cssi/SciSciGPT-SciSciNet huggingface dataset:
Qiskit Pulse: Programming Quantum Computers Through the Cloud with Pulses

The quantum circuit model is an abstraction that hides the underlying physical implementation of gates and measurements on a quantum computer. For precise control of real quantum hardware, the ability to execute pulse and readout-level instructions is required. To that end, we introduce Qiskit Pulse, a pulse-level programming paradigm implemented as a module within Qiskit-Terra \cite{Qiskit}. To demonstrate the capabilities of Qiskit Pulse, we calibrate both un-echoed and echoed variants of the cross-resonance entangling gate with a pair of qubits on an IBM Quantum system accessible through the cloud. We perform Hamiltonian characterization of both single and two-pulse variants of the cross-resonance entangling gate with varying amplitudes on a cloud-based IBM Quantum system. We then transform these calibrated sequences into a high-fidelity CNOT gate by applying pre and post local-rotations to the qubits, achieving average gate fidelities of $F=0.981$ and $F=0.979$ for the un-echoed and echoed respectively. This is comparable to the standard backend CNOT fidelity of $F_{CX}=0.984$. Furthermore, to illustrate how users can access their results at different levels of the readout chain, we build a custom discriminator to investigate qubit readout correlations. Qiskit Pulse allows users to explore advanced control schemes such as optimal control theory, dynamical decoupling, and error mitigation that are not available within the circuit model.
A fast direct solver for two dimensional quasi-periodic multilayered media scattering problems

This manuscript presents a fast direct solution technique for solving two dimensional wave scattering problems from quasi-periodic multilayered structures. When the interface geometries are complex, the dominant term in the computational cost of creating the direct solver scales O(NI) where N is the number of discretization points on each interface and I is the number of interfaces. The bulk of the precomputation can be re-used for any choice of incident wave. As a result, the direct solver can solve over 200 scattering problems involving an eleven layer geometry with complex interfaces 100 times faster than building a new fast direct solver from scratch for each new set of boundary data. An added benefit of the presented solver is that building an updated solver for a new geometry involving a replaced interface or a change in material property in one layer is inexpensive compared to building a new fast direct solver from scratch.
The Chinese Dream: Hukou, Social Mobility, and Trust in Government

Objective Social mobility plays an important role in stabilizing the political order. This article leverages China's hukou (household registration) reforms to examine the effects of state‐engineered social mobility on individuals’ trust in the government. Methods Using China General Social Survey data for 2010 and entropy balancing for causal effects, this article provides empirical evidence for the attitudinal effects of social mobility at the individual level. Results It finds that, first, individuals with the rural‐to‐urban hukou change are more likely to experience upward mobility, while individuals with the nonlocal‐to‐local hukou change are more pessimistic about their prospects of upward mobility; second, the rural‐to‐urban hukou change increases beneficiaries’ trust in the central government, while the nonlocal‐to‐local hukou change increases beneficiaries’ trust in the local government. Conclusion The Chinese authoritarian regime's co‐optation tactic of engineering upward social mobility via the hukou reforms contributes to its performance‐based political legitimacy as it effectively bolsters individuals’ trust in government.
Spatial and temporal patterns of nitric oxide diffusion and degradation drive emergent cerebrovascular dynamics

Nitric oxide (NO) is a gaseous signaling molecule that plays an important role in neurovascular coupling. NO produced by neurons diffuses into the smooth muscle surrounding cerebral arterioles, driving vasodilation. However, the rate of NO degradation in hemoglobin is orders of magnitude higher than in brain tissue, though how this might impact NO signaling dynamics is not completely understood. We used simulations to investigate how the spatial and temporal patterns of NO generation and degradation impacted dilation of a penetrating arteriole in cortex. We found that the spatial location of NO production and the size of the vessel both played an important role in determining its responsiveness to NO. The much higher rate of NO degradation and scavenging of NO in the blood relative to the tissue drove emergent vascular dynamics. Large vasodilation events could be followed by post-stimulus constrictions driven by the increased degradation of NO by the blood, and vasomotion-like 0.1-0.3 Hz oscillations could also be generated. We found that these dynamics could be enhanced by elevation of free hemoglobin in the plasma, which occurs in diseases such as malaria and sickle cell anemia, or following blood transfusions. Finally, we show that changes in blood flow during hypoxia or hyperoxia could be explained by altered NO degradation in the parenchyma. Our simulations suggest that many common vascular dynamics may be emergent phenomena generated by NO degradation by the blood or parenchyma.
Lower Bounds for XOR of Forrelations

