cssi/SciSciGPT-SciSciNet huggingface dataset:
Measurement of binding specificity between T cell lymphomas and murine leukemia viruses

We have previously reported the presence of receptors on radiation leukemia virus (RadLV)-induced thymomas and malignant thymocytes from AKR mice which specifically bind retrovirus produced by these T cell clones. These receptors have been shown to have specificity for virus reminiscent of an immune-specific receptor. Previous studies on T cell lymphoma binding to retroviruses have involved measurement of the interaction of labelled virus with cells using fluorescence-activated cell sorter (FACS) analysis (McGrath et al., J. Virol. (1978) 25, 923; McGrath and Weissman, Cell (1979) 17, 65; Weissman and McGrath, Curr. Top. Microbiol. Immunol. (1982) 98, 103). Here we report development of an assay for measuring lymphoma binding to virus, prepared as an immunoabsorbent adhered to a microtiter plate. Using this assay, we have shown that only T and not B cell lymphomas can bind to T cell-tropic viruses, and some cell lines have greatest specificity for homologous virus. The AKR-derived T cell lymphomas, SL3 and KKT-2, show greater specificity for leukemogenic AKR viruses, than for an AKR xenotropic virus or the recombinant AKR virus, MCF247. The RadLV-induced T cell lines, C6VL/1 and BL/VL3, have been found to bind cross-reactively to several different murine leukemia viruses (MuLVs). RadLV-induced T cell lymphomas do have greater specificity for their cognate retroviruses since free, homologous retrovirus can best block the interaction between cells and virus adhered to the wells of a microtiter plate. Cross-reactive interactions are more easily demonstrated by this assay, probably because low avidity interactions are stabilized as a result of the mode of virus presentation. Binding specificity for retroviral envelope determinants has been demonstrated using a rat anti-retroviral antiserum prepared as an F(ab)1 fragment. This antiserum can inhibit the interaction between the C6VL/1 thymoma and its RadLV virus. Specificity of this antibody for a gp70-like protein was confirmed by NaDodSO4-polyacrylamide gel electrophoresis (PAGE) and by loss of this activity after absorption of antibody on virus. Antibodies specific for RadLV/VL3 gp70 determinants can inhibit the interaction of C6VL/1 with RadLV/VL3 suggesting that cross-reactive binding to heterologous virus is also specific for a gp70 viral env determinant.
Measuring Districts' Monthly Economic Activity from Outer Space

Evening-hour luminosity observed using satellites is a good proxy for economic activity. The strengths of measuring economic activity using nightlight measurements include that the data capture informal activity, are available in near real-time, are cheap to obtain, and can be used to conduct very spatially granular analysis. This paper presents a measure of monthly economic activity at the district level based on cleaned Visible Infrared Imaging Radiometer Suite nightlight and rural population. The paper demonstrates that this new method can shed light on recent episodes in South Asia: first, the 2015 earthquake in Nepal; second, demonetization in India; and, third, violent conflict outbreaks in Afghanistan.
Measurement of nitrate and nitrite in biopsy-sized muscle samples using HPLC.

Measurement of nitrate and especially nitrite in small, i.e., biopsy-sized, muscle samples is analytically challenging. We have developed a precise, accurate, and convenient method for doing so usi...
Market Failure in Kidney Exchange

We show that kidney exchange markets suffer from market failures whose remedy could increase transplants by 30 to 63 percent. First, we document that the market is fragmented and inefficient; most transplants are arranged by hospitals instead of national platforms. Second, we propose a model to show two sources of inefficiency: hospitals only partly internalize their patients' benefits from exchange, and current platforms suboptimally reward hospitals for submitting patients and donors. Third, we calibrate a production function and show that individual hospitals operate below efficient scale. Eliminating this inefficiency requires either a mandate or a combination of new mechanisms and reimbursement reforms.
Rapid Adaptation of Neural Machine Translation to New Languages

This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods based on starting with massively multilingual “seed models”, which can be trained ahead-of-time, and then continuing training on data related to the LRL. We contrast a number of strategies, leading to a novel, simple, yet effective method of “similar-language regularization”, where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data. Experiments demonstrate that massively multilingual models, even without any explicit adaptation, are surprisingly effective, achieving BLEU scores of up to 15.5 with no data from the LRL, and that the proposed similar-language regularization method improves over other adaptation methods by 1.7 BLEU points average over 4 LRL settings.
Identification and Biochemical Characterization of a Novel Protein Phosphatase 2C-Like Ser/Thr Phosphatase in Escherichia coli.

