cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimal approximation for unconstrained non-submodular minimization

Submodular function minimization is a well studied problem; existing algorithms solve it exactly or up to arbitrary accuracy. However, in many applications, the objective function is not exactly submodular. No theoretical guarantees exist in this case. While submodular minimization algorithms rely on intricate connections between submodularity and convexity, we show that these relations can be extended sufficiently to obtain approximation guarantees for non-submodular minimization. In particular, we prove how a projected subgradient method can perform well even for certain non-submodular functions. This includes important examples, such as objectives for structured sparse learning and variance reduction in Bayesian optimization. We also extend this result to noisy function evaluations. Our algorithm works in the value oracle model. We prove that in this model, the approximation result we obtain is the best possible with a subexponential number of queries.
The influence of training-induced sarcomerogenesis on the history dependence of force.

The increase or decrease in isometric force following active muscle lengthening or shortening, relative to a reference isometric contraction at the same muscle length and level of activation, are referred to as residual force enhancement (rFE) and residual force depression (rFD), respectively. The purpose of these experiments was to investigate the trainability of rFE and rFD on the basis of serial sarcomere number (SSN) alterations to history-dependent force properties. Maximal rFE/rFD measures from the soleus and extensor digitorum longus (EDL) of rats were compared after 4 weeks of uphill/downhill running to a no-running control. SSN adapted to the training: soleus SSN was greater with downhill compared to uphill running, while EDL demonstrated a trend towards more SSN for downhill compared to no running. In contrast, rFE and rFD did not differ across training groups for either muscle. As such, it appears that training-induced SSN adaptations do not modify rFE/rFD at the whole-muscle level.
A Cyclically-Trained Adversarial Network for Invariant Representation Learning

Recent studies show that deep neural networks are vulnerable to adversarial examples which can be generated via certain types of transformations. Being robust to a desired family of adversarial attacks is then equivalent to being invariant to a family of transformations. Learning invariant representations then naturally emerges as an important goal to achieve which we explore in this paper within specific application contexts. Specifically, we propose a cyclically-trained adversarial network to learn a mapping from image space to latent representation space and back such that the latent representation is invariant to a specified factor of variation (e.g., identity). The learned mapping assures that the synthesized image is not only realistic, but has the same values for unspecified factors (e.g., pose and illumination) as the original image and a desired value of the specified factor. Unlike disentangled representation learning, which requires two latent spaces, one for specified and another for unspecified factors, invariant representation learning needs only one such space. We encourage invariance to a specified factor by applying adversarial training using a variational autoencoder in the image space as opposed to the latent space. We strengthen this invariance by introducing a cyclic training process (forward and backward cycle). We also propose a new method to evaluate conditional generative networks. It compares how well different factors of variation can be predicted from the synthesized, as opposed to real, images. In quantitative terms, our approach attains state-of-the-art performance in experiments spanning three datasets with factors such as identity, pose, illumination or style. Our method produces sharp, high-quality synthetic images with little visible artefacts compared to previous approaches.
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages

Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA—a question answering dataset covering 11 typologically diverse languages with ...
Multitask learning deep neural networks to combine revealed and stated preference data

Abstract It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze individual choices. While the nested logit (NL) model is the classical way to address the question, this study presents multitask learning deep neural networks (MTLDNNs) as an alternative framework, and discusses its theoretical foundation, empirical performance, and behavioral intuition. We first demonstrate that the MTLDNNs are theoretically more general than the NL models because of MTLDNNs’ automatic feature learning, flexible regularizations, and diverse architectures. By analyzing the adoption of autonomous vehicles (AVs), we illustrate that the MTLDNNs outperform the NL models in terms of prediction accuracy but underperform in terms of cross-entropy losses. To interpret the MTLDNNs, we compute the elasticities and visualize the relationship between choice probabilities and input variables. The MTLDNNs reveal that AVs mainly substitute driving and ride hailing, and that the variables specific to AVs are more important than the socio-economic variables in determining AV adoption. Overall, this work demonstrates that MTLDNNs are theoretically appealing in leveraging the information shared by RP and SP and capable of revealing meaningful behavioral patterns, although its performance gain over the classical NL model is still limited. To improve upon this work, future studies can investigate the inconsistency between prediction accuracy and cross-entropy losses, novel MTLDNN architectures, regularization design for the RP-SP question, MTLDNN applications to other choice scenarios, and deeper theoretical connections between choice models and the MTLDNN framework.
Confidence in the dynamic spread of epidemics under biased sampling conditions

