cssi/SciSciGPT-SciSciNet huggingface dataset:
Multivariate tests for phase capacity

To combat the potentially detrimental effects of nonresponse, most surveys repeatedly follow-up with nonrespondents, often targeting a response rate or predetermined number of completes. Each additional recruitment attempt generally brings in a new wave of data, but returns gradually diminish over the course of a static data collection protocol. This is because each subsequent wave tends to contain fewer and fewer new responses, thereby rendering smaller and smaller changes in point estimates. Consequently, point estimates calculated from the accumulating data begin to stabilize. This is the notion of phase capacity, suggesting some form of design change is warranted, such as switching modes, increasing the incentive, or simply discontinuing nonrespondent follow-up. Phase capacity testing methods that have appeared in the literature to date are generally only applicable to a single point estimate. It is unclear how to proceed if conflicting results are obtained following independent tests on two or more point estimates. The purpose of this paper is to introduce two multivariate phase capacity tests, each designed with the aim of providing a universal, yes-or-no phase capacity determination for a battery of point estimates. The two competing methods’ performance is compared via simulation and application using data from the 2011 Federal Employee Viewpoint Survey.
Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents

Images and text co-occur constantly on the web, but explicit links between images and sentences (or other intra-document textual units) are often not present. We present algorithms that discover image-sentence relationships without relying on explicit multimodal annotation in training. We experiment on seven datasets of varying difficulty, ranging from documents consisting of groups of images captioned post hoc by crowdworkers to naturally-occurring user-generated multimodal documents. We find that a structured training objective based on identifying whether collections of images and sentences co-occur in documents can suffice to predict links between specific sentences and specific images within the same document at test time.
Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems

Innovations in Next-Generation Sequencing are enabling generation of DNA sequence data at ever faster rates and at very low cost. For example, the Illumina NovaSeq 6000 sequencer can generate 6 Terabases of data in less than two days, sequencing nearly 20 Billion short DNA fragments called reads at the low cost of $1000 per human genome. Large sequencing centers typically employ hundreds of such systems. Such highthroughput and low-cost generation of data underscores the need for commensurate acceleration in downstream computational analysis of the sequencing data. A fundamental step in downstream analysis is mapping of the reads to a long reference DNA sequence, such as a reference human genome. Sequence mapping is a compute-intensive step that accounts for more than 30% of the overall time of the GATK (Genome Analysis ToolKit) best practices workflow. BWA-MEM is one of the most widely used tools for sequence mapping and has tens of thousands of users. In this work, we focus on accelerating BWA-MEM through an efficient architecture aware implementation, while maintaining identical output. The volume of data requires distributed computing and is usually processed on clusters or cloud deployments with multicore processors usually being the platform of choice. Since the application can be easily parallelized across multiple sockets (even across distributed memory systems) by simply distributing the reads equally, we focus on performance improvements on a single socket multicore processor. BWA-MEM run time is dominated by three kernels, collectively responsible for more than 85% of the overall compute time. We improved the performance of the three kernels by 1) using techniques to improve cache reuse, 2) simplifying the algorithms, 3) replacing many small memory allocations with a few large contiguous ones to improve hardware prefetching of data, 4) software prefetching of data, and 5) utilization of SIMD wherever applicable and massive reorganization of the source code to enable these improvements. As a result, we achieved nearly 2×, 183×, and 8× speedups on the three kernels, respectively, resulting in up to 3:5× and 2:4× speedups on end-to-end compute time over the original BWA-MEM on single thread and single socket of Intel Xeon Skylake processor. To the best of our knowledge, this is the highest reported speedup over BWA-MEM (running on a single CPU) while using a single CPU or a single CPU-single GPGPU/FPGA combination. Source-code: https://github.com/bwa-mem2/bwa-mem2
Extractive institutions? Investor returns to Indian railway companies in the age of high imperialism

Did colonial policies in India deliver excessive returns to British investors? We answer this question using annual data on Indian securities trading on the London Stock Exchange. We present new series on market capitalization, capital gains, dividend yields, and total returns of railway securities from 1880 to 1929. The average annual total return on the largest and most important Indian railway securities was 3.7%. These returns were not excessive by any financial standard. Indeed, they were lower than the return on railway securities in North America, Latin America, and Asia. We also undertake an event study analysis to assess whether Indian railways significantly benefited British investors. When the Government of India purchased large positions in the private railway companies between 1880 and 1910, there were opportunities for profit making. However, we find no evidence of abnormal investor returns in the years leading to the purchase of railway companies. Broadly our findings call into question the extractive nature of colonial railway policy.
Resolution of singularities of complex algebraic varieties and their families