ABSTRACT In bacteria, signaling phosphorylation is thought to occur primarily on His and Asp residues. However, phosphoproteomic surveys over the past decade in phylogenetically diverse bacteria have identified numerous proteins that are phosphorylated on Ser and/or Thr residues. Consistently, genes encoding Ser/Thr kinases are present in many bacterial genomes, such as that of Escherichia coli, which encodes at least three Ser/Thr kinases. Since Ser/Thr phosphorylation is a stable modification, a dedicated phosphatase is necessary to allow reversible regulation. Ser/Thr phosphatases belonging to several conserved families are found in bacteria. One family of particular interest are Ser/Thr phosphatases, which have extensive sequence and structural homology to eukaryotic Ser/Thr protein phosphatase 2C (PP2C) phosphatases. These proteins, called eukaryote-like Ser/Thr phosphatases (eSTPs), have been identified in a number of bacteria but not in E. coli. Here, we describe a previously unknown eSTP encoded by an E. coli open reading frame (ORF), yegK, and characterize its biochemical properties, including its kinetics, substrate specificity, and sensitivity to known phosphatase inhibitors. We investigate differences in the activity of this protein in closely related E. coli strains. Finally, we demonstrate that this eSTP acts to dephosphorylate a novel Ser/Thr kinase that is encoded in the same operon. IMPORTANCE Regulatory protein phosphorylation is a conserved mechanism of signaling in all biological systems. Recent phosphoproteomic analyses of phylogenetically diverse bacteria, including the model Gram-negative bacterium Escherichia coli, demonstrate that many proteins are phosphorylated on serine or threonine residues. In contrast to phosphorylation on histidine or aspartate residues, phosphorylation of serine and threonine residues is stable and requires the action of a partner Ser/Thr phosphatase to remove the modification. Although a number of Ser/Thr kinases have been reported in E. coli, no partner Ser/Thr phosphatases have been identified. Here, we biochemically characterize a novel Ser/Thr phosphatase that acts to dephosphorylate a Ser/Thr kinase that is encoded in the same operon.
Bottom-Up Abstractive Summarization

Neural summarization produces outputs that are fluent and readable, but which can be poor at content selection, for instance often copying full sentences from the source document. This work explores the use of data-efficient content selectors to over-determine phrases in a source document that should be part of the summary. We use this selector as a bottom-up attention step to constrain the model to likely phrases. We show that this approach improves the ability to compress text, while still generating fluent summaries. This two-step process is both simpler and higher performing than other end-to-end content selection models, leading to significant improvements on ROUGE for both the CNN-DM and NYT corpus. Furthermore, the content selector can be trained with as little as 1,000 sentences making it easy to transfer a trained summarizer to a new domain.
Search for an axion-induced oscillating electric dipole moment for electrons using atomic magnetometers

We propose an experimental search for an axion-induced oscillating electric dipole moment (OEDM) for electrons using state-of-the-art alkali vapor-cell atomic magnetometers. The axion is a hypothesized new fundamental particle which can resolve the strong charge-parity problem and be a prominent dark matter candidate. This experiment utilizes an atomic magnetometer as both a source of optically polarized electron spins and a magnetic-field sensor. The interaction of the axion field, oscillating at a frequency equal to the axion mass, with an electron spin induces a sizable OEDM of the electron at the same frequency as the axion field. When the alkali vapor is subjected to an electric field and a magnetic field, the electron OEDM interacts with the electric field, resulting in an electron spin precession at the spin's Larmor frequency in the magnetic field. The resulting precession signal can be sensitively detected with a probe laser beam of the atomic magnetometer. We estimate that the experiment is sensitive to the axion-photon interaction in ultralight axion masses from ${10}^{\ensuremath{-}15}$ to ${10}^{\ensuremath{-}10}\text{ }\text{ }\mathrm{eV}$. It is able to improve the current experimental limit up to 5 orders of magnitude, exploring new axion parameter spaces.
Large-scale GW calculations on pre-exascale HPC systems

Abstract The ab initio GW approach is a rigorous Green’s-function-based framework that can be employed to compute electronic excitation properties of a wide variety of materials such as extended systems, molecules, as well as confined and nanostructured materials with a very satisfactory accuracy. However, GW calculations on complex systems are often hindered by the high computational cost associated with the method. Here, we demonstrate how to significantly speedup GW calculations with a novel algorithm for the computationally intense kernel based on a non-blocking chunked cyclic communication scheme that minimizes latency in MPI messages, allows for overlapping of communication and computation, and improves cache usage in matrix multiplication operations. The optimized version of the code, implemented in the BerkeleyGW software package, is capable of scaling well to the full Cori computer at NERSC (Cray XC40) and achieves over 11 Peta FLOP/s of sustained performance. We showcase our work by performing large-scale GW calculations of defect structures on silicon, which require simulation cells containing over 1700 atoms, and which can now be efficiently executed in just a few minutes on large pre-exascale high-performance computing systems.
A Deterministic Algorithm for Bridging Anaphora Resolution