The interpretation of sampling data plays a crucial role in policy response to the spread of a disease during an epidemic, such as the COVID-19 epidemic of 2020. However, this is a non-trivial endeavor due to the complexity of real world conditions and limits to the availability of diagnostic tests, which necessitate a bias in testing favoring symptomatic individuals. A thorough understanding of sampling confidence and bias is necessary in order make accurate conclusions. In this manuscript, we provide a stochastic model of sampling for assessing confidence in disease metrics such as trend detection, peak detection and disease spread estimation. Our model simulates testing for a disease in an epidemic with known dynamics, allowing us to use Monte-Carlo sampling to assess metric confidence. This model can provide realistic simulated data which can be used in the design and calibration of data analysis and prediction methods. As an example, we use this method to show that trends in the disease may be identified using under 10,000 biased samples each day, and an estimate of disease spread can be made with additional 1,000-2,000 unbiased samples each day. We also demonstrate that the model can be used to assess more advanced metrics by finding the precision and recall of a strategy for finding peaks in the dynamics.
Exploring the maturation of a monocytic cell line using self-organizing maps of single-cell Raman spectra

Phorbol myristate acetate (PMA)-differentiated THP-1 cells are routinely used in lieu of primary macrophages to study macrophage polarization during host-pathogen interactions and disease progression. The phenotypes of THP-1 macrophages are influenced by the level and duration of PMA stimulation and possibly also by the presence of adhesion factors. Here, we use self-organizing maps (SOMs) of single-cell Raman spectra to probe the effects of PMA stimulation conditions and adhesion factors on THP-1 cell differentiation. Raman spectra encoding for biochemical composition were acquired from individual cells on substrates coated with fibronectin or poly-l-lysine before and after stimulation with 20 or 200 nM PMA for two different time intervals. SOMs constructed from these spectra showed the extent of spectral dissimilarity between different chronological cell populations. For all conditions, the SOMs indicated that the spectra acquired from cells after three-day treatment had diverged from those of untreated cells. The SOMs also showed that the higher PMA concentration produced both fully and partially differentiated cells for both adhesion factors after three days, whereas the outcome of stimulation for three days with the lower PMA concentration depended on the adhesion factor. On poly-l-lysine, treatment with 20 nM PMA for three days induced an intermediate stage of differentiation, but the same treatment produced partially and fully differentiated cells when applied to THP-1 cells on fibronectin. These results are consistent with the modulation of the transition of THP-1 monocytes into macrophage-like cells by integrin-binding interactions. Furthermore, differences in culture and stimulation conditions may confound comparison of results from separate studies.
Zygomorphic flowers have fewer potential pollinator species

Botanists have long identified bilaterally symmetrical (zygomorphic) flowers with more specialized pollination interactions than radially symmetrical (actinomorphic) flowers. Zygomorphic flowers fa...
Brazil’s Missing Infants: Zika Risk Changes Reproductive Behavior

Zika virus epidemics have potential large-scale population effects. Controlled studies of mice and nonhuman primates indicate that Zika affects fecundity, raising concerns about miscarriage in human populations. In regions of Brazil, Zika risk peaked months before residents learned about the epidemic and its relation to congenital anomalies. This spatiotemporal variation supports analysis of both biological effects of Zika infection on fertility and the effects of learning about Zika risk on reproductive behavior. Causal inference techniques used with vital statistics indicate that the epidemic caused reductions in birth cohort size of approximately one-quarter 18 months after Zika infection risk peaked but 10 months after public health messages advocated childbearing delay. The evidence is consistent with small but not statistically detectable biological reductions in fecundity, as well as large strategic changes in reproductive behavior to temporally align childbearing with reduced risk to infant health. The behavioral effects are larger for more-educated and older women, which may reflect facilitated access to information and to family planning services within high-risk, mosquito-infested urban locations as well as perceptions about the opportunity costs of risks to pregnancy and infant survival.
Microlocal Analysis of the Bulk-Edge Correspondence

The bulk-edge correspondence predicts that interfaces between topological insulators support robust currents. We prove this principle for PDEs that are periodic away from an interface. Our approach relies on semiclassical methods. It suggests novel perspectives for the analysis of topologically protected transport.
Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

While most robotics simulation libraries are built for low-dimensional and intrinsically serial tasks, soft-body and multi-agent robotics have created a demand for simulation environments that can model many interacting bodies in parallel. Despite the increasing interest in these fields, no existing simulation library addresses the challenge of providing a unified, highly-parallelized, GPU-accelerated interface for simulating large robotic systems. Titan is a versatile CUDA-based C++ robotics simulation library that employs a novel asynchronous computing model for GPU-accelerated simulations of robotics primitives. The innovative GPU architecture design permits simultaneous optimization and control on the CPU while the GPU runs asynchronously, enabling rapid topology optimization and reinforcement learning iterations. Kinematics are solved with a massively parallel integration scheme that incorporates constraints and environmental forces. We report dramatically improved performance over CPU baselines, simulating as many as 300 million primitive updates per second, while allowing flexibility for a wide range of research applications. We present several applications of Titan to high-performance simulations of soft-body and multi-agent robots.
Efficient Oct Image Segmentation Using Neural Architecture Search

