cssi/SciSciGPT-SciSciNet huggingface dataset:
Importation and Innovation

Importation of drugs into the US may soon become legal. Since prices of drugs are lower in most other countries than they are in the US, importation would result in a decline in US drug prices. The purpose of this paper is to assess the consequences of importation for new drug development. First, the author presents a simple theoretical model of drug development which suggests that the elasticity of innovation with respect to the expected price of drugs should be at least as great as the elasticity of innovation with respect to expected market size (disease incidence). Then, the cross-sectional relationship between pharmaceutical innovation and market size among a set of diseases (different types of cancer) exhibiting substantial exogenous variation in expected market size is examined. Two different measures of pharmaceutical innovation are analysed: the number of distinct chemotherapy regimens for treating a cancer site and the number of articles published in scientific journals pertaining to drug therap...
Gametophyte and Sporophyte

First page of article Keywords: gametophyte and sporophyte; pollen grains and embryo sacs
Chapter 1 – Geology of Geothermal Regions

Geothermal energy—Earth’s heat—can be found anywhere in the world. But the high-temperature energy that is needed to drive electric generation stations is found in relatively few places. The purpose of this opening chapter is to provide the geologic framework within which high-temperature geothermal resources can be understood, both with regard to their occurrence and their nature.
The Political Economy of Intellectual Property Treaties

Intellectual property treaties have two main types of provisions: national treatment of foreign inventors, and harmonization of protections. I characterize the circumstances in which countries would want to treat foreign inventors the same as national inventors. I then argue that national treatment of foreign inventors leads to stronger intellectual property protection than is optimal, and that this effect is exacerbated when protections must be harmonized. However levels of public and private R&D spending will be lower than if each country took account of the uncompensated externalities that its R&D spending confers on other countries. The stronger protection engendered by attempts at harmonization are a partial remedy.
Telling from Discrete Data Whether the Underlying Continuous‐Time Model Is a Diffusion

Can discretely sampled financial data help us decide which continuous-time models are sensible? Diffusion processes are characterized by the continuity of their sample paths. This cannot be verified from the discrete sample path: Even if the underlying path were continuous, data sampled at discrete times will always appear as a succession of jumps. Instead, I rely on the transition density to determine whether the discontinuities observed are the result of the discreteness of sampling, or rather evidence of genuine jump dynamics for the underlying continuous-time process. I then focus on the implications of this approach for option pricing models. IN MANY INSTANCES IN F INANCIAL ECONOMETRICS, we make inference about a pos
Chapter 5 – Water Chemistry

Soil and environmental chemists simulate water chemistry using a host of chemical equilibrium reactions and constants. In most water chemistry problems we need to adjust concentrations using activity coefficients; otherwise the ion concentration product will yield an equilibrium coefficient whose numerical value depends on the concentration of dissolved ions in solution: the ionic strength. General chemistry introduced several methods for solving simple equilibrium problems but these methods are impractical when faced with actual water chemistry problems. Computer-based numerical methods are required to efficiently and reliably solve complex equilibrium systems. A firm grasp of chemistry and chemical equilibrium is absolutely essential when assessing the validity of any water chemistry simulation. A central issue in any simulation of natural water samples is identifying the minerals that control water chemistry. The Gibbs Phase Rule places an upper limit on the number of mineral phases that can actively control water chemistry. With this rule in hand, the practitioner can use water chemistry simulations to compute the saturation index for candidate minerals or plot ion activities from your simulation in solubility diagrams. Either method is a suitable strategy for identifying minerals controlling the chemistry of the water samples.
Mathematical Laboratories: A New Power for the Physical Sciences