Previous work on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013) use syntactic preposition patterns to calculate word relatedness. However, such patterns only consider NPs’ head nouns and hence do not fully capture the semantics of NPs. Recently, Hou (2018) created word embeddings (embeddings_PP) to capture associative similarity (i.e., relatedness) between nouns by exploring the syntactic structure of noun phrases. But embeddings_PP only contains word representations for nouns. In this paper, we create new word vectors by combining embeddings_PP with GloVe. This new word embeddings (embeddings_bridging) are a more general lexical knowledge resource for bridging and allow us to represent the meaning of an NP beyond its head easily. We therefore develop a deterministic approach for bridging anaphora resolution, which represents the semantics of an NP based on its head noun and modifications. We show that this simple approach achieves the competitive results compared to the best system in Hou et al. (2013) which explores Markov Logic Networks to model the problem. Additionally, we further improve the results for bridging anaphora resolution reported in Hou (2018) by combining our simple deterministic approach with Hou et al. (2013)’s best system MLN II.
Defining the developmental program leading to meiosis in maize

In multicellular organisms, the entry into meiosis is a complex process characterized by increasing meiotic specialization. Using single-cell RNA sequencing, we reconstructed the developmental program into maize male meiosis. A smooth continuum of expression stages before meiosis was followed by a two-step transcriptome reorganization in leptotene, during which 26.7% of transcripts changed in abundance by twofold or more. Analysis of cell-cycle gene expression indicated that nearly all pregerminal cells proliferate, eliminating a stem-cell model to generate meiotic cells. Mutants defective in somatic differentiation or meiotic commitment expressed transcripts normally present in early meiosis after a delay; thus, the germinal transcriptional program is cell autonomous and can proceed despite meiotic failure.
Cyclin B2 is required for progression through meiosis in mouse oocytes

Cyclins associate with cyclin-dependent serine/threonine kinase 1 (CDK1) to generate the M phase-promoting factor (MPF) activity essential for progression through mitosis and meiosis. Although cyclin B1 (CCNB1) is required for embryo development, previous studies concluded that CCNB2 is dispensable for cell cycle progression. Given previous findings of high Ccnb2 mRNA translation rates in prophase-arrested oocytes, we re-evaluated the role of this cyclin during meiosis. Ccnb2 -/- oocytes underwent delayed germinal vesicle breakdown and showed defects during the metaphase-to-anaphase transition. This defective maturation was associated with compromised Ccnb1 and Moloney sarcoma oncogene (Mos) mRNA translation, delayed spindle assembly and increased errors in chromosome segregation. Given these defects, a significant percentage of oocytes failed to complete meiosis I because the spindle assembly checkpoint remained active and anaphase-promoting complex/cyclosome function was inhibited. In vivo, CCNB2 depletion caused ovulation of immature oocytes, premature ovarian failure, and compromised female fecundity. These findings demonstrate that CCNB2 is required to assemble sufficient pre-MPF for timely meiosis re-entry and progression. Although endogenous cyclins cannot compensate, overexpression of CCNB1/2 rescues the meiotic phenotypes, indicating similar molecular properties but divergent modes of regulation of these cyclins.
Abuse, mastery, and health among lesbian, bisexual, and two-spirit American Indian and Alaska Native women

This reprinted article originally appeared in Cultural Diversity and Ethnic Minority Psychology, 2009, Vol. 15, (No. 3), 275–284. (The following abstract of the original article appeared in record 2009-10166-008). American Indian and Alaska Native (AIAN) women have endured a history of colonial oppression in the United States. Current manifestations of colonization include an epidemic of violence toward AIAN women, who often are sexually and physically abused from early on in life. Such violence may erode AIAN women's sense of agency or mastery and contribute to their poor physical and mental health outcomes. AIAN women who identify as lesbian, bisexual, or “two-spirit” appear to experience disproportionate levels of violence and may be particularly vulnerable to disparities in health outcomes. In this study, 152 sexual minority AIAN women were interviewed as part of an investigation addressing the health concerns of two-spirit persons. Participants reported disturbingly high prevalence of both sexual (85%) and physical (78%) assault, both of which were associated with worse overall mental and physical health. These relationships generally were mediated by a diminished sense of control or mastery. The need to indigenize the concept of mastery is discussed, as is the urgency of interventions to work toward decreasing levels of abuse and increasing mastery among sexual minority AIAN women. (PsycINFO Database Record (c) 2010 APA, all rights reserved)
Government Procurement and Changes in Firm Transparency

The government requires its suppliers to have certain internal information processes to reduce uncertainty about their ability to fulfill their commitments. I argue that these requirements improve ...
Generalizability of findings from randomized controlled trials is limited in the leading general medical journals.