The Forrelation problem, first introduced by Aaronson [Scott Aaronson, 2010] and Aaronson and Ambainis [Scott Aaronson and Andris Ambainis, 2015], is a well studied computational problem in the context of separating quantum and classical computational models. Variants of this problem were used to give tight separations between quantum and classical query complexity [Scott Aaronson and Andris Ambainis, 2015]; the first separation between poly-logarithmic quantum query complexity and bounded-depth circuits of super-polynomial size, a result that also implied an oracle separation of the classes BQP and PH [Ran Raz and Avishay Tal, 2019]; and improved separations between quantum and classical communication complexity [Uma Girish et al., 2021]. In all these separations, the lower bound for the classical model only holds when the advantage of the protocol (over a random guess) is more than ≈ 1/√N, that is, the success probability is larger than ≈ 1/2 + 1/√N. This is unavoidable as ≈ 1/√N is the correlation between two coordinates of an input that is sampled from the Forrelation distribution, and hence there are simple classical protocols that achieve advantage ≈ 1/√N, in all these models. To achieve separations when the classical protocol has smaller advantage, we study in this work the xor of k independent copies of (a variant of) the Forrelation function (where k≪ N). We prove a very general result that shows that any family of Boolean functions that is closed under restrictions, whose Fourier mass at level 2k is bounded by α^k (that is, the sum of the absolute values of all Fourier coefficients at level 2k is bounded by α^k), cannot compute the xor of k independent copies of the Forrelation function with advantage better than O((α^k)/(N^{k/2})). This is a strengthening of a result of [Eshan Chattopadhyay et al., 2019], that gave a similar statement for k = 1, using the technique of [Ran Raz and Avishay Tal, 2019]. We give several applications of our result. In particular, we obtain the following separations: Quantum versus Classical Communication Complexity. We give the first example of a partial Boolean function that can be computed by a simultaneous-message quantum protocol with communication complexity polylog(N) (where Alice and Bob also share polylog(N) EPR pairs), and such that, any classical randomized protocol of communication complexity at most o(N^{1/4}), with any number of rounds, has quasipolynomially small advantage over a random guess. Previously, only separations where the classical protocol has polynomially small advantage were known between these models [Dmitry Gavinsky, 2016; Uma Girish et al., 2021]. Quantum Query Complexity versus Bounded Depth Circuits. We give the first example of a partial Boolean function that has a quantum query algorithm with query complexity polylog(N), and such that, any constant-depth circuit of quasipolynomial size has quasipolynomially small advantage over a random guess. Previously, only separations where the constant-depth circuit has polynomially small advantage were known [Ran Raz and Avishay Tal, 2019].
Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.
Factors Contributing to Attitudinal Gains in Introductory Astronomy Courses

Most students do not enroll in introductory astronomy as part of their major; For many, it is the last science course they will ever take. Thus, it has great potential to shape students’ attitudes toward STEM fields for the rest of their life. We therefore argue that it is less important, when assessing the effectiveness of introductory astronomy courses, to explore traditional curricular learning gains than to explore the effects that various course components have on this attitude. We describe the results of our analysis of end-of-semester surveys returned by a total of 749 students in 2014-2015, at 10 institutions that employed at least part of the introductory astronomy lecture and lab curriculum we first implemented at the University of North Carolina at Chapel Hill in 2009. Surveys were designed to measure each student’s attitude, and to probe the correlation of attitude with their utilization of, and satisfaction with, various course components, along with other measures of their academic background and their self-assessed performance in the course. We find that students’ attitudes are significantly positively correlated with the grade they expect to receive, and with their rating of the course’s overall effectiveness. To a lesser degree, we find that students’ attitudes are positively correlated with their mathematical background, with whether they intend to major or pursue a career in STEM, and with their rating of the effectiveness of the instructor. We find that students’ attitudes are negatively correlated with the amount of work they perceived the course to involve, and, surprisingly, with the size and reputation of their home institution. We also find that, for the subsets of students who were exposed to them, students’ attitudes are positively correlated with their perception of the helpfulness of the lecture component of the course, and of telescope-based labs that utilized UNC-CH’s Skynet Robotic Telescope Network.
Online Versus Offline Rate in Streaming Codes for Variable-Size Messages

Providing high quality-of-service for live communication is a pervasive challenge which is plagued by packet losses during transmission. Streaming codes are a class of erasure codes specifically designed for such low-latency streaming communication settings. We consider the recently proposed setting of streaming codes under variable-size messages which reflects the requirements of applications such as live video streaming. In practice, streaming codes often need to operate in an "online" setting where the sizes of the future messages are unknown. Yet, previously studied upper bounds on the rate apply to "offline" coding schemes with access to all (including future) message sizes.In this paper, we evaluate whether the optimal offline rate is a feasible goal for online streaming codes when communicating over a burst-only packet loss channel. We identify two broad parameter regimes where, perhaps surprisingly, online streaming codes can, in fact, match the optimal offline rate. For both of these settings, we present rate-optimal online code constructions. For all remaining parameter settings, we establish that it is impossible for online schemes to attain the optimal offline rate.
Single Cell Transcriptomics of Human Native Kidney and Kidney Allografts and New Insights into the Mechanism of Fibrosis

