cssi/SciSciGPT-SciSciNet huggingface dataset:
Model-based Reconstruction for Single Particle Cryo-Electron Microscopy

Single particle cryo-electron microscopy is a vital tool for 3D characterization of protein structures. A typical workflow involves acquiring projection images of a collection of randomly oriented particles, picking and classifying individual particle projections by orientation, and finally using the individual particle projections to reconstruct a 3D map of the electron density profile. The reconstruction is challenging because of the low signal-to-noise ratio of the data, the unknown orientation of the particles, and the sparsity of data especially when dealing with flexible proteins where there may not be sufficient data corresponding to each class to obtain an accurate reconstruction using standard algorithms. In this paper we present a model-based image reconstruction technique that uses a regularized cost function to reconstruct the 3D density map by assuming known orientations for the particles. Our method casts the reconstruction as minimizing a cost function involving a novel forward model term that accounts for the contrast transfer function of the microscope, the orientation of the particles and the center of rotation offsets. We combine the forward model term with a regularizer that enforces desirable properties in the volume to be reconstructed. Using simulated data, we demonstrate how our method can significantly improve upon the typically used approach.
Understanding the Bounds of Legitimacy: Weber’s Facets of Legitimacy and the Police Empowerment Hypothesis

Despite considerable evidence that police legitimacy results in beneficial outcomes like compliance, cooperation, and empowerment, scholars have yet to agree on how to define and operationalize leg...
An all-digital associated particle imaging system for the 3D determination of isotopic distributions.

Associated particle imaging (API) is a non-destructive nuclear technique for the 3D determination of isotopic distributions. By detecting the alpha particle associated with the emitted neutron in the deuterium-tritium fusion reaction with a position- and time-resolving detector, the direction of the 14.1 MeV neutron and its time of emission can be determined. Employing this method, isotope characteristic gamma rays emitted in inelastic neutron scattering events can be correlated with the neutron interaction location. An API system consisting of a sealed-type neutron generator, gamma detectors, and a position-sensitive alpha detector was designed, constructed, and characterized. The system was tested with common soil elements and shown to be sensitive to 12C, 16O, 28Si, 27Al, and 56Fe. New aspects of our approach are the use of a yttrium-aluminum-perovskite scintillator, using a sapphire window instead of a fiber-optic faceplate for light transport to the photomultiplier, and the all-digital data acquisition system. We present a description of the system with simulations and experimental results that show a position resolution on the alpha detector of 1 mm, a depth resolution using a LaBr3 detector of 6.2 cm, and an angular resolution of 4.5°. Additionally, we present single-element gamma response measurements for the elements mentioned above together with a comparison to Monte Carlo simulations (MCNP6).
Deducing the positive odd density of p(n) from that of a multipartition function: An unconditional proof

Abstract A famous conjecture of Parkin-Shanks predicts that p ( n ) is odd with density 1/2. Despite the remarkable amount of work of the last several decades, however, even showing this density is positive seems out of reach. In a 2018 paper with Judge, we introduced a different approach and conjectured the ‘‘striking” fact that, if for any A ≡ ± 1 ( mod 6 ) the multipartition function p A ( n ) has positive odd density, then so does p ( n ) . Similarly, the positive odd density of any p A ( n ) with A ≡ 3 ( mod 6 ) would imply that of p 3 ( n ) . Our conjecture was shown to be a corollary of an earlier conjecture of the same paper. In this brief note, we provide an unconditional proof of it. An important tool will be Chen's recent breakthrough on a special case of our earlier conjecture.
Sequences of three dimensional manifolds with positive scalar curvature

Abstract We develop two new methods of constructing sequences of manifolds with positive scalar curvature that converge in the Gromov-Hausdorff and Intrinsic Flat sense to limit spaces with “pulled regions”. The examples created rigorously using these methods were announced a few years ago and have influenced the statements of some of Gromov's conjectures concerning sequences of manifolds with positive scalar curvature. Both methods extend the notion of “sewing along a curve” developed in prior work of the authors with Dodziuk to create limits that are pulled string spaces. The first method allows us to sew any compact set in a fixed initial manifold to create a limit space in which that compact set has been scrunched to a single point. The second method allows us to edit a sequence of regions or curves in a sequence of distinct manifolds.
Example of exponentially enhanced magnetic reconnection driven by a spatially bounded and laminar ideal flow

