cssi/SciSciGPT-SciSciNet huggingface dataset:
Simulations of a distributed intelligent array trigger for the cherenkov telescope array

It is anticipated that the forthcoming Cherenkov Telescope Array (CTA) will include a number of medium-sized telescopes that are constructed using a dual-mirror Schwarzschild-Couder configuration. These telescopes will sample a wide (8◦) field of view using a densely pixelated camera comprising over 104 individual readout channels. A readout frequency congruent with the expected single-telescope trigger rates would result in substantial data rates. To ameliorate these data rates, a novel, hardware-level Distributed Intelligent Array Trigger (DIAT) is envisioned. A copy of the DIAT operates autonomously at each telescope and uses reduced metadata from a limited subset of nearby telescopes to veto events prior to camera readout. We present the results of Monte-Carlo simulations that evaluate the efficacy of a "Parallax width" discriminator that can be used by the DIAT to efficiently distinguish between genuine gamma-ray initiated events and unwanted background events that are initiated by hadronic cosmic rays.
A Recount of Sunspot Groups on Staudach's Drawings

We have examined the more than 1100 drawings of the solar disk made by the German amateur astronomer Johann Caspar Staudach during 1749 – 1799 and counted the spots on each image. Using the modern perception of how to group spots into active regions, we regrouped the spots as a modern observer would. The resulting number of groups was found to be higher on average by 25 % than the first count of groups performed by Wolf in 1857, which was used by Hoyt and Schatten (Solar Phys. 181, 491, 1998) in their construction of the group sunspot number. Compared to other observers at the time, Staudach’s drawings have a very low average number, about two, of spots per group, possibly indicating an inferior telescope that probably suffered from spherical and chromatic aberration, as would be typical of amateur telescopes of the day. We have initiated an ongoing project aiming at observing sunspots with antique telescopes having similar defects in order to determine the factor necessary to bring the Staudach observations onto a modern scale.
Dynamic interference management

A linear interference network is considered. Long-term fluctuations (shadow fading) in the wireless channel can lead to any link being erased with probability p. Each receiver is interested in one unique message that can be available at M transmitters. In a cellular downlink scenario, the case where M = 1 reflects the cell association problem, and the case where M > 1 reflects the problem of setting up the backhaul links for Coordinated Multi-Point (CoMP) transmission. For M = 1, we characterize the average per user DoF, and identify the optimal assignment of messages to transmitters at each value of p. For general values of M, we show that there is no strategy for assigning messages to transmitters in large networks that is optimal for all values of p.
The symplectic displacement energy

We define the symplectic displacement energy of a non-empty subset of a compact symplectic manifold as the infimum of the Hofer-like norm [5] of symplectic diffeomorphisms that displace the set. We show that this energy (like the usual displacement energy defined using Hamiltonian diffeomorphisms) is a strictly positive number on sets with non-empty interior. As a consequence we prove a result justifying the introduction of the notion of strong symplectic homeomorphisms [4].
Electrical Control of Metallic Heavy-Metal–Ferromagnet Interfacial States

C-SPIN, one of six centers of STARnet; Semiconductor Research Corporation program; MARCO; DARPA; National Science Foundation [ECCS-1310338]
Efficient dispersion of mobile robots on graphs

The dispersion problem on graphs requires k robots placed arbitrarily at the n nodes of an anonymous graph, where k ≤ n, to coordinate with each other to reach a final configuration in which each robot is at a distinct node of the graph. The dispersion problem is important due to its relationship to graph exploration by mobile robots, scattering on a graph, and load balancing on a graph. In addition, an intrinsic application of dispersion has been shown to be the relocation of self-driven electric cars (robots) to recharge stations (nodes). We propose five algorithms to solve dispersion on graphs. The first three algorithms require O(k log Δ) bits at each robot and O(m) steps running time, where m is the number of edges and Δ is the degree of the graph. The algorithms differ in whether they address the synchronous or the asynchronous system model, and in what, where, and how data structures are maintained. The fourth algorithm, for the asynchronous model, has a space usage of O(D log Δ) bits at each robot and uses O(ΔD) steps, where D is the graph diameter. The fifth algorithm, for the asynchronous model, has a space usage of O(max(log k, log Δ)) bits at each robot and uses O((m - n)k) steps.
Concentration Fluctuations and Capacitive Response in Dense Ionic Solutions

