cssi/SciSciGPT-SciSciNet huggingface dataset:
Nonreciprocal metasurface with space-time phase modulation.

Creating materials with time-variant properties is critical for breaking reciprocity that imposes fundamental limitations on wave propagation. However, it is challenging to realize efficient and ultrafast temporal modulation in a photonic system. Here, leveraging both spatial and temporal phase manipulation offered by an ultrathin nonlinear metasurface, we experimentally demonstrated nonreciprocal light reflection at wavelengths around 860 nm. The metasurface, with travelling-wave modulation upon nonlinear Kerr building blocks, creates spatial phase gradient and multi-terahertz temporal phase wobbling, which leads to unidirectional photonic transitions in both the momentum and energy spaces. We observed completely asymmetric reflections in forward and backward light propagations over a large bandwidth around 5.77 THz within a sub-wavelength interaction length of 150 nm. Our approach highlights a potential means for creating miniaturized and integratable nonreciprocal optical components. Light propagating in one direction only could pave the way towards miniaturized components for faster, more efficient data transfer in optical circuits. Xingjie Ni and colleagues at the Pennsylvania State University demonstrated the unidirectional flow of near-infrared light in free space based on a 150-nanometre-thick nano-engineered surface, i.e., metasurface. Their approach adds to the body of research examining ways to disrupt the common wisdom that light travelling in one direction can also travel in the other. In the new system, the ultrathin metasurface consisting of a silver reflector plate supporting block-shaped, silicon nanoantennaswas dynamically modulated to generate time-varying parameters. The infrared light interacts with the ultrathin metasurface, ultimately resulting in a reflection that does not propagate in the opposite direction.
A Simple and More General Approach to Stokes’ Theorem

Summary. Oftentimes, Stokes’ theorem is derived by using, more or less explicitly, the invariance of the curl of the vector field with respect to translations and rotations. However, this invarianc...
Aspects of Rich Accessibility User Experiences

We present an approach to providing broad-based accessibility to graphical applications by employing the capabilities of the Eclipse platform and aspect-oriented programming (AOP) in a way that leverages the strengths of each. We first describe the Eclipse platform and the variety of frameworks that can be useful to the effort. We also present the advantages of aspect-oriented programming and contrast aspect-oriented programming with object-oriented programming (OOP). Then, we discuss how to use the Eclipse frameworks and AOP in the design of accessible Eclipse-based applications and share some situations of when to leverage these frameworks and when we do not from the perspective of robustness and efficiency.
An Analysis Framework for Metric Voting based on LP Duality

Distortion-based analysis has established itself as a fruitful framework for comparing voting mechanisms. m voters and n candidates are jointly embedded in an (unknown) metric space, and the voters submit rankings of candidates by non-decreasing distance from themselves. Based on the submitted rankings, the social choice rule chooses a winning candidate; the quality of the winner is the sum of the (unknown) distances to the voters. The rule's choice will in general be suboptimal, and the worst-case ratio between the cost of its chosen candidate and the optimal candidate is called the rule's distortion. It was shown in prior work that every deterministic rule has distortion at least 3, while the Copeland rule and related rules guarantee distortion at most 5; a very recent result gave a rule with distortion 2 + √5 ≈ 4.236.We provide a framework based on LP-duality and flow interpretations of the dual which provides a simpler and more unified way for proving upper bounds on the distortion of social choice rules. We illustrate the utility of this approach with three examples. First, we show that the Ranked Pairs and Schulze rules have distortion Θ(√n). Second, we give a fairly simple proof of a strong generalization of the upper bound of 5 on the distortion of Copeland, to social choice rules with short paths from the winning candidate to the optimal candidate in generalized weak preference graphs. A special case of this result recovers the recent 2 + √5 guarantee. Finally, our framework naturally suggests a combinatorial rule that is a strong candidate for achieving distortion 3, which had also been proposed in recent work. We prove that the distortion bound of 3 would follow from any of three combinatorial conjectures we formulate.
General nonlinear stochastic systems motivated by chemostat models: Complete characterization of long-time behavior, optimal controls, and applications to wastewater treatment

