin firing. We found that neither rif1-∆1322 nor rif1HOOK affected firing of telomeric origins. We conclude that telomeric origin firing does not cause telomere elongation, and the role of Rif1 in regulating origin firing is separable from its role in regulating telomere length.
A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging

To analyze data from multisubject experiments in neuroimaging studies, we develop a modeling framework for joint community detection in a group of related networks that can be considered as a sample from a population of networks. The proposed random effects stochastic block model facilitates the study of group differences and subject-specific variations in the community structure. The model proposes a putative mean community structure, which is representative of the group or the population under consideration but is not the community structure of any individual component network. Instead, the community memberships of nodes vary in each component network with a transition matrix, thus modeling the variation in community structure across a group of subjects. To estimate the quantities of interest, we propose two methods: a variational EM algorithm and a model-free “two-step” method called Co-OSNTF which is based on nonnegative matrix factorization. We also develop a resampling-based hypothesis test for differences between community structure in two populations both at the whole network level and node level. The methodology is applied to the COBRE dataset, a publicly available fMRI dataset from multisubject experiments involving schizophrenia patients. Our methods reveal an overall putative community structure representative of the group as well as subject-specific variations within each of the two groups, healthy controls and schizophrenia patients. The model has good predictive ability for predicting community structure in subjects from the same population but outside the training sample. Using our network level hypothesis tests, we are able to ascertain statistically significant difference in community structure between the two groups, while our node level tests help determine the nodes that are driving the difference.
Does the Disclosure of PCAOB Inspection Findings Increase Audit Firms' Litigation Exposure?

We examine whether PCAOB inspection reports increase auditors' litigation risk. We find that inspection reports with audit deficiencies are positively associated with the number of lawsuits subsequently filed against the inspected auditor. These results are strongest when client-level lawsuit triggering events have already occurred and when PCAOB inspection content is arguably more persuasive. Importantly, these results pertain exclusively to triennially inspected audit firms for which the set of other publicly available signals of audit quality is limited. Furthermore, we do not argue that inspection reports in isolation trigger lawsuits. Instead, once events such as restatement announcements or bankruptcies create the potential for legal action against the auditor, inspection reports provide a public signal about past noncompliance with auditing standards. This signal likely increases lawyers' perceived strength of case against the auditor before the lawsuit is filed and before lawyers have access to the audit workpapers.
Western Diet Promotes Renal Injury, Inflammation, and Fibrosis in a Murine Model of Alström Syndrome.

Introduction: Alstrom syndrome is a rare recessive genetic disease caused by mutations in ALMS1, which encodes a protein that is related to cilia function and intracellular endosome trafficking. The syndrome has been linked to impaired glucose metabolism and CKD. Polymorphisms in Alms1 have likewise been linked to CKD, but little is known about the modification of the phenotype by environmental factors. Methods: To gain further insights, the fat aussie (foz) mouse strain, a genetic murine model of Alstrom syndrome, was exposed to a normal chow (NC) or to a Western diet (WD, 20% fat, 34% sucrose by weight, and 0.2% cholesterol) and renal outcomes were measured. Results: Body weight and albuminuria were higher in foz than in wild-type (WT) mice on both diets but WD significantly increased the difference. Measurement of plasma creatinine and cystatin C indicated that glomerular filtration rate was preserved in foz versus WT independent of diet. Renal markers of injury, inflammation, and fibrosis were similar in both genotypes on NC but significantly greater in foz than in WT mice on WD. A glucose tolerance test performed in foz and WT mice on WD revealed similar basal blood glucose levels and subsequent blood glucose profiles. Conclusions: WD sensitizes a murine model of Alstrom syndrome to kidney injury, inflammation, and fibrosis, an effect that may not be solely due to effects on glucose metabolism. Polymorphisms in Alms1 may induce CKD in part by modulating the deleterious effects of high dietary fat and sucrose on kidney outcome.
A rapidly evolved shift in life‐history timing during ecological speciation is driven by the transition between developmental phases