Abstract Background Single-cell RNA-sequencing (scRNA-seq) provides unique opportunity to study cell types and cell states at a hitherto unavailable level of precision. We tested the hypothesis that scRNA-seq and computational analysis of human kidney allograft biopsies will reveal new cell types and cell states and yield insights to personalize the care of transplant recipients. Methods We selected 3 kidney biopsies from 3 individuals for scRNA-seq using the 10x Chromium Single Cell platform; (i) HK: native kidney biopsy from a living kidney, (ii) AK1: allograft kidney biopsy with transplant glomerulopathy, fibrosis, and worsening kidney function but with undetectable circulating anti-HLA antibodies, and (iii) AK2: allograft kidney biopsy after successful treatment of active antibody-mediated rejection but with persistent circulating donor-specific anti-HLA antibodies. Results We generated 7,217 high-quality single cell transcriptomes. Taking advantage of the recipient-donor sex mismatches, we determined that in the AK1 biopsy with fibrosis, more than half of the kidney allograft fibroblasts were—unexpectedly—recipient-derived and therefore likely migratory and graft infiltrative, whereas in the AK2 biopsy without fibrosis, all the fibroblasts were donor-derived. Furthermore, tubular progenitor cells that overexpress profibrotic extracellular matrix genes potentially contributing to fibrosis, were enriched in AK1 biopsy. Eight months after successful treatment of antibody-mediated rejection, AK2 biopsy contained endothelial cells that expressed mRNA for T-cell chemoattractant cytokines. In addition to these key findings, our analysis revealed unique cell types and cell states in the kidney. Conclusions Altogether, single cell transcriptomics complemented histopathology and yielded novel mechanistic insights for individualizing the care of transplant recipients.
A numerical study of mass transfer from laminar liquid films

The paper presents results of numerical simulations of a dissolved substance diffusing out of a liquid film in a two-dimensional, gravity-driven laminar flow down a vertical solid plane. The fluid mechanic problem is solved separately subject to periodicity conditions in the flow direction. After steady-state is reached, up to a hundred copies of the calculated wave and associated flow fields are efficiently ‘glued’ together to generate a long computational domain for the diffusion process which is solved as an initial-value problem. This approach renders it possible to follow the diffusion process over a long distance and to elucidate its various stages. It is found that large and small waves, with a maximum liquid velocity larger or smaller than the wave speed, respectively, behave differently. For the latter, the Sherwood number reaches an asymptotic value by the time the film still contains a significant amount of solute. From this point on, the mass transfer is very similar to that of a flat film with a smaller thickness (quantified in this paper). For large waves, the contributions of the various parts of the wave – main crest, capillary waves, nearly flat substrate – evolve differently with time and conditions and may negatively affect the mass transfer process if they get out of balance. Thus, the presence of recirculation is, in and by itself, insufficient to judge the mass transfer performance of a falling film.
Lebesgue Decomposition of Non-Commutative Measures

The Riesz-Markov theorem identifies any positive, finite, and regular Borel measure on the complex unit circle with a positive linear functional on the continuous functions. By the Weierstrass approximation theorem, the continuous functions are obtained as the norm closure of the Disk Algebra and its conjugates. Here, the Disk Algebra can be viewed as the unital norm-closed operator algebra of the shift operator on the Hardy Space, $H^2$ of the disk. Replacing square-summable Taylor series indexed by the non-negative integers, i.e. $H^2$ of the disk, with square-summable power series indexed by the free (universal) monoid on $d$ generators, we show that the concepts of absolutely continuity and singularity of measures, Lebesgue Decomposition and related results have faithful extensions to the setting of `non-commutative measures' defined as positive linear functionals on a non-commutative multi-variable `Disk Algebra' and its conjugates.
Deep reinforcement learning for optical systems: A case study of mode-locked lasers

We demonstrate that deep reinforcement learning (deep RL) provides a highly effective strategy for the control and self-tuning of optical systems. Deep RL integrates the two leading machine learning architectures of deep neural networks and reinforcement learning to produce robust and stable learning for control. Deep RL is ideally suited for optical systems as the tuning and control relies on interactions with its environment with a goal-oriented objective to achieve optimal immediate or delayed rewards. This allows the optical system to recognize bi-stable structures and navigate, via trajectory planning, to optimally performing solutions, the first such algorithm demonstrated to do so in optical systems. We specifically demonstrate the deep RL architecture on a mode-locked laser, where robust self-tuning and control can be established through access of the deep RL agent to its waveplates and polarizers. We further integrate transfer learning to help the deep RL agent rapidly learn new parameter regimes and generalize its control authority. Additionally, the deep RL learning can be easily integrated with other control paradigms to provide a broad framework to control any optical system.
Living the Full Catastrophe: A Mindfulness-Based Program to Support Recovery from Stroke.