We use molecular dynamics simulations in a constant potential ensemble to study the effects of solution composition on the electrochemical response of a double layer capacitor. We find that the capacitance first increases with ion concentration following its expected ideal solution behavior but decreases upon approaching a pure ionic liquid in agreement with recent experimental observations. The nonmonotonic behavior of the capacitance as a function of ion concentration results from the competition between the independent motion of solvated ions in the dilute regime and solvation fluctuations in the concentrated regime. Mirroring the capacitance, we find that the characteristic decay length of charge density correlations away from the electrode is also nonmonotonic. The correlation length first decreases with ion concentration as a result of better electrostatic screening but increases with ion concentration as a result of enhanced steric interactions. When charge fluctuations induced by correlated ion–so...
Optimal Bidding in Repeated Wireless Spectrum Auctions with Budget Constraints

Small operators who take part in secondary wireless spectrum markets typically have strict budget limits. In this paper, we study the bidding problem of a budget constrained operator in repeated secondary spectrum auctions. In existing truthful auctions, truthful bidding is the optimal strategy of a bidder. However, budget limits impact bidding behaviors and make bidding decisions complicated, since bidders may behave differently to avoid running out of money. We formulate the problem as a dynamic auction game between operators, where knowledge of other operators is limited due to the distributed nature of wireless networks/markets. We first present a Markov Decision Process (MDP) formulation of the problem and characterize the optimal bidding strategy of an operator, provided that opponents' bids are i.i.d. Next, we generalize the formulation to a Markov game that, in conjunction with model-free reinforcement learning approaches, enables an operator to make inferences about its opponents based on local observations. Finally, we present a fully distributed learning-based bidding algorithm which relies only on local information. Our numerical results show that our proposed learning-based bidding results in a better utility than truthful bidding.
Non-commutative projective Calabi–Yau schemes

Abstract The objective of the present article is to construct the first examples of (non-trivial) non-commutative projective Calabi–Yau schemes in the sense of Artin and Zhang [1] .
Ricci curvature and the manifold learning problem

Abstract Consider a sample of n points taken i.i.d. from a submanifold Σ of Euclidean space. We show that there is a way to estimate the Ricci curvature of Σ with respect to the induced metric from the sample. Our method is grounded in the notions of Carre du Champ for diffusion semi-groups, the theory of empirical processes and local Principal Component Analysis.
Reduced word manipulation: patterns and enumeration

We develop the technique of reduced word manipulation to give a range of results concerning reduced words and permutations more generally. We prove a broad connection between pattern containment and reduced words, which specializes to our previous work for vexillary permutations. We also analyze general tilings of Elnitsky’s polygon and demonstrate that these are closely related to the patterns in a permutation. Building on previous work for commutation classes, we show that reduced word enumeration is monotonically increasing with respect to pattern containment. Finally, we give several applications of this work. We show that a permutation and a pattern have equally many reduced words if and only if they have the same length (equivalently, the same number of 21-patterns) and that they have equally many commutation classes if and only if they have the same number of 321-patterns. We also apply our techniques to enumeration problems of pattern avoidance and give a bijection between 132-avoiding permutations of a given length and partitions of that same size, as well as refinements of these data and a connection to the Catalan numbers.
SCALAR CURVATURES OF HERMITIAN METRICS ON THE MODULI SPACE OF RIEMANN SURFACES

In this article we show that any finite cover of the moduli space of closed Riemann surfaces of g genus with g > 2 does not ad- mit any complete finite-volume Hermitian metric of non-negative scalar curvature. Moreover, we also show that the total mass of the scalar curvature of any almost Hermitian metric, which is equivalent to the Teichmuller metric, on any finite cover of the moduli space is negative provided that the scalar curvature is bounded from below.
Patterns in words of ordered set partitions

An ordered set partition of $\{1,2,\ldots,n\}$ is a partition with an ordering on the parts. Let $\mathcal{OP}_{n,k}$ be the set of ordered set partitions of $[n]$ with $k$ blocks. Godbole, Goyt, Herdan and Pudwell defined $\mathcal{OP}_{n,k}(\sigma)$ to be the set of ordered set partitions in $\mathcal{OP}_{n,k}$ avoiding a permutation pattern $\sigma$ and obtained the formula for $|\mathcal{OP}_{n,k}(\sigma)|$ when the pattern $\sigma$ is of length $2$. Later, Chen, Dai and Zhou found a formula algebraically for $|\mathcal{OP}_{n,k}(\sigma)|$ when the pattern $\sigma$ is of length $3$. In this paper, we define a new pattern avoidance for the set $\mathcal{OP}_{n,k}$, called $\mathcal{WOP}_{n,k}(\sigma)$, which includes the questions proposed by Godbole, Goyt, Herdan and Pudwell. We obtain formulas for $|\mathcal{WOP}_{n,k}(\sigma)|$ combinatorially for any $\sigma$ of length $ 3$. We also define 3 kinds of descent statistics on ordered set partitions and study the distribution of the descent statistics on $\mathcal{WOP}_{n,k}(\sigma)$ for $\sigma$ of length $3$.
Measuring Offensive Speech in Online Political Discourse