Abstract Objectives To document reporting of study characteristics of randomized controlled trials (RCTs) in the four leading general medical journals and to appraise the generalizability of the evidence. Study Design and Setting All RCTs in BMJ, JAMA, Lancet, and NEJM from January 1, 2017 to September 30, 2017 were searched by hand, and data were extracted according to the benchmarking method. Results Hundred sixty-one RCTs were found; 67% assessed pharmacological therapy. The percentages of adequate documentation were patients’ path before randomization 3% to 33% of trials; characteristics of the health care settings 0% to 75%; at least two comorbid conditions 25% to 50%; at least one measure was reported of functioning 42% to 54%, of behavioral factors 25% to 58%, of environmental factors 3% to 25%, and of inequity-related factors 28% to 68%; cointerventions 6% to 25%; and reasons for dropping out of follow-up 39% to 100%. Conclusion Almost all RCTs showed deficiencies in description of patient selection and study setting and in reporting of patient characteristics related to functioning, comorbidities, and to behavioral, environmental, and inequity factors. The findings indicate that generalizability of this evidence may be limited. The benchmarking method can be used for planning and appraisal of clinical trials and systematic reviews.
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling

Many real-world systems studied are governed by complex, nonlinear dynamics. By modeling these dynamics, we can gain insight into how these systems work, make predictions about how they will behave, and develop strategies for controlling them. While there are many methods for modeling nonlinear dynamical systems, existing techniques face a trade off between offering interpretable descriptions and making accurate predictions. Here, we develop a class of models that aims to achieve both simultaneously, smoothly interpolating between simple descriptions and more complex, yet also more accurate models. Our probabilistic model achieves this multi-scale property through a hierarchy of locally linear dynamics that jointly approximate global nonlinear dynamics. We call it the tree-structured recurrent switching linear dynamical system. To fit this model, we present a fully-Bayesian sampling procedure using Polya-Gamma data augmentation to allow for fast and conjugate Gibbs sampling. Through a variety of synthetic and real examples, we show how these models outperform existing methods in both interpretability and predictive capability.
Chern-Simons theory from M5-branes and calibrated M2-branes

We study a sector of the 5d maximally supersymmetric Yang-Mills theory on S5 consisting of 1/8-BPS Wilson loop operators contained within a great S3 inside S5. We conjecture that these observables are described by a 3d Chern Simons theory on S3, analytically continued to a pure imaginary Chern-Simons level. Therefore, the expectation values of these 5d Wilson loops compute knot invariants. We verify this conjecture in the weakly-coupled regime from explicit Feynman diagram computations. At strong coupling, these Wilson loop operators lift to 1/8-BPS surface operators in the 6d (2, 0) theory on S1 × S5. Using AdS/CFT, we show that these surface operators are dual to M2-branes subject to certain calibration conditions required in order to preserve supersymmetry. We compute the renormalized action of a large class of calibrated M2-branes and obtain a perfect match with the field theory prediction. Finally, we present a derivation of the 3d Chern-Simons theory from 5d super-Yang-Mills theory using supersymmetric localization, modulo a subtle issue that we discuss.
Pseudorandomness for Regular Branching Programs via Fourier Analysis.

We present an explicit pseudorandom generator for oblivious, read-once, permutation branching programs of constant width that can read their input bits in any order. The seed length is $O(\log^2 n)$, where $n$ is the length of the branching program. The previous best seed length known for this model was $n^{1/2+o(1)}$, which follows as a special case of a generator due to Impagliazzo, Meka, and Zuckerman (FOCS 2012) (which gives a seed length of $s^{1/2+o(1)}$ for arbitrary branching programs of size $s$). Our techniques also give seed length $n^{1/2+o(1)}$ for general oblivious, read-once branching programs of width $2^{n^{o(1)}}$, which is incomparable to the results of Impagliazzo et al.Our pseudorandom generator is similar to the one used by Gopalan et al. (FOCS 2012) for read-once CNFs, but the analysis is quite different; ours is based on Fourier analysis of branching programs. In particular, we show that an oblivious, read-once, regular branching program of width $w$ has Fourier mass at most $(2w^2)^k$ at level $k$, independent of the length of the program.
Optimal Structure Synthesis for Environment Augmenting Robots

Building structures can allow a robot to surmount large obstacles, expanding the set of areas it can reach. This letter presents a planning algorithm to automatically determine what structures a construction-capable robot must build in order to traverse its entire environment. Given an environment, a set of building blocks, and a robot capable of building structures, we seek an optimal set of structures (using a minimum number of building blocks) that could be built to make the entire environment traversable with respect to the robot's movement capabilities. We show that this problem is NP-Hard, and present a complete, optimal algorithm that solves it using a branch-and-bound strategy. The algorithm runs in exponential time in the worst case, but solves typical problems with practical speed. In hardware experiments, we show that the algorithm solves three-dimensional maps of real indoor environments in about 1 min, and that the structures selected by the algorithm allow a robot to traverse the entire environment. An accompanying video is available online at https://youtu.be/B9WM557NP44 .
Generalized Score Matching for Non-Negative Data.