In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Optical Coherence Tomography (OCT) scans. We incorporate the Unet architecture in the NAS framework as its backbone for the segmentation of the retinal layers in our collected and preprocessed OCT image dataset. At the pre-processing stage, we conduct super resolution and image processing techniques on the raw OCT scans to improve the quality of the raw images. For our search strategy, different primitive operations are suggested to find the down- & up-sampling cell blocks, and the binary gate method is applied to make the search strategy practical for the task in hand. We empirically evaluated our method on our in-house OCT dataset. The experimental results demonstrate that the self-adapting NAS-Unet architecture substantially outperformed the competitive human-designed architecture by achieving 95.4% in mean Intersection over Union metric and 78.7% in Dice similarity coefficient.
Control of Electron Recollision and Molecular Nonsequential Double Ionization

Control of electron recollision and molecular nonsequential double ionization via pulse shaping was obtained on ethane. A model based on sudden jump in driving frequency is found to be in excellent agreement with experimental results.
User-Friendly Design of Cryptographically-Enforced Hierarchical Role-based Access Control Models

Data access control is a critical issue for any organization generating, recording or leveraging sensitive information. The popular Role-based Access Control (RBAC) model is well- suited for large organizations with various groups of personnel, each needing their own set of data access privileges. Unfortunately, the traditional RBAC model does not involve the use of cryptographic keys needed to enforce access control policies and protect data privacy. Cryptography-based Hierarchical Access Control (CHAC) models, on the other hand, have been proposed to facilitate RBAC models and directly enforce data privacy and access controls through the use of key management schemes. Though CHAC models and efficient key management schemes can support large and dynamic organizations, they are difficult to design and maintain without intimate knowledge of symmetric encryption, key management and hierarchical access control models. Therefore, in this paper we propose an efficient algorithm which automatically generates a fine-grained CHAC model based on the input of a highly user-friendly representation of access control policies. The generated CHAC model, the dual-level key management (DLKM) scheme, leverages the collusion-resistant Access Control Polynomial (ACP) and Atallah’s Efficient Key Management scheme in order to provide privacy at both the data and user levels. As a result, the proposed model generation algorithm serves to democratize the use of CHAC. We analyze each component of our proposed system and evaluate the resulting performance of the user-friendly CHAC model generation algorithm, as well as the DLKM model itself, along several dimensions.
The vimentin cytoskeleton: when polymer physics meets cell biology.

The proper functions of tissues depend on the ability of cells to withstand stress and maintain shape. Central to this process is the cytoskeleton, comprised of three polymeric networks: F-actin, microtubules, and intermediate filaments. Intermediate filament proteins are among the most abundant cytoskeletal proteins in cells; yet they remain some of the least understood. Their structure and function deviate from those of their cytoskeletal partners, F-actin and microtubules. Intermediate filament networks show a unique combination of extensibility, flexibility and toughness that confers mechanical resilience to the cell. Vimentin is an intermediate filament protein expressed in mesenchymal cells. This review highlights exciting new results on the physical biology of vimentin intermediate filaments and their role in allowing whole cells and tissues to cope with stress.
Remittance Concentration and Volatility: Evidence from 72 Developing Countries

This paper contributes to the literature by introducing the role of geographic concentration of the source of remittances. Specifically, using data over 2010–2015 for 72 developing countries, we st...
Numerical simulations for the energy-supercritical nonlinear wave equation

We carry out numerical simulations of the defocusing energy-supercritical nonlinear wave equation for a range of spherically-symmetric initial conditions. We demonstrate numerically that the critical Sobolev norm of solutions remains bounded in time. This lends support to conditional scattering results that have been recently established for nonlinear wave equations.
Impact of tocilizumab administration on mortality in severe COVID-19