In laboratory and natural plasmas of practical interest, the spatial scale Δd at which magnetic field lines lose distinguishability differs enormously from the scale a of magnetic reconnection across the field lines. In the solar corona, plasma resistivity gives a / Δ d ∼ 10 12, which is the magnetic Reynolds number Rm. The traditional resolution of the paradox of disparate scales is for the current density j associated with the reconnecting field Brec to be concentrated by a factor of Rm by the ideal evolution, so j ∼ B rec / μ 0 Δ d. A second resolution is for the ideal evolution to increase the ratio of the maximum to minimum separation between pairs of arbitrary chosen magnetic field lines, Δ max / Δ min, when calculated at various points in time. Reconnection becomes inevitable where Δ max / Δ min ∼ R m. A simple model of the solar corona will be used for a numerical illustration that the natural rate of increase in time is linear for the current density but exponential for Δ max / Δ min. Reconnection occurs on a timescale and with a current density enhanced by only ln ( a / Δ d ) from the ideal evolution time and from the current density B rec / μ 0 a. In both resolutions, once a sufficiently wide region, Δr, has undergone reconnection, the magnetic field loses static force balance and evolves on an Alfvenic timescale. The Alfvenic evolution is intrinsically ideal but expands the region in which Δ max / Δ min is large.
Epigenetic clocks reveal a rejuvenation event during embryogenesis followed by aging

The notion that the germ line does not age goes back to the 19th-century ideas of August Weismann. However, being metabolically active, the germ line accumulates damage and other changes over time, i.e., it ages. For new life to begin in the same young state, the germ line must be rejuvenated in the offspring. Here, we developed a multi-tissue epigenetic clock and applied it, together with other aging clocks, to track changes in biological age during mouse and human prenatal development. This analysis revealed a significant decrease in biological age, i.e., rejuvenation, during early stages of embryogenesis, followed by an increase in later stages. We further found that pluripotent stem cells do not age even after extensive passaging and that the examined epigenetic age dynamics is conserved across species. Overall, this study uncovers a natural rejuvenation event during embryogenesis and suggests that the minimal biological age (ground zero) marks the beginning of organismal aging.
Physarum Powered Differentiable Linear Programming Layers and Applications

Consider a learning algorithm, which involves an internal call to an optimization routine such as a generalized eigenvalue problem, a cone programming problem or even sorting. Integrating such a method as a layer(s) within a trainable deep neural network (DNN) in an efficient and numerically stable way is not straightforward - for instance, only recently, strategies have emerged for eigendecomposition and differentiable sorting. We propose an efficient and differentiable solver for general linear programming problems which can be used in a plug and play manner within DNNs as a layer. Our development is inspired by a fascinating but not widely used link between dynamics of slime mold (physarum) and optimization schemes such as steepest descent. We describe our development and show the use of our solver in a video segmentation task and meta-learning for few-shot learning. We review the existing results and provide a technical analysis describing its applicability for our use cases. Our solver performs comparably with a customized projected gradient descent method on the first task and outperforms the differentiable CVXPY-SCS solver on the second task. Experiments show that our solver converges quickly without the need for a feasible initial point. Our proposal is easy to implement and can easily serve as layers whenever a learning procedure needs a fast approximate solution to a LP, within a larger network.
ESTO Investments in Innovative Sensor Technologies for Remote Sensing

For more then 18 years NASA Earth Science Technology Office has been investing in remote sensing technologies. During this period ESTO has invested in more then 900 tasks. These tasks are managed under multiple programs like Instrument Incubator Program (IIP), Advanced Component Technology (ACT), Advanced Information Systems Technology (AIST), In-Space Validation of Earth Science Technologies (InVEST), Sustainable Land Imaging - Technology (SLI-T) and others. This covers the whole spectrum of technologies from component to full up satellite in space and software. Over the years many of these technologies have been infused into space missions like Aquarius, SMAP, CYGNSS, SWOT, TEMPO and others. Over the years ESTO is actively investing in Infrared sensor technologies for space applications. Recent investments have been for SLI-T and InVEST program. On these tasks technology development is from simple Bolometers to Advanced Photonic waveguide based spectrometers. Some of the details on these missions and technologies will be presented.
Multiple control of few-layer Janus MoSSe systems

In this computational work based on density functional theory, we study the electronic and electron transport properties of asymmetric multilayer MoSSe junctions, known as Janus junctions. Focusing on four-layer systems, we investigate the influence of electric field, electrostatic doping, strain, and interlayer stacking on the electronic structure. We discover that a metal-to-semiconductor transition can be induced by an out-of-plane electric field. The critical electric field for such a transition can be reduced by in-plane biaxial compressive strain. Due to an intrinsic electric field, a four-layer MoSSe can rectify out-of-plane electric current. The rectifying ratio reaches 34.1 in a model junction Zr/four-layer MoSSe/Zr and can be further enhanced by increasing the number of MoSSe layers. In addition, we show a drastic sudden vertical compression of four-layer MoSSe due to in-plane biaxial tensile strain, indicating a second phase transition. Furthermore, an odd-even effect on electron transmission at the Fermi energy for Zr/$n$-layer MoSSe/Zr junctions with $n=1,\phantom{\rule{0.16em}{0ex}}2,\phantom{\rule{0.16em}{0ex}}3,\ensuremath{\cdots},10$ is observed. These findings reveal the richness of physics in this asymmetric system, and they strongly suggest that the properties of four-layer MoSSe are highly tunable, thus providing a guide to future experiments relating to materials research and nanoelectronics.
Distinguishing Orthogonality Graphs

