cssi/SciSciGPT-SciSciNet huggingface dataset:
A linearised model for calculating inertial forces on a particle in the presence of a permeate flow

Understanding particle transport and localisation in porous channels, especially at moderate Reynolds numbers, is relevant for many applications ranging from water reclamation to biological studies. Recently, researchers experimentally demonstrated that the interplay between axial and permeate flow in a porous microchannel results in a wide range of focusing positions of finite-sized particles (Garcia & Pennathur, Phys. Rev. Fluids, vol. 2 (4), 2017, 042201). We numerically explore this interplay by computing the lateral forces on a neutrally buoyant spherical particle that is subject to both inertial and permeate forces over a range of experimentally relevant particle sizes and channel Reynolds numbers. Interestingly, we show that the lateral forces on the particle are well represented using a linearised model across a range of permeate-to-axial flow rate ratios. Specifically, our model linearises the effects of the permeate flow, which suggests that the interplay between axial and permeate flow on the lateral force on a particle can be represented as a superposition between the lateral (inertial) forces in pure axial flow and the viscous forces in pure permeate flow. We experimentally validate this observation for a range of flow conditions. The linearised behaviour observed significantly reduces the complexity and time required to predict the migration of inertial particles in permeate channels.
MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features

In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in USA. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of the previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MRIs. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-words approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex-matched healthy controls) and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions.
Homology operations in symmetric homology

Symmetric homology of a unital algebra A over a commutative ground ring k has been defined using derived functors and the symmetric bar construction of Fiedorowicz, in an analogous way as cyclic, dihedral or quaternionic homology has been defined. In this paper, it is found that the HS�(A) admits Dyer-Lashoff homology operations, and indeed, there is a Pontryagin product structure making HS�(A) into an associative commutative graded algebra. Some explicit computations are shown in low degree.
Integrating Testing and Interactive Theorem Proving

Using an interactive theorem prover to reason about programs involves a sequence of interactions where the user challenges the theorem prover with conjectures. Invariably, many of the conjectures posed are in fact false, and users often spend considerable effort examining the theorem prover’s output before realizing this. We present a synergistic integration of testing with theorem proving, implemented in the ACL2 Sedan (ACL2s), for automatically generating concrete counterexamples. Our method uses the full power of the theorem prover and associated libraries to simplify conjectures; this simplification can transform conjectures for which finding counterexamples is hard into conjectures where finding counterexamples is trivial. In fact, our approach even leads to better theorem proving, e.g., if testing shows that a generalization step leads to a false conjecture, we force the theorem prover to backtrack, allowing it to pursue more fruitful options that may yield a proof. The focus of the paper is on the engineering of a synergistic integration of testing with interactive theorem proving; this includes extending ACL2 with new functionality that we expect to be of general interest. We also discuss our experience in using ACL2s to teach freshman students how to reason about their programs.
Zero-Shot Learning via Semantic Similarity Embedding

In this paper we consider a version of the zero-shot learning problem where seen class source and target domain data are provided. The goal during test-time is to accurately predict the class label of an unseen target domain instance based on revealed source domain side information (e.g. attributes) for unseen classes. Our method is based on viewing each source or target data as a mixture of seen class proportions and we postulate that the mixture patterns have to be similar if the two instances belong to the same unseen class. This perspective leads us to learning source/target embedding functions that map an arbitrary source/target domain data into a same semantic space where similarity can be readily measured. We develop a max-margin framework to learn these similarity functions and jointly optimize parameters by means of cross validation. Our test results are compelling, leading to significant improvement in terms of accuracy on most benchmark datasets for zero-shot recognition.
Locally-smeared operator product expansions

We propose a "locally-smeared Operator Product Expansion" (sOPE) to decompose non-local operators in terms of a basis of locally-smeared operators. The sOPE formally connects nonperturbative matrix elements of smeared degrees of freedom, determined numerically using the gradient flow, to non-local operators in the continuum. The nonperturbative matrix elements do not suffer from power-divergent mixing on the lattice, provided the smearing scale is kept fixed in the continuum limit. The presence of this smearing scale prevents a simple connection to the standard operator product expansion and therefore requires the construction of a two-scale formalism. We demonstrate the feasibility of our approach using the example of real scalar field theory.
On model misspecification and KL separation for Gaussian graphical models