A common challenge in estimating parameters of probability density functions is the intractability of the normalizing constant. While in such cases maximum likelihood estimation may be implemented using numerical integration, the approach becomes computationally intensive. The score matching method of Hyvarinen [2005] avoids direct calculation of the normalizing constant and yields closed-form estimates for exponential families of continuous distributions over . Hyvarinen [2007] extended the approach to distributions supported on the non-negative orthant, . In this paper, we give a generalized form of score matching for non-negative data that improves estimation efficiency. As an example, we consider a general class of pairwise interaction models. Addressing an overlooked inexistence problem, we generalize the regularized score matching method of Lin et al.[2016] and improve its theoretical guarantees for non-negative Gaussian graphical models.
Approximating the Medial Axis from the Voronoi Diagram with a Convergence Guarantee

The medial axis of a surface in 3D is the closure of all points that have two or more closest points on the surface. It is an essential geometric structure in a number of applications involving 3D geometric shapes. Since exact computation of the medial axis is difficult in general, efforts continue to improve their approximations. Voronoi diagrams turn out to be useful for this approximation. Although it is known that Voronoi vertices for a sample of points from a curve in 2D approximate its medial axis, a similar result does not hold in 3D. Recently, it has been discovered that only a subset of Voronoi vertices converge to the medial axis as sample density approaches infinity. However, most applications need a nondiscrete approximation as opposed to a discrete one. To date no known algorithm can compute this approximation straight from the Voronoi diagram with a guarantee of convergence. We present such an algorithm and its convergence analysis in this paper. One salient feature of the algorithm is that it is scale and density independent. Experimental results corroborate our theoretical claims.
Non-Markovian Monte Carlo on Directed Graphs

Markov Chain Monte Carlo (MCMC) has been the de facto technique for sampling and inference of large graphs such as online social networks. At the heart of MCMC lies the ability to construct an ergodic Markov chain that attains any given stationary distribution $\boldsymbolπ $, often in the form of random walks or crawling agents on the graph. Most of the works around MCMC, however, presume that the graph is undirected or has reciprocal edges, and become inapplicable when the graph is directed and non-reciprocal. Here we develop a similar framework for directed graphs, which we call Non-Markovian Monte Carlo (NMMC), by establishing a mapping to convert $\boldsymbolπ into the quasi-stationary distribution of a carefully constructed transient Markov chain on an extended state space. As applications, we demonstrate how to achieve any given distribution $\boldsymbolπ $ on a directed graph and estimate the eigenvector centrality using a set of non-Markovian, history-dependent random walks on the same graph in a distributed manner. We also provide numerical results on various real-world directed graphs to confirm our theoretical findings, and present several practical enhancements to make our NMMC method ready for practical use in most directed graphs. To the best of our knowledge, the proposed NMMC framework for directed graphs is the first of its kind, unlocking all the limitations set by the standard MCMC methods for undirected graphs.
Making Brazil work? Brazilian coalitional presidentialism at 30 and its post-Lava Jato prospects

The article analyzes the question of coalition presidentialism in the countries of Latin America in general and in Brazil in particular. It offers the reader a close look at coalition presidentialism in all Brazilian governments since the democratization of the country to the present day, presenting the complex relations between Legislative Branch and the Executive Branch in these different moments of Brazilian political reality. The main focus of the article is to relate the impacts of “Lava Jato” operation to this process, seeking to indicate possible consequences of this operation, which caused great repercussion on Brazilian politics, to this scenario of relationship between the Executive and Legislative branches.
Strategy purification

There has been significant recent interest in computing good strategies for large games. Most prior work involves computing an approximate equilibrium strategy in a smaller abstract game, then playing this strategy in the full game. In this paper, we present a modification of this approach that works by constructing a deterministic strategy in the full game from the solution to the abstract game; we refer to this procedure as purification. We show that purification, and its generalization which we call thresholding, lead to significantly stronger play than the standard approach in a wide variety of experimental domains. One can view these approaches as ways of achieving robustness against one's own lossy abstraction.
Guided meditation as an adjunct to enhance postoperative recovery after cardiac surgery: study protocol for a prospective randomized controlled feasibility trial