A graph $G$ is said to be $d$-distinguishable if there is a labeling of the vertices with $d$ labels so that only the trivial automorphism preserves the labels. The smallest such $d$ is the distinguishing number, Dist($G$). A subset of vertices $S$ is a determining set for $G$ if every automorphism of $G$ is uniquely determined by its action on $S$. The size of a smallest determining set for $G$ is called the determining number, Det($G$). The orthogonality graph $\Omega_{2k}$ has vertices which are bitstrings of length $2k$ with an edge between two vertices if they differ in precisely $k$ bits. This paper shows that Det($\Omega_{2k}$) $= 2^{2k-1}$ and that if $\binom{m}{2} \geq 2k$ then $2<$ Dist($\Omega_{2k}$) $\leq m$.
Number theoretic applications of a class of Cantor series fractal functions. I

Suppose that $${{(P, Q) \in {\mathbb{N}_{2}^\mathbb{N}} \times {\mathbb{N}_{2}^\mathbb{N}}}}$$ and x = E 0.E 1 E 2 · · · is the P-Cantor series expansion of $${x \in \mathbb{R}}$$ . We define $$\psi_{P,Q}(x) := {\sum_{n=1}^{\infty}} \frac{{\rm min}(E_n, q_{n}-1)}{q_1 \cdots q_n}.$$ The functions $${\psi_{P,Q}}$$ are used to construct many pathological examples of normal numbers. These constructions are used to give the complete containment relation between the sets of Q-normal, Q-ratio normal, and Q-distribution normal numbers and their pairwise intersections for fully divergent Q that are infinite in limit. We analyze the Holder continuity of $${\psi_{P,Q}}$$ restricted to some judiciously chosen fractals. This allows us to compute the Hausdorff dimension of some sets of numbers defined through restrictions on their Cantor series expansions. In particular, the main theorem of a paper by Y. Wang et al. [29] is improved. Properties of the functions $${\psi_{P,Q}}$$ are also analyzed. Multifractal analysis is given for a large class of these functions and continuity is fully characterized. We also study the behavior of $${\psi_{P,Q}}$$ on both rational and irrational points, monotonicity, and bounded variation. For different classes of ergodic shift invariant Borel probability measures $${\mu_1}$$ and $${\mu_2}$$ on $${{\mathbb{N}_2^\mathbb{N}}}$$ , we study which of these properties $${\psi_{P,Q}}$$ satisfies for $${\mu_1 \times \mu_2}$$ -almost every (P,Q) $${{\in {\mathbb{N}_{2}^{\mathbb{N}}} \times {\mathbb{N}_{2}^{\mathbb{N}}}}}$$ . Related classes of random fractals are also studied.
The Tachocline Revisited

The solar tachocline is a shear layer located at the base of the solar convection zone. The horizontal shear in the tachocline is likely turbulent, and it is often assumed that this turbulence would be strongly anisotropic as a result of the local stratification. What role this turbulence plays in the tachocline dynamics, however, remains to be determined. In particular, it is not clear whether it would result in a turbulent eddy diffusivity, or anti-diffusivity, or something else entirely. In this paper, we present the first direct numerical simulations of turbulence in horizontal shear flows at low Prandtl number, in an idealized model that ignores rotation and magnetic fields. We find that several regimes exist, depending on the relative importance of the stratification, viscosity and thermal diffusivity. Our results suggest that the tachocline is in the stratified turbulence regime, which has very specific properties controlled by a balance between buoyancy, inertia, and thermal diffusion.
Balian-Low Theorems in Several Variables

Recently, Nitzan and Olsen showed that Balian-Low theorems (BLTs) hold for discrete Gabor systems defined on \(\mathbb {Z}_d\). Here we extend these results to a multivariable setting. Additionally, we show a variety of applications of the Quantitative BLT, proving in particular nonsymmetric BLTs in both the discrete and continuous setting for functions with more than one argument. Finally, in direct analogy of the continuous setting, we show the Quantitative Finite BLT implies the Finite BLT.
Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures

Abstract Smoldering combustion plays an important role in forest and wildland fires. Fires from smoldering combustion can last for long periods of time, emit more pollutants, and be difficult to extinguish. This makes the study of smoldering in woody fuels and forest duff important. Cellulose, hemicellulose, and lignin are the major constituents in these type of fuels, in different proportions for different fuels. In this article, we developed a 1-D model using the open-source software Gpyro to study the smoldering combustion of cellulose and hemicellulose mixtures. We first validated our simulations against experimentally obtained values of propagation speed for mixtures with fuel compositions including 100%, 75%, 50%, and 25% cellulose, with the remaining proportion of hemicellulose. Then, we studied the effects of varying fuel composition, density, and moisture content on smoldering combustion. We found that propagation speed of smoldering increases with decreasing density and increasing hemicellulose content, which we attribute to the role of oxygen diffusion. Propagation speed increases with moisture content for pure cellulose up to a certain limiting value, after which the propagation speed drops by up to 70%. The mean peak temperature of smoldering increases with hemicellulose content and density, and decreases with increasing moisture content.
Casimir light in dispersive nanophotonics