Decades of research suggest that Mindfulness-Based Stress Reduction (MBSR) training supports a greater capacity to live with chronic medical conditions and contributes to lowering stress levels. This paper introduces a model for a Mindfulness-Based Recovery from Stroke (MBRfS) for promoting stroke recovery, informed by the lived experience of the author (a stroke survivor and certified MBSR instructor), the research literature regarding MBSR training, and the specific challenges of stroke recovery. Four themes emerged from the autoethnographic analysis that informed the proposed model: Readiness to accept the stroke event and the acquired brain injury, navigating uncertainties of stroke recovery with awareness and self-responsibility for outcomes, trusting the inherent wisdom of the body as a stroke recovery “teacher”, and increased capacity to integrate complex emotions with self-compassion, and a sense of wholeness. A four-component MBRfS model is offered, which consists of an integration amongst a modified MBSR framework, emergent attitudinal themes, and insights from the autoethnographic vignettes. The MBRfS model offers a path for providing participants with a supportive experience within stroke recovery. Recommendations and suggestions for future studies are offered to support the development of MBRfS for stroke survivors and their caregivers, as well as contributing to healthcare providers.
The range of repetition in reduced decompositions

Abstract Given a permutation w, we look at the range of how often a simple reflection σ k appears in reduced decompositions of w. We compute the minimum and give a sharp upper bound on the maximum. That bound is in terms of 321- and 3412-patterns in w, specifically as they relate in value and position to k. We also characterize when that minimum and maximum are equal, refining a previous result that braid moves are equivalent to 321-patterns.
Provisioning Spot Instances Without Employing Fault-Tolerance Mechanisms

Cloud computing offers a variable-cost payment scheme that allows cloud customers to specify the price they are willing to pay for renting spot instances to run their applications at much lower costs than fixed payment schemes, and depending on the varying demand from cloud customers, cloud platforms could revoke spot instances at any time. To alleviate the effect of spot instance revocations, applications often employ different fault-tolerance mechanisms to minimize or even eliminate the lost work for each spot instance revocation. However, these fault-tolerance mechanisms incur additional overhead related to application completion time and deployment cost. We propose a novel cloud market-based approach that leverages cloud spot market features to provision spot instances without employing fault-tolerance mechanisms to reduce the deployment cost and completion time of applications. We evaluate our approach in simulations and use Amazon spot instances that contain jobs in Docker containers and realistic price traces from EC2 markets. Our simulation results show that our approach reduces the deployment cost and completion time compared to approaches based on faulttolerance mechanisms.
Towards grounding nuclear physics in QCD

Abstract Exascale computing could soon enable a predictive theory of nuclear structure and reactions rooted in the Standard Model, with quantifiable and systematically improvable uncertainties. Such a predictive theory will help exploit experiments that use nucleons and nuclei as laboratories for testing the Standard Model and its limitations. Examples include direct dark matter detection, neutrinoless double beta decay, and searches for permanent electric dipole moments of the neutron and atoms. It will also help connect QCD to the properties of cold neutron stars and hot supernova cores. We discuss how a quantitative bridge between QCD and the properties of nuclei and nuclear matter will require a synthesis of lattice QCD (especially as applied to two- and three-nucleon interactions), effective field theory, and ab initio methods for solving the nuclear many-body problem. While there are significant challenges that must be addressed in developing this triad of theoretical tools, the rapid advance of computing is accelerating progress. In particular, we focus this review on the anticipated advances from lattice QCD and how these advances will impact few-body effective theories of nuclear physics by providing critical input, such as constraints on unknown low-energy constants of the effective (field) theories. We also review particular challenges that must be overcome for the successful application of lattice QCD for low-energy nuclear physics. We describe progress in developing few-body effective (field) theories of nuclear physics, with an emphasis on HOBET, a non-relativistic effective theory of nuclear physics, which is less common in the literature. We use the examples of neutrinoless double beta decay and the nuclear-matter equation of state to illustrate how the coupling of lattice QCD to effective theory might impact our understanding of symmetries and exotic astrophysical environments.
GhostImage: Remote perception attacks against camera-based image classification systems