The novel coronavirus disease 2019 (COVID-19) worldwide pandemic has placed a significant burden on hospitals and healthcare providers. The immune response to this disease is thought to lead to an aberrant inflammatory response or cytokine storm, which contributes to the severity of illness. There is an urgent need to confirm whether the use of tocilizumab provides a benefit in individuals with COVID-19. A single-center propensity-score matched cohort study, including all consecutive COVID-19 patients, admitted to the medical center who were either discharged from the medical center or expired between March 1, 2020, and May 5, 2020, was performed. Patients were stratified according to the receipt of tocilizumab for cytokine storm and matched to controls using propensity scores. The primary outcome was in-hospital mortality. A total of 274 patients meeting inclusion and exclusion criteria were identified and 132 patients were included in the matched dataset (tocilizumab = 66; no tocilizumab = 66). Approximately 73% of the patients were male. Hypertension (55%), diabetes mellitus (31%), and chronic pulmonary disease (15%) were the most common comorbidities present. There were 18 deaths (27.3%) in the tocilizumab group and 18 deaths (27.3%) in the no tocilizumab group (odds ratio, 1.0; 95% confidence interval, 0.465 - 2.151; p = 1.00). Advanced age, history of myocardial infarction, dementia, chronic pulmonary disease, heart failure, and malignancy were significantly more common in patients who died. The current analysis does not support the use of tocilizumab for the management of cytokine storm in patients with COVID-19. Use of this therapeutic agent should be limited to the context of a clinical trial until more evidence is available.
Missing links as a source of seemingly variable constants in complex reaction networks

The authors show that the omission of small-sensitivity links can create large variations in the estimated rate constants of chemical reaction networks.
Understanding contact gating in Schottky barrier transistors from 2D channels

In this article, a novel two-path model is proposed to quantitatively explain sub-threshold characteristics of back-gated Schottky barrier FETs (SB-FETs) from 2D channel materials. The model integrates the “conventional” model for SB-FETs with the phenomenon of contact gating – an effect that significantly affects the carrier injection from the source electrode in back-gated field effect transistors. The two-path model is validated by a careful comparison with experimental characteristics obtained from a large number of back-gated WSe2 devices with various channel thicknesses. Our findings are believed to be of critical importance for the quantitative analysis of many three-terminal devices with ultrathin body channels.
Stochastic Sampling Simulation for Pedestrian Trajectory Prediction

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to correctly understand and predict the future trajectories of pedestrians to avoid collision and plan a safe path. Deep neural networks (DNNs) have shown promising results in accurately predicting pedestrian trajectories, relying on large amounts of annotated real-world data to learn pedestrian behavior. However, collecting and annotating these large real-world pedestrian datasets is costly in both time and labor. This paper describes a novel method using a stochastic sampling-based simulation to train DNNs for pedestrian trajectory prediction with social interaction. Our novel simulation method can generate vast amounts of automatically-annotated, realistic, and naturalistic synthetic pedestrian trajectories based on small amounts of real annotation. We then use such synthetic trajectories to train an off-the-shelf state-of-the-art deep learning approach Social GAN (Generative Adversarial Network) to perform pedestrian trajectory prediction. Our proposed architecture, trained only using synthetic trajectories, achieves better prediction results compared to those trained on human-annotated real-world data using the same network. Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.
Solving parametric PDE problems with artificial neural networks

The curse of dimensionality is commonly encountered in numerical partial differential equations (PDE), especially when uncertainties have to be modelled into the equations as random coefficients. However, very often the variability of physical quantities derived from PDE can be captured by a few features on the space of the coefficient fields. Based on such observation, we propose using neural network to parameterise the physical quantity of interest as a function of input coefficients. The representability of such quantity using a neural network can be justified by viewing the neural network as performing time evolution to find the solutions to the PDE. We further demonstrate the simplicity and accuracy of the approach through notable examples of PDEs in engineering and physics.
An uplifting discussion of T-duality

It is well known that string theory has a T-duality symmetry relating circle compactifications of large and small radius. This symmetry plays a foundational role in string theory. We note here that while T-duality is order two acting on the moduli space of compactifications, it is order four in its action on the conformal field theory state space. More generally, involutions in the Weyl group W (G) which act at points of enhanced G symmetry have canonical lifts to order four elements of G, a phenomenon first investigated by J. Tits in the mathematical literature on Lie groups and generalized here to conformal field theory. This simple fact has a number of interesting consequences. One consequence is a reevaluation of a mod two condition appearing in asymmetric orbifold constructions. We also briefly discuss the implications for the idea that T-duality and its generalizations should be thought of as discrete gauge symmetries in spacetime.
Uniqueness of the bowl soliton

We prove that any translating soliton for the mean curvature flow which is noncollapsed and uniformly 2-convex must be the rotationally symmetric bowl soliton. In particular, this proves a conjecture of White and Wang, in the 2-convex case in arbitrary dimension.
EXCITATION OF ACOUSTIC WAVES BY VORTICES IN THE QUIET SUN