Abstract This paper focuses on a general class of systems of nonlinear stochastic differential equations, inspired by stochastic chemostat models. In the first part, the system is formulated as a hybrid switching diffusion. A complete characterization of the asymptotic behavior of the system under consideration is provided. It is shown that the long-term properties of the system can be classified by using a real-valued parameter λ . If λ ≤ 0 , the bacteria will die out, which means that the process does not operate. If λ > 0 , the system has an invariant probability measure and the transition probability of the solution process converges to that of the invariant measure. The rate of convergence is also obtained. One of the distinct features of this paper is that the critical case λ = 0 is also considered. Moreover, numerical examples are given to illustrate our results. In the second part of the paper, controlled diffusions with a long-run average objective function are treated. The associated Hamilton–Jacobi–Bellman (HJB) equation is derived and the existence of an optimal Markov control is established. The techniques and methods of analysis in this paper can be applied to many other stochastic Kolmogorov systems.
Reference glycan structure libraries of primary human cardiomyocytes and pluripotent stem cell-derived cardiomyocytes reveal cell-type and culture stage-specific glycan phenotypes

Abstract Cell surface glycoproteins play critical roles in maintaining cardiac structure and function in health and disease and the glycan-moiety attached to the protein is critical for proper protein folding, stability and signaling [ 1 ]. However, despite mounting evidence that glycan structures are key modulators of heart function and must be considered when developing cardiac biomarkers, we currently do not have a comprehensive view of the glycans present in the normal human heart. In the current study, we used porous graphitized carbon liquid chromatography interfaced with mass spectrometry (PGC-LC-MS) to generate glycan structure libraries for primary human heart tissue homogenate, cardiomyocytes (CM) enriched from human heart tissue, and human induced pluripotent stem cell derived CM (hiPSC-CM). Altogether, we established the first reference structure libraries of the cardiac glycome containing 265 N- and O-glycans. Comparing the N-glycome of CM enriched from primary heart tissue to that of heart tissue homogenate, the same pool of N-glycan structures was detected in each sample type but the relative signal of 21 structures significantly differed between samples, with the high mannose class increased in enriched CM. Moreover, by comparing primary CM to hiPSC-CM collected during 20–100 days of differentiation, dynamic changes in the glycan profile throughout in vitro differentiation were observed and differences between primary and hiPSC-CM were revealed. Namely, >30% of the N-glycome significantly changed across these time-points of differentiation and only 23% of the N-glycan structures were shared between hiPSC-CM and primary CM. These observations are an important complement to current genomic, transcriptomic, and proteomic profiling and reveal new considerations for the use and interpretation of hiPSC-CM models for studies of human development, disease, and drug testing. Finally, these data are expected to support future regenerative medicine efforts by informing targets for evaluating the immunogenic potential of hiPSC-CM and harnessing differences between immature, proliferative hiPSC-CM and adult primary CM.
A Highly Efficient Bad Data Identification Approach for Very Large Scale Power Systems

The well-known largest normalized residual (LNR) test for bad data identification becomes computationally inefficient for large-scale power systems containing a large volume of bad data, given the fact that it identifies and removes bad measurements sequentially, one at a time. In this paper, a highly efficient alternative implementation of the LNR test will be presented where the computational efficiency will be significantly improved. The main idea is based on the classification of suspect measurements into groups, which have negligible interaction. Then, the LNR test can be applied simultaneously to each individual group, allowing simultaneous identification of multiple bad data in different groups. Consequently, the number of identification/correction cycles for processing a large volume of bad data will be significantly reduced. Simulations carried out on a large utility system show drastic reductions in the CPU time for bad data processing while maintaining highly accurate results. This work is expected to facilitate implementation and more effective use of the LNR test for identifying and correcting measurement errors in very large power systems.
Quantum speed limits and the maximal rate of information production

Special thanks goes to Eric Lutz, who more than a decade ago posed the rather innocent looking question, “Can we derive the Bremermann-Bekenstein bound by means of Quantum Thermodynamics?” [25]. After having developed a more comprehensive framework for quantum speed limits the answer can finally be given to be “yes.” Enlightening discussions with Akram Touil that helped to streamline the narrative are gratefully acknowledged. This research was supported by Grant No. FQXi-RFP-1808 from the Foundational Questions Institute and Fetzer Franklin Fund, a donor advised fund of the Silicon Valley Community Foundation.
Concentration inequalities for random matrix products

Abstract Suppose { X k } k ∈ Z is a sequence of bounded independent random matrices with common dimension d × d and common expectation E [ X k ] = X . Under these general assumptions, the normalized random matrix product Z n = ( I d + 1 n X n ) ( I d + 1 n X n − 1 ) ⋯ ( I d + 1 n X 1 ) converges to e X as n → ∞ . Normalized random matrix products of this form arise naturally in stochastic iterative algorithms, such as Oja's algorithm for streaming Principal Component Analysis. Here, we derive nonasymptotic concentration inequalities for such random matrix products. In particular, we show that the spectral norm error satisfies ‖ Z n − e X ‖ = O ( ( log ⁡ ( n ) ) 2 log ⁡ ( d / δ ) / n ) with probability exceeding 1 − δ . This rate is sharp in n, d, and δ, up to logarithmic factors. The proof relies on two key points of theory: the Matrix Bernstein inequality concerning the concentration of sums of random matrices, and Baranyai's theorem from combinatorial mathematics. Concentration bounds for general classes of random matrix products are hard to come by in the literature, and we hope that our result will inspire further work in this direction.
Molecular dynamics analysis of Mg2+ -dependent cleavage of a pistol ribozyme reveals a fail-safe secondary ion for catalysis.