The Internet and online forums such as Reddit have become an increasingly popular medium for citizens to engage in political conversations. However, the online disinhibition effect resulting from the ability to use pseudonymous identities may manifest in the form of offensive speech, consequently making political discussions more aggressive and polarizing than they already are. Such environments may result in harassment and self-censorship from its targets. In this paper, we present preliminary results from a large-scale temporal measurement aimed at quantifying offensiveness in online political discussions. To enable our measurements, we develop and evaluate an offensive speech classifier. We then use this classifier to quantify and compare offensiveness in the political and general contexts. We perform our study using a database of over 168M Reddit comments made by over 7M pseudonyms between January 2015 and January 2017 -- a period covering several divisive political events including the 2016 US presidential elections.
EXPLICIT UPPER BOUNDS FOR L-FUNCTIONS ON THE CRITICAL LINE

We find an explicit upper bound for general L-functions on the critical line, assuming the Generalized Riemann Hypothesis, and give as illus- trative examples its application to some families of L-functions and Dedekind zeta functions. Further, this upper bound is used to obtain lower bounds be- yond which all eligible integers are represented by Ramanujan's ternary form and Kaplansky's ternary forms. This improves on previous work of Ono and Soundararajan (7) on Ramanujan's form and Reinke (8) on Kaplansky's form with a substantially easier proof.
Generalizing the Divisibility Property of Rectangle Domino Tilings

We introduce a class of graphs called compound graphs , which are constructed out of copies of a planar bipartite base graph , and explore the number of perfect matchings of compound graphs. The main result is that the number of matchings of every compound graph is divisible by the number of matchings of its base graph. Our approach is to use Kasteleyn's theorem to prove a key lemma, from which the divisibility theorem follows combinatorially. This theorem is then applied to provide a proof of Problem 21 of Propp's Enumeration of Matchings , a divisibility property of rectangles. Finally, we present a new proof, in the same spirit, of Ciucu's factorization theorem.
Models for homotopy categories of injectives and Gorenstein injectives

A natural generalization of locally noetherian and locally coherent categories leads us to define locally type FP∞ categories. They include not just all categories of modules over a ring, but also ...
Deep Sequential Models for Sampling-Based Planning

We demonstrate how a sequence model and a sampling-based planner can influence each other to produce efficient plans and how such a model can automatically learn to take advantage of observations of the environment. Sampling-based planners such as RRT generally know nothing of their environments even if they have traversed similar spaces many times. A sequence model, such as an HMM or LSTM, guides the search for good paths. The resulting model, called DeRRT*, observes the state of the planner and the local environment to bias the next move and next planner state. The neural-network-based models avoid manual feature engineering by co-training a convolutional network which processes map features and observations from sensors. We incorporate this sequence model in a manner that combines its likelihood with the existing bias for searching large unexplored Voronoi regions. This leads to more efficient trajectories with fewer rejected samples even in difficult domains such as when escaping bug traps. This model can also be used for dimensionality reduction in multi-agent environments with dynamic obstacles. Instead of planning in a high-dimensional space that includes the configurations of the other agents, we plan in a low-dimensional subspace relying on the sequence model to bias samples using the observed behavior of the other agents. The techniques presented here are general, include both graphical models and deep learning approaches, and can be adapted to a range of planners.
Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach

Syntactic tracking aims to classify a target's spatio-temporal trajectory by using natural language processing models. This paper proposes constrained stochastic context-free grammar (CSCFG) models for target trajectories confined to a roadmap. We present a particle filtering algorithm that exploits the CSCFG model structure to estimate the target's trajectory. This metalevel algorithm operates in conjunction with a base-level target tracking algorithm. Extensive numerical results using simulated ground moving target indicator radar measurements show useful improvement in both trajectory classification and target state (both coordinates and velocity) estimation.
Information-theoretically optimal sparse PCA

Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a lowrank representation of a data matrix with additional sparsity constraints on the obtained representation. We consider two probabilistic formulations of sparse PCA: a spiked Wigner and spiked Wishart (or spiked covariance) model. We analyze an Approximate Message Passing (AMP) algorithm to estimate the underlying signal and show, in the high dimensional limit, that the AMP estimates are information-theoretically optimal. As an immediate corollary, our results demonstrate that the posterior expectation of the underlying signal, which is often intractable to compute, can be obtained using a polynomial-time scheme. Our results also effectively provide a single-letter characterization of the sparse PCA problem.
Persistent Homology Lower Bounds on High-Order Network Distances