We present a theory of entangled photon pair emission through the dynamical Casimir effect in systems which are simultaneously dispersive and time dependent. We show surface phonon polariton emission in time-modulated polar insulators.
The time has come to permanently retire all our Caribbean currencies

Abstract. The currencies of Caribbean countries have now outlived their usefulness, and have become a liability. They were devised at a time when most payments were made using notes and coin, issued in distant metropolitan centres. Scarcity of the means of payment was a severe hindrance to commerce. In response Currency Boards were set up, to issue local currency as needed in the colonies. The system worked well because the local currency issue was backed by an equivalent value of Sterling, in a global system of fixed exchange rates. In contrast, nowadays payments are made mostly by electronic communication, credit and debit cards, cheques and drafts, with settlement over digitized bank accounts. In today’s world an own currency has become a liability for small economies, limiting access to international goods and services, exposing residents to risks of currency devaluation and inflation, eroding the value of domestic savings, increasing economic inequalities, providing a tool for unproductive government spending, and diverting attention from the need to increase productivity and enhance international competitiveness. Keywords. Dollarisation, Exchange rate, Fixed exchange rate, Foreign currency, Currency board, Open economy. JEL. F31, F32 .
Risk-Sensitive Rendezvous Algorithm for Heterogeneous Agents in Urban Environments*

Demand for fast and inexpensive parcel deliveries in urban environments has risen considerably in recent years. A framework is envisioned to enforce efficient last-mile delivery in urban environments by leveraging a network of ride-sharing vehicles, where Unmanned Aerial Systems (UASs) drop packages on said vehicles, which then cover the majority of the distance before final aerial delivery. This approach presents many engineering challenges, including the safe rendezvous of both agents: the UAS and the human-operated ground vehicle. This paper introduces a framework to minimize the risk of failure while allowing for the controlled agent's optimal usage. We discuss the downfalls of traditional approaches and formulate a fast, compact planner to drive a UAS to a passive ground vehicle with inexact behavior. To account for uncertainty, we learn driver behavior while leveraging historical data, and a Model Predictive Controller minimizes a risk-enabled cost function. The resulting algorithm is shown to be fast and implementable in real-time in qualitative scenarios.
Evanescent-wave Johnson noise in small devices

In many quantum computer architectures, the qubits are in close proximity to metallic device elements. The fluctuating currents in the metal give rise to noisy electromagnetic fields that leak out into the surrounding region. These fields are known as evanescent-wave Johnson noise. The noise can decohere the qubits. We present the general theory of this effect for charge qubits subject to electric noise and for spin and magnetic qubits subject to magnetic noise. A mapping of the quantum-mechanical problem onto a problem in classical electrodynamics simplifies the calculations. The focus is on relatively simple geometries in which analytical calculations can be done. New results are presented for the local noise spectral density in the vicinity of cylindrical conductors such as small antennae, noise from objects that can be treated as dipoles, and noise correlation functions for several geometries. We summarize the current state of the comparison of theory with experimental results on decoherence times of qubits. Emphasis is placed on qualitative understanding of the basic concepts and phenomena.
Regulation by the Pitcher Plant Sarracenia purpurea of the Structure of its Inquiline Food Web

Phytotelmata, the water-filled habitats in pitcher plants, bromeliad tanks, and tree-holes, host multitrophic food webs that are model experimental systems for studying food-web structure and dynamics. However, the plant usually is considered simply as an inert container, not as an interacting part of the food web. We used a manipulative field experiment with a response-surface design to determine effects of nutrient enrichment (multiple levels of NH4NO3, PO4, and captured prey), top predators (removed or present), and the plant itself (with or without plastic tubes inserted into the pitchers to isolate the food web from the plant) on the macrobial food web within the modified leaves (“pitchers”) of the carnivorous pitcher plant Sarracenia purpurea. Connection to the plant, addition of NH4NO3, and removal of the top predator significantly increased the food web's saturation, defined as its trophic depth and number of interactions. No effects on food-web saturation resulted from addition of PO4 or supplemental prey. Plants such as S. purpurea that create phytotelmata are more than inert containers and their inhabitants are more than commensal inquilines. Rather, both the plant and the inquilines are partners in a complex network of interactions.
Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries

Mathematical modeling of lithium-ion batteries (LiBs) is a central challenge in advanced battery management. This paper presents a new approach to integrate a physics-based model with machine learning to achieve high-precision modeling for LiBs. This approach uniquely proposes to inform the machine learning model of the dynamic state of the physical model, enabling a deep integration between physics and machine learning. We propose two hybrid physics-machine learning models based on the approach, which blend a single particle model with thermal dynamics (SPMT) with a feedforward neural network (FNN) to perform physics-informed learning of a LiB's dynamic behavior. The proposed models are relatively parsimonious in structure and can provide considerable predictive accuracy even at high C-rates, as shown by extensive simulations.
On the Role of Asymptomatic Carriers in Epidemic Spread Processes