The five-minute oscillations are one of the basic properties of solar convection. Observations show a mixture of a large number of acoustic wave fronts propagating from their sources. We investigate the process of acoustic waves excitation from the point of view of individual events, by using a realistic three-dimensional radiative hydrodynamic simulation of the quiet Sun. The results show that the excitation events are related to the dynamics of vortex tubes (or swirls) in intergranular lanes of solar convection. These whirlpool-like flows are characterized by very strong horizontal velocities (7-11 km s–1) and downflows (≈7 km s–1), and are accompanied by strong decreases of temperature, density, and pressure at the surface and 0.5-1 Mm below the surface. High-speed whirlpool flows can attract and capture other vortices. According to our simulation results the processes of vortex interaction, such as vortex annihilation, can cause excitation of acoustic waves on the Sun.
Penetration of Wind-Generated Near-Inertial Waves into a Turbulent Ocean

AbstractAn idealized storm scenario is examined in which a wind-generated inertial wave interacts with a turbulent baroclinic quasigeostrophic flow. The flow is initialized by spinning up an Eady m...
On arithmetical structures on complete graphs

An arithmetical structure on the complete graph Kn with n vertices is given by a collection of n positive integers with no common factor, each of which divides their sum. We show that, for all positive integers c less than a certain bound depending on n, there is an arithmetical structure on Kn with largest value c. We also show that, if each prime factor of c is greater than (n+1)2∕4, there is no arithmetical structure on Kn with largest value c. We apply these results to study which prime numbers can occur as the largest value of an arithmetical structure on Kn.
The Rational Cohomology of the Mapping Class Group Vanishes in its Virtual Cohomological Dimension

Let Mod_g be the mapping class group of a genus g >= 2 surface. The group Mod_g has virtual cohomological dimension 4g-5. In this note we use a theorem of Broaddus and the combinatorics of chord diagrams to prove that H^{4g-5}(Mod_g; Q) = 0.
A new kinetic theory model of granular flows that incorporates particle stiffness

Granular materials of practical interest in general have finite stiffness; therefore, the particle collision is a process that takes finite time to complete. Soft-sphere Discrete Element Method (DEM) simulations suggest that there are three regimes for granular shear flows: inertial regime (or rapid flow regime), elastic regime (or quasistatic regime), and the transition regime (or elastic-inertial regime). If we use tf to represent the mean free flight time for a particle between two consecutive collisions and tc to represent the binary collision duration, these regimes are implicitly related to the ratio tc/tf. Granular flows can be successfully predicted by the classical Kinetic Theory (KT) when they are in the inertial regime of low particle-particle collision frequencies and short time contacts (tc/tf ≈ 0). However, we find that KT becomes less accurate in the transition regime where the collision duration tc is no longer small compared with the collision interval tf (tc/tf > 0.05). To address this issue, we develop a soft-sphere KT (SSKT) model that takes particle stiffness k as an input parameter since tc/tf is mainly determined by k. This is achieved by proposing a modified expression for the collision frequency and introducing an elastic granular temperature Te. Compared with the classical KT that only considers the kinetic granular temperature Tk, a redefined total granular temperature (Tg = Tk + Te/3) that takes both kinetic fluctuation energy and elastic potential energy into consideration is used in the SSKT model. The model is developed for identical frictionless particles with the linear-spring-dashpot collision scheme; however, it can be extended to frictional systems as well after the modification of constitutive equations. We show that the proposed SSKT extends the applicability of the KT framework to the transition regime without losing significant accuracy. The rheological crossover has been explained physically, and the regime boundaries that separate the inertial regime and the elastic regime are quantitatively determined, showing good agreement with the previous regime map that was based on the DEM simulations. Our SSKT predictions also show that for unsteady flows such as homogeneous cooling, the particle stiffness could have a large impact on the granular flow behavior due to the energy transfer between Te and Tk.
Pulsation Period Change & Classical Cepheids: Probing the Details of Stellar Evolution

Measurements of secular period change probe real-time stellar evolution of classical Cepheids making these measurements powerful constraints for stellar evolution models, espe- cially when coupled with interferometric measurements. In this work, we present stellar evolution models and measured rates of period change for two Galactic Cepheids: Polaris and l Carinae, both important Cepheids for anchoring the Cepheid Leavitt law (period-luminosity relation). The combination of previously-measured parallaxes, interferometric angular diameters and rates of period change allows for predictions of Cepheid mass loss and stellar mass. Using the stellar evolution models, We find that l Car has a mass of about 9 M⊙ consistent with stellar pulsation models, but is not undergoing enhanced stellar mass loss. Conversely, the rate of period change for Polaris requires including enhanced mass-loss rates. We discuss what these different results imply for Cepheid evolution and the mass-loss mechanism on the Cepheid instability strip.
Service Oriented Paradigm for Massive Multiplayer Online Games