Cardiac surgical procedures are associated with postoperative neurological complications such as cognitive decline and delirium, which can complicate recovery and impair quality of life. Perioperative depression and anxiety may be associated with increased mortality after cardiac surgeries. Surgical prehabilitation is an emerging concept that includes preoperative interventions to potentially reduce postoperative complications. While most current prehabilitation interventions focus on optimizing physical health, mind–body interventions are an area of growing interest. Preoperative mind–body interventions such as Isha Kriya meditation, may hold significant potential to improve postsurgical outcomes. This is a prospective, randomized controlled feasibility trial. A total of 40 adult patients undergoing cardiac surgery will be randomized to one of three study groups. Participants randomized to either of the two intervention groups will receive meditative intervention: (1) commencing two weeks before surgery; or (2) commencing only from the day after surgery. Meditative intervention will last for four weeks after the surgery in these groups. Participants in the third control group will receive the current standard of care with no meditative intervention. All participants will undergo assessments using neurocognitive, sleep, depression, anxiety, and pain questionnaires at various time points in the perioperative period. Blood samples will be collected at baseline, preoperatively, and postoperatively to assess for inflammatory biomarkers. The primary aim of this trial is to assess the feasibility of implementing a perioperative meditative intervention program. Other objectives include studying the effect of meditation on postoperative pain, sleep, psychological wellbeing, cognitive function, and delirium. These will be used to calculate effect size to design future studies. This study serves as the first step towards understanding the feasibility of implementing a mind–body intervention as a prehabilitative intervention to improve postoperative surgical outcomes after cardiac surgery. Clinicaltrials.gov, NCT03198039 . Registered on 23 June 2017.
Lost in Machine Translation: A Method to Reduce Meaning Loss

A desideratum of high-quality translation systems is that they preserve meaning, in the sense that two sentences with different meanings should not translate to one and the same sentence in another language. However, state-of-the-art systems often fail in this regard, particularly in cases where the source and target languages partition the “meaning space” in different ways. For instance, “I cut my finger.” and “I cut my finger off.” describe different states of the world but are translated to French (by both Fairseq and Google Translate) as “Je me suis coupe le doigt.”, which is ambiguous as to whether the finger is detached. More generally, translation systems are typically many-to-one (non-injective) functions from source to target language, which in many cases results in important distinctions in meaning being lost in translation. Building on Bayesian models of informative utterance production, we present a method to define a less ambiguous translation system in terms of an underlying pre-trained neural sequence-to-sequence model. This method increases injectivity, resulting in greater preservation of meaning as measured by improvement in cycle-consistency, without impeding translation quality (measured by BLEU score).
Eventual linear convergence of the Douglas-Rachford iteration for basis pursuit

We provide a simple analysis of the Douglas-Rachford splitting algorithm in the context of ‘ 1 minimization with linear constraints, and quantify the asymptotic linear convergence rate in terms of principal angles between relevant vector spaces. In the compressed sensing setting, we show how to bound this rate in terms of the restricted isometry constant. More general iterative schemes obtained by ‘ 2 -regularization and over-relaxation including the dual split Bregman method [27] are also treated, which answers the question how to choose the relaxation and soft-thresholding parameters to accelerate the asymptotic convergence rate. We make no attempt at characterizing the transient regime preceding the onset of linear convergence.
Monthly Payment Targeting and the Demand for Maturity

In this paper, we provide evidence of the importance of monthly payments in the market for consumer installment debt. Auto debt in particular has grown rapidly since the Great Recession and has eclipsed credit cards in total debt outstanding. Auto-loan maturities have also increased such that most auto-loan originations now have a term of over 72 months. We document three phenomena we jointly refer to as monthly payment targeting. First, using data from 500,000 used auto loans and discontinuities in contract terms offered by hundreds of lenders, we show that demand is more sensitive to maturity than interest rate, consistent with consumers managing payment size when making debt decisions. Second, many consumers appear to employ segregated mental accounts, spending exogenous payment savings on larger loans. Third, consumers bunch at round-number monthly payment amounts, consistent with the use of budgeting heuristics. These patterns hold in subsamples of constrained and unconstrained borrowers, challenging liquidity constraints as a complete explanation. Our estimates suggest that borrower focus on payment size, combined with credit-supply shocks to maturity, could significantly affect aggregate outstanding debt.
The Dark Side of Morality: Group Polarization and Moral Epistemology

This article argues that philosophers and laypeople commonly conceptualize moral truths or justified moral beliefs as discoverable through intuition, argument, or some other purely cognitive or affective process. It then contends that three empirically well-supported theories all predict that this ‘Discovery Model’ of morality plays a substantial role in causing social polarization. The same three theories are then used to argue that an alternative ‘Negotiation Model’ of morality — according to which moral truths are not discovered but instead created by actively negotiating compromises — promises to reduce polarization by fostering a progressive willingness to ‘work across the aisle’ to settle moral issues cooperatively. This article then examines potential methods for normatively evaluating polarization, arguing there are prima facie reasons to favor the Negotiation Model over the Discovery Model based on their hypothesized effects on polarization. Finally, I outline avenues for further empirical and philosophical research.
Photon-Flooded Single-Photon 3D Cameras