Pistol ribozymes comprise a class of small, self-cleaving RNAs discovered via comparative genomic analysis. Prior work in the field has probed the kinetics of the cleavage reaction, as well as the influence of various metal ion cofactors that accelerate the process. In the current study, we performed unbiased and unconstrained molecular dynamics simulations from two current high-resolution pistol crystal structures, and we analyzed trajectory data within the context of the currently accepted ribozyme mechanistic framework. Root-mean-squared deviations, radial distribution functions, and distributions of nucleophilic angle-of-attack reveal insights into the potential roles of three magnesium ions with respect to catalysis and overall conformational stability of the molecule. A series of simulation trajectories containing in silico mutations reveal the relatively flexible and partially interchangeable roles of two particular magnesium ions within solvated hydrogen-bonding distances from the catalytic center.
TDP-43 α-helical structure tunes liquid–liquid phase separation and function

Liquid-liquid phase separation (LLPS) is involved in the formation of membraneless organelles (MLOs) associated with RNA processing. The RNA-binding protein TDP-43 is present in several MLOs, undergoes LLPS, and has been linked to the pathogenesis of amyotrophic lateral sclerosis (ALS). While some ALS-associated mutations in TDP-43 disrupt self-interaction and function, here we show that designed single mutations can enhance TDP-43 assembly and function via modulating helical structure. Using molecular simulation and NMR spectroscopy, we observe large structural changes upon dimerization of TDP-43. Two conserved glycine residues (G335 and G338) are potent inhibitors of helical extension and helix-helix interaction, which are removed in part by variants at these positions, including the ALS-associated G335D. Substitution to helix-enhancing alanine at either of these positions dramatically enhances phase separation in vitro and decreases fluidity of phase-separated TDP-43 reporter compartments in cells. Furthermore, G335A increases TDP-43 splicing function in a minigene assay. Therefore, the TDP-43 helical region serves as a short but uniquely tunable module where application of biophysical principles can precisely control assembly and function in cellular and synthetic biology applications of LLPS.
Education and health over the life cycle

Abstract There is little theoretical and empirical research on the effects of education on health over the life cycle. In this article, we extend the Grossman (1972) model of the demand for health and use the extended model to analyze the effect of education on health at different ages. The main conclusion from our model is that it is unlikely that the relationship between education and health will be constant over the life cycle and that education is likely to have little effect on health at younger ages when there is little depreciation of the health stock. We also present an extensive empirical analysis documenting the association between education and health over the life cycle. Results of our analysis suggest that in terms of mortality, education has little effect until age 60, but then lowers the hazard rate of death. For measures of morbidity, education has an effect at most ages between 45 and 60, but after age 60 has apparently little effect most likely due to selective mortality. In addition, most of the apparent beneficial effect of education stems from obtaining a high school degree or more. It is the health and mortality of lowest education group—those with less than a high school degree—that diverges from the health and mortality of other education groups. Finally, we find that the educational differences in health have become larger for more recent birth cohorts.
The complexity of some ordinal determined classes of operators

We compute the complexity of the classes of operators $${\mathfrak {G}}_{\xi , \zeta }\cap {\mathcal {L}}$$ and $${\mathfrak {M}}_{\xi , \zeta }\cap {\mathcal {L}}$$ in the coding of operators between separable Banach spaces. We also prove the non-existence of universal factoring operators for both $$\complement {\mathfrak {G}}_{\xi , \zeta }$$ and $$\complement {\mathfrak {M}}_{\xi , \zeta }$$. The latter result is an ordinal extension of a result of Johnson and Girardi.
InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache

Internet-scale web applications are becoming increasingly storage-intensive and rely heavily on in-memory object caching to attain required I/O performance. We argue that the emerging serverless computing paradigm provides a well-suited, cost-effective platform for object caching. We present InfiniCache, a first-of-its-kind in-memory object caching system that is completely built and deployed atop ephemeral serverless functions. InfiniCache exploits and orchestrates serverless functions' memory resources to enable elastic pay-per-use caching. InfiniCache's design combines erasure coding, intelligent billed duration control, and an efficient data backup mechanism to maximize data availability and cost-effectiveness while balancing the risk of losing cached state and performance. We implement InfiniCache on AWS Lambda and show that it: (1) achieves 31 -- 96X tenant-side cost savings compared to AWS ElastiCache for a large-object-only production workload, (2) can effectively provide 95.4% data availability for each one hour window, and (3) enables comparative performance seen in a typical in-memory cache.
Deletion of Mouse Setd4 Promotes the Recovery of Hematopoietic Failure.

Purpose Acquired hematopoietic failure is commonly caused by therapeutic and accidental exposure of the bone marrow (BM) to toxic agents. Efficient recovery from BM failure is dictated not only by the intrinsic sensitivity and proliferation capacity of the hematopoietic stem and progenitor cells but also by the BM environment niche. Identification of genetic factors that improve recovery from hematopoietic failure is essential. Vertebrate SETD4 is a poorly characterized and putatively nonhistone methyltransferase. This study aims to identify the roles of SETD4 in BM recovery. Methods and Materials An inducible SETD4 knockout mouse model (Setd4flox/flox;Rosa26-CreERT2+) was used. Adult sex-matched littermates were treated with tamoxifen to induce Setd4 deletion or oil as the control. Tamoxifen-treated Setd4wt/wt;Rosa26-CreERT2+ mice were included as another control. Those mice were irradiated to induce hematopoietic syndrome and analyzed to identify the roles and mechanisms of Setd4 in of BM recovery. Results Loss of Setd4 in adult mice improved the survival of whole-body irradiation–induced BM failure. This was associated with improved recoveries of long-term and short-term hematopoietic stem cells (HSCs) and early progenitor cells. BM transplantation analyses surprisingly showed that the improved recovery was not due to radiation resistance of the Setd4-deficient HSCs but that Setd4-deficient HSCs were actually more sensitive to radiation. However, the Setd4-deficient mice were better recipients for allogeneic HSC transplantation. Furthermore, there was enhanced splenic erythropoiesis in Setd4-deficient mice. Conclusion These findings not only revealed a previously unrecognized role of Setd4 as a unique modulator of hematopoiesis but also underscored the critical role of the BM niche in recovery from hematopoietic failure. Our study also implicated Setd4 as a potential target for therapeutic inhibition to improve the conditioning of the BM niche before allogeneic transplantation.
Camera-Based Adaptive Trajectory Guidance via Neural Networks

In this paper, we introduce a novel method to capture visual trajectories for navigating an indoor robot in dynamic settings using streaming image data. First, an image processing pipeline is proposed to accurately segment trajectories from noisy backgrounds. Next, the captured trajectories are used to design, train, and compare two neural network architectures for predicting acceleration and steering commands for a line following robot over a continuous space in real time. Lastly, experimental results demonstrate the performance of the neural networks versus human teleoperation of the robot and the viability of the system in environments with occlusions and/or low-light conditions.
Classical liberals on ‘social justice’

In the summer of 2019, The Independent Review published a symposium on classical liberalism and social justice. We give an overview and commentary on the symposium papers. Rather than adopting the term social justice, we recommend returning to the three senses of justice maintained by Adam Smith and explained by Daniel Klein (2019). We articulate and explore Smith’s tripartite understanding of justice in contrast with an understanding that gives place to the expression social justice. Smith’s tri-layered understanding is in the spirit of addressing the micro-foundations of macrophenomena, the spirit of Thomas Schelling’s Micromotives and Macrobehavior.
Efficient Rules for Monetary Policy

This paper defines an efficient rule for monetary policy as one that minimizes a weighted sum of output variance and inflation variance. It derives several results about the efficiency of alternative rules in a simple macroeconomic model. First, efficient rules can be expressed as ‘Taylor rules’ in which interest rates respond to output and inflation. But the coefficients in efficient Taylor rules differ from the coefficients that fit actual policy in the United States. Second, inflation targets are efficient. Indeed, the set of efficient rules is equivalent to the set of inflation-target policies with different speeds of adjustment. Finally, nominal-income targets are highly inefficient: they create great volatility in both inflation and output.
Securitization and the fixed-rate mortgage