In recent times Massive Multiplayer Online Game has appeared as a computer game that enables hundreds of players from all parts of the world to interact in a game world (common platform) at the same time instance. Current architecture used for MMOGs based on the classic tightly coupled distributed system. While, MMOGs are getting more interactive same time number of interacting users is increasing, classic implementation architecture may raise scalability and interdependence issues. This requires a loosely coupled service oriented architecture to support evolution in MMOG application. Data flow architecture, Event driven architecture and client server architecture are basic date orchestration approaches used by any service oriented architecture. Real time service is hottest issue for service oriented architecture. The basic requirement of any real time service oriented architecture is to ensure the quality of service. In this paper we have proposed a service oriented architecture for massive multiplayer online game and a specific middleware (based on open source DDS) in MMOG's for fulfilling real time constraints.
On relative rational chain connectedness of threefolds with anti-big canonical divisors in positive characteristics

In this paper we prove two results about the rational chain connectedness for klt threefolds with anti-big canonical divisors in the relative setting.
Intel Quantum Simulator: A cloud-ready high-performance simulator of quantum circuits

Classical simulation of quantum computers will continue to play an essential role in the progress of quantum information science, both for numerical studies of quantum algorithms and for modeling noise and errors. Here we introduce the latest release of Intel Quantum Simulator (IQS), formerly known as qHiPSTER. The high-performance computing (HPC) capability of the software allows users to leverage the available hardware resources provided by supercomputers, as well as available public cloud computing infrastructure. To take advantage of the latter platform, together with the distributed simulation of each separate quantum state, IQS allows to subdivide the computational resources to simulate a pool of related circuits in parallel. We highlight the technical implementation of the distributed algorithm and details about the new pool functionality. We also include some basic benchmarks (up to 42 qubits) and performance results obtained using HPC infrastructure. Finally, we use IQS to emulate a scenario in which many quantum devices are running in parallel to implement the quantum approximate optimization algorithm, using particle swarm optimization as the classical subroutine. The results demonstrate that the hyperparameters of this classical optimization algorithm depends on the total number of quantum circuit simulations one has the bandwidth to perform. Intel Quantum Simulator has been released open-source with permissive licensing and is designed to simulate a large number of qubits, to emulate multiple quantum devices running in parallel, and/or to study the effects of decoherence and other hardware errors on calculation results.
Most Lensed Quasars at z > 6 are Missed by Current Surveys

The discovery of the first strongly lensed (μ ≈ 50) quasar at z > 6 (J0439+1634) represents a breakthrough in our understanding of the early universe. We derive the theoretical consequences of the new discovery. We predict that the observed population of z > 6 quasars should contain many sources with magnifications μ 10 and with image separations below the resolution threshold. Additionally, current selection criteria could have missed a substantial population of lensed z > 6 quasars, due to the contamination of the drop-out photometric bands by lens galaxies. We argue that this predicted population of lensed z > 6 quasars would be misclassified and mixed up with low-z galaxies. We quantify the fraction of undetected quasars as a function of the slope of the bright end of the quasar luminosity function, β. For β 3.6, we predict that the undetected lensed quasars could reach half of the population, whereas for β 4.5 the vast majority of the z > 6 quasar population is lensed and still undetected. This would significantly affect the z > 6 quasar luminosity function and inferred black hole mass distributions, with profound implications for the ultraviolet, X-ray, and infrared cosmic backgrounds and the growth of early quasars.
Toward Verification of the Riemann Hypothesis: Application of the Li Criterion

We substantially apply the Li criterion for the Riemann hypothesis to hold. Based upon a series representation for the sequence {λk}, which are certain logarithmic derivatives of the Riemann xi function evaluated at unity, we determine new bounds for relevant Riemann zeta function sums and the sequence itself. We find that the Riemann hypothesis holds if certain conjectured properties of a sequence ηj are valid. The constants ηj enter the Laurent expansion of the logarithmic derivative of the zeta function about s=1 and appear to have remarkable characteristics. On our conjecture, not only does the Riemann hypothesis follow, but an inequality governing the values λn and inequalities for the sums of reciprocal powers of the nontrivial zeros of the zeta function.
Correlation Structures, Many-Body Scattering Processes, and the Derivation of the Gross–Pitaevskii Hierarchy