The concept of a mathematical laboratory has been developing throughout the lifetime of computers. The capabilities made available in systems supporting these laboratories range from symbolic integration, differentiation, and polynomial and power series manipulation, through mathematical simulation, to direct control experimental systems. About 1961 two trends, one toward what has become known as "on-line" computation, the other toward "time-sharing" had gained enough recognition to develop national support, and subsequently they have come to represent what is now known as modern computation. An on-line system provides interactive facilities by which a user can exert deterministic influence over the computation sequence; a time-sharing system provides a means by which partial computations on several different problems may be interleaved in time and may share facilities according to predetermined sharing algorithms. For reasons of economy it is hard to put a single user in direct personal control (on-line, that is) of a large-scale computer. It is equally (or even more) difficult to get adequate computation power for significant scientific applications out of any small-scale economical computer. Consequently, on-line computing has come to depend upon time-sharing as its justifiable mode of implementation. On the other hand, valuable on-line applications have formed one of the major reasons for pushing forward the development of time-sharing systems. At present, both efforts have reached such a stage of fruition that we find many systems incorporating selective aspects of the early experimental systems of both types.
Individual account investment options and portfolio choice: Behavioral lessons from 401(k) plans

Abstract This paper examines how the menu of investment options made available to workers in defined contribution plans influences portfolio choice. Using unique panel data of 401(k) plans in the U.S., we present three principle findings. First, we show that the share of investment options in a particular asset class (i.e., company stock, equities, fixed income, and balanced funds) has a significant effect on aggregate participant portfolio allocations across these asset classes. Second, we document that the vast majority of the new funds added to 401(k) plans are high-cost actively-managed equity funds, as opposed to lower-cost equity index funds. Third, because the average share of assets invested in low-cost equity index funds declines with an increase in the number of options, average portfolio expenses increase and average portfolio performance is thus depressed. All of these findings are obtained from a panel data set, enabling us to control for heterogeneity in the investment preferences of workers across firms and across time.
Pituitary-ovarian responses to nafarelin testing in the polycystic ovary syndrome

To investigate the basis of polycystic ovary syndrome, we examined the responses of patients to nafarelin, a specific gonadotropin-releasing-hormone agonist, given to stimulate pituitary and gonadal secretion. We compared 16 normal women in the follicular phase, 5 normal men, 8 women with polycystic ovary syndrome, and 1 woman with polycystic ovary syndrome caused by a 3 beta-hydroxysteroid dehydrogenase deficiency. After 100 micrograms of nafarelin was given subcutaneously, serum follicle-stimulating hormone and luteinizing hormone increased rapidly to peak levels within four hours. The women with polycystic ovary syndrome had a pattern similar to that of the men, with greater early luteinizing-hormone responses (30 minutes to 1 hour) and lower peak follicle-stimulating-hormone responses than normal women (P less than 0.05). Patients with polycystic ovary syndrome responded to gonadotropin stimulation with normal to increased production of plasma estrogens and increased levels of androstenedione at 16 to 24 hours (P less than 0.05). Elevated production of 17 alpha-hydroxyprogesterone was found in all the women with polycystic ovary syndrome and in the men. These abnormal responses were unchanged by pretreatment with dexamethasone to suppress adrenal function. In the patient with the 3 beta-hydroxysteroid dehydrogenase deficiency, both basal and stimulated plasma levels of delta 5-3 beta-hydroxysteroids before the enzymatic block were elevated, whereas plasma levels of 17 alpha-hydroxyprogesterone and androstenedione--the steroids immediately beyond the block--were low. We conclude that women with polycystic ovary syndrome have masculinized pituitary and ovarian responses to stimulation by nafarelin. Our findings suggest that the regulation of the ovarian 17-hydroxylase and C-17,20-lyase activities is abnormal in such women.
Elements of finite order in the Riordan group and their eigenvectors