We establish bounds on the KL divergence between two multivariate Gaussian distributions in terms of the Hamming distance between the edge sets of the corresponding graphical models. We show that the KL divergence is bounded below by a constant when the graphs differ by at least one edge; this is essentially the tightest possible bound, since classes of graphs exist for which the edge discrepancy increases but the KL divergence remains bounded above by a constant. As a natural corollary to our KL lower bound, we also establish a sample size requirement for correct model selection via maximum likelihood estimation. Our results rigorize the notion that it is essential to estimate the edge structure of a Gaussian graphical model accurately in order to approximate the true distribution to close precision.
Approximating the Permanent of a Random Matrix with Vanishing Mean

The permanent is #P-hard to compute exactly on average for natural random matrices including matrices over finite fields or Gaussian ensembles. Should we expect that it remains #P-hard to compute on average if we only care about approximation instead of exact computation? In this work we take a first step towards resolving this question: We present a quasi-polynomial time deterministic algorithm for approximating the permanent of a typical n × n random matrix with unit variance and vanishing mean µ = O(ln ln n)^-1/8 to within inverse polynomial multiplicative error. (alternatively, one can achieve permanent approximation for matrices with mean µ = 1/polylog(n) in time 2^n^e, for arbitrarily small e>0). The proposed algorithm significantly extends the regime of matrices for which efficient approximation of the permanent is known. This is because unlike previous algorithms which require a stringent correlation between the signs of the entries of the matrix [1], [2] it can tolerate random ensembles in which this correlation is negligible (albeit non-zero). Among important special cases we note: 1) Biased Gaussian: each entry is a complex Gaussian with unit variance 1 and mean µ. 2) Biased Bernoulli: each entry is -1 + µ with probability 1/2, and 1 with probability 1/2. These results counter the common intuition that the difficulty of computing the permanent, even approximately, stems merely from our inability to treat matrices with many opposing signs. The Gaussian ensemble approaches the threshold of a conjectured hardness [3] of computing the permanent of a zero mean Gaussian matrix. This conjecture is one of the baseline assumptions of the BosonSampling paradigm that has received vast attention in recent years in the context of quantum supremacy experiments. We furthermore show that the permanent of the biased Gaussian ensemble is #P-hard to compute exactly on average. To our knowledge, this is the first natural example of a counting problem that becomes easy only when average case analysis and approximation are combined. On a technical level, our approach stems from a recent approach taken by Barvinok [1], [4], [5], [6] who used Taylor series approximation of the logarithm of a certain univariate polynomial related to the permanent. Our main contribution is to introduce an average-case analysis of such related polynomials. We complement our approach with a new technique for iteratively computing a Taylor series approximation of a function that is analytical in the vicinity of a curve in the complex plane. This method can be viewed as a computational version of analytic continuation in complex analysis.
Efficient Time Domain Decomposition Algorithms for Parabolic PDE-Constrained Optimization Problems

Abstract Optimization with time-dependent partial differential equations (PDEs) as constraints appears in many science and engineering applications. The associated first-order necessary optimality system consists of one forward and one backward time-dependent PDE coupled with optimality conditions. An optimization process by using the one-shot method determines the optimal control, state and adjoint state at once, with the cost of solving a large scale, fully discrete optimality system. Hence, such a one-shot method could easily become computationally prohibitive when the time span is long or time step is small. To overcome this difficulty, we propose several time domain decomposition algorithms for improving the computational efficiency of the one-shot method. In these algorithms, the optimality system is split into many small subsystems over a much smaller time interval, which are coupled by appropriate continuity matching conditions. Both one-level and two-level multiplicative and additive Schwarz algorithms are developed for iteratively solving the decomposed subsystems in parallel. In particular, the convergence of the one-level, non-overlapping algorithms is proved. The effectiveness of our proposed algorithms is demonstrated by both 1D and 2D numerical experiments, where the developed two-level algorithms show convergence rates that are scalable with respect to the number of subdomains.
A rigidity phenomenon for the Hardy-Littlewood maximal function