Fixed-rate mortgages (FRMs) dominate the U.S. mortgage market, with important consequences for monetary policy, household risk management, and financial stability. We show that the FRM market share is sharply lower when mortgages are difficult to securitize, exploiting plausibly exogenous variation in access to liquid securitization markets generated by a regulatory cutoff and time variation in private securitization activity. We interpret our findings as evidence that lenders are reluctant to retain the prepayment and interest rate risk embedded in FRMs. The form of securitization (private versus government backed) has little effect on FRM supply during periods in which private securitization markets are well functioning.
Should Hospitals Keep Their Patients Longer? The Role of Inpatient Care in Reducing Post-Discharge Mortality

The Centers for Medicare & Medicaid Services (CMS) and the National Quality Forum have endorsed the 30-day mortality rate as an important indicator of hospital quality. Concerns have been raised, h...
Phase diagram calculations in teaching, research, and industry

I have a long-standing interest in alloy thermodynamics/phase diagrams and in utilizing the principles of this subject for materials research and engineering applications. At the same time, I also have a long association with ASM International as a member and a former Trustee of the Society. The Society’s initiative in promoting critical assessments of phase diagrams beginning in the late 1970s rekindled this field and stimulated further research, particularly in phase diagram calculations. Significant advancements have been made in phase diagram calculations using the Calphad approach since the late 1980s due primarily to the availability of inexpensive computers and robust software. In this article, I first present the use of computational thermodynamics including phase diagram calculation in teaching, next the use of calculated phase diagrams, particularly for multicomponent systems, for materials research/development, and manufacturing, and last describe some current research in advancing this methodology when the phases involve ordering with decreasing temperature.
Gallium Antimonide (GaSb)

Publisher Summary Few reports can be found in the literature of the fundamental optical properties of gallium antimonide (GaSb). This chapter presents an analysis of some experimental work done on the optical properties of gallium GaSb. Three recent technical advances have been made that allow for more accurate determination of the optical properties of semiconductors, GaSb included. The first and most important is the development of their new etching and cleanup procedures. These procedures minimize the thickness of the undesirable surface layer that forms on semiconductors. As a result, their ellipsometric measurements and calculated pseudo-dielectric functions more accurately represent the intrinsic bulk values. The second advance is the development of the automatic spectroscopic ellipsometer. This instrument determines both components of the complex pseudo-dielectric function over a broad spectral range. No wavelength extrapolations are needed as in the Kramers–Kronig analysis of reflectance data. The third technical advance is the development of unambiguous criteria for optically determining the best surfaces. These surfaces are the ones most free of roughness, unintentional overlayers, absorbed contaminants, and subsurface damage. Several reports have been made of the reflectance of the optical constants of GaSb in the near-ultraviolet and visible regions. The chapter reviews these reports.
The power of one: Evaluating the impact of a single multi-disciplinary treatment visit on time to treatment.

Breast cancer treatment often requires multi-disciplinary evaluation, which can require multiple visits, delaying time to treatment initiation (TTI). The present analysis evaluated the impact of system-wide initiatives to reduce TTI by evaluating TTI for patients completing treatment evaluation in a single visit compared with those having multiple visits. The results demonstrated that patients who completed multi-disciplinary evaluation in a single visit had a reduced median TTI (27 vs 32 days, P = .002), which was seen for patients undergoing initial surgery (28.0 vs 33.5 days, P = .01) as well as for those undergoing neoadjuvant systemic therapy (22.5 vs 29 days, P = .05).
Synthesis of β- and γ-carbolines by the palladium-catalyzed iminoannulation of alkynes

A variety of substituted beta- and gamma-carbolines have been prepared in moderate to excellent yields by the palladium-catalyzed annulation of internal and terminal acetylenes by the tert-butylimines of N-substituted 3-iodoindole-2-carboxaldehydes and 2-haloindole-3-carboxaldehydes, respectively. This annulation chemistry is effective for a wide range of alkynes, including aryl-, alkyl-, hydroxymethyl-, ethoxycarbonyl-, and trimethylsilyl-substituted alkynes. When an unsymmetrical internal alkyne is employed, this method generally gives two regioisomers. When a terminal alkyne is employed, only one regioisomer has been isolated. This palladium-catalyzed annulation chemistry has also been successfully applied to the synthesis of two biologically interesting beta-carboline alkaloids, ZK93423 and abecarnil (ZK112119).
Stay-at-home works to fight against COVID-19: International evidence from Google mobility data

Daily Google mobility data covering 130 countries over the period between February 15, 2020 and May 2, 2020 suggest that less mobility is associated with lower COVID-19 cases and deaths. This obser...
The psychological costs of war: Military combat and mental health ☆