High-order networks are weighted hypergraphs collecting relationships between elements of tuples, not necessarily pairs. Valid metric distances between high-order networks have been defined but they are difficult to compute when the number of nodes is large. The goal here is to find tractable approximations of these network distances. The paper does so by mapping high-order networks to filtrations of simplicial complexes and showing that the distance between networks can be lower bounded by the difference between the homological features of their respective filtrations. Practical implications are explored by classifying weighted pairwise networks constructed from different generative processes and by comparing the coauthorship networks of engineering and mathematics academic journals. The persistent homology methods succeed in identifying different generative models, in discriminating engineering and mathematics communities, as well as in differentiating engineering communities with different research interests.
Mass equidistribution for Hecke eigenforms

We prove a conjecture of Rudnick and Sarnak on the mass equidistribution of Hecke eigenforms. This builds upon independent work of the authors.
Beauty Premiums Among Academics

Abstract This paper examines the effects of instructors’ attractiveness on student evaluations of their teaching. We build on previous studies by holding both observed and unobserved characteristics of the instructor and classes constant. Our identification strategy exploits the fact that many instructors, in addition to traditional teaching in the classroom, also teach in the online environment, where attractiveness is either unknown or less salient. We utilize multiple attractiveness measures, including facial symmetry software, subjective evaluations, and a novel, proxy methodology that resembles a “Keynesian Beauty Contest.” We identify a substantial beauty premium in face-to-face classes for women but not for men. While gender on its own does not impact teaching evaluation scores, female instructors rated as more attractive receive higher instructional ratings. This result holds across several beauty measures, given a multitude of controls and while controlling for unobserved instructor characteristics and skills. Notably, the positive relationship between beauty and teaching effectiveness is not found in the online environment, suggesting the observed premium may be due to discrimination.
On Asymptotic Standard Normality of the Two Sample Pivot

The asymptotic solution to the problem of comparing the means of two heteroscedastic populations, based on two random samples from the populations, hinges on the pivot underpinning the construction...
Disaster privacy/privacy disaster

Abstract Privacy expectations during disasters differ significantly from nonemergency situations. This paper explores the actual privacy practices of popular disaster apps, highlighting location in...
The Power of Context in Networks: Ideal Point Models with Social Interactions.

Game theory has been widely used for modeling strategic behaviors in networked multiagent systems. However, the context within which these strategic behaviors take place has received limited attention. We present a model of strategic behavior in networks that incorporates the behavioral context. We focus on the contextual aspects of congressional voting. A senator's decision to vote yea or nay on a bill comes as a result of their ideologies, agendas, and their interactions with other senators. One salient model in political science is the ideal point model , which assigns each senator and each bill a number on the real line of political spectrum. These points then allow for prediction of future voting behavior. We extend the classical ideal point model with network-structured interactions among senators. In contrast to the ideal point model's prediction of individual voting behavior, we predict joint voting behaviors in a game-theoretic fashion. Our model also includes the characteristics of a bill. This allows it to outperform previous models that solely focus on the networked interactions among senators with no bill-specific parameters. We focus on two fundamental questions: learning the model using real-world data and computing stable outcomes of the model in order to predict joint voting behaviors. We demonstrate the effectiveness of our model through experiments using data from the 114th U.S. Congress.
GuSTO: Guaranteed Sequential Trajectory optimization via Sequential Convex Programming

Sequential Convex Programming (SCP) has recently seen a surge of interest as a tool for trajectory optimization. However, most available methods lack rigorous performance guarantees and they are often tailored to specific optimal control setups. In this paper, we present GuSTO (Guaranteed Sequential Trajectory optimization), an algorithmic framework to solve trajectory optimization problems for control-affine systems with drift. GuSTO generalizes earlier SCP-based methods for trajectory optimization (by addressing, for example, goal-set constraints and problems with either fixed or free final time) and enjoys theoretical convergence guarantees in terms of convergence to, at least, a stationary point. The theoretical analysis is further leveraged to devise an accelerated implementation of GuSTO, which originally infuses ideas from indirect optimal control into an SCP context. Numerical experiments on a variety of trajectory optimization setups show that GuSTO generally outperforms current state-of-the-art approaches in terms of success rates, solution quality, and computation times.
PIKfyve Deficiency in Myeloid Cells Impairs Lysosomal Homeostasis in Macrophages and Promotes Systemic Inflammation in Mice.