We present an epidemiological compartment model, SAIR(S), that explicitly captures the dynamics of asymptomatic infected individuals in an epidemic spread process. We first present a group model and then discuss networked versions. We provide an investigation of equilibria and stability properties for these models, and present simulation results illustrating the effects of asymptomatic-infected individuals on the spread of the disease. We also discuss local isolation effects on the epidemic dynamics in terms of the networked models. Finally, we provide initial parameter estimation results based on simple least-squares approaches and local test-site data.
Dark zone maintenance results for segmented aperture wavefront error drift in a high contrast space coronagraph

Due to the limited number of photons, directly imaging planets requires long integration times with a coronagraphic instrument. The wavefront must be stable on the same time scale, which is often difficult in space due to thermal variations and other mechanical instabilities. In this paper, we discuss the implications on future space mission observing conditions of our recent laboratory demonstration of a dark hole maintenance (DHM) algorithm. The experiments are performed on the High-contrast imager for Complex Aperture Telescopes (HiCAT) at the Space Telescope Science Institute (STScI). The testbed contains a segmented aperture, a pair of deformable mirrors (DMs), and a lyot coronagraph. The segmented aperture injects high order zernike wavefront aberration drifts into the system which are then corrected by the DMs downstream via the DHM algorithm. We investigate various drift modes including segmented aperture drift, all DMs drift, and drift correction at multiple wavelengths.
COVID-19 and Its Impact on Financial Markets and the Real Economy

The COVID-19 pandemic severely disrupted financial markets and the real economy worldwide. These extraordinary events prompted large monetary and fiscal policy interventions. Recognizing the unusual nature of the shock, the academic community has produced an impressive amount of research during the last year. Macro-finance models have been extended to analyze the impact of epidemics. Empirical papers study the origins and consequences of the disruptions and the impact of policy interventions. New research evaluates the ongoing financial fragility and its relation to previous episodes and regulations. This special issue contains early contributions to this important and rapidly developing literature.(1)
Efficient verification of anticoncentrated quantum states

I present a method for estimating the fidelity F(μ, τ) between a preparable quantum state μ and a classically specified pure target state $$\tau =\left|\tau \right\rangle \left\langle \tau \right|$$ , using simple quantum circuits and on-the-fly classical calculation (or lookup) of selected amplitudes of $$\left|\tau \right\rangle$$ . The method is sample efficient for anticoncentrated states (including many states that are hard to simulate classically), with approximate cost 4ϵ−2(1 − F)dpcoll where ϵ is the desired precision of the estimate, d is the dimension of the Hilbert space, and pcoll is the collision probability of the target distribution. This scaling is exponentially better than that of any method based on classical sampling. I also present a more sophisticated version of the method that uses any efficiently preparable and well-characterized quantum state as an importance sampler to further reduce the number of copies of μ needed. Though some challenges remain, this work takes a significant step toward scalable verification of complex states produced by quantum processors.
Clinical evaluation of the molecular-based BD SARS-CoV-2/flu for the BD MAX™ System

ABSTRACT Background COVID-19 and influenza (Flu) share similar clinical symptoms. Therefore, differential detection of these three viruses during the respiratory virus seasons will be an important component for proper patient triage, management, and treatment. Objectives Establish the diagnostic performance related to SARS-CoV-2 and Flu A/B detection for the BD SARS-CoV-2/Flu for BD MAX™ System (“MAX SARS-CoV-2/Flu”) multiplex assay. Materials and Methods Two hundred and thirty-five (235) retrospective nasopharyngeal specimens were obtained from external vendors. The BD BioGx SARS-CoV-2 Reagents for BD MAX™ System (“BioGx SARS-CoV-2”) and the Cepheid Xpert® Xpress Flu/RSV (“Xpert Flu/RSV”) were utilized as reference methods. Results By reference methods, 52 specimens were SARS-CoV-2-positive, 59 were Flu A-positive, and 60 were Flu B-positive. MAX SARS-CoV-2/Flu had positive percent agreement (PPA) and negative percent agreement (NPA) values for SARS-CoV-2 detection of 96.2% ([95%CI]:87.0-98.9) and 100% [95%CI:88.7-100], respectively; PPA values for Flu A and Flu B of 100% [95%CI:93.9-100] and 98.3% [95%CI:91.1-99.7], respectively, and NPA values for Flu A and Flu B of 98.9% [95%CI:94.0- 99.8] and 100% [95%CI:95.9-100], respectively. Conclusions The MAX SARS-CoV-2/Flu assay met FDA-EUA performance criteria for SARS-CoV-2 (≥95% for PPA and NPA) and FDA clearance criteria for Flu A/B (PPA ≥90%; lower bound of the 95%CI ≥80%) and (NPA ≥95%; lower bound of the 95%CI ≥90%).
Bounding the finite-size error of quantum many-body dynamics simulations

The authors derive upper bounds on the finite-size error of numerical simulations of ground states and real time dynamics of quantum many-body systems.
Vertical-Axis Wind Turbine Steady and Unsteady Aerodynamics for Curved Deforming Blades