For insect species in temperate environments, seasonal timing is often governed by the regulation of diapause, a complex developmental programme that allows insects to weather unfavourable conditions and synchronize their life cycles with available resources. Diapause development consists of a series of distinct phases including initiation, maintenance, termination and post-diapause development. The evolution of insect seasonal timing depends in part on how these phases of diapause development and post-diapause development interact to affect variation in phenology. Here, we dissect the physiological basis of a recently evolved phenological shift in Rhagoletis pomonella (Diptera: Tephritidae), a model system for ecological divergence. A recently derived population of R. pomonella shifted from specializing on native hawthorn fruit to earlier fruiting introduced apples, resulting in a 3-4 week shift in adult emergence timing. We tracked metabolic rates of individual flies across post-winter development to test which phases of development may act either independently or in combination to contribute to this recently evolved divergence in timing. Apple and hawthorn flies differed in a number of facets of their post-winter developmental trajectories. However, divergent adaptation in adult emergence phenology in these flies was due almost entirely to the end of the pupal diapause maintenance phase, with post-diapause development having a very small effect. The relatively simple underpinnings of variation in adult emergence phenology suggest that further adaptation to seasonal change in these flies for this trait might be largely due to the timing of diapause termination unhindered by strong covariance among different components of post-diapause development.
Direct and high-throughput assays for human cell killing through trogocytosis by Entamoeba histolytica

Entamoeba histolytica is the causative agent of amoebiasis. Pathogenesis is associated with profound damage to human tissues. We previously showed that amoebae kill human cells through trogocytosis. Trogocytosis is likely to underlie tissue damage during infection, although the mechanism is still unknown. Trogocytosis is difficult to assay quantitatively, which makes it difficult to study. Here, we developed two new, complementary assays to measure trogocytosis by quantifying human cell death. One assay uses CellTiterGlo, a luminescent readout for ATP, as a proxy for cell death. We found that the CellTiterGlo could be used to detect death of human cells after co-incubation with amoebae, and that it was sensitive to inhibition of actin or the amoeba surface Gal/GalNAc lectin, two conditions that are known to inhibit amoebic trogocytosis. The other assay uses two fluorescent nuclear stains to directly differentiate live and dead human cells by microscopy, and is also sensitive to inhibition of amoebic trogocytosis through interference with actin. Both assays are simple and inexpensive, can be used with suspension and adherent human cell types, and are amenable to high-throughput approaches. These new assays are tools to improve understanding of trogocytosis and amoebiasis pathogenesis.
MEC-Enhanced Information Freshness for Safety-Critical C-V2X Communications

Information freshness is a status update timeliness indicator of utmost importance to several real-time applications, such as connected and autonomous driving. The Ageof- Information (AoI) metric is widely considered as useful to quantify the information freshness of delivered messages to the involved entities. Recently, the advent of Multi-access Edge Computing (MEC) promises several performance benefits for Vehicular-to-Everything (V2X) communications, emphasizing on the experienced End-to-End (E2E) message delay. In this paper, we argue that, when it comes to safety-critical use cases, such as the one of Vulnerable Road User (VRU), additional metrics can be more insightful to evaluate and address scalability issues in dense urban environments. In particular, the impact of the packet inter-arrival time on the timeliness of VRU messages arriving at nearby vehicles can be directly assessed by exploiting the AoI metric. For that purpose, assuming a MEC-enabled multi-VRU system setting, we model the AoI and, by means of a performance comparison to the state-of-the-art network architecture based on numerical evaluations, we provide evidence of the information freshness and system scalability enhancements offered by MEC infrastructure deployment for different system parameter settings involving a large number of connected entities.
Scale versus Scope in the Diffusion of New Technology: Evidence from the Farm Tractor

Using the farm tractor as a case study, I show that lags in technology diffusion arise along two distinct margins, which I term scale and scope. Though tractors are now used in nearly every agricultural field operation and in the production of nearly all crops, they first developed with much more limited application. Early diffusion was accordingly rapid in these narrower applications, but limited in scope until tractor technology generalized. The sequence of diffusion is consistent with a model of R&D in specific- versus general-purpose attributes and with other historical examples, suggesting that the key to understanding technology diffusion lies not only in explaining the number of different users, but also in explaining the number of different uses.
Response to: Management of fluids in paediatric diabetic ketoacidosis: concerns over new guidance.