We discuss Hironaka's theorem on resolution of singularities in charactetistic 0 as well as more recent progress, both on simplifying and improving Hironaka's method of proof and on new results and directions on families of varieties, leading to joint work on toroidal orbifolds with Michael Temkin and Jaros{\l}aw W{\l}odarczyk.
Non-asymptotic Identification of LTI Systems from a Single Trajectory

We consider the problem of learning a realization for a linear time-invariant (LTI) dynamical system from input/output data. Given a single input/output trajectory, we provide finite time analysis for learning the system's Markov parameters, from which a balanced realization is obtained using the classical Ho-Kalman algorithm. By proving a stability result for the Ho-Kalman algorithm and combining it with the sample complexity results for Markov parameters, we show how much data is needed to learn a balanced realization of the system up to a desired accuracy with high probability.
Modeling Imprecision in Perception, Valuation, and Choice

Traditional decision theory assumes that people respond to the exact features of the options available to them, but observed behavior seems much less precise. This review considers ways of introduc...
Universal mixers in all dimensions

Abstract We construct universal mixers, incompressible flows that mix arbitrarily well general solutions to the corresponding transport equation, in all dimensions. This mixing is exponential in time (i.e., essentially optimal) for any initial condition with at least some regularity, and we also show that a uniform mixing rate for all initial conditions cannot be achieved. The flows are time periodic and uniformly-in-time bounded in spaces W s , p for a range of ( s , p ) that includes points with s > 1 and p > 2 .
On Multiterminal Communication over MIMO Channels with One-bit ADCs at the Receivers

The fundamental limits of communication over multiple-input multiple-output (MIMO) networks are considered when a limited number of one-bit analog to digital converters (ADC) are used at the receiver terminals. Prior works have mainly focused on point-to-point communications, where receiver architectures consisting of a concatenation of an analog processing module, a limited number of one-bit ADCs with non-adaptive thresholds, and a digital processing module are considered. In this work, a new receiver architecture is proposed which utilizes adaptive threshold one-bit ADCs — where the ADC thresholds at each channel-use are dependent on the channel outputs in the previous channel-uses — to mitigate the quantization rate-loss. Coding schemes are proposed for communication over the point-to-point and broadcast channels, and achievable rate regions are derived. In the high SNR regime, it is shown that using the proposed architectures and coding schemes leads to the largest achievable rate regions among all receiver architectures with the same number of one-bit ADCs.
Detection of an electron spin in a MRFM cantilever experiment

In the magnetic resonance force microscopy (MRFM) experiment that we consider, a cantilever is used to detect the presence of an electron spin. If an electron spin is present, the equation governing the motion of the cantilever is nonlinear. In the absence of a spin, the equation is linear. We propose a detector model that uses two Kalman filter (KF) state estimators. The estimator for the spin hypothesis is an adaptation of the standard KF, and can be viewed as a 0th order mixed-time extended Kalman filter (EKF). Simulation results are presented for several system parameter values.
ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation.

Change detection involves segmenting sequential data such that observations in the same segment share some desired properties. Multivariate change detection continues to be a challenging problem due to the variety of ways change points can be correlated across channels and the potentially poor signal-to-noise ratio on individual channels. In this paper, we are interested in locating additive outliers (AO) and level shifts (LS) in the unsupervised setting. We propose ABACUS, Automatic BAyesian Changepoints Under Sparsity, a Bayesian source separation technique to recover latent signals while also detecting changes in model parameters. Multi-level sparsity achieves both dimension reduction and modeling of signal changes. We show ABACUS has competitive or superior performance in simulation studies against state-of-the-art change detection methods and established latent variable models. We also illustrate ABACUS on two real application, modeling genomic profiles and analyzing household electricity consumption.
Generalized Greatest Common Divisors for the Orbits under Rational Functions

Assume Vojta’s Conjecture for blowups of $${\mathbb {P}}^1 \times {\mathbb {P}}^1$$. Suppose $$a, b, \alpha ,\beta \in {\mathbb {Z}}$$, and $$f(x),g(x)\in {\mathbb {Z}}[x]$$ are polynomials of degree $$d\ge 2$$. Assume that the sequence $$(f^{\circ n}(a), g^{\circ n}(b))_n$$ is generic and $$\alpha ,\beta $$ are not exceptional for f, g respectively. We prove that for each given $$\varepsilon >0$$, there exists a constant $$C = C(\varepsilon ,a,b,\alpha ,\beta ,f,g)>0 $$, such that for all $$n\ge 1$$, we have $$\begin{aligned} \gcd (f^{\circ n}(a)-\alpha , g^{\circ n}(b) -\beta ) \le C\cdot \exp ({\varepsilon \cdot d^n}). \end{aligned}$$We prove an estimate for rational functions and for a more general gcd and then obtain the above inequality as a consequence.
Know when to fold ‘em: An empirical description of risk management in public research funding