In vision-based object classification systems imaging sensors perceive the environment and machine learning is then used to detect and classify objects for decision-making purposes; e.g., to maneuver an automated vehicle around an obstacle or to raise an alarm to indicate the presence of an intruder in surveillance settings. In this work we demonstrate how the perception domain can be remotely and unobtrusively exploited to enable an attacker to create spurious objects or alter an existing object. An automated system relying on a detection/classification framework subject to our attack could be made to undertake actions with catastrophic results due to attacker-induced misperception. We focus on camera-based systems and show that it is possible to remotely project adversarial patterns into camera systems by exploiting two common effects in optical imaging systems, viz., lens flare/ghost effects and auto-exposure control. To improve the robustness of the attack to channel effects, we generate optimal patterns by integrating adversarial machine learning techniques with a trained end-to-end channel model. We experimentally demonstrate our attacks using a low-cost projector, on three different image datasets, in indoor and outdoor environments, and with three different cameras. Experimental results show that, depending on the projector-camera distance, attack success rates can reach as high as 100% and under targeted conditions.
Polymorphisms in STING Affect Human Innate Immune Responses to Poxviruses

We conducted a large genome-wide association study (GWAS) of the immune responses to primary smallpox vaccination in a combined cohort of 1,653 subjects. We did not observe any polymorphisms associated with standard vaccine response outcomes (e.g., neutralizing antibody, T cell ELISPOT response, or T cell cytokine production); however, we did identify a cluster of SNPs on chromosome 5 (5q31.2) that were significantly associated (p-value: 1.3 x 10-12 - 1.5x10-36) with IFNα response to in vitro poxvirus stimulation. Examination of these SNPs led to the functional testing of rs1131769, a non-synonymous SNP in TMEM173 causing an Arg-to-His change at position 232 in the STING protein-a major regulator of innate immune responses to viral infections. Our findings demonstrate differences in the ability of the two STING variants to phosphorylate the downstream intermediates TBK1 and IRF3 in response to multiple STING ligands. Further downstream in the STING pathway, we observed significantly reduced expression of type I IFNs (including IFNα) and IFN-response genes in cells carrying the H232 variant. Subsequent molecular modeling of both alleles predicted altered ligand binding characteristics between the two variants, providing a potential mechanism underlying differences in inter-individual responses to poxvirus infection. Our data indicate that possession of the H232 variant may impair STING-mediated innate immunity to poxviruses. These results clarify prior studies evaluating functional effects of genetic variants in TMEM173 and provide novel data regarding genetic control of poxvirus immunity.
A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine Resilience

We study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses), which generates their joint observation profiles. To solve this problem, we propose a distributed learning rule that differs fundamentally from existing approaches, in that it does not employ any form of “belief-averaging.” Instead, agents update their beliefs based on a min-rule. Under standard assumptions on the observation model and the network structure, we establish that each agent learns the truth asymptotically almost surely. As our main contribution, we prove that with probability 1, each false hypothesis is ruled out by every agent exponentially fast, at a network-independent rate that is strictly larger than existing rates. We then develop a computationally efficient variant of our learning rule that is provably resilient to agents who do not behave as expected (as represented by a Byzantine adversary model) and deliberately try to spread misinformation.
A Novel Tampering Attack on AES Cores with Hardware Trojans

The implementation of cryptographic primitives in integrated circuits (ICs) continues to increase over the years due to the recent advancement of semiconductor manufacturing and reduction of cost per transistors. The hardware implementation makes cryptographic operations faster and more energy-efficient. However, various hardware attacks have been proposed aiming to extract the secret key in order to undermine the security of these primitives. In this paper, we focus on the widely used advanced encryption standard (AES) block cipher and demonstrate its vulnerability against tampering attack. Our proposed attack relies on implanting a hardware Trojan in the netlist by an untrusted foundry, which can design and implement such a Trojan as it has access to the design layout and mask information. The hardware Trojan’s activation modifies a particular round’s input data by preventing the effect of all previous rounds’ key-dependent computation. We propose to use a sequential hardware Trojan to deliver the payload at the input of an internal round for achieving this modification of data. All the internal subkeys, and finally, the secret key can be computed from the observed ciphertext once the Trojan is activated. We implement our proposed tampering attack with a sequential hardware Trojan inserted into a 128-bit AES design from OpenCores benchmark suite and report the area overhead to demonstrate the feasibility of the proposed tampering attack.
Hochschild Cohomology of Torus Equivariant D-modules

We discuss the Hochschild cohomology of the category of D-modules associated to an algebraic stack. In particular we describe the Hochschild cohomology of the category of torus-equivariant D-modules as the cohomology of a D-module on the loop space of the quotient stack. Finally, we give an approach for understanding the Hochschild cohomology of D-modules on general stacks via a relative compactification of the diagonal. This work is motivated by a desire to understand the support theory (in the sense of Benson-Iyengar-Krause) of D-modules on stacks.
Proximal gradient flow and Douglas–Rachford splitting dynamics: Global exponential stability via integral quadratic constraints