Vertical-axis wind turbines’ simpler design and low center of gravity make them ideal for floating wind applications. However, efficient design optimization of floating systems requires fast and ac...
Covariate-Adjusted Inference for Differential Analysis of High-Dimensional Networks

Differences between biological networks corresponding to disease conditions can help delineate the underlying disease mechanisms. Existing methods for differential network analysis do not account for dependence of networks on covariates. As a result, these approaches may detect spurious differential connections induced by the effect of the covariates on both the disease condition and the network. To address this issue, we propose a general covariate-adjusted test for differential network analysis. Our method assesses differential network connectivity by testing the null hypothesis that the network is the same for individuals who have identical covariates and only differ in disease status. We show empirically in a simulation study that the covariate-adjusted test exhibits improved type-I error control compared with naive hypothesis testing procedures that do not account for covariates. We additionally show that there are settings in which our proposed methodology provides improved power to detect differential connections. We illustrate our method by applying it to detect differences in breast cancer gene co-expression networks by subtype.
Inferring simple but precise quantitative models of human oocyte and early embryo development.

Macroscopic, phenomenological models are useful as concise framings of our understandings in fields from statistical physics to finance to biology. Constructing a phenomenological model for development would provide a framework for understanding the complicated, regulatory nature of oogenesis and embryogenesis. Here, we use a data-driven approach to infer quantitative, precise models of human oocyte maturation and pre-implantation embryo development, by analysing clinical in-vitro fertilization (IVF) data on 7399 IVF cycles resulting in 57 827 embryos. Surprisingly, we find that both oocyte maturation and early embryo development are quantitatively described by simple models with minimal interactions. This simplicity suggests that oogenesis and embryogenesis are composed of modular processes that are relatively siloed from one another. In particular, our analysis provides strong evidence that (i) pre-antral follicles produce anti-Mullerian hormone independently of effects from other follicles, (ii) oocytes mature to metaphase-II independently of the woman's age, her BMI and other factors, (iii) early embryo development is memoryless for the variables assessed here, in that the probability of an embryo transitioning from its current developmental stage to the next is independent of its previous stage. Our results both provide insight into the fundamentals of oogenesis and embryogenesis and have implications for the clinical IVF.
SeQUeNCe: a customizable discrete-event simulator of quantum networks

Recent advances in quantum information science enabled the development of quantum communication network prototypes and created an opportunity to study full-stack quantum network architectures. This work develops SeQUeNCe, a comprehensive, customizable quantum network simulator. Our simulator consists of five modules: Hardware models, Entanglement Management protocols, Resource Management, Network Management, and Application. This framework is suitable for simulation of quantum network prototypes that capture the breadth of current and future hardware technologies and protocols. We implement a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a photonic quantum network with nine routers equipped with quantum memories. The simulation capabilities are illustrated in three use cases. We show the dependence of quantum network throughput on several key hardware parameters and study the impact of classical control message latency. We also investigate quantum memory usage efficiency in routers and demonstrate that redistributing memory according to anticipated load increases network capacity by 69.1% and throughput by 6.8%. We design SeQUeNCe to enable comparisons of alternative quantum network technologies, experiment planning, and validation and to aid with new protocol design. We are releasing SeQUeNCe as an open source tool and aim to generate community interest in extending it.
Barbero–Immirzi value from experiment

We consider general relativity as a limit case of the scalar–tensor theory with Barbero–Immirzi (BI) field when the field tends to a constant. We use Shapiro time delay experimental value of 1/w(x)...
Improving RNN-T ASR accuracy using context audio

We present a training scheme for streaming automatic speech recognition (ASR) based on recurrent neural network transducers (RNN-T) which allows the encoder network to learn to exploit context audio from a stream, using segmented or partially labeled sequences of the stream during training. We show that the use of context audio during training and inference can lead to word error rate reductions of more than 6% in a realistic production setting for a voice assistant ASR system. We investigate the effect of the proposed training approach on acoustically challenging data containing background speech and present data points which indicate that this approach helps the network learn both speaker and environment adaptation. To gain further insight into the ability of a long short-term memory (LSTM) based ASR encoder to exploit long-term context, we also visualize RNN-T loss gradients with respect to the input.
AuTO: a framework for Automatic differentiation in Topology Optimization

A critical step in topology optimization (TO) is finding sensitivities. Manual derivation and implementation of sensitivities can be quite laborious and error-prone, especially for non-trivial objectives, constraints and material models. An alternate approach is to utilize automatic differentiation (AD). While AD has been around for decades, and has also been applied in TO, its wider adoption has largely been absent. In this educational paper, we aim to reintroduce AD for TO, making it easily accessible through illustrative codes. In particular, we employ JAX, a high-performance Python library for automatically computing sensitivities from a user-defined TO problem. The resulting framework, referred to here as AuTO, is illustrated through several examples in compliance minimization, compliant mechanism design and microstructural design.
Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning

Modeling tap or click sequences of users on a mobile device can improve our understandings of interaction behavior and offers opportunities for UI optimization by recommending next element the user might want to click on. We analyzed a large-scale dataset of over 20 million clicks from more than 4,000 mobile users who opted in. We then designed a deep learning model that predicts the next element that the user clicks given the user’s click history, the structural information of the UI screen, and the current context such as the time of the day. We thoroughly investigated the deep model by comparing it with a set of baseline methods based on the dataset. The experiments show that our model achieves 48% and 71% accuracy (top-1 and top-3) for predicting next clicks based on a held-out dataset of test users, which significantly outperformed all the baseline methods with a large margin. We discussed a few scenarios for integrating the model in mobile interaction and how users can potentially benefit from the model.
Attack Prediction using Hidden Markov Model

It is important to predict any adversarial attacks and their types to enable effective defense systems. Often it is hard to label such activities as malicious ones without adequate analytical reasoning. We propose the use of Hidden Markov Model (HMM) to predict the family of related attacks. Our proposed model is based on the observations often agglomerated in the form of log files and from the target or the victim’s perspective. We have built an HMM-based prediction model and implemented our proposed approach using Viterbi algorithm, which generates a sequence of states corresponding to stages of a particular attack. As a proof of concept and also to demonstrate the performance of the model, we have conducted a case study on predicting a family of attacks called Action Spoofing.
Interleukin-6 mediates PSAT1 expression and serine metabolism in TSC2-deficient cells.

Tuberous sclerosis complex (TSC) and lymphangioleiomyomatosis (LAM) are caused by aberrant mechanistic Target of Rapamycin Complex 1 (mTORC1) activation due to loss of either TSC1 or TSC2. Cytokine profiling of TSC2-deficient LAM patient–derived cells revealed striking up-regulation of Interleukin-6 (IL-6). LAM patient plasma contained increased circulating IL-6 compared with healthy controls, and TSC2-deficient cells showed up-regulation of IL-6 transcription and secretion compared to wild-type cells. IL-6 blockade repressed the proliferation and migration of TSC2-deficient cells and reduced oxygen consumption and extracellular acidification. U-13C glucose tracing revealed that IL-6 knockout reduced 3-phosphoserine and serine production in TSC2-deficient cells, implicating IL-6 in de novo serine metabolism. IL-6 knockout reduced expression of phosphoserine aminotransferase 1 (PSAT1), an essential enzyme in serine biosynthesis. Importantly, recombinant IL-6 treatment rescued PSAT1 expression in the TSC2-deficient, IL-6 knockout clones selectively and had no effect on wild-type cells. Treatment with anti–IL-6 (αIL-6) antibody similarly reduced cell proliferation and migration and reduced renal tumors in Tsc2+/− mice while reducing PSAT1 expression. These data reveal a mechanism through which IL-6 regulates serine biosynthesis, with potential relevance to the therapy of tumors with mTORC1 hyperactivity.
Retrosynthesis prediction using grammar-based neural machine translation: An information-theoretic approach

Abstract Retrosynthetic prediction is one of the main challenges in chemical synthesis because it requires a search over the space of plausible chemical reactions that often results in complex, multi-step, branched synthesis trees for even moderately complex organic reactions. Here, we propose an approach that performs single-step retrosynthesis prediction using SMILES grammar-based representations in a neural machine translation framework. Information-theoretic analyses of such grammar-representations reveal that they are superior to SMILES representations and are better-suited for machine learning tasks due to their underlying redundancy and high information capacity. We report the top-1 prediction accuracy of 43.8 % (syntactic validity 95.6 % ) and maximal fragment (MaxFrag) accuracy of 50.4 % . Comparing our model’s performance with previous work that used character-based SMILES representations demonstrate significant reduction in grammatically invalid predictions and improved prediction accuracy. Fewer invalid predictions for both known and unknown reaction class scenarios demonstrate the model’s ability to learn the underlying SMILES grammar efficiently.
The Gamma Function via Interpolation

A new computational framework for evaluation of the gamma function Γ(z) over the complex plane is developed. The algorithm is based on interpolation by rational functions, and generalizes the classical methods of Lanczos (SIAM J. Numer. Anal. B 1:86–96, (1964) and Spouge (SIAM J. Numer. Anal., 31(3):931–944, (1994) (which we show are also interpolatory). This framework utilizes the exact poles of the gamma function. By relaxing this condition and allowing the poles to vary, a near-optimal rational approximation is possible, which is demonstrated using the adaptive Antoulous Anderson (AAA) algorithm, developed in Nakatsukasa et al. (Appl. Math. Comp., 40:1494-1522, (2016)) and Nakatsukasa and Trefethen (SIAM J. Sci. Comp., 42(5):A3157–A3179, (2020)). The resulting approximations are competitive with Stirling’s formula in terms of overall efficiency.
Political booms and currency crises