Despite the introduction of more restrictive fluid in the management of diabetic ketoacidosis (DKA) such as that advocated by Lillie et al ,1 there is little epidemiological evidence to suggest that more restrictive regimens have reduced the incidence of cerebral oedema. Cerebral oedema occurs prior to the administration of fluid in up to 19% of cases, and subclinical oedema has been demonstrated on MRI in up to 50% of cases.2 3 Why some patients evolve cerebral oedema and others do not and the role of fluids remain unclear and contentious. Experts suggest that it results from initial hypoperfusion followed by vasogenic/reperfusion injury rather than the traditional view of osmotic changes. In 2019, a multidisciplinary expert working group (including paediatric intensivists) was convened by the British Society of Paediatric Endocrinology and Diabetes to review the previous guideline published in 2015 …
Overproduction of the AlgT Sigma Factor Is Lethal to Mucoid Pseudomonas aeruginosa

ABSTRACT Pseudomonas aeruginosa isolates from chronic lung infections often overproduce alginate, giving rise to the mucoid phenotype. Isolation of mucoid strains from chronic lung infections correlates with a poor patient outcome. The most common mutation that causes the mucoid phenotype is called mucA22 and results in a truncated form of the anti-sigma factor MucA that is continuously subjected to proteolysis. When a functional MucA is absent, the cognate sigma factor, AlgT, is no longer sequestered and continuously transcribes the alginate biosynthesis operon, leading to alginate overproduction. In this work, we report that in the absence of wild-type MucA, providing exogenous AlgT is toxic. This is intriguing, since mucoid strains endogenously possess high levels of AlgT. Furthermore, we show that suppressors of toxic AlgT production have mutations in mucP, a protease involved in MucA degradation, and provide the first atomistic model of MucP. Based on our findings, we speculate that mutations in mucP stabilize the truncated form of MucA22, rendering it functional and therefore able to reduce toxicity by properly sequestering AlgT. IMPORTANCEPseudomonas aeruginosa is an opportunistic bacterial pathogen capable of causing chronic lung infections. Phenotypes important for the long-term persistence and adaption to this unique lung ecosystem are largely regulated by the AlgT sigma factor. Chronic infection isolates often contain mutations in the anti-sigma factor mucA, resulting in uncontrolled AlgT and continuous production of alginate in addition to the expression of ∼300 additional genes. Here, we report that in the absence of wild-type MucA, AlgT overproduction is lethal and that suppressors of toxic AlgT production have mutations in the MucA protease, MucP. Since AlgT contributes to the establishment of chronic infections, understanding how AlgT is regulated will provide vital information on how P. aeruginosa is capable of causing long-term infections.
Non-local model for surface tension in fluid-fluid simulations

Abstract We propose a non-local model for surface tension obtained in the form of an integral of a molecular-force-like function with support 3.5e added to the Navier-Stokes momentum conservation equation. We demonstrate analytically and numerically that with the non-local model interfaces with a radius of curvature larger than the support length behave macroscopically and microscopically, otherwise. For static droplets, the pressure difference P e , i n − P e , o u t satisfies the Young-Laplace law for droplet radius greater than 3.5e and otherwise deviates from the Young-Laplace law. The latter indicates that the surface tension in the proposed model decreases with decreasing radius of curvature, which agrees with molecular dynamics and experimental studies of nanodroplets. Using the non-local model we perform numerical simulations of droplets under dynamic conditions, including a rising droplet, a droplet in shear flow, and two colliding droplets in shear flow, and compare results with a standard Navier-Stokes model subject to the Young-Laplace boundary condition at the fluid-fluid interface implemented via the Conservative Level Set (CLS) method. We find good agreement with existing numerical methods and analytical results for a rising macroscopic droplet and a droplet in a shear flow. For colliding droplets in shear flow, the non-local model converges (with respect to the grid size) to the correct behavior, including sliding, coalescence, and merging and breaking of two droplets depending on the capillary number. In contrast, we find that the results of the CLS model are highly grid-size dependent.
Estimating the genome-wide contribution of selection to temporal allele frequency change