Abstract Public research funding programs typically make grants with minimal intervention by program staff, rather than using a hands-on approach to project management, which is more common in the private sector. In contrast, program staff at the US Department of Energy's Advanced Research Projects Agency – Energy (ARPA-E) are given a set of real options with which to manage funded projects: abandon, contract or expand project budgets or timelines. Using internal data from ARPA-E, we show that active project management enables risk mitigation across a portfolio of research projects. We find that program staff modify projects frequently, especially project timelines, and these changes are more sensitive to poor performance than to strong performance. We also find that projects with a shortened timeline or reduced budget are less likely to generate short-term research outputs, compared to those of ultimately similar size. This evidence suggests that the practice of active project management, when combined with high upfront risk tolerance, can be used to enhance the productivity of mission-oriented public research funding.
Introduction to Multi-Armed Bandits

Multi-armed bandits is a rich, multi-disciplinary area that has been studied since 1933, with a surge of activity in the past 10-15 years. This is the first monograph to provide a textbook like treatment of the subject. The work on multi-armed bandits can be partitioned into a dozen or so directions. Each chapter tackles one line of work, providing a self-contained introduction and pointers for further reading. Introduction to Multi-Armed Bandits concentrates on fundamental ideas and elementary, teachable proofs over the strongest possible results. It emphasizes accessibility of the material; while exposure to machine learning and probability/statistics would certainly help, a standard undergraduate course on algorithms should suffice for background. The first four chapters are devoted to IID rewards with adversarial rewards being covered in the next 3 chapters. Contextual bandits are discussed in a separate chapter before the monograph concludes with connections to economics. Each chapter contains a section on bibliographic notes and further directions. Many of the chapters conclude with some exercises. Introduction to Multi-Armed Bandits provides an accessible treatment for students of a topic that has gained importance in the last decade. Lecturers can use it as a text for an introductory course on the subject.
Establishing Institution-Specific Normative Data for the BREAST-Q Reconstruction Module: A Prospective Study.

Background Breast satisfaction in general female populations is relatively unknown and prior research populations do not reflect our community. Objectives We sought to assess breast satisfaction in a cohort of female participants utilizing the BREAST-Q and determine the impact of participant-related factors. Methods Females with no history of breast cancer or breast surgery attending gynecology appointments completed preoperative BREAST-Q reconstruction modules and demographic forms in this prospective, single-center, patient-reported outcomes study. We also assessed participant-related factors capable of influencing BREAST-Q scores. Results Three hundred females were included. Increasing body mass index had significant associations with lower Satisfaction with Breasts and Psychosocial Well-being scores. Increasing age was associated with significantly lower Sexual Well-being scores. African Americans had significantly higher scores for Satisfaction with Breasts, Psychosocial Well-being, and Sexual Well-being compared with Caucasians. Bra cup sizes A and C were associated with significantly higher Psychosocial Well-being scores than other sizes. Bra cup sizes A, B, and C were associated with significantly higher Sexual Well-being and Physical Well-being: Chest scores than larger sizes. Bra cup sizes B and C were associated with significantly higher Physical Well-being: Abdomen scores than size DD. Bra cup size A was associated with significantly higher Satisfaction with Breasts scores than sizes DD and >DD. Bra cup size C was associated with significantly higher Satisfaction with Breasts scores than larger sizes. Conclusions Body mass index, age, race, and bra cup sizes significantly impact BREAST-Q scores in our population. Determining normative BREAST-Q scores in female populations could represent important baselines for breast outcomes research. Level of Evidence: 2.
Deep learning for automated classification and characterization of amorphous materials

It is difficult to quantify structure–property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide a procedure to identify important structural features in materials that could be missed by standard techniques and give unique insight into how these neural networks process data.
Bott vanishing for algebraic surfaces