Single-photon avalanche diodes (SPADs) are starting to play a pivotal role in the development of photon-efficient, long-range LiDAR systems. However, due to non-linearities in their image formation model, a high photon flux (e.g., due to strong sunlight) leads to distortion of the incident temporal waveform, and potentially, large depth errors. Operating SPADs in low flux regimes can mitigate these distortions, but, often requires attenuating the signal and thus, results in low signal-to-noise ratio. In this paper, we address the following basic question: what is the optimal photon flux that a SPAD-based LiDAR should be operated in? We derive a closed form expression for the optimal flux, which is quasi-depth-invariant, and depends on the ambient light strength. The optimal flux is lower than what a SPAD typically measures in real world scenarios, but surprisingly, considerably higher than what is conventionally suggested for avoiding distortions. We propose a simple, adaptive approach for achieving the optimal flux by attenuating incident flux based on an estimate of ambient light strength. Using extensive simulations and a hardware prototype, we show that the optimal flux criterion holds for several depth estimators, under a wide range of illumination conditions.
An empirical comparison of some experimental designs for the valuation of large variable annuity portfolios

Variable annuities contain complex guarantees, whose fair market value cannot be calculated in closed form. To value the guarantees, insurance companies rely heavily on Monte Carlo simulation, which is extremely computationally demanding for large portfolios of variable annuity policies. Metamodeling approaches have been proposed to address these computational issues. An important step of metamodeling approaches is the experimental design that selects a small number of representative variable annuity policies for building metamodels. In this paper, we compare empirically several multivariate experimental design methods for the GB2 regression model, which has been recently discovered to be an attractive model to estimate the fair market value of variable annuity guarantees. Among the experimental design methods examined, we found that the data clustering method and the conditional Latin hypercube sampling method produce the most accurate results.
Corrosion of carbon steel by aqueous piperazine protected by FeCO3

Abstract The effect of piperazine (PZ) degradation on carbon steel corrosion was studied at extremely reducing stripper conditions. The corrosion rate was measured with electrical resistance (ER) corrosion probes, and coupons were used to collect corrosion products for characterization. The measured corrosion rate increased from below 80 μm/yr to around 2000 μm/yr at a critical concentration of degradation products. Measurements with clean PZ and different levels of ethylenediamine (EDA) produced the same effect and suggest that EDA may be one of the corrosive agents in degraded PZ. The corrosion product on the coupon surface was identified as FeCO3 at both low and high corrosion rate, although the appearance of this film was more open with high corrosion rate. These films might dissolve back into solution or be damaged by fluid flow, resulting in incomplete or porous films, and reduced effectiveness in protecting carbon steel from corrosion.
Counterintuitive results from observational data: a case study and discussion.

Objective To explore the issue of counterintuitive data via analysis of a representative case in which the data obtained was unexpected and inconsistent with current knowledge. We then discuss the issue of counterintuitive data while developing a framework for approaching such findings. Design The case study is a retrospective analysis of a cohort of coronary artery bypass graft (CABG) patients. Regression was used to examine the association between perceived pain in the intensive care unit (ICU) and selected outcomes. Setting Medical Information Mart for Intensive Care-III, a publicly available, de-identified critical care patient database. Participants 844 adult patients from the database who underwent CABG surgery and were extubated within 24 hours after ICU admission. Outcomes 30 day mortality, 1 year mortality and hospital length of stay (LOS). Results Increased pain levels were found to be significantly associated with reduced mortality at 30 days and 1 year, and shorter hospital LOS. A one-point increase in mean pain level was found to be associated with a reduction in the odds of 30 day and 1 year mortality by a factor of 0.457 (95% CI 0.304 to 0.687, p Conclusion The finding of an association between increased pain and improved outcomes was unexpected and clinically counterintuitive. In an increasingly digitised age of medical big data, such results are likely to become more common. The reliability of such counterintuitive results must be carefully examined. We suggest several issues to consider in this analytic process. If the data is determined to be valid, consideration must then be made towards alternative explanations for the counterintuitive results observed. Such results may in fact indicate that current clinical knowledge is incomplete or not have been firmly based on empirical evidence and function to inspire further research into the factors involved.
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees

We examine popular gradient-based algorithms for nonlinear control in the light of the modern complexity analysis of first-order optimization algorithms. The examination reveals that the complexity bounds can be clearly stated in terms of calls to a computational oracle related to dynamic programming and implementable by gradient back-propagation using machine learning software libraries such as PyTorch or TensorFlow. Finally, we propose a regularized Gauss-Newton algorithm enjoying worst-case complexity bounds and improved convergence behavior in practice. The software library based on PyTorch is publicly available.
Response to vocal music in Angelman syndrome contrasts with Prader-Willi syndrome