We exploit plausibly exogenous variation in overseas deployment assignment to estimate the effect of combat exposure on psychological well-being. Controlling for pre-deployment mental health, we find that active-duty soldiers deployed to combat zones are more likely to suffer from post-traumatic stress disorder (PTSD) than their counterparts deployed outside the United States in non-combat zones. Among those deployed to combat zones, those deployed to locales where they engage in enemy firefight or witness allied or civilian deaths are at an increased risk for suicidal ideation and PTSD relative to their active-duty counterparts deployed to combat zones without enemy firefight.
Age-Conditioned Synthesis of Pediatric Computed Tomography with Auxiliary Classifier Generative Adversarial Networks

Deep learning is a popular and powerful tool in computed tomography (CT) image processing such as organ segmentation, but its requirement of large training datasets remains a challenge. Even though there is a large anatomical variability for children during their growth, the training datasets for pediatric CT scans are especially hard to obtain due to risks of radiation to children. In this paper, we propose a method to conditionally synthesize realistic pediatric CT images using a new auxiliary classifier generative adversarial network (ACGAN) architecture by taking age information into account. The proposed network generated age-conditioned high-resolution CT images to enrich pediatric training datasets.
Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. In comparison, the previous mission to Saturn, Voyager over 20 years earlier, had onboard a ~70 kilobyte 8-track storage ability. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of ‘black box’ machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. The present work uses Cassini data as a case study as these data are similar to space physics and planetary missions at Earth and other solar system objects. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetic environment, or magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine leaning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.
Joint Uplink-Downlink Cooperative Interference Management With Flexible Cell Associations

We study information theoretic models of interference networks that consist of $K$ Base Station (BS) - Mobile Terminal (MT) pairs. BS $i$ is connected to the MTs with indices in the set $\{i,i+1,\ldots,i+L\}$ . We fix the value of $L$ and study the per user Degrees of Freedom (puDoF) in large networks. We assume that each MT can be associated with $N_{c}$ BSs, and these associations are determined by a cloud-based controller that has a global view of the network. An MT has to be associated with a BS, for the BS to transmit its message in the downlink, or have its decoded message in the uplink. We propose puDoF inner bounds for arbitrary values of $L$ for the uplink, and prove their optimality when only zero-forcing schemes are allowed. We then introduce new achievable average uplink-downlink puDoF values, and show their optimality for the range when $N_{c} \leq \frac {L}{2}$ and when we restrict our attention to zero-forcing schemes. Additionally, for the remaining range, we characterize the optimal downlink scheme when the uplink-optimal associations are used. Finally, we show that the proposed scheme is information theoretically optimal for Wyner’s linear interference network.
Bridging the Gap Between f-GANs and Wasserstein GANs

Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions. Learning objectives approximately minimize an $f$-divergence ($f$-GANs) or an integral probability metric (Wasserstein GANs) between the model and the data distribution using a discriminator. Wasserstein GANs enjoy superior empirical performance, but in $f$-GANs the discriminator can be interpreted as a density ratio estimator which is necessary in some GAN applications. In this paper, we bridge the gap between $f$-GANs and Wasserstein GANs (WGANs). First, we list two constraints over variational $f$-divergence estimation objectives that preserves the optimal solution. Next, we minimize over a Lagrangian relaxation of the constrained objective, and show that it generalizes critic objectives of both $f$-GAN and WGAN. Based on this generalization, we propose a novel practical objective, named KL-Wasserstein GAN (KL-WGAN). We demonstrate empirical success of KL-WGAN on synthetic datasets and real-world image generation benchmarks, and achieve state-of-the-art FID scores on CIFAR10 image generation.
Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.

Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-e, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-e efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-e to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways.
Stationary discs and finite jet determination for CR mappings in higher codimension

Abstract We discuss stationary discs for generic CR manifolds and apply them to the problem of finite jet determination for CR mappings. We prove that a C 2 -smooth CR diffeomorphism of two C 4 -smooth strictly pseudoconvex Levi generating CR manifolds is uniquely determined by its 2-jet at a given point. A new key element of the proof is the existence of non-defective stationary discs.
A Library of Second-Order Models for Synchronous Machines

This article presents a library of second-order models for synchronous machines that can be utilized in power system dynamic performance analysis and control design. The models have a similar structure to that of the so-called classical model in that they consist of two dynamic states, the power angle and the angular speed. However, unlike the classical model, they find applications beyond first swing stability analysis, i.e., they can be utilized for multi-swing transient stability analysis. The models are developed through a systematic reduction of a nineteenth-order model using singular perturbation techniques. They are validated by comparing their response with that of the high-order model from which they were derived, as well as that of other synchronous machine models existing in the literature, such as the so-called two-axis model and the so-called one-axis model.
Efficiently Solving MDPs with Stochastic Mirror Descent