Bott proved a strong vanishing theorem for sheaf cohomology on projective space. It holds for toric varieties, but not for most other varieties. We prove Bott vanishing for the quintic del Pezzo surface, also known as the moduli space M_{0,5}^bar of 5-pointed stable curves of genus zero. This is the first non-toric Fano variety for which Bott vanishing has been shown, answering a question by Achinger, Witaszek, and Zdanowicz. In another direction, we prove sharp results on which K3 surfaces satisfy Bott vanishing. For example, a K3 surface with Picard number 1 satisfies Bott vanishing if and only if the degree is 20 or at least 24. For K3 surfaces of any Picard number, we have complete results when the degree is big enough. We build on Beauville, Mori, and Mukai's work on moduli spaces of K3 surfaces, as well as recent advances by Arbarello-Bruno-Sernesi, Ciliberto-Dedieu-Sernesi, and Feyzbakhsh. The most novel aspect of the paper is our analysis of what happens when there is an elliptic curve of low degree. (In other terminology, this concerns K3 surfaces that are unigonal, hyperelliptic, trigonal, or tetragonal.) It turns out that the crucial issue is whether an elliptic fibration has a certain special type of singular fiber.
DDSP: Differentiable Digital Signal Processing

Most generative models of audio directly generate samples in one of two domains: time or frequency. While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived. A third approach (vocoders/synthesizers) successfully incorporates strong domain knowledge of signal processing and perception, but has been less actively researched due to limited expressivity and difficulty integrating with modern auto-differentiation-based machine learning methods. In this paper, we introduce the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods. Focusing on audio synthesis, we achieve high-fidelity generation without the need for large autoregressive models or adversarial losses, demonstrating that DDSP enables utilizing strong inductive biases without losing the expressive power of neural networks. Further, we show that combining interpretable modules permits manipulation of each separate model component, with applications such as independent control of pitch and loudness, realistic extrapolation to pitches not seen during training, blind dereverberation of room acoustics, transfer of extracted room acoustics to new environments, and transformation of timbre between disparate sources. In short, DDSP enables an interpretable and modular approach to generative modeling, without sacrificing the benefits of deep learning. The library will be made available upon paper acceptance and we encourage further contributions from the community and domain experts.
The McDonald's equilibrium Advertising, empty calories, and the endogenous determination of dietary preferences

A comparison of accepted nutritional advice with actual American dietary practice suggests that many people fail to eat well in spite of well-documented health consequences. Popular culture often labels the worst offenders as lacking in “self-control,” and many blame the aggressive advertising campaigns of the fast-food and snack-food industries for manipulating consumers into poor diets, but these conclusions are not easily reconciled with a neoclassical approach to economic decision theory. This essay considers the consumer’s “diet problem” in light of emerging evidence from the medical and behavioral sciences. In particular, it is argued that human evolution in the distant past resulted in an elegant solution to this problem (of search for a suitable diet in an uncertain environment), which any neoclassical economist would recognize. In modern environments, however, the signals that formerly provided information in the consumer’s search problem are subject to manipulation by food-producing firms. Confirmation by molecular biologists that many human responses to these signals are firmly encoded in our genes suggests a need to re-evaluate the welfare economics of the food industry.
MicroRNAs Cause Accelerated Decay of Short-Tailed Target mRNAs

MicroRNAs (miRNAs) specify the recruitment of deadenylases to mRNA targets. Despite this recruitment, we find that miRNAs have almost no effect on steady-state poly(A)-tail lengths of their targets in mouse fibroblasts, which motivates the acquisition of pre-steady-state measurements of the effects of miRNAs on tail lengths, mRNA levels, and translational efficiencies. Effects on translational efficiency are minimal compared to effects on mRNA levels, even for newly transcribed target mRNAs. Effects on target mRNA levels accumulate as the mRNA population approaches steady state, whereas effects on tail lengths peak for recently transcribed target mRNAs and then subside. Computational modeling of this phenomenon reveals that miRNAs cause not only accelerated deadenylation of their targets but also accelerated decay of short-tailed target molecules. This unanticipated effect of miRNAs largely prevents short-tailed target mRNAs from accumulating despite accelerated target deadenylation. The net result is a nearly imperceptible change to the steady-state tail-length distribution of targeted mRNAs.
Hidden Hamiltonian Cycle Recovery via Linear Programming