The Hardy-Littlewood maximal function $\mathcal{M}$ and the trigonometric function $\sin{x}$ are two central objects in harmonic analysis. We prove that $\mathcal{M}$ characterizes $\sin{x}$ in the following way: let $f \in C^{\alpha}(\mathbb{R}, \mathbb{R})$ be a periodic function and $\alpha > 1/2$. If there exists a real number $0 < \gamma < \infty$ such that the averaging operator $$ (A_xf)(r) = \frac{1}{2r}\int_{x-r}^{x+r}{f(z)dz}$$ has a critical point in $r = \gamma$ for every $x \in \mathbb{R}$, then $$f(x) = a+b\sin{(cx + d)} \qquad \mbox{for some}~a,b,c,d \in \mathbb{R}.$$ This statement can be used to derive a characterization of trigonometric functions as those nonconstant functions for which the computation of the maximal function $\mathcal{M}$ is as simple as possible. The proof uses the Lindemann-Weierstrass theorem from transcendental number theory.
Noise Contrastive Priors for Functional Uncertainty

Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of an independent normal prior in weight space imposes relatively weak constraints on the function posterior, allowing it to generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results.
A predicted deleterious allele of the essential meiosis gene MND1, present in ~ 3% of East Asians, does not disrupt reproduction in mice.

Infertility is a major health problem affecting ~15% of couples worldwide. Except for cases involving readily detectable chromosome aberrations, confident identification of a causative genetic defect is problematic. Despite the advent of genome sequencing for diagnostic purposes, the preponderance of segregating genetic variants complicates identification of culprit genetic alleles or mutations. Many algorithms have been developed to predict the effects of 'variants of unknown significance', typically single nucleotide polymorphisms (SNPs), but these predictions are not sufficiently accurate for clinical action. As part of a project to identify population variants that impact fertility, we have been generating clustered regularly interspaced short palindromic repeats-Cas9 edited mouse models of suspect SNPs in genes that are known to be required for fertility in mice. Here, we present data on a non-synonymous (amino acid altering) SNP (rs140107488) in the meiosis gene Mnd1, which is predicted bioinformatically to be deleterious to protein function. We report that when modeled in mice, this allele (MND1K85M), which is present at an allele frequency of ~ 3% in East Asians, has no discernable effect upon fertility, fecundity or gametogenesis, although it may cause sex skewing of progeny from homozygous males. In sum, assuming the mouse model accurately reflects the impact of this variant in humans, rs140107488 appears to be a benign allele that can be eliminated or de-prioritized in clinical genomic analyses of infertility patients.
Loss of Skin Microbial Diversity and Alteration of Bacterial Metabolic Function in Hidradenitis Suppurativa.

Hidradenitis Suppurativa (HS) is a chronic, scarring, inflammatory skin disease affecting hair follicles in axillae, inguinal, and anogenital regions. Dysbiosis in HS patients compared to healthy subjects is documented. However, whether dysbiosis is specific to particular body sites or skin niches is unknown. We investigated the follicular and skin surface microbiome of the axilla and groin of HS patients (n=11) and healthy individuals (n=10) using 16S rRNA gene sequencing (V3-V4). We sampled non-lesional (HSN) and lesional skin (HSL) of HS patients. {beta}-diversity was significantly decreased (p<0.05) in HSN and HSL skin compared to normal skin with loss of body site and skin niche heterogeneity in HS samples. The relative bacterial abundance of specific microbes was also significantly different between normal and HSN (15 genera) or HSL (21 genera) skin. Smoking and alcohol use influenced the {beta}-diversity (p<0.08) in HS skin. We investigated metabolic profiles of bacterial communities in HS and normal skin using a computational approach. Metabolism, Genetic Information Processing, and Environmental Information Processing were significantly different between normal and HS samples. Altered metabolic pathways associated with dysbiosis of HS skin suggest mechanisms underlying the disease pathology and information about treatment with drugs targeting those pathways.
Coupling of deterministically activated quantum emitters in hexagonal boron nitride to plasmonic surface lattice resonances

Cooperative phenomena stemming from radiation-field-mediated coupling between individual quantum emitters are presently attracting broad interest for on-chip photonic quantum memories and long-range entanglement. Common to these applications is the generation of electromagnetic modes over macroscopic distances. Much research, however, is still needed before such systems can be deployed in the form of practical devices, starting with the investigation of alternate physical platforms. Quantum emitters in two-dimensional (2D) systems provide an intriguing route because these materials can be adapted to arbitrarily shaped substrates to form hybrid systems where emitters are near-field-coupled to suitable optical modes. Here, we report a scalable coupling method allowing color center ensembles in a van der Waals material - hexagonal boron nitride - to couple to a delocalized high quality plasmonic surface lattice resonance. This type of architecture is promising for photonic applications, especially given the ability of the hexagonal boron nitride emitters to operate as single-photon sources at room temperature.
Daily ambient temperature is associated with biomarkers of kidney injury in older Americans.