Abstract Many large-scale and distributed optimization problems can be brought into a composite form in which the objective function is given by the sum of a smooth term and a nonsmooth regularizer. Such problems can be solved via a proximal gradient method and its variants, thereby generalizing gradient descent to a nonsmooth setup. In this paper, we view proximal algorithms as dynamical systems and leverage techniques from control theory to study their global properties. In particular, for problems with strongly convex objective functions, we utilize the theory of integral quadratic constraints to prove the global exponential stability of the equilibrium points of the differential equations that govern the evolution of proximal gradient and Douglas–Rachford splitting flows. In our analysis, we use the fact that these algorithms can be interpreted as variable-metric gradient methods on the suitable envelopes and exploit structural properties of the nonlinear terms that arise from the gradient of the smooth part of the objective function and the proximal operator associated with the nonsmooth regularizer. We also demonstrate that these envelopes can be obtained from the augmented Lagrangian associated with the original nonsmooth problem and establish conditions for global exponential convergence even in the absence of strong convexity.
Quenching of inclusive and tagged b-jets at the LHC

We present theoretical predictions for the nuclear-induced attenuation of the differential cross sections for inclusive and tagged b-jet production in heavy ion collisions at the LHC. We find that for inclusive b-jet production at high transverse momentum the mass effects are negligible, and that the attenuation is comparable to the one observed for light jets. On the other hand, for isolated-photon and B-meson-tagged b-jets the sample of events with heavy quarks produced at the early stages of the collision is greatly enhanced. Thus, these tagged b-jet final-states have a much more direct connection to the physics of b-quark energy loss. We present theoretical predictions for the quenching of such tagged b-jet events at the LHC and the QGP-induced modification of the related momentum imbalance and asymmetry. We demonstrate that these tagged processes can be used to accurately study the physics of heavy quark production and propagation in dense QCD matter.
Spectra and statistics of cosmic string perturbations on the microwave background: a Monte Carlo approach

Abstract Using Monte Carlo simulations of perturbations induced by cosmic strings on the microwave background, we demonstrate the scale invariance of string fluctuation patterns. By comparing string-induced fluctuation patterns with gaussian random phase ones, we show that the non-gaussian signatures of the string patterns are detectable by tests based on the moments of the distributions only for angular scales smaller than a few arcminutes and for maps based on the gradient of temperature fluctuations. However, we find that tests of the gaussianity of the moments fail when we include a reasonable amount of instrumental noise in a pattern. Signal to noise ratios of 3.3 or greater completely suppress a string pattern's non-gaussian features even at the highest resolutions.
HECKE-TYPE CONGRUENCES FOR ANDREWS' SPT-FUNCTION MODULO 16 AND 32

Inspired by recent congruences by Andersen with varying powers of 2 in the modulus for partition related functions, we extend the modulo 32760 congruences of the first author for the function spt(n). We show that a normalized form of the generating function of spt(n) is an eigenform modulo 32 for the Hecke operators T(l2) for primes l ≥ 5 with l ≡ 1, 11, 17, 19 (mod 24), and an eigenform modulo 16 for l ≡ 13, 23 (mod 24).
Partially quenched QCD and staggered fermions

Abstract We summarize results for partially quenched chiral perturbation theory and indicate an application to staggered fermion QCD in which the square root of the determinant is taken to reduce the number of flavors from four to two.
Nanoparticle diffusion in sheared cellular blood flow

Using a multiscale blood flow solver, the complete diffusion tensor of nanoparticles (NPs) in sheared cellular blood flow is calculated over a wide range of shear rate and haematocrit. In the short-time regime, NPs exhibit anomalous dispersive behaviors under high shear and high haematocrit due to the transient elongation and alignment of the red blood cells (RBCs). In the long-time regime, the NP diffusion tensor features high anisotropy. Particularly, there exists a critical shear rate () around which the shear-rate dependence of the diffusivity tensor changes from linear to nonlinear scale. Above the critical shear rate, the cross-stream diffusivity terms vary sublinearly with shear rate, while the longitudinal term varies superlinearly. The dependence on haematocrit is linear in general except at high shear rates, where a sublinear scale is found for the vorticity term and a quadratic scale for the longitudinal term. Through analysis of the suspension microstructure and numerical experiments, the nonlinear haemorheological dependence of the NP diffusion tensor is attributed to the streamwise elongation and cross-stream contraction of RBCs under high shear, quantified by a capillary number. The RBC size is shown to be the characteristic length scale affecting the RBC-enhanced shear-induced diffusion (RESID), while the NP submicrometre size exhibits negligible influence on the RESID. Based on the observed scaling behaviours, empirical correlations are proposed to bridge the NP diffusion tensor to specific shear rate and haematocrit. The characterized NP diffusion tensor provides a constitutive relation that can lead to more effective continuum models to tackle large-scale NP biotransport applications.
Temperature-dependent mechanical properties of ZrC and HfC from first principles