We introduce the problem of hidden Hamiltonian cycle recovery, where there is an unknown Hamiltonian cycle in an n -vertex complete graph that needs to be inferred from noisy edge measurements. The measurements are independent and distributed according to P n for edges in the cycle and Q n otherwise. This formulation is motivated by a problem in genome assembly, where the goal is to order a set of contigs (genome subsequences) according to their positions on the genome using long-range linking measurements between the contigs. Computing the maximum likelihood estimate in this model reduces to a traveling salesman problem (TSP). Despite the NP-hardness of TSP, we show that a simple linear programming (LP) relaxation—namely, the fractional 2-factor (F2F) LP—recovers the hidden Hamiltonian cycle with high probability as n → ∞ provided that α n − log n → ∞ , where α n ≜ − 2 log ∫ d P n d Q n is the Renyi divergence of order 1 2 . This condition is information-theoretically optimal in the sense that, under mild distributional assumptions, α n ≥ ( 1 + o ( 1 ) ) log n is necessary for any algorithm to succeed regardless of the computational cost. Departing from the usual proof techniques based on dual witness construction, the analysis relies on the combinatorial characterization (in particular, the half-integrality) of the extreme points of the F2F polytope. Represented as bicolored multigraphs, these extreme points are further decomposed into simpler “blossom-type” structures for the large deviation analysis and counting arguments. Evaluation of the algorithm on real data shows improvements over existing approaches.
An Efficient Frontier Approach to Scoring and Ranking Hospital Performance

For many years, stakeholders have been complaining about how hospital scores are computed in the Centers for Medicare and Medicaid Services (CMS) hospital star ratings. In “An Efficient Frontier Ap...
Rerandomization and regression adjustment

Author(s): Li, X; Ding, P | Abstract: Randomization is a basis for the statistical inference of treatment effects without strong assumptions on the outcome-generating process. Appropriately using covariates further yields more precise estimators in randomized experiments. R. A. Fisher suggested blocking on discrete covariates in the design stage or conducting analysis of covariance in the analysis stage. We can embed blocking in a wider class of experimental design called rerandomization, and extend the classical analysis of covariance to more general regression adjustment. Rerandomization trumps complete randomization in the design stage, and regression adjustment trumps the simple difference-in-means estimator in the analysis stage. It is then intuitive to use both rerandomization and regression adjustment. Under the randomization inference framework, we establish a unified theory allowing the designer and analyser to have access to different sets of covariates. We find that asymptotically, for any given estimator with or without regression adjustment, rerandomization never hurts either the sampling precision or the estimated precision, and, for any given design with or without rerandomization, our regression-adjusted estimator never hurts the estimated precision. Therefore, combining rerandomization and regression adjustment yields better coverage properties and thus improves statistical inference. To quantify these statements theoretically, we discuss optimal regression-adjusted estimators in terms of the sampling precision and the estimated precision, and then measure the additional gains of the designer and the analyser. We finally suggest the use of rerandomization in the design and regression adjustment in the analysis followed by the Huber–White robust standard error.
Pathogenic siderophore ABC importer YbtPQ adopts a surprising fold of exporter

To fight for essential metal ions, human pathogens secrete virulence-associated siderophores and retake the metal-chelated siderophores through a subfamily of adenosine triphosphate (ATP)–binding cassette (ABC) importer, whose molecular mechanisms are completely unknown. We have determined multiple structures of the yersiniabactin importer YbtPQ from uropathogenic Escherichia coli (UPEC) at inward-open conformation in both apo and substrate-bound states by cryo–electron microscopy. YbtPQ does not adopt any known fold of ABC importers but surprisingly adopts the fold of type IV ABC exporters. To our knowledge, it is the first time an exporter fold of ABC importer has been reported. We have also observed two unique features in YbtPQ: unwinding of a transmembrane helix in YbtP upon substrate release and tightly associated nucleotide-binding domains without bound nucleotides. Together, our study suggests that siderophore ABC importers have a distinct transport mechanism and should be classified as a separate subfamily of ABC importers.
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks

Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep neural networks (DNNs). Even for the baseline of a constant learning rate, it is non-trivial to choose a good constant value for training a DNN. Dynamic learning rates involve multi-step tuning of LR values at various stages of the training process and offer high accuracy and fast convergence. However, they are much harder to tune. In this paper, we present a comprehensive study of 13 learning rate functions and their associated LR policies by examining their range parameters, step parameters, and value update parameters. We propose a set of metrics for evaluating and selecting LR policies, including the classification confidence, variance, cost, and robustness, and implement them in LRBench, an LR benchmarking system. LRBench can assist end-users and DNN developers to select good LR policies and avoid bad LR policies for training their DNNs. We tested LRBench on Caffe, an open source deep learning framework, to showcase the tuning optimization of LR policies. Evaluated through extensive experiments, we attempt to demystify the tuning of LR policies by identifying good LR policies with effective LR value ranges and step sizes for LR update schedules.
Multi-resolution localization of causal variants across the genome.