Macrophages are professional phagocytes that are essential for host defense and tissue homeostasis. Proper membrane trafficking and degradative functions of the endolysosomal system are known to be critical for the function of these cells. We have found that PIKfyve, the kinase that synthesizes the endosomal phosphoinositide phosphatidylinositol-3,5-bisphosphate, is an essential regulator of lysosomal biogenesis and degradative functions in macrophages. Genetically engineered mice lacking PIKfyve in their myeloid cells (PIKfyvefl/fl LysM-Cre) develop diffuse tissue infiltration of foamy macrophages, hepatosplenomegaly, and systemic inflammation. PIKfyve loss in macrophages causes enlarged endolysosomal compartments and impairs the lysosomal degradative function. Moreover, PIKfyve deficiency increases the cellular levels of lysosomal proteins. Although PIKfyve deficiency reduced the activation of mTORC1 pathway and was associated with increased cleavage of TFEB proteins, this does not translate into transcriptional activation of lysosomal genes, suggesting that PIKfyve modulates the abundance of lysosomal proteins by affecting the degradation of these proteins. Our study shows that PIKfyve modulation of lysosomal degradative activity and protein expression is essential to maintain lysosomal homeostasis in macrophages.
Sunflowers of convex open sets

Abstract A sunflower is a collection of sets { U 1 , … , U n } such that the pairwise intersection U i ∩ U j is the same for all choices of distinct i and j. We study sunflowers of convex open sets in R d , and provide a Helly-type theorem describing a certain “rigidity” that they possess. In particular we show that if { U 1 , … , U d + 1 } is a sunflower in R d , then any hyperplane that intersects all U i must also intersect ⋂ i = 1 d + 1 U i . We use our results to describe a combinatorial code C n for all n ≥ 2 which is on the one hand minimally non-convex, and on the other hand has no local obstructions. Along the way we further develop the theory of morphisms of codes, and establish results on the covering relation in the poset P Code .
Policy Evaluation with Latent Confounders via Optimal Balance

Evaluating novel contextual bandit policies using logged data is crucial in applications where exploration is costly, such as medicine. But it usually relies on the assumption of no unobserved confounders, which is bound to fail in practice. We study the question of policy evaluation when we instead have proxies for the latent confounders and develop an importance weighting method that avoids fitting a latent outcome regression model. Surprisingly, we show that there exist no single set of weights that give unbiased evaluation regardless of outcome model, unlike the case with no unobserved confounders where density ratios are sufficient. Instead, we propose an adversarial objective and weights that minimize it, ensuring sufficient balance in the latent confounders regardless of outcome model. We develop theory characterizing the consistency of our method and tractable algorithms for it. Empirical results validate the power of our method when confounders are latent.
Large-scale multiple hypothesis testing with the normal-beta prime prior

ABSTRACTWe revisit the problem of simultaneously testing the means of n independent normal observations under sparsity. We take a Bayesian approach to this problem by studying a scale-mixture prior...
Three-dimensional crystals of adaptive knots

Starting with Gauss and Kelvin, knots in fields were postulated to behave like particles, but experimentally they were found only as transient features or required complex boundary conditions to exist and could not self-assemble into three-dimensional crystals. We introduce energetically stable, micrometer-sized knots in helical fields of chiral liquid crystals. While spatially localized and freely diffusing in all directions, they resemble colloidal particles and atoms, self-assembling into crystalline lattices with open and closed structures. These knots are robust and topologically distinct from the host medium, though they can be morphed and reconfigured by weak stimuli under conditions such as those in displays. A combination of energy-minimizing numerical modeling and optical imaging uncovers the internal structure and topology of individual helical field knots and the various hierarchical crystalline organizations that they form.
F-BAR Cdc15 Promotes Cdc42 Activation During Cytokinesis and Cell Polarization in Schizosaccharomyces pombe