Parent-offspring conflict-conflict over resource distribution within families due to differences in genetic relatedness-is the biological foundation for many psychological phenomena. In genomic imprinting disorders, parent-specific genetic expression is altered causing imbalances in behaviors influenced by parental investment. We use this natural experiment to test the theory that parent-offspring conflict contributed to the evolution of vocal music by moderating infant demands for parental attention. Individuals with Prader-Willi syndrome, a genomic imprinting disorder resulting from increased relative maternal genetic contribution, show enhanced relaxation responses to song, consistent with reduced demand for parental investment (Mehr et al., 2017, Psychological Science). We report the necessary complementary pattern here: individuals with Angelman syndrome, a genomic imprinting disorder resulting from increased relative paternal genetic contribution, demonstrate a relatively reduced relaxation response to song, suggesting increased demand for parental attention. These results support the extension of genetic conflict theories to psychological resources like parental attention.
Market stability with machine learning agents

Abstract We consider the effect of adaptive model selection and regularization by agents on price volatility and market stability in a simple agent-based model of a financial market. The agents base their trading behavior on forecasts of future returns, which they update adaptively and asynchronously through a process of model selection, estimation, and prediction. The addition of model selection and regularization methods to the traders’ learning algorithm is shown to reduce but not eliminate overfitting and resulting excess volatility. Our results suggest that even a high degree of attention to overfitting on the part of traders who are engaged in data mining is unlikely to entirely eliminate destabilizing speculation. They also accord well with the empirical “sparse signals” and “pockets of predictability” findings of Chinco et al. (2019) and Farmer et al. (2019).
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging

We address the statistical and optimization impacts of the classical sketch and Hessian sketch used to approximately solve the Matrix Ridge Regression (MRR) problem. Prior research has quantified the effects of classical sketch on the strictly simpler least squares regression (LSR) problem. We establish that classical sketch has a similar effect upon the optimization properties of MRR as it does on those of LSR: namely, it recovers nearly optimal solutions. By contrast, Hessian sketch does not have this guarantee; instead, the approximation error is governed by a subtle interplay between the "mass" in the responses and the optimal objective value. For both types of approximation, the regularization in the sketched MRR problem results in significantly different statistical properties from those of the sketched LSR problem. In particular, there is a bias-variance trade-off in sketched MRR that is not present in sketched LSR. We provide upper and lower bounds on the bias and variance of sketched MRR; these bounds show that classical sketch significantly increases the variance, while Hessian sketch significantly increases the bias. Empirically, sketched MRR solutions can have risks that are higher by an order-of-magnitude than those of the optimal MRR solutions. We establish theoretically and empirically that model averaging greatly decreases the gap between the risks of the true and sketched solutions to the MRR problem. Thus, in parallel or distributed settings, sketching combined with model averaging is a powerful technique that quickly obtains near-optimal solutions to the MRR problem while greatly mitigating the increased statistical risk incurred by sketching.
Synthesis and Characterization of Nanosized Halogenated and Interhalogenated Metal Oxide Adducts

Nonpolar halogens (Cl2, Br2, and I2) and polar interhalogen molecules (ICl, IBr, and ICl3) have been adsorbed on the surface of three different nanosized metal oxides (NanoActive (NA) Al2O3 Plus, NA-TiO2, and NA-CeO2). The prepared halogen and interhalogen adducts have been characterized in detail by thermogravimetric analysis (TGA), UV−vis, Raman, and X-ray photoelectron spectroscopies (XPS), and the results are discussed herein. The different metal oxides lead to varying strength of adsorption of the halogen/interhalogen in the prepared adducts. Adsorption was also tested on their macrocrystalline available counterparts but with no success. Nanosized metal oxide halogen adducts possess high surface reactivities due to their unique surface morphologies. These adducts can be used as halogenating agents in organic and inorganic synthesis, for disinfection and decontamination.
Structured illumination to spatially map chromatin motions.

We describe a simple optical method that creates structured illumination of a photoactivatable probe and apply this method to characterize chromatin motions in nuclei of live cells. A laser beam coupled to a diffractive optical element at the back focal plane of an excitation objective generates an array of near diffraction-limited beamlets with FWHM of 340 ± 30 nm, which simultaneously photoactivate a 7 × 7 matrix pattern of GFP-labeled histones, with spots 1.70 μm apart. From the movements of the photoactivated spots, we map chromatin diffusion coefficients at multiple microdomains of the cell nucleus. The results show correlated motions of nearest chromatin microdomain neighbors, whereas chromatin movements are uncorrelated at the global scale of the nucleus. The method also reveals a DNA damage-dependent decrease in chromatin diffusion. The diffractive optical element instrumentation can be easily and cheaply implemented on commercial inverted fluorescence microscopes to analyze adherent cell culture models. A protocol to measure chromatin motions in nonadherent human hematopoietic stem and progenitor cells is also described. We anticipate that the method will contribute to the identification of the mechanisms regulating chromatin mobility, which influences most genomic processes and may underlie the biogenesis of genomic translocations associated with hematologic malignancies.