In the statistical analysis of genome-wide association data, it is challenging to precisely localize the variants that affect complex traits, due to linkage disequilibrium, and to maximize power while limiting spurious findings. Here we report on KnockoffZoom: a flexible method that localizes causal variants at multiple resolutions by testing the conditional associations of genetic segments of decreasing width, while provably controlling the false discovery rate. Our method utilizes artificial genotypes as negative controls and is equally valid for quantitative and binary phenotypes, without requiring any assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings. GWAS analysis currently relies mostly on linear mixed models, which do not account for linkage disequilibrium (LD) between tested variants. Here, Sesia et al. propose KnockoffZoom, a non-parametric statistical method for the simultaneous discovery and fine-mapping of causal variants, assuming only that LD is described by hidden Markov models (HMMs).
Real orientations of Lubin–Tate spectra

We show that Lubin–Tate spectra at the prime 2 are Real oriented and Real Landweber exact. The proof is by application of the Goerss–Hopkins–Miller theorem to algebras with involution. For each height n, we compute the entire homotopy fixed point spectral sequence for $$E_n$$ with its $$C_2$$ -action given by the formal inverse. We study, as the height varies, the Hurewicz images of the stable homotopy groups of spheres in the homotopy of these $$C_2$$ -fixed points.
GAN-Based Projector for Faster Recovery With Convergence Guarantees in Linear Inverse Problems

A Generative Adversarial Network (GAN) with generator G trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems. Here, we propose a new method of deploying a GAN-based prior to solve linear inverse problems using projected gradient descent (PGD). Our method learns a network-based projector for use in the PGD algorithm, eliminating expensive computation of the Jacobian of G. Experiments show that our approach provides a speed-up of 60-80x over earlier GAN-based recovery methods along with better accuracy in compressed sensing. Our main theoretical result is that if the measurement matrix is moderately conditioned on the manifold range(G) and the projector is \delta-approximate, then the algorithm is guaranteed to reach O(\delta) reconstruction error in O(log(1/\delta)) steps in the low noise regime. Additionally, we propose a fast method to design such measurement matrices for a given G. Extensive experiments demonstrate the efficacy of this method by requiring 5-10x fewer measurements than random Gaussian measurement matrices for comparable recovery performance. Because the learning of the GAN and projector is decoupled from the measurement operator, our GAN-based projector and recovery algorithm are applicable without retraining to all linear inverse problems in which the measurement operator is moderately conditioned for range(G), as confirmed by experiments on compressed sensing, super-resolution, and inpainting.
Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text

There is inherent information captured in the order in which we write words in a list. The orderings of binomials — lists of two words separated by ‘and’ or ‘or’ — has been studied for more than a century. These binomials are common across many areas of speech, in both formal and informal text. In the last century, numerous explanations have been given to describe what order people use for these binomials, from differences in semantics to differences in phonology. These rules describe primarily ‘frozen’ binomials that exist in exactly one ordering and have lacked large-scale trials to determine efficacy. Text in online social media such as Reddit provides a unique opportunity to study these lists in the context of informal text at a very large scale. In this work, we expand the view of binomials to include a large-scale analysis of both frozen and non-frozen binomials in a quantitative way. Using this data, we then demonstrate that most previously proposed rules are ineffective at predicting binomial ordering. By tracking the order of these binomials across time and communities we are able to establish additional, unexplored dimensions central to these predictions and demonstrate the global structure of the binomials across communities. Expanding beyond the question of individual binomials, we also explore the global structure of binomials in various communities, establishing a new model for these lists and analyzing this structure for non-frozen and frozen binomials. Additionally, novel analysis of trinomials — lists of length three — suggests that none of the binomials analysis applies in these cases. Finally, we demonstrate how large data sets gleaned from the web can be used in conjunction with older theories to expand and improve on old questions.
Liminal movement by digital platform‐based sharing economy ventures: The case of Uber Technologies

We explore the challenges that digital platform-based sharing economy ventures confront in establishing legitimacy for their business models by examining the dynamics that ensued when Uber Technologies deployed its ride-sharing business model in four U.S. cities. Uber entered each city to jump-start network effects by establishing cognitive legitimacy while deepening a sociopolitical legitimacy crisis that emerged due to mismatches between its business model and existing regulations. Operating between existing categories, Uber used a series of interrelated market and non-market strategies, which we label as liminal movement, to generate cognitive and sociopolitical legitimacy.
An inexact matching approach for the comparison of plane curves with general elastic metrics

This paper introduces a new mathematical formulation and numerical approach for the computation of distances and geodesics between immersed planar curves. Our approach combines the general simplifying transform for first-order elastic metrics that was recently introduced by Kurtek and Needham, together with a relaxation of the matching constraint using parametrization-invariant fidelity metrics. The main advantages of this formulation are that it leads to a simple optimization problem for discretized curves, and that it provides a flexible approach to deal with noisy, inconsistent or corrupted data. These benefits are illustrated via a few preliminary numerical results.
ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data.