Cdc42, a Rho-family GTPase, is a master regulator of cell polarity. Recently, it has been shown that Cdc42 also facilitates proper cytokinesis in the fission yeast Schizosaccharomyces pombe. Cdc42 is activated by two partially redundant GEFs, Gef1 and Scd1. Although both GEFs activate Cdc42, their deletion mutants display distinct phenotypes, indicating that they are differentially regulated by an unknown mechanism. During cytokinesis, Gef1 localizes to the division site and activates Cdc42 to initiate ring constriction and septum ingression. Here, we report that the F-BAR protein Cdc15 promotes Gef1 localization to its functional sites. We show that cdc15 promotes Gef1 association with cortical puncta at the incipient division site to activate Cdc42 during ring assembly. Moreover, cdc15 phospho-mutants phenocopy the polarity phenotypes of gef1 mutants. In a hypermorphic cdc15 mutant, Gef1 localizes precociously to the division site and is readily detected at the cortical patches and the cell cortex. Correspondingly, the hypermorphic cdc15 mutant shows increased bipolarity during interphase and precocious Cdc42 activation at the division site during cytokinesis. Finally, loss of gef1 in hypermorphic cdc15 mutants abrogates the increased bipolarity and precocious Cdc42 activation phenotype. We did not see any change in the localization of the other GEF Scd1 in a Cdc15-dependent manner. Our data indicate that Cdc15 facilitates Cdc42 activation at the division site during cytokinesis at the cell cortex to promote bipolarity and this is mediated by promoting Gef1 localization to these sites.
Robotic Assisted Laparoscopic Resection of Anterior Adenomyoma

Video Objective This video demonstrates a unique technique of anterior adenomyoma resection and repair of the defect laparoscopically. Setting Our patient is a 40 yo G 3P0020 with secondary infertility and had multiple failed euploid frozen embryo transfers. She was diagnosed to have an anterior adenomyoma and decision was taken to excise the adenomyoma to optimize the fertility outcome. Interventions We performed a robotic assisted laparoscopic anterior adenomyomectomy where we excised the adenomyoma leaving approximately 1 cm of myometrium to the endometrium and serosa and then close d the defect by the overlap flap technique. Conclusion We describe an unique technique for optimal adenomyoma resection and then the effective way to close the uterine defect. This technique is easily adaptable and reproducible. This repair technique achieves good myometrial thickness after closure that reduces the chance of uterine rupture and the addition of extra suture layer decreases the chance of hematoma formation.
Item Aggregation and Column Generation for Online-Retail Inventory Placement

Problem definition : This paper studies an online retailer’s problem of choosing fulfillment centers in which to place items. We formulate the problem as a mixed-integer program that models thousan...
Not Learning From Failure—the Greatest Failure of All

Our society celebrates failure as a teachable moment. Yet in five studies (total N = 1,674), failure did the opposite: It undermined learning. Across studies, participants answered binary-choice questions, following which they were told they answered correctly (success feedback) or incorrectly (failure feedback). Both types of feedback conveyed the correct answer, because there were only two answer choices. However, on a follow-up test, participants learned less from failure feedback than from success feedback. This effect was replicated across professional, linguistic, and social domains-even when learning from failure was less cognitively taxing than learning from success and even when learning was incentivized. Participants who received failure feedback also remembered fewer of their answer choices. Why does failure undermine learning? Failure is ego threatening, which causes people to tune out. Participants learned less from personal failure than from personal success, yet they learned just as much from other people's failure as from others' success. Thus, when ego concerns are muted, people tune in and learn from failure.
Ends and Simple Coarse Structures

This paper is devoted to introducing coarse structures in a very simple way, namely as an equivalence relation on the set of simple ends. As an application we show that Gromov boundary of every hyperbolic space is an example of a Higson corona and each Freudenthal compactification is an example of a Higson compactification.
The Cyclic AMP Receptor Protein Regulates Quorum Sensing and Global Gene Expression in Yersinia pestis during Planktonic Growth and Growth in Biofilms.

Cyclic AMP (cAMP) receptor protein (Crp) is an important transcriptional regulator of Yersinia pestis Expression of crp increases during pneumonic plague as the pathogen depletes glucose and forms large biofilms within lungs. To better understand control of Y. pestis Crp, we determined a 1.8-A crystal structure of the protein-cAMP complex. We found that compared to Escherichia coli Crp, C helix amino acid substitutions in Y. pestis Crp did not impact the cAMP dependency of Crp to bind DNA promoters. To investigate Y. pestis Crp-regulated genes during plague pneumonia, we performed RNA sequencing on both wild-type and Δcrp mutant bacteria growing in planktonic and biofilm states in minimal media with glucose or glycerol. Y. pestis Crp was found to dramatically alter expression of hundreds of genes in a manner dependent upon carbon source and growth state. Gel shift assays confirmed direct regulation of the malT and ptsG promoters, and Crp was then linked to Y. pestis growth on maltose as a sole carbon source. Iron regulation genes ybtA and fyuA were found to be indirectly regulated by Crp. A new connection between carbon source and quorum sensing was revealed as Crp was found to regulate production of acyl-homoserine lactones (AHLs) through direct and indirect regulation of genes for AHL synthetases and receptors. AHLs were subsequently identified in the lungs of Y. pestis-infected mice when crp expression was highest in Y. pestis biofilms. Thus, in addition to the well-studied pla gene, other Crp-regulated genes likely have important functions during plague infection.IMPORTANCE Bacterial pathogens have evolved extensive signaling pathways to translate environmental signals into changes in gene expression. While Crp has long been appreciated for its role in regulating metabolism of carbon sources in many bacterial species, transcriptional profiling has revealed that this protein regulates many other aspects of bacterial physiology. The plague pathogen Y. pestis requires this global regulator to survive in blood, skin, and lungs. During disease progression, this organism adapts to changes within these niches. In addition to regulating genes for metabolism of nonglucose sugars, we found that Crp regulates genes for virulence, metal acquisition, and quorum sensing by direct or indirect mechanisms. Thus, this single transcriptional regulator, which responds to changes in available carbon sources, can regulate multiple critical behaviors for causing disease.
Nanoscale conditions for ductile void nucleation in copper: Vacancy condensation and the growth-limited microstructural state