We consider the dynamics of N bosons in three dimensions. We assume the pair interaction is given by N3β−1V (N ·) . By studying an associated many-body wave operator, we introduce a BBGKY hierarchy which takes into account all of the interparticle singular correlation structures developed by the many-body evolution from the beginning. Assuming energy conditions on the N -body wave function, for β ∈ (0, 1], we derive the Gross-Pitaevskii hierarchy with 2-body interaction. In particular, we establish that, in the N →∞ limit, all k-body scattering processes vanishes if k > 3 and thus provide a direct answer to a question raised by Erdos, Schlein, and Yau in [31]. Moreover, this new BBGKY hierarchy shares the limit points with the ordinary BBGKY hierarchy strongly for β ∈ (0, 1) and weakly for β = 1. Since this new BBGKY hierarchy converts the problem from a two-body estimate to a weaker three-body estimate for which we have the estimates to achieve β < 1, it then allows us to prove that all limit points of the ordinary BBGKY hierarchy satisfy the space-time bound conjectured by Klainerman and Machedon in [47] for β ∈ (0, 1).
The Multiverse and Particle Physics

The possibility of fundamental theories with very many ground states, each with different physical parameters, changes the way that we approach the major questions of particle physics. Most importantly, it raises the possibility that these different parameters could be realized in different domains in the larger universe. In this review, I survey the motivations for the multiverse and the impact of the idea of the multiverse on the search for new physics beyond the Standard Model.
Twisted traces of quantum intertwiners and quantum dynamical R-matrices corresponding to generalized Belavin-Drinfeld triples

We consider weighted traces of products of intertwining operators for quantum groups Uq(?), suitably twisted by a “generalized Belavin–Drinfeld triple”. We derive two commuting sets of difference equations – the (twisted) Macdonald–Ruijsenaars system and the (twisted) quantum Knizhnik–Zamolodchikov–Bernard (qKZB) system. These systems involve the nonstandard quantum R-matrices defined in a previous joint work with T. Schedler ([ESS]). When the generalized Belavin–Drinfeld triple comes from an automorphism of the Lie algebra ?, we also derive two additional sets of difference equations, the dual Macdonald–Ruijsenaars system and the \textit{dual} qKZB equations.
Towards Classification of Fracton Phases: The Multipole Algebra

Author(s): Gromov, A | Abstract: We present an effective field theory approach to the fracton phases. The approach is based on the notion of a multipole algebra. It is an extension of space(time) symmetries of a charge-conserving matter that includes global symmetries responsible for the conservation of various components of the multipole moments of the charge density. We explain how to construct field theories invariant under the action of the algebra. These field theories generally break rotational invariance and exhibit anisotropic scaling. We further explain how to partially gauge the multipole algebra. Such gauging makes the symmetries responsible for the conservation of multipole moments local, while keeping rotation and translations symmetries global. It is shown that upon such gauging one finds the symmetric tensor gauge theories, as well as the generalized gauge theories discussed recently in the literature. We refer to all such theories as multipole gauge theories. The outcome of the gauging procedure depends on the choice of the multipole algebra. In particular, we show how to construct an effective theory for the U(1) version of the Haah code based on the principles of symmetry and provide a two-dimensional example with operators supported on a Sierpinski triangle. We show that upon condensation of charged excitations, fracton phases of both types as well as various Symmetry-protected topological phases emerge. Finally, the relation between the present approach and the formalism based on polynomials over finite fields is discussed.
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning

Diffusion-weighted magnetic resonance imaging (DWI) is the only noninvasive method for quantifying microstructure and reconstructing white-matter pathways in the living human brain. Fluctuations from multiple sources create significant additive noise in DWI data which must be suppressed before subsequent microstructure analysis. We introduce a self-supervised learning method for denoising DWI data, Patch2Self, which uses the entire volume to learn a full-rank locally linear denoiser for that volume. By taking advantage of the oversampled q-space of DWI data, Patch2Self can separate structure from noise without requiring an explicit model for either. We demonstrate the effectiveness of Patch2Self via quantitative and qualitative improvements in microstructure modeling, tracking (via fiber bundle coherency) and model estimation relative to other unsupervised methods on real and simulated data.
Biochemical filter with sigmoidal response: increasing the complexity of biomolecular logic.