Rapid phenotypic adaptation is often observed in natural populations and selection experiments. However, detecting the genome-wide impact of this selection is difficult since adaptation often proceeds from standing variation and selection on polygenic traits, both of which may leave faint genomic signals indistinguishable from a noisy background of genetic drift. One promising signal comes from the genome-wide covariance between allele frequency changes observable from temporal genomic data (e.g., evolve-and-resequence studies). These temporal covariances reflect how heritable fitness variation in the population leads changes in allele frequencies at one time point to be predictive of the changes at later time points, as alleles are indirectly selected due to remaining associations with selected alleles. Since genetic drift does not lead to temporal covariance, we can use these covariances to estimate what fraction of the variation in allele frequency change through time is driven by linked selection. Here, we reanalyze three selection experiments to quantify the effects of linked selection over short timescales using covariance among time points and across replicates. We estimate that at least 17 to 37% of allele frequency change is driven by selection in these experiments. Against this background of positive genome-wide temporal covariances, we also identify signals of negative temporal covariance corresponding to reversals in the direction of selection for a reasonable proportion of loci over the time course of a selection experiment. Overall, we find that in the three studies we analyzed, linked selection has a large impact on short-term allele frequency dynamics that is readily distinguishable from genetic drift.
Illuminating Regulatory Guidance

Administrative agencies issue many guidance documents each year in an effort to provide clarity and direction to the public about important programs, policies, and rules. But these guidance documents are only helpful to the public if they can be readily found by those who they will benefit. Unfortunately, too many agency guidance documents are inaccessible, reaching the point where some observers even worry that guidance has become a form of regulatory “dark matter.” This article identifies a series of measures for agencies to take to bring their guidance documents better into the light. It begins by explaining why, unlike the disclosure requirements for binding agency rules, existing legal requirements have failed to make guidance documents more accessible. The basic problem is that the law on guidance disclosure is not self-enforcing. As a result, guidance availability is ultimately a managerial challenge for agencies—dependent on the adoption of internal disclosure practices—as much as it is a problem with a legal or technological fix. To aid agency leaders in meeting their managerial challenge, this article reviews best practices in guidance disclosure across the federal government. It considers these practices in light of four key criteria for assessing guidance management—comprehensiveness, currency, accessibility, and comprehensibility—and provides a series of practical steps that agencies can take to illuminate their guidance. Given the important role that guidance plays in the modern administrative state, meaningful governmental transparency today dictates that agencies take seriously their responsibility to manage the production and release of their guidance documents in a manner that makes them consistently and readily available to the public.
GCSA Codes with Noise Alignment for Secure Coded Multi-Party Batch Matrix Multiplication

A secure multi-party batch matrix multiplication problem (SMBMM) is considered, where the goal is to allow a master to efficiently compute the pairwise products of two batches of massive matrices, by distributing the computation across S servers. Any X colluding servers gain no information about the input, and the master gains no additional information about the input beyond the product. A solution called Generalized Cross Subspace Alignment codes with Noise Alignment (GCSA- NA) is proposed in this work, based on cross-subspace alignment codes. The state of art solution to SMBMM is a coding scheme called polynomial sharing (PS) that was proposed by Nodehi and Maddah-Ali. GCSA-NA outperforms PS codes in several key aspects — more efficient and secure inter-server communication, lower latency, flexible inter-server network topology, efficient batch processing, and tolerance to stragglers.
College Credit on the Table? Advanced Placement Course and Exam Taking

Abstract Millions of high school students who take an Advanced Placement (AP) course in one of over 30 subjects can earn college credit by performing well on the corresponding AP exam. Using data from four metro-Atlanta public school districts, we find that 15 percent of students’ AP courses do not result in an AP exam. We predict that up to 32 percent of the AP courses that do not result in an AP exam would result in a score of 3 or higher, which generally commands college credit at colleges and universities across the United States. Next, we examine disparities in AP exam-taking rates by demographics and course taking patterns. Most immediately policy relevant, we find evidence consistent with the positive impact of school district exam subsidies on AP exam-taking rates. In fact, students on free and reduced-price lunch (FRL) in the districts that provide a higher subsidy to FRL students than non-FRL students are more likely to take an AP exam than their non-FRL counterparts, after controlling for demographic and academic covariates.
Can Market Participants Report their Preferences Accurately (Enough)