BACKGROUND Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are used in diverse life science research domains. Algorithm choice can affect classification accuracy dramatically, so it is crucial that researchers optimize the choice of which algorithm(s) to apply in a given research domain on the basis of empirical evidence. In benchmark studies, multiple algorithms are applied to multiple datasets, and the researcher examines overall trends. In addition, the researcher may evaluate multiple hyperparameter combinations for each algorithm and use feature selection to reduce data dimensionality. Although software implementations of classification algorithms are widely available, robust benchmark comparisons are difficult to perform when researchers wish to compare algorithms that span multiple software packages. Programming interfaces, data formats, and evaluation procedures differ across software packages; and dependency conflicts may arise during installation. FINDINGS To address these challenges, we created ShinyLearner, an open-source project for integrating machine-learning packages into software containers. ShinyLearner provides a uniform interface for performing classification, irrespective of the library that implements each algorithm, thus facilitating benchmark comparisons. In addition, ShinyLearner enables researchers to optimize hyperparameters and select features via nested cross-validation; it tracks all nested operations and generates output files that make these steps transparent. ShinyLearner includes a Web interface to help users more easily construct the commands necessary to perform benchmark comparisons. ShinyLearner is freely available at https://github.com/srp33/ShinyLearner. CONCLUSIONS This software is a resource to researchers who wish to benchmark multiple classification or feature-selection algorithms on a given dataset. We hope it will serve as example of combining the benefits of software containerization with a user-friendly approach.
Consequences of analytic boundary conditions in AdS

We investigate the effects of an analytic boundary metric for smooth asymptotically anti-de Sitter gravitational solutions. The boundary dynamics is then completely determined by the initial data due to corner conditions that all smooth solutions must obey. We perturb a number of familiar static solutions and explore the boundary dynamics that results. We find evidence for a nonlinear asymptotic instability of the planar black hole in four and six dimensions. In four dimensions we find indications of at least exponential growth, while in six dimensions, it appears that a singularity may form in finite time on the boundary. This instability extends to pure AdS (at least in the Poincare patch). For the class of perturbations we consider, there is no sign of this instability in five dimensions.
Improved surrogates in inertial confinement fusion with manifold and cycle consistencies

Neural networks have become the method of choice in surrogate modeling because of their ability to characterize arbitrary, high-dimensional functions in a data-driven fashion. This paper advocates for the training of surrogates that are 1) consistent with the physical manifold, resulting in physically meaningful predictions, and 2) cyclically consistent with a jointly trained inverse model; i.e., backmapping predictions through the inverse results in the original input parameters. We find that these two consistencies lead to surrogates that are superior in terms of predictive performance, are more resilient to sampling artifacts, and tend to be more data efficient. Using inertial confinement fusion (ICF) as a test-bed problem, we model a one-dimensional semianalytic numerical simulator and demonstrate the effectiveness of our approach.
Factor Saving Innovation

It has been argued that concave models exhibit less “endogeneity of growth” than models with increasing returns to scale. Here we study a simple model of factor saving technological improvement in a concave framework. Capital can be used either to reproduce itself, or, at some additional cost, to produce a higher quality of capital, which requires less labor input. If better quality capital can be produced quickly, we get a model of exogenous balanced growth as a special case of ours. If, however, better quality capital can be produced slowly, we get a model of “endogenous growth” in which the growth rate of the economy and the rate of adoption of new technologies is determined by preferences, technology and initial conditions. Moreover, in the latter case, the process of growth is necessarily uneven, exhibiting a natural cycle with alternating periods of high and slow growth. Growth paths and technological innovations also exhibit dependence upon initial conditions. The model provides a step toward a theory of endogenous innovation under conditions of perfect competition. Journal of Economic Literature Classification Numbers: O30, O40, D92, C61, D24, D41.
The Effect of Trust on Public Support for Biotechnology: Evidence from the U.S. Biotechnology Study, 1997-1998

The purpose of this paper is to examine the extent to which trust directly affects public support for biotechnology, particularly in applications to food production and genetic modification of crop plants. Unlike previous research in which trust is assumed to be exogenous, this paper posits that trust is endogenously determined. An econometric model is developed that controls for the endogeneity of trust using instrumental variable and selection correction techniques. Using data from the U.S. Biotechnology Study, this study finds that the effect of trust on public support is substantially stronger than previous estimates.
Chapter 3 – Geothermal Well Drilling