In order to gain insight into the effect of elevated temperature on the mechanical performance of zirconium carbide (ZrC) and hafnium carbide (HfC), their temperature-dependent elastic constants have been systematically studied. For both ZrC and HfC, isoentropic $$C_{11}$$ gradually decreases with the increase in temperature, while the values of $$C_{44}$$ and $$C_{12}$$ of both are nearly temperature-independent. Temperature effects on modulus of elasticity, Poisson’s ratio, elastic anisotropy, hardness, and fracture toughness are further explored and discussed. A good agreement is observed between the predicted isoentropic Young’s modulus E and the available experiments for ZrC. Using quasistatic approximation can underestimate the decline rate of $$C_{11}$$ , bulk modulus B, shear modulus G, and Young’s modulus E at high temperatures, especially above 298 K. This suggests the importance of the vibrational component of the free energy to calculate mechanical properties.
FERMION MASSES IN NONCOMMUTATIVE GEOMETRY

Recent indications of neutrino oscillations raise the question of the possibility of incorporating massive neutrinos in the formulation of the Standard Model (SM) within noncommutative geometry (NCG). We find that the NCG requirement of Poincare duality constrains the numbers of massless quarks and neutrinos to be unequal unless new fermions are introduced. Possible scenarios in which this constraint is satisfied are discussed.
Constraining modified growth patterns with tomographic surveys

Viable models of modified gravity designed to produce cosmic acceleration at the current epoch, closely mimic the ΛCDM model at the level of background cosmology. However, this degeneracy is generically broken at the level of linear perturbations, where the modifications induce a peculiar scale-dependent pattern. A similar pattern is expected in models of coupled dark energy. I present the main results published in Pogosian and Silvestri [L. Pogosian and A. Silvestri, Phys. Rev. D 77 , 023503 (2008) [ arXiv:0709.0296 [astro-ph] ], on the growth of structure in f ( R ) theories of gravity, and in Zhao et al. [G.B. Zhao, L. Pogosian, A. Silvestri and J. Zylberberg, arXiv:0809.3791 [astro-ph] ], on the potential of upcoming and future tomographic surveys to detect departures from the growth of cosmic structure expected within General Relativity with a cosmological constant.
Scattering into one-dimensional waveguides from a coherently-driven quantum-optical system

We develop a new computational tool and framework for characterizing the scattering of photons by energy-nonconserving Hamiltonians into unidirectional (chiral) waveguides, for example, with coherent pulsed excitation. The temporal waveguide modes are a natural basis for characterizing scattering in quantum optics, and afford a powerful technique based on a coarse discretization of time. This overcomes limitations imposed by singularities in the waveguide-system coupling. Moreover, the integrated discretized equations can be faithfully converted to a continuous-time result by taking the appropriate limit. This approach provides a complete solution to the scattered photon field in the waveguide, and can also be used to track system-waveguide entanglement during evolution. We further develop a direct connection between quantum measurement theory and evolution of the scattered field, demonstrating the correspondence between quantum trajectories and the scattered photon state. Our method is most applicable when the number of photons scattered is known to be small, i.e. for a single-photon or photon-pair source. We illustrate two examples: analytical solutions for short laser pulses scattering off a two-level system and numerically exact solutions for short laser pulses scattering off a spontaneous parametric downconversion (SPDC) or spontaneous four-wave mixing (SFWM) source. Finally, we note that our technique can easily be extended to systems with multiple ground states and generalized scattering problems with both finite photon number input and coherent state drive, potentially enhancing the understanding of, e.g., light-matter entanglement and photon phase gates.
Nonlocal kinetic energy functionals by functional integration.

Since the seminal studies of Thomas and Fermi, researchers in the Density-Functional Theory (DFT) community are searching for accurate electron density functionals. Arguably, the toughest functional to approximate is the noninteracting kinetic energy, Ts[ρ], the subject of this work. The typical paradigm is to first approximate the energy functional and then take its functional derivative, δTs[ρ]δρ(r), yielding a potential that can be used in orbital-free DFT or subsystem DFT simulations. Here, this paradigm is challenged by constructing the potential from the second-functional derivative via functional integration. A new nonlocal functional for Ts[ρ] is prescribed [which we dub Mi-Genova-Pavanello (MGP)] having a density independent kernel. MGP is constructed to satisfy three exact conditions: (1) a nonzero “Kinetic electron” arising from a nonzero exchange hole; (2) the second functional derivative must reduce to the inverse Lindhard function in the limit of homogenous densities; (3) the potential is derived from functional integration of the second functional derivative. Pilot calculations show that MGP is capable of reproducing accurate equilibrium volumes, bulk moduli, total energy, and electron densities for metallic (body-centered cubic, face-centered cubic) and semiconducting (crystal diamond) phases of silicon as well as of III-V semiconductors. The MGP functional is found to be numerically stable typically reaching self-consistency within 12 iterations of a truncated Newton minimization algorithm. MGP’s computational cost and memory requirements are low and comparable to the Wang-Teter nonlocal functional or any generalized gradient approximation functional.
Numerical Simulations of Collisional Cascades at the Roche Limits of White Dwarf Stars