In mechanism design theory it is common to assume that agents can perfectly report their preferences, even in complex settings in which this assumption strains reality. We experimentally test wheth...
An entropy-maximization approach to automated training set generation for interatomic potentials

Machine learning-based interatomic potentials are currently garnering a lot of attention as they strive to achieve the accuracy of electronic structure methods at the computational cost of empirical potentials. Given their generic functional forms, the transferability of these potentials is highly dependent on the quality of the training set, the generation of which can be highly labor-intensive. Good training sets should at once contain a very diverse set of configurations while avoiding redundancies that incur cost without providing benefits. We formalize these requirements in a local entropy-maximization framework and propose an automated sampling scheme to sample from this objective function. We show that this approach generates much more diverse training sets than unbiased sampling and is competitive with hand-crafted training sets.
Seven deadly sins of tech

Abstract There is currently a great deal of interest in online competition, particularly involving large tech firms such as Google, Apple, Facebook, Amazon, and Microsoft (a group commonly known as “GAFAM”). In this essay I examine several issues involving these firms that have often come up both in the popular press and academic discussions. The goal of this paper is to examine the facts about the alleged seven deadly sins of tech: competition, innovation, acquisitions, entry, switching costs, entry barriers, and size. I argue that when you look at the facts, it is clear that competition among tech firms is working well, and this has yielded many positive outcomes for consumers and the economy as a whole.
Crowdsourced Behavior-Driven Development

Abstract Key to the effectiveness of crowdsourcing approaches for software engineering is workflow design, describing how complex work is organized into small, relatively independent microtasks. This paper, we introduce a Behavior-Driven Development (BDD) workflow for accomplishing programming work through self-contained microtasks, implemented as a preconfigured environment called CrowdMicroservices. In our approach, a client, acting on behalf of a software team, describes a microservice as a set of endpoints with paths, requests, and responses. A crowd then implements the endpoints, identifying individual endpoint behaviors that they test, implement, debug, create new functions, and interact with persistence APIs as needed. To evaluate our approach, we conducted a feasibility study in which a small crowd worked to implement a small ToDo microservice. The crowd created an implementation with only four defects, completing 350 microtasks and implementing 13 functions. We discuss the implications of these findings for incorporating crowdsourced programming contributions into traditional software projects.
Normed barrelled spaces

Several conditions are given that imply barrelledness of a normed linear space. Also, examples of spaces that satisfy these properties are given.
KETO: Learning Keypoint Representations for Tool Manipulation

We aim to develop an algorithm for robots to manipulate novel objects as tools for completing different task goals. An efficient and informative representation would facilitate the effectiveness and generalization of such algorithms. For this purpose, we present KETO, a framework of learning keypoint representations of tool-based manipulation. For each task, a set of task-specific keypoints is jointly predicted from 3D point clouds of the tool object by a deep neural network. These keypoints offer a concise and informative description of the object to determine grasps and subsequent manipulation actions. The model is learned from self-supervised robot interactions in the task environment without the need for explicit human annotations. We evaluate our framework in three manipulation tasks with tool use. Our model consistently outperforms state-of-the-art methods in terms of task success rates. Qualitative results of keypoint prediction and tool generation are shown to visualize the learned representations.
Product forms for FCFS queueing models with arbitrary server-job compatibilities: an overview

In recent years, a number of models involving different compatibilities between jobs and servers in queueing systems, or between agents and resources in matching systems, have been studied, and, under Markov assumptions and appropriate stability conditions, the stationary distributions have been shown to have product forms. We survey these results and show how, under an appropriate detailed description of the state, many existing product-form results are corollaries of similar results for the Order Independent queue. We also discuss how to use the product-form results to determine distributions for steady-state response times.
Stability Inequalities for Lawson Cones

We prove stability inequalities for Lawson cones $M_{kh}$ with $$(k,h),(h,k)\in\{(3,5),(2,7),(2,8),(2,9),(2,10),(2,11)\}.$$ This extends the results of G. De. Philippis and F. Maggi to all area-minimizing Lawson cones.