Abstract This paper shows evidence that political booms, defined as the rise in governments’ popularity, are associated with a higher likelihood of currency crises. The reasoning behind this finding is that prudent economic policies to address underlying weaknesses in the economy may be political costly for incumbent governments in the short-term. Hence, popularity-concerned governments may not have enough incentives to take such corrective actions in a timely manner. This approach, in turn, can deteriorate economic fundamentals and increase related risks in the economy which can eventually lead to crises. This paper sheds light on this phenomenon in the case of currency crises, suggesting that currency crises can be viewed as “political booms gone bust” events. Moreover, it finds that higher international reserves, higher exports, and a higher degree of financial openness alleviate the effect of political booms on currency crises.
Risk and Ambiguity in Turbulent Times

This paper focuses on the 2008-2020 period during which two major crises, affecting the economy and the financial markets, occurred. Between 2008 and 2020, there were less extreme tail events, including the lingering Eurozone and Greece crises. In particular, after extremely high stock market volatility and volatility of volatility (VoV) during 2008, the long-run average volatility declined to about 20% and the VoV to around 100%. This paper analyzes this period through the lens of risk and ambiguity (uncertainty). It aims to address the question: what are the financial markets that trade risk - the volatility derivatives markets - telling us? To this end, this paper uses several measures of uncertainty. It reviews the history of volatility and uncertainty measures and discusses their informativeness. It then discusses the information derived from volatility derivatives. © 2022 World Scientific Publishing Company. Midwest Finance Association.
Generalization in Reinforcement Learning by Soft Data Augmentation

Extensive efforts have been made to improve the generalization ability of Reinforcement Learning (RL) methods via domain randomization and data augmentation. However, as more factors of variation are introduced during training, optimization becomes increasingly challenging, and empirically may result in lower sample efficiency and unstable training. Instead of learning policies directly from augmented data, we propose SOft Data Augmentation (SODA), a method that decouples augmentation from policy learning. Specifically, SODA imposes a soft constraint on the encoder that aims to maximize the mutual information between latent representations of augmented and non-augmented data, while the RL optimization process uses strictly non-augmented data. Empirical evaluations are performed on diverse tasks from DeepMind Control suite as well as a robotic manipulation task, and we find SODA to significantly advance sample efficiency, generalization, and stability in training over state-of-the-art vision-based RL methods.1
Double-Prong ConvLSTM for Spatiotemporal Occupancy Prediction in Dynamic Environments

Predicting the future occupancy state of an environment is important to enable informed decisions for autonomous vehicles. Common challenges in occupancy prediction include vanishing dynamic objects and blurred predictions, especially for long prediction horizons. In this work, we propose a double-prong neural network architecture to predict the spatiotemporal evolution of the occupancy state. One prong is dedicated to predicting how the static environment will be observed by the moving ego vehicle. The other prong predicts how the dynamic objects in the environment will move. Experiments conducted on the real-world Waymo Open Dataset indicate that the fused output of the two prongs is capable of retaining dynamic objects and reducing blurriness in the predictions for longer time horizons than baseline models.
Large scale model predictive control with neural networks and primal active sets

Abstract This work presents an explicit–implicit procedure to compute a model predictive control (MPC) law with guarantees on recursive feasibility and asymptotic stability . The approach combines an offline-trained fully-connected neural network with an online primal active set solver. The neural network provides a control input initialization while the primal active set method ensures recursive feasibility and asymptotic stability. The neural network is trained with a primal–dual loss function, aiming to generate control sequences that are primal feasible and meet a desired level of suboptimality. Since the neural network alone does not guarantee constraint satisfaction, its output is used to warm start the primal active set method online. We demonstrate that this approach scales to large problems with thousands of optimization variables, which are challenging for current approaches. Our method achieves a 2 × reduction in online inference time compared to the best method in a benchmark suite of different solver and initialization strategies.
Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA

Dual Bounds of Sparse Principal Component AnalysisSparse principal component analysis (PCA) is a widely used dimensionality reduction tool in machine learning and statistics. Compared with PCA, spa...
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate

In design, fabrication, and control problems, we are often faced with the task of synthesis, in which we must generate an object or configuration that satisfies a set of constraints while maximizing one or more objective functions. The synthesis problem is typically characterized by a physical process in which many different realizations may achieve the goal. This many-to-one map presents challenges to the supervised learning of feed-forward synthesis, as the set of viable designs may have a complex structure. In addition, the non-differentiable nature of many physical simulations prevents direct optimization. We address both of these problems with a two-stage neural network architecture that we may consider to be an autoencoder. We first learn the decoder: a differentiable surrogate that approximates the many-to-one physical realization process. We then learn the encoder, which maps from goal to design, while using the fixed decoder to evaluate the quality of the realization. We evaluate the approach on two case studies: extruder path planning in additive manufacturing and constrained soft robot inverse kinematics. We compare our approach to direct optimization of design using the learned surrogate, and to supervised learning of the synthesis problem. We find that our approach produces higher quality solutions than supervised learning, while being competitive in quality with direct optimization, at a greatly reduced computational cost.