We consider the long-term collisional and dynamical evolution of solid material orbiting in a narrow annulus near the Roche limit of a white dwarf. With orbital velocities of 300 km/sec, systems of solids with initial eccentricity $e \gtrsim 10^{-3}$ generate a collisional cascade where objects with radii $r \lesssim$ 100--300 km are ground to dust. This process converts 1-100 km asteroids into 1 $\mu$m particles in $10^2 - 10^6$ yr. Throughout this evolution, the swarm maintains an initially large vertical scale height $H$. Adding solids at a rate $\dot{M}$ enables the system to find an equilibrium where the mass in solids is roughly constant. This equilibrium depends on $\dot{M}$ and $r_0$, the radius of the largest solid added to the swarm. When $r_0 \lesssim$ 10 km, this equilibrium is stable. For larger $r_0$, the mass oscillates between high and low states; the fraction of time spent in high states ranges from 100% for large $\dot{M}$ to much less than 1% for small $\dot{M}$. During high states, the stellar luminosity reprocessed by the solids is comparable to the excess infrared emission observed in many metallic line white dwarfs.
Exploring One Aspect of Pedagogical Content Knowledge of Teaching Assistants Using the Conceptual Survey of Electricity and Magnetism.

Teaching assistants often cannot predict what conceptual difficulties introductory physics students are likely to have.
Simple, low-noise piezo driver with feed-forward for broad tuning of external cavity diode lasers

We present an inexpensive, low-noise (<260 μVrms, 0.1 Hz-100 kHz) design for a piezo driver suitable for frequency tuning of external-cavity diode lasers. This simple driver improves upon many commercially available drivers by incorporating circuitry to produce a “feed-forward” signal appropriate for making simultaneous adjustments to the piezo voltage and laser current, enabling dramatic improvements in a mode-hop-free laser frequency tuning range. We present the theory behind our driver’s operation, characterize its output noise, and demonstrate its use in absorption spectroscopy on the rubidium D1 line.
The Lerch zeta function IV. Hecke operators

This paper studies algebraic and analytic structures associated with the Lerch zeta function. It defines a family of two-variable Hecke operators $$\{ \mathrm{T}_m: \, m \ge 1\}$$ given by $$\mathrm{T}_m(f)(a, c) = \frac{1}{m} \sum _{k=0}^{m-1} f(\frac{a+k}{m}, mc)$$ acting on certain spaces of real-analytic functions, including Lerch zeta functions for various parameter values. The actions of various related operators on these function spaces are determined. It is shown that, for each $$s \in {\mathbb C}$$ , there is a two-dimensional vector space spanned by linear combinations of Lerch zeta functions characterized as a maximal space of simultaneous eigenfunctions for this family of Hecke operators. This is an analog of a result of Milnor for the Hurwitz zeta function. We also relate these functions to a linear partial differential operator in the (a, c)-variables having the Lerch zeta function as an eigenfunction.
Apparent wave function collapse caused by scattering

Some experimental implications of the recent progress on wave function collapse are calculated. Exact results are derived for the center-of-mass wave function collapse caused by random scatterings and applied to a range of specific examples. The results show that recently proposed experiments to measure the GRW effect are likely to fail, since the effect of naturally occurring scatterings is of the same form as the GRW effect but generally much stronger. The same goes for attempts to measure the collapse caused by quantum gravity as suggested by Hawking and others. The results also indicate that macroscopic systems tend to be found in states with ΔxΔp =ħ/ $$\sqrt 2$$ , but microscopic systems in highly tiltedly squeezed states with ΔxΔp »ħ.
An Algebraic Classification Of Entangled States

We provide a classification of entangled states that uses new discrete entanglement invariants. The invariants are defined by algebraic properties of linear maps associated with the states. We prove a theorem on a correspondence between the invariants and sets of equivalent classes of entangled states. The new method works for an arbitrary finite number of finite-dimensional state subspaces. As an application of the method, we considered a large selection of cases of three subspaces of various dimensions. We also obtain an entanglement classification of four qubits, where we find 27 fundamental sets of classes.