Abstract Ductile rupture or tearing usually involves structural degradation from the nucleation and growth of voids and their coalescence into cracks. Although some materials contain preexisting pores, the first step in failure is often the formation of voids. Because this step can govern both the failure strain and the fracture mechanism, it is critical to understand the mechanisms of void nucleation and the enabling microstructural configurations which give rise to nucleation. To understand the role of dislocations during void nucleation, the present study presents ex-situ cross-sectional observations of interrupted deformation experiments revealing incipient, subsurface voids in a copper material containing copper oxide inclusions. The local microstructural state was evaluated using electron backscatter diffraction (EBSD), electron channeling contrast (ECC), transmission electron microscopy (TEM), and transmission kikuchi diffraction (TKD). Surprisingly, before substantial growth and coalescence had occurred, the deformation process had resulted in the nucleation of a high density of nanoscale (≈50 nm) voids in the deeply deformed neck region where strains were on the order of 1.5. Such a proliferation of nucleation sites immediately suggests that the rupture process is limited by void growth, not nucleation. With regard to void growth, analysis of more than 20 microscale voids suggests that dislocation boundaries facilitate the growth process. The present observations call into question prior assumptions on the role of dislocation pile-ups and provide new context for the formulation of revised ductile rupture models. While the focus of this study is on damage accumulation in a highly ductile metal containing small, well-dispersed particles, these results are also applicable to understanding void nucleation in engineering alloys.
Bayesian sparse multiple regression for simultaneous rank reduction and variable selection

We develop a Bayesian methodology aimed at simultaneously estimating low-rank and row-sparse matrices in a high-dimensional multiple-response linear regression model. We consider a carefully devised shrinkage prior on the matrix of regression coefficients which obviates the need to specify a prior on the rank, and shrinks the regression matrix towards low-rank and row-sparse structures. We provide theoretical support to the proposed methodology by proving minimax optimality of the posterior mean under the prediction risk in ultra-high dimensional settings where the number of predictors can grow sub-exponentially relative to the sample size. A one-step post-processing scheme induced by group lasso penalties on the rows of the estimated coefficient matrix is proposed for variable selection, with default choices of tuning parameters. We additionally provide an estimate of the rank using a novel optimization function achieving dimension reduction in the covariate space. We exhibit the performance of the proposed methodology in an extensive simulation study and a real data example.
Evolutionary Clustering via Message Passing

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this article we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.
Unexpected entanglement dynamics in semidilute blends of supercoiled and ring DNA.

Blends of polymers of different topologies, such as ring and supercoiled, naturally occur in biology and often exhibit emergent viscoelastic properties coveted in industry. However, due to their complexity, along with the difficulty of producing polymers of different topologies, the dynamics of topological polymer blends remains poorly understood. We address this void by using both passive and active microrheology to characterize the linear and nonlinear rheological properties of blends of relaxed circular and supercoiled DNA. We characterize the dynamics as we vary the concentration from below the overlap concentration c* to above (0.5c* to 2c*). Surprisingly, despite working at the dilute–semidilute crossover, entanglement dynamics, such as elastic plateaus and multiple relaxation modes, emerge. Finally, blends exhibit an unexpected sustained elastic response to nonlinear strains not previously observed even in well-entangled linear polymer solutions.
A Two-stage Universal Input Charger with Wide Output Voltage Range

This paper presents the design of a two-stage 50W portable charger architecture with universal ac input and 5V/25W, 9V/45W, 12V/50W output. A 98.5% efficient valley-switched boost converter operating at 0.5-2MHz enables a 47% reduction in the buffer capacitor volume while a stacked-bridge LLC converter with a Variable-Inverter-Rectifier-Transformer (VIRT) offers greater than 96% efficiency across the wide output voltage range. The design demonstrates the potential and trade-offs of a two-stage architecture at this operating power.
Public’s views of risk assessment algorithms and pretrial decision making.