In this chapter we will briefly describe the drilling process. The confirmation of the optimistic outcome of the exploration phase comes with the successful drilling of the first exploratory well.
Chapter 23 – High-Throughput Analysis of Gene Expression

Microarrays are widely regarded both for side-by-side comparisons of two or more samples and as remarkable tools for high-throughput screening. A microarray, also known simply as an array or a gene chip, is typically a glass slide, a silicon wafer, or even a plastic substrate upon which very large numbers of individual gene sequences have been permanently applied. Microarrays were originally developed for the assay of mRNA expression from various biological sources. Microarrays are considered high-throughput tools though they are not especially quantitative. Candidate genes are generally identified by high-throughput microarray analysis; more refined quantitative gene expression data results from ensuing real-time PCR. Heat maps are one of the most useful methods for reporting data that often represent many samples, many genes, or both. A heat map is a graphical two-dimensional representation of microarray data in which the values of the data are represented in color. The most obvious advantage of microarray-based profiling of gene expression is the ability to assay very large numbers of genes in a single hybridization experiment. As a result of this rapid analysis of literally thousands of genes, microarray data are now being used to implicate specific genes and gene relays in the onset and progression of various diseases. The basic steps performed in a typical microarray analysis are acquisition of the microarray, isolation of high-quality RNA, cDNA synthesis, linkage of fluorescent dyes, purification of Cy-labeled probes, microarray hybridization, acquisition of data from the microarray slide or the filter, and determination of the biological significance and relevance of data.
Dissociable neural correlates of uncertainty underlie different exploration strategies.

Most real-world decisions involve a delicate balance between exploring unfamiliar alternatives and committing to the best known option. Previous work has shown that humans rely on different forms of uncertainty to negotiate this "explore-exploit” trade-off, yet the neural basis of the underlying computations remains unclear. Using fMRI (n = 31), we find that relative uncertainty is represented in right rostrolateral prefrontal cortex and drives directed exploration, while total uncertainty is represented in right dorsolateral prefrontal cortex and drives random exploration. The decision value signal combining relative and total uncertainty to compute choice is reflected in motor cortex activity. The variance of this signal scales with total uncertainty, consistent with a sampling mechanism for random exploration. Overall, these results are consistent with a hybrid computational architecture in which different uncertainty computations are performed separately and then combined by downstream decision circuits to compute choice. Humans explore the world by optimistically directing choices to less familiar options and by choosing more randomly when options are uncertain. Here, the authors show that these two exploration strategies rely on distinct uncertainty estimates represented in different parts of the prefrontal cortex.
The valley version of the Extended Delta Conjecture

Abstract The Shuffle Theorem of Carlsson and Mellit gives a combinatorial expression for the bigraded Frobenius characteristic of the ring of diagonal harmonics, and the Delta Conjecture of Haglund, Remmel and the second author provides two generalizations of the Shuffle Theorem to the delta operator expression Δ e k ′ e n . Haglund et al. also propose the Extended Delta Conjecture for the delta operator expression Δ e k ′ Δ h r e n , which is analogous to the rise version of the Delta Conjecture. Recently, D'Adderio, Iraci and Wyngaerd proved the rise version of the Extended Delta Conjecture at the case when t = 0 . In this paper, we propose a new valley version of the Extended Delta Conjecture. Then, we work on the combinatorics of extended ordered multiset partitions to prove that the two conjectures for Δ e k ′ Δ h r e n are equivalent when t or q equals 0, thus proving the valley version of the Extended Delta Conjecture when t or q equals 0.
Imaging Switchable Protein Interactions with an Active Porous Polymer Support

Mechanistic details about how local physicochemistry of porous interfaces drives protein transport mechanisms are necessary to optimize biomaterial applications. Cross-linked hydrogels made of stimuli-responsive polymers have potential for active protein capture and release through tunable steric and chemical transformations. Simultaneous monitoring of dynamic changes in both protein transport and interfacial polymer structure is an experimental challenge. We use single-particle tracking (SPT) and fluorescence correlation spectroscopy Super-resolution Optical Fluctuation Imaging (fcsSOFI) to relate the switchable changes in size and structure of a pH-responsive hydrogel to the interfacial transport properties of a model protein, lysozyme. SPT analysis reveals the reversible switching of protein transport dynamics in and at the hydrogel polymer in response to pH changes. fcsSOFI allows us to relate tunable heterogeneity of the hydrogels and pores to reversible changes in the distribution of confined diffusion and adsorption/desorption. We find that physicochemical heterogeneity of the hydrogels dictates protein confinement and desorption dynamics, particularly at pH conditions in which the hydrogels are swollen.