Abstract Background Increases in ambient temperature have recently been associated with increased emergency department visits and hospital admissions for acute renal failure. However, potential biological mechanisms through which short-term ambient temperature affects kidney function are not known. Methods We used multiple regression models to evaluate the association between 1- and 3-day average, ambient temperature levels and two biomarkers of kidney injury (neutrophil gelatinase-associated lipocalin (NGAL) and adiponectin), among 3377 individuals over 57 years of age enrolled in the National Social Life, Health, and Aging Project. Ambient temperature was estimated on a 6-km grid covering the conterminous United States using ambient temperature measurements obtained from the National Climatic Data Center (NCDC). NGAL and adiponectin levels were measured from whole blood collected for each participant. All health effect models were adjusted for a number of demographics, socioeconomic, health behavior, medical history variables, with non-linear exposure-response relationships examined using natural cubic splines. Results The relationship between 1- and 3-day average temperature and both NGAL and adiponectin levels was significant and non-linear, with largely null associations below 10 °C, and positive association for temperatures >10 °C. In fully adjusted, linear multiple regression models restricted to >10 °C, NGAL and adiponectin levels increased by 1.89% (95% CI: 0.77, 3.91) and 2.51% (95% CI: 1.34, 3.69), respectively, for a 1 °C increase in daily average temperature. Additionally, every 1 °C increase in temperature over 10 °C was associated with a 1.83% increased odds of having plasma NGAL levels consistent with acute kidney injury (>150 μg/L). Conclusions In a cohort of older men and women in the United States, our study is the first to observe that short-term ambient temperature exposures were significantly associated with biomarkers of kidney injury. These associations suggest that ambient temperature exposures could be an important risk factor for renal pathology.
History and Examples of Environmental Justice

Environmental justice has become an influential policy discourse and analysis tool over the last few decades with particular importance for risk management. This article examines the evolution of the environmental justice movement in the United States, the main research tools used in the field, and their potential use and adaptation for incorporating justice into risk problems. The environmental justice discourse in international policy issues such as climate change and transboundary movements of hazardous and toxic substances, and its use within the sustainable development field are also discussed. Although literature reviews of environmental justice analysis suggest that there is strong evidence of environmental inequities, many research challenges exist related to the measurement of individual and multiple exposures to environmental risks and their link to human health outcomes. Understanding these links among different population groups is an area of active research and represents critical future research directions in the field. Keywords: environmental justice; environmental equity; environmental risk; proximity analysis; sustainable development; geographic information systems (GIS); infrastructure; risk assessment; risk management
Light-driven deracemization enabled by excited-state electron transfer

Deracemization is an attractive strategy for asymmetric synthesis, but intrinsic energetic challenges have limited its development. Here, we report a deracemization method in which amine derivatives undergo spontaneous optical enrichment upon exposure to visible light in the presence of three distinct molecular catalysts. Initiated by an excited-state iridium chromophore, this reaction proceeds through a sequence of favorable electron, proton, and hydrogen-atom transfer steps that serve to break and reform a stereogenic C–H bond. The enantioselectivity in these reactions is jointly determined by two independent stereoselective steps that occur in sequence within the catalytic cycle, giving rise to a composite selectivity that is higher than that of either step individually. These reactions represent a distinct approach to creating out-of-equilibrium product distributions between substrate enantiomers using excited-state redox events.
Measuring discourse by algorithm

Abstract Scholars increasingly use machine learning techniques such as Latent Dirichlet Allocation (LDA) to reduce the dimensionality of textual data and to study discourse in collective bodies. However, measures of discourse based on algorithmic results typically have no intuitive meaning or obvious relationship to humanly observed discourse. Such measures of discourse must be carefully validated before relied on and interpreted. We examine several common measures of discourse based on algorithmic results, and propose a number of ways to validate them in the setting of Federal Open Market Committee meetings. We also suggest that validation techniques may be used as a principled approach to model selection and parameterization.
A Hypothetical Learning Progression for Quantifying Phenomena in Science