We present a unified framework based on primal-dual stochastic mirror descent for approximately solving infinite-horizon Markov decision processes (MDPs) given a generative model. When applied to an average-reward MDP with $A_{tot}$ total state-action pairs and mixing time bound $t_{mix}$ our method computes an $\epsilon$-optimal policy with an expected $\widetilde{O}(t_{mix}^2 A_{tot} \epsilon^{-2})$ samples from the state-transition matrix, removing the ergodicity dependence of prior art. When applied to a $\gamma$-discounted MDP with $A_{tot}$ total state-action pairs our method computes an $\epsilon$-optimal policy with an expected $\widetilde{O}((1-\gamma)^{-4} A_{tot} \epsilon^{-2})$ samples, matching the previous state-of-the-art up to a $(1-\gamma)^{-1}$ factor. Both methods are model-free, update state values and policies simultaneously, and run in time linear in the number of samples taken. We achieve these results through a more general stochastic mirror descent framework for solving bilinear saddle-point problems with simplex and box domains and we demonstrate the flexibility of this framework by providing further applications to constrained MDPs.
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks

Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the ...
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC

Stochastic gradient Hamiltonian Monte Carlo (SGHMC) is an efficient method for sampling from continuous distributions. It is a faster alternative to HMC: instead of using the whole dataset at each iteration, SGHMC uses only a subsample. This improves performance, but introduces bias that can cause SGHMC to converge to the wrong distribution. One can prevent this using a step size that decays to zero, but such a step size schedule can drastically slow down convergence. To address this tension, we propose a novel second-order SG-MCMC algorithm---AMAGOLD---that infrequently uses Metropolis-Hastings (M-H) corrections to remove bias. The infrequency of corrections amortizes their cost. We prove AMAGOLD converges to the target distribution with a fixed, rather than a diminishing, step size, and that its convergence rate is at most a constant factor slower than a full-batch baseline. We empirically demonstrate AMAGOLD's effectiveness on synthetic distributions, Bayesian logistic regression, and Bayesian neural networks.
Some New Positive Observations

Abstract We revisit Bressoud’s generalized Borwein conjecture. Making use of the new positivity-preserving transformations for q -binomial coefficients we establish the truth of infinitely many cases of the Bressoud conjecture. In addition, we prove new bounded versions of Lebesgue’s identity and of Euler’s Pentagonal Number Theorem. Finally, we discuss new companions to Andrews–Gordon m o d 21 and Bressoud m o d 20 identities.
Insights on Streamflow Predictability Across Scales Using Horizontal Visibility Graph Based Networks

Streamflow is a dynamical process that integrates water movement in space and time within basin boundaries. The authors characterize the dynamics associated with streamflow time series data from about seventy-one U.S. Geological Survey (USGS) stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG). It uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical system. The authors focus on a simple variant of VG algorithm called horizontal visibility graph (HVG). The tracking of dynamics and hence, the predictability of streamflow processes, are carried out by extracting two key pieces of information called characteristic exponent, λ of degree distribution and global clustering coefficient, GC pertaining to HVG derived network. The authors use these two measures to identify whether streamflow process has its origin in random or chaotic processes. They show that the characterization of streamflow dynamics is sensitive to data attributes. Through a systematic and comprehensive analysis, the authors illustrate that streamflow dynamics characterization is sensitive to the normalization, and the time-scale of streamflow time-series. At daily scale, streamflow at all stations used in the analysis, reveals randomness with strong spatial scale (basin size) dependence. This has implications for predictability of streamflow and floods. The authors demonstrate that dynamics transition through potentially chaotic to randomly correlated process as the averaging time-scale increases. Finally, the temporal trends of λ and GC are statistically significant at about 40% of the total number of stations analyzed. Attributing this trend to factors such as changing climate or land use requires further research.
On an enhancement of RNA probing data using information theory

Identifying the secondary structure of an RNA is crucial for understanding its diverse regulatory functions. This paper focuses on how to enhance target identification in a Boltzmann ensemble of structures via chemical probing data. We employ an information-theoretic approach to solve the problem, via considering a variant of the Renyi-Ulam game. Our framework is centered around the ensemble tree, a hierarchical bi-partition of the input ensemble, that is constructed by recursively querying about whether or not a base pair of maximum information entropy is contained in the target. These queries are answered via relating local with global probing data, employing the modularity in RNA secondary structures. We present that leaves of the tree are comprised of sub-samples exhibiting a distinguished structure with high probability. In particular, for a Boltzmann ensemble incorporating probing data, which is well established in the literature, the probability of our framework correctly identifying the target in the leaf is greater than $$90\%$$ .
Infinite-order perturbative treatment for quantum evolution with exchange