The first realization of a designed, rather than natural, biochemical filter process is reported and analyzed as a promising network component for increasing the complexity of biomolecular logic systems. Key challenge in biochemical logic research has been achieving scalability for complex network designs. Various logic gates have been realized, but a “toolbox” of analog elements for interconnectivity and signal processing has remained elusive. Filters are important as network elements that allow control of noise in signal transmission and conversion. We report a versatile biochemical filtering mechanism designed to have sigmoidal response in combination with signal-conversion process. Horseradish peroxidase-catalyzed oxidation of chromogenic electron donor by H2O2 was altered by adding ascorbate, allowing to selectively suppress the output signal, modifying the response from convex to sigmoidal. A kinetic model was developed for evaluation of the quality of filtering. The results offer improved capabilit...
A geometric approach to orthogonal Higgs bundles

We give a geometric characterisation of the topological invariants associated to -Higgs bundles through KO-theory and the Langlands correspondence between orthogonal and symplectic Hitchin systems. By defining the split orthogonal spectral data, we obtain geometric description of the intersection of the moduli space of those Higgs bundles with the -Hitchin fibration in terms of a collection of compact abelian varieties, and provide a natural stratification of the moduli space of -Higgs bundles.
Spectral gaps without the pressure condition

For all convex co-compact hyperbolic surfaces, we prove the existence of an essential spectral gap, that is a strip beyond the unitarity axis in which the Selberg zeta function has only finitely many zeroes. We make no assumption on the dimension $\delta$ of the limit set, in particular we do not require the pressure condition $\delta\leq {1\over 2}$. This is the first result of this kind for quantum Hamiltonians. Our proof follows the strategy developed by Dyatlov-Zahl [arXiv:1504.06589]. The main new ingredient is the fractal uncertainty principle for $\delta$-regular sets with $\delta<1$, which may be of independent interest.
Molecular graph convolutions: moving beyond fingerprints

Molecular “fingerprints” encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.
The First Candidate Colliding-wind Binary in M33

We present the detection of the first candidate colliding-wind binary (CWB) in M33, located in the giant H II region NGC 604. The source was first identified in archival {\it Chandra} imaging as a relatively soft X-ray point source, with the likely primary star determined from precise astrometric alignment between archival {\it Hubble Space Telescope} and {\it Chandra} imaging. The candidate primary star in the CWB is classified for the first time in this work as a carbon-rich Wolf-Rayet star with a likely O star companion based on spectroscopy obtained from Gemini-North. We model the X-ray spectrum using {\it Chandra} and {\it XMM-Newton} observations, and find the CWB is well-fit as a $\sim$ 1 keV thermal plasma with a median unabsorbed luminosity in the 0.5--2.0 keV band of $L_{\rm X}$ $\sim$ 3 $\times$ 10$^{35}$ erg s$^{-1}$, making this source among the brightest of CWBs observed to date. We present a long term light curve for the candidate CWB from archival {\it Chandra} and {\it XMM-Newton} observations, and discuss the constraints placed on the binary by this light curve, as well as the X-ray luminosity at maximum. Finally, we compare this candidate CWB in M33 to other well-studied, bright CWBs in the Galaxy and Magellanic Clouds, such as $\eta$ Car.
Symbolic regression of generative network models

Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied "out of the box" to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world networks. We were able to find programs that are simple enough to lead to an actual understanding of the mechanisms proposed, namely for a simple brain and a social network.
Dual field theories of quantum computation

Given two quantum states of N q-bits we are interested to find the shortest quantum circuit consisting of only one- and two- q-bit gates that would transfer one state into another. We call it the quantum maze problem for the reasons described in the paper. We argue that in a large N limit the quantum maze problem is equivalent to the problem of finding a semiclassical trajectory of some lattice field theory (the dual theory) on an N +1 dimensional space-time with geometrically flat, but topologically compact spatial slices. The spatial fundamental domain is an N dimensional hyper-rhombohedron, and the temporal direction describes transitions from an arbitrary initial state to an arbitrary target state and so the initial and final dual field theory conditions are described by these two quantum computational states. We first consider a complex Klein-Gordon field theory and argue that it can only be used to study the shortest quantum circuits which do not involve generators composed of tensor products of multiple Pauli Z matrices. Since such situation is not generic we call it the Z-problem. On the dual field theory side the Z-problem corresponds to massless excitations of the phase (Goldstone modes) that we attempt to fix using Higgs mechanism. The simplest dual theory which does not suffer from the massless excitation (or from the Z-problem) is the Abelian-Higgs model which we argue can be used for finding the shortest quantum circuits. Since every trajectory of the field theory is mapped directly to a quantum circuit, the shortest quantum circuits are identified with semiclassical trajectories. We also discuss the complexity of an actual algorithm that uses a dual theory prospective for solving the quantum maze problem and compare it with a geometric approach. We argue that it might be possible to solve the problem in sub-exponential time in 2 N , but for that we must consider the Klein-Gordon theory on curved spatial geometry and/or more complicated (than N -torus) topology.