In this article, we report on a three-pronged effort to create a hypothetical learning progression for quantification in science. First, we drew from history and philosophy of science to define the quantification competency and develop hypothetical levels of the learning progression. More specifically, the quantification competency refers to the ability to analyze phenomena through (a) abstracting relevant measurable variables from phenomena and observations, (b) investigating the mathematical relationships among the variables, and (c) conceptualizing scientific ideas that explain the mathematical relationships. The quantification learning progression contains four levels of increasing sophistication: level 1, holistic observation; level 2, attributes; level 3, measurable variables; and level 4, relational complexity. Second, we analyzed the practices in the Next Generation Science Standards for current, largely tacit, assumptions about how quantification develops (or ought to develop) through K-12 education. While several pieces of evidence support the learning progression, we found that quantification was described inconsistently across practices. Third, we used empirical student data from a field test of items in physical and life sciences to illustrate qualitative differences in student thinking that align with levels in the hypothetical learning progression for quantification. By generating a hypothetical learning progression for quantification, we lay the groundwork for future standards development efforts to include this key practice and provide guidance for curriculum developers and instructors in helping students develop robust scientific understanding.
Square Sierpiński carpets and Lattès maps

We prove that every quasisymmetric homeomorphism of a standard square Sierpinski carpet $$S_p$$ , $$p\ge 3$$ odd, is an isometry. This strengthens and completes earlier work by the authors (Bonk and Merenkov in Ann Math (2) 177:591–643, 2013, Theorem 1.2). We also show that a similar conclusion holds for quasisymmetries of the double of $$S_p$$ across the outer peripheral circle. Finally, as an application of the techniques developed in this paper, we prove that no standard square carpet $$S_p$$ is quasisymmetrically equivalent to the Julia set of a postcritically-finite rational map.
Local Exposure to School Shootings and Youth Antidepressant Use

While over 240,000 American students experienced a school shooting in the last two decades, little is known about the impacts of these events on the mental health of surviving youth. Using large-scale prescription data from 2006 to 2015, we examine the effects of 44 school shootings on youth antidepressant use. Our empirical strategy compares the number of antidepressant prescriptions written by providers practicing 0 to 5 miles from a school that experienced a shooting (treatment areas) to the number of prescriptions written by providers practicing 10 to 15 miles away (reference areas), both before and after the shooting. We include month-by-year and school-by-area fixed effects in all specifications, thereby controlling for overall trends in antidepressant use and all time-invariant differences across locations. We find that local exposure to fatal school shootings increases youth antidepressant use by 21.4% in the following 2 y. These effects are smaller in areas with a higher density of mental health providers who focus on behavioral, rather than pharmacological, interventions.
Wave physics as an analog recurrent neural network

Analog machine learning hardware platforms promise to be faster and more energy efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for building analog processors for time-varying signals. Here, we identify a mapping between the dynamics of wave physics and the computation in recurrent neural networks. This mapping indicates that physical wave systems can be trained to learn complex features in temporal data, using standard training techniques for neural networks. As a demonstration, we show that an inverse-designed inhomogeneous medium can perform vowel classification on raw audio signals as their waveforms scatter and propagate through it, achieving performance comparable to a standard digital implementation of a recurrent neural network. These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain.
Active binary mixtures of fast and slow hard spheres

We computationally studied the phase behavior and dynamics of binary mixtures of active particles, where each species had distinct activities leading to distinct velocities, fast and slow. We obtained phase diagrams demonstrating motility-induced phase separation (MIPS) upon varying the activity and concentration of each species, and extended current kinetic theory of active/passive mixtures to active/active mixtures. We discovered two regimes of behavior quantified through the participation of each species in the dense phase compared to their monodisperse counterparts. In regime I (active/passive and active/weakly-active), we found that the dense phase was segregated by particle type into domains of fast and slow particles. Moreover, fast particles were suppressed from entering the dense phase while slow particles were enhanced entering the dense phase, compared to monodisperse systems of all-fast or all-slow particles. These effects decayed asymptotically as the activity of the slow species increased, approaching the activity of the fast species until they were negligible (regime II). In regime II, the dense phase was homogeneously mixed and each species participated in the dense phase as if it were it a monodisperse system (i.e. not mixed at all). Finally, we showed that a weighted average of constituent particle activities, which we term the net activity, defines a binodal for the MIPS transition in active/active binary mixtures. We examined the critical point of the transition and found a critical exponent (β = 0.45) in agreement with similar studies on monodisperse systems, and distinct from equilibrium systems.
Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise

In tracking of time-varying low-rank models of time-varying matrices, we present a method robust to both uniformly-distributed measurement noise and arbitrarily-distributed “sparse” noise. In theory, we bound the tracking error. In practice, our use of randomised coordinate descent is scalable and allows for encouraging results on changedetection.net, a benchmark.
Hessian estimate for semiconvex solutions to the sigma-2 equation

We derive a priori interior Hessian estimates for semiconvex solutions to the sigma-2 equation. An elusive Jacobi inequality, a transformation rule under the Legendre–Lewy transform, and a mean value inequality for the still nonuniformly elliptic equation without area structure are the key to our arguments. Previously, this result was known for almost convex solutions.
A quantum solution for efficient use of symmetries in the simulation of many-body systems

A many-body Hamiltonian can be block-diagonalized by expressing it in terms of symmetry-adapted basis states. Finding the group orbit representatives of these basis states and their corresponding symmetries is currently a memory/computational bottleneck on classical computers during exact diagonalization. We apply Grover’s search in the form of a minimization procedure to solve this problem. Our quantum solution provides an exponential reduction in memory, and a quadratic speedup in time over classical methods. We discuss explicitly the full circuit implementation of Grover minimization as applied to this problem, finding that the oracle only scales as polylog in the size of the group, which acts as the search space. Further, we design an error mitigation scheme that, with no additional qubits, reduces the impact of bit-flip errors on the computation, with the magnitude of mitigation directly correlated with the error rate, improving the utility of the algorithm in the Noisy Intermediate Scale Quantum era.
Wide-field, high-resolution lensless on-chip microscopy via near-field blind ptychographic modulation.

We report a novel lensless on-chip microscopy platform based on near-field blind ptychographic modulation. In this platform, we place a thin diffuser in between the object and the image sensor for light wave modulation. By blindly scanning the unknown diffuser to different x–y positions, we acquire a sequence of modulated intensity images for quantitative object recovery. Different from previous ptychographic implementations, we employ a unit magnification configuration with a Fresnel number of ∼50 000, which is orders of magnitude higher than those of previous ptychographic setups. The unit magnification configuration allows us to have the entire sensor area, 6.4 mm by 4.6 mm, as the imaging field of view. The ultra-high Fresnel number enables us to directly recover the positional shift of the diffuser in the phase retrieval process, addressing the positioning accuracy issue plaguing regular ptychographic experiments. In our implementation, we use a low-cost, DIY scanning stage to perform blind diffuser modulation. Precise mechanical scanning that is critical in conventional ptychography experiments is no longer needed in our setup. We further employ an up-sampling phase retrieval scheme to bypass the resolution limit set by the imager pixel size and demonstrate a half-pitch resolution of 0.78 μm. We validate the imaging performance via in vitro cell cultures, transparent and stained tissue sections, and a thick biological sample. We show that the recovered quantitative phase map can be used to perform effective cell segmentation of a dense yeast culture. We also demonstrate 3D digital refocusing of the thick biological sample based on the recovered wavefront. The reported platform provides a cost-effective and turnkey solution for large field-of-view, high-resolution, and quantitative on-chip microscopy. It is adaptable for a wide range of point-of-care-, global-health-, and telemedicine-related applications.
"Family Values Don't Stop at the Rio Grande..." : Can the Republican Party Convert Hispanic Voters?

As the Hispanic community becomes increasingly important in American politics there are competing views about whether they can be converted to the Republican Party. One perspective argues that Hispanics’ religion and traditional social values makes them natural constituents of the Republican Party. Alternatively, Hispanics are primarily concerned about issues promoting their well-being and topics such as moral values or religion are private. We use a novel approach to test whether traditional social values might attract Hispanic voters to the Republican Party. Using exit poll results for ballot propositions on moral issues from Arizona, Colorado, and Florida we find weak evidence that traditional values will convert Hispanics to the Republican Party. Instead, our results indicate that traditional social values issues reinforce the polarization between the two parties.
Do the most prominent firms really make the worst deals? How selection issues affect inferences from M&A studies