Many important applications in biochemistry, materials science, and catalysis sit squarely at the interface between quantum and statistical mechanics: Coherent evolution is interrupted by discrete events, such as binding of a substrate or isomerization. Theoretical models for such dynamics usually truncate the incorporation of these events to the linear response limit, thus requiring small step sizes. Here, we completely reassess the foundations of chemical exchange models and redesign a master equation treatment for exchange accurate to infinite order in perturbation theory. The net result is an astonishingly simple correction to the traditional picture, which vastly improves convergence with no increased computational cost. We demonstrate that this approach accurately and efficiently extracts physical parameters from complex experimental data, such as coherent hyperpolarization dynamics in magnetic resonance, and is applicable to a wide range of other systems.
Epithelial morphogenesis in the perinatal mouse uterus

Background The uterus is the location where multiple events occur that are required for the start of new life in mammals. The adult uterus contains endometrial or uterine glands that are essential for female fertility. In the mouse, uterine glands are located in the lateral and antimesometrial regions of the uterine horn. Previous three-dimensional (3D)-imaging of the adult uterus, its glands, and implanting embryos has been performed by multiple groups, using fluorescent microscopy. Adenogenesis, the formation of uterine glands, initiates after birth. Recently, we created a 3D-staging system of mouse uterine gland development at postnatal time points, using light sheet fluorescent microscopy. Here, using a similar approach, we examine the morphological changes in the epithelium of the perinatal mouse uterus. Results The uterine epithelium exhibits dorsoventral (mesometrial-antimesometrial) patterning as early as 3 days after birth (P3), marked by the presence of the dorsally positioned developing uterine rail. Uterine gland buds are present beginning at P4. Novel morphological epithelial structures, including a ventral ridge and uterine segments were identified. Conclusions The perinatal mouse uterine luminal epithelium develops dorsal-ventral morphologies at 3 to 4 days postpartum. Between 5 and 6 days postpartum uterine epithelial folds form, defining alternating left-right segments.
Optical communications in the mid-wave IR spectral band

The mid-wave IR (MWIR) spectral band extending from 3 to 5 microns is considered to be a low loss atmospheric window. The MWIR wavelengths are eye safe and are attractive for several free-space applications including remote sensing of chemical and biological species, hard target imaging, range finding, target illumination, and free-space communications. Due to the nature of light-matter interaction characteristics, MWIR wavelength based systems can provide unique advantages over other spectral bands for these applications. The MWIR wavelengths are found to effectively penetrate natural and anthropogenic obscurants. Consequently, MWIR systems offer increased range performance at reduced power levels. Free-space, line-of-sight optical communication links for terrestrial as well as space based platforms using MWIR wavelengths can be designed to operate under low visibility conditions. Combined with high-bandwidth, eye-safe, covert and jam proof features, a MWIR wavelength based optical communication link could play a vital role in hostile environments. A free-space optical communication link basically consists of a transmitter, a receiver and a scheme for directing the beam towards a target. Coherent radiation in the MWIR spectral band can be generated using various types of lasers and nonlinear optical devices. Traditional modulation techniques are applicable to these optical sources. Novel detector and other subcomponent technologies with enhanced characteristics for a MWIR based system are advancing. Depending on the transmitter beam characteristics, atmospheric conditions may adversely influence the beam propagation and thereby increasing the bit error rate. For satisfactory transmission over a given range, the influence of atmosphere on beam propagation has to be analyzed. In this chapter, salient features of atmospheric modeling required for wavelength selection and performance prediction is presented. Potential optical sources and detectors for building a practical MWIR communication link are surveyed. As an illustration, the design configuration and experimental results of a recently demonstrated free-space, obscurant penetrating optical data communication link suitable for battlefield applications is discussed. In this case, the MWIR wavelength was derived using an all solid-state, compact, optical parametric oscillator device. With this device, weapon codes pertaining to small and large weapon platforms were transmitted over a range of 5 km. Furthermore, image transmission through light fog, accomplished using this hardware, is also presented. Advances in source and detector technologies are contributing to the development of cost effective systems compatible with various platforms requirements. In coming years, MWIR wavelengths are anticipated to play a vital role in various human endeavors.