Risk assessment algorithms are increasingly–and controversially–being used to inform whether criminal defendants are released or held in custody prior to their adjudications. A representative sample of Californians (n=420)–the most recent state to consider eliminating cash bail and adopting an algorithmic approach to pretrial detention–was used to assess public knowledge and general support for the new law. The sample evidenced limited awareness of bail reform, was mixed in support of change to the current system, and believed that an algorithm would augment rather than decrease racial and socioeconomic disparities. In terms of actually implementing a risk assessment algorithm for the purpose of pretrial decision making, it is ultimately a decision maker who must apply a decision threshold and determine whether a given risk level is sufficient to occasion a particular course of action (e.g., deny pretrial release). The sample was also queried about their pretrial decision thresholds. The average respondent’s decision threshold for “low risk” (or pretrial release) was 33%, indicating a 33% or less likelihood of failing to reappear or committing a new crime was tolerable, and 60% for “high risk” (or confinement), indicating a likelihood of 60% or greater of failing to reappear or committing a new crime was acceptable to deny a defendant release. Incorporating the public’s values into the decision-making process is likely to promote the legitimacy of the use of risk assessment algorithms in the criminal justice system more generally.
Financing Direct Democracy: Revisiting the Research on Campaign Spending and Citizen Initiatives

The conventional view in the direct democracy literature is that spending against a measure is more effective than spending in favor of a measure, but the empirical results underlying this conclusion have been questioned by recent research. We argue that the conventional finding is driven by the endogenous nature of campaign spending: initiative proponents spend more to support their ballot measure when, without that spending, their ballot measure is more likely to fail. We address this endogeneity by using an instrumental variables approach to analyze a comprehensive data set of ballot propositions in California from 1976 to 2004. We find that both support and opposition spending on citizen initiatives have strong, statistically significant, and countervailing effects. We confirm this finding by looking at time series data from early polling on a subset of these measures. Both analyses show that spending in favor of citizen initiatives substantially increases their chances of passage, just as opposition spending decreases this likelihood. The Author 2011. Published by Oxford University Press on behalf of Yale University. All rights reserved. For Permissions, please email: journals.permissions@oup.com, Oxford University Press.
Central Values for Clebsch–Gordan Coefficients

We develop further properties of the matrices M(m, n, k) defined by the author and W. G. Kim in a previous work. In particular, we continue an alternative approach to the theory of Clebsch–Gordan coefficients in terms of combinatorics and convex geometry. New features include a censorship rule for zeros, a sequence of 36-pointed stars of zeros, and another proof of Dixon’s Identity. As a major application, we reinterpret the work of Raynal et al. on vanishing Clebsch–Gordan coefficients as a “middle-out” approach to computing M(m, n, k).
Deterministic Debt Cycles in Open Economies with Flow Collateral Constraints

Abstract This paper establishes the existence of endogenous cycles in infinite-horizon open economy models with a flow collateral constraint. It shows that for plausible parameter configurations, the economy has a unique equilibrium exhibiting deterministic cycles in which periods of debt growth are followed by periods of debt deleveraging. In particular, three-period cycles exist, which implies by the Li-Yorke Theorem the presence of cycles of any periodicity and chaos. The paper also shows that deterministic cycles are absent in the Ramsey optimal allocation providing a justification for macroprudential policies even in the absence of uncertainty.
On the translates of general dyadic systems on $\mathbb{R}$

Many techniques in harmonic analysis use the fact that a continuous object can be written as a sum (or an intersection) of dyadic counterparts, as long as those counterparts belong to an adjacent dyadic system. Here we generalize the notion of adjacent dyadic system and explore when it occurs, leading to some new and perhaps surprising classifications. In particular, we show that every dyadic grid is determined by two parameters, the shift and the location; moreover two dyadic grids form an adjacent dyadic system if and only if their shifts and locations satisfy readily verifiable conditions.
Rank Aggregation via Heterogeneous Thurstone Preference Models

We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Luce (BTL) model for pairwise comparisons to heterogeneous populations of users. Under this framework, we also propose a rank aggregation algorithm based on alternating gradient descent to estimate the underlying item scores and accuracy levels of different users simultaneously from noisy pairwise comparisons. We theoretically prove that the proposed algorithm converges linearly up to a statistical error which matches that of the state-of-the-art method for the single-user BTL model. We evaluate the proposed HTM model and algorithm on both synthetic and real data, demonstrating that it outperforms existing methods.