Abstract Many studies find a negative relationship between acquirers’ stock returns and their size, past acquisitiveness, and performance. This counter-intuitively suggests that large, well-performing, and acquisitive firms are worse-than-average acquirers. We hypothesize that these findings stem from bias related to an omitted variable: the predictability of acquisitive behavior. We theoretically model the direction of this bias for each variable and test these predictions using Heckman's two-stage procedure with a relevant and plausibly excludable variable related to tax avoidance. By mitigating this bias, our approach generates several new insights about acquisition value that are obscured by OLS regressions of deal announcement returns.
Gain saturation in Nd-doped phosphate laser glasses

We have studied saturation of gain in several Nd-doped phosphate glasses which are being evaluated for possible use in the Nova laser facility. Phosphate glass of excellent optical quality can be produced in sizes adequate to fabricate the large amplifier rods and disks needed for fusion lasers.
Evolution Rapidly Optimizes Stability and Aggregation in Lattice Proteins Despite Pervasive Landscape Valleys and Mazes.

The “fitness” landscapes of genetic sequences are characterized by high dimensionality and “ruggedness” due to sign epistasis. Ascending from low to high fitness on such landscapes can be difficult because adaptive trajectories get stuck at low-fitness local peaks. Compounding matters, recent theoretical arguments have proposed that extremely long, winding adaptive paths may be required to reach even local peaks: a “maze-like” landscape topography. The extent to which peaks and mazes shape the mode and tempo of evolution is poorly understood, due to empirical limitations and the abstractness of many landscape models. We explore the prevalence, scale, and evolutionary consequences of landscape mazes in a biophysically grounded computational model of protein evolution that captures the “frustration” between “stability” and aggregation propensity. Our stability-aggregation landscape exhibits extensive sign epistasis and local peaks galore. Although this frequently obstructs adaptive ascent to high fitness and virtually eliminates reproducibility of evolutionary outcomes, many adaptive paths do successfully complete the ascent from low to high fitness, with hydrophobicity a critical mediator of success. These successful paths exhibit maze-like properties on a global landscape scale, in which taking an indirect path helps to avoid low-fitness local peaks. This delicate balance of “hard but possible” adaptation could occur more broadly in other biological settings where competing interactions and frustration are important.
On the Spectrum of the XXZ-Chain at Roots of Unity

In a recent paper (cond-mat/0009279), Fabricius and McCoy studied the spectrum of the spin 1/2 XXZ model at roots of unity, i.e., Δ=(q+q −1)/2 with q 2N =1 for integer N≥2. They found a certain pattern of degeneracies and linked it to the sl 2-loop symmetry present in the commensurable spin sector—S z ≡0 modN. We show that the degeneracies are due to an unusual type of zero-energy “transparent” excitation, the cyclic bound state. The cyclic bound states exist both in the commensurable and in the incommensurable sectors indicating a symmetry group present, of which sl 2-loop algebra is a partial manifestation. Our approach treats both sectors on even footing and allows us to obtain analytically an explicit expression for the degeneracies in the case N=3.
Embedded spectral descriptors: learning the point-wise correspondence metric via Siamese neural networks

A robust and informative local shape descriptor plays an important role in mesh registration. In this regard, spectral descriptors that are based on the spectrum of the Laplace-Beltrami operator have been a popular subject of research for the last decade due to their advantageous properties, such as isometry invariance. Despite such, however, spectral descriptors often fail to give a correct similarity measure for non-isometric cases where the metric distortion between the models is large. Hence, they are not reliable for correspondence matching problems when the models are not isometric. In this paper, it is proposed a method to improve the similarity metric of spectral descriptors for correspondence matching problems. We embed a spectral shape descriptor into a different metric space where the Euclidean distance between the elements directly indicates the geometric dissimilarity. We design and train a Siamese neural network to find such an embedding, where the embedded descriptors are promoted to rearrange based on the geometric similarity. We demonstrate our approach can significantly enhance the performance of the conventional spectral descriptors by the simple augmentation achieved via the Siamese neural network in comparison to other state-of-the-art methods.
Shared Linear Quadratic Regulation Control: A Reinforcement Learning Approach

We propose controller synthesis for state regulation problems in which a human operator shares control with an autonomy system, running in parallel. The autonomy system continuously improves over human action, with minimal intervention, and can take over full-control if necessary. It additively combines user input with an adaptive optimal corrective signal to drive the plant. It is adaptive in the sense that it neither estimates nor requires a model of the human’s action policy, or the internal dynamics of the plant, and can adjust to changes in both. Our contribution is twofold; first, a new controller synthesis for shared control which we formulate as an adaptive optimal control problem for continuous-time linear systems and solve it online as a human-in-the-loop reinforcement learning. The result is an architecture that we call shared linear quadratic regulator (sLQR). Second, we provide new analysis of reinforcement learning for continuous-time linear systems in two parts. In the first analysis part, we avoid learning along a single state-space trajectory which we show leads to data collinearity under certain conditions. In doing so, we make a clear separation between exploitation of learned policies and exploration of the state-space, and propose an exploration scheme that requires switching to new state-space trajectories rather than injecting noise continuously while learning. This avoidance of continuous noise injection minimizes interference with human action, and avoids bias in the convergence to the stabilizing solution of the underlying algebraic Riccati equation. We show that exploring a minimum number of pairwise distinct state-space trajectories is necessary to avoid collinearity in the learning data. In the second analysis part, we show conditions under which existence and uniqueness of solutions can be established for off-policy reinforcement learning in continuous-time linear systems; namely, prior knowledge of the input matrix.
Toward Autonomy and Conditional Independence: A Standardized Script Improves Patient Acceptance of Surgical Trainee Roles

Background Progressive autonomy leading to conditional independence is necessary to achieve competence in surgical skills and decision making. Trust and transparency are ethical imperatives, but practices vary regarding the extent of disclosure of specific resident roles. We tested whether a standardized preoperative script would improve patient acceptance of resident involvement in perioperative care. Methods Patients admitted to a resident-run acute care general surgery service between October 2017 and October 2018 were enrolled in an IRB-approved study. During the first half of the rotation (control), operative consent was obtained according to individual practice without specified explanation of resident roles. During the second half (intervention), the senior resident read a short semistructured script specifically explaining team roles and responsibilities, including the degree of resident independence and supervision by attendings. On postoperative day 3, patients completed a survey assessing understanding of their surgical care. Results Sixty-two patients under the care of 10 rotating chief residents were enrolled; 46 patients completed the survey, 23 in each arm (74% response rate). Ten patients in the control arm (43%) compared to only 3 (13%) in the intervention arm indicated that residents should not be allowed to perform portions of operations (odds ratio 4.94, p = 0.047). Patients in the intervention arm felt that care team roles were more adequately explained to them before their operation (p = 0.002). There was no difference in the number of patients naming a resident as "their doctor." Conclusions Use of a short script specifying resident roles improves patient acceptance of trainee participation in perioperative care.
Herding an Adversarial Swarm in an Obstacle Environment

This paper studies a defense approach against a swarm of adversarial agents. We employ a closed formation (‘StringNet’) of defending agents around the adversarial agents to restrict their motion and guide them to a safe area while navigating in an obstacle-populated environment. Control laws for forming the StringNet and guiding it to a safe area are developed, and the stability of the closed-loop system is analyzed formally. The adversarial swarm is assumed to move as a flock in the presence of rectangular obstacles. Simulation results are provided to demonstrate the efficacy of the approach.
Failure study of fiber/epoxy composite laminate interface using cohesive multiscale model:

In this study, finite element modeling is performed to investigate the compressive failure of the composite sandwich structures with layered composite shells. An embedded debond area between the la...
The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.
Channel-Attention Dense U-Net for Multichannel Speech Enhancement

Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary mask that is applied to the mixture in the time-frequency domain to produce the clean speech. Despite the great performance in the single-channel setting, these frameworks lag in performance in the multichannel setting as the majority of these methods a) fail to exploit the available spatial information fully, and b) still treat the deep architecture as a black box which may not be well-suited for multichannel audio processing. This paper addresses these drawbacks, a) by utilizing complex ratio masking instead of masking on the magnitude of the spectrogram, and more importantly, b) by introducing a channel-attention mechanism inside the deep architecture to mimic beamforming. We propose Channel-Attention Dense U-Net, in which we apply the channel-attention unit recursively on feature maps at every layer of the network, enabling the network to perform non-linear beamforming. We demonstrate the superior performance of the network against the state-of-the-art approaches on the CHiME-3 dataset.