Abstract We consider elements of finite order in the Riordan group R over a field F of characteristic 0. We solve, for all n ≥ 2 , two foundational questions posed by L. Shapiro (2001) for the case n = 2 (“involutions”): Given F ( x ) of finite compositional order n, Theorem 1 gives a formula for g ( x ) showing that those g ( x ) which make ( g ( x ) , F ( x ) ) ∈ R a Riordan element of order n are precisely those for which the matrix ( g ( x ) , F ( x ) ) has an eigenvector ( h 0 , h 1 , … , ) T with h 0 ≠ 0 . Theorem 2 classifies finite-order Riordan group elements up to conjugation in R and gives a normal form for finite order Riordan arrays under similarity. This leads to Theorem 3, a formula for all eigenvectors of finite order Riordan arrays, and results in interesting combinatorial identities. We relate our work to 2008 and 2013 papers of Cheon and Kim which motivated this paper; and we solve their Open question. This circle of ideas closes with a new proof of C. Marshall's theorem, which finds the unique F ( x ) , given bi-invertible g ( x ) , such that ( g ( x ) , F ( x ) ) is an involution.
Observation of Laughlin States Made of Light

Much of the richness in nature emerges because simple constituents form an endless variety of ordered states1. Whereas many such states are fully characterized by symmetries2, interacting quantum systems can exhibit topological order and are instead characterized by intricate patterns of entanglement3,4. A paradigmatic example of topological order is the Laughlin state5, which minimizes the interaction energy of charged particles in a magnetic field and underlies the fractional quantum Hall effect6. Efforts have been made to enhance our understanding of topological order by forming Laughlin states in synthetic systems of ultracold atoms7,8 or photons9-11. Nonetheless, electron gases remain the only systems in which such topological states have been definitively observed6,12-14. Here we create Laughlin-ordered photon pairs using a gas of strongly interacting, lowest-Landau-level polaritons as a photon collider. Initially uncorrelated photons enter a cavity and hybridize with atomic Rydberg excitations to form polaritons15-17, quasiparticles that here behave like electrons in the lowest Landau level owing to a synthetic magnetic field created by Floquet engineering18 a twisted cavity11,19 and by Rydberg-mediated interactions between them16,17,20,21. Polariton pairs collide and self-organize to avoid each other while conserving angular momentum. Our finite-lifetime polaritons only weakly prefer such organization. Therefore, we harness the unique tunability of Floquet polaritons to distil high-fidelity Laughlin states of photons outside the cavity. Particle-resolved measurements show that these photons avoid each other and exhibit angular momentum correlations, the hallmarks of Laughlin physics. This work provides broad prospects for the study of topological quantum light22.
Hyposkillia – Deficiency of Clinical Skills

*Editor's Note: This article is being reprinted with permission from Dr. Fred and the Texas Heart Institute Journal.
Winning Big: Scale and Success in Retail Entrepreneurship

In 2014, Washington State used a lottery system to allocate licenses to firms in the newly legalized retail cannabis industry, generating random variation in how many stores entrepreneurs were able to own. We observe highly detailed data on all subsequent industry transactions, including prices, wholesale costs, markups, and product assortments. We find that entrepreneurs who are randomly allocated more store licenses ultimately earn substantially higher per store profits than do single-store firms, suggesting that the returns to scale in the mom-and-pop retail sector are quite large. Despite these firms having greater market power, this increase in profits does not come at the expense of consumers. Rather, retailers in multi-store chains ultimately charge significantly lower prices and margins and offer greater product variety. This gap in prices is not initially present but grows substantially over time, as does the difference in assortment size and profits between stores in multi-store chains and stores operating alone. Using the full history of outcomes, we track the evolution of firms in this new market and show that multi-store retailers use an initial advantage in offering larger assortments to position themselves as the low-price, large-assortment retail option and attract a larger but more price-sensitive set of customers. These results have implications for the study of retail concentration and mergers, countervailing buyer power, and consumer search. Our results suggest that policies to help entrepreneurs expand in retail may have large benefits to both firms and consumers.
Understanding the Political Ideology of Legislators from Social Media Images

In this paper, we seek to understand how politicians use images to express ideological rhetoric through Facebook images posted by members of the U.S. House and Senate. In the era of social media, politics has become saturated with imagery, a potent and emotionally salient form of political rhetoric which has been used by politicians and political organizations to influence public sentiment and voting behavior for well over a century. To date, however, little is known about how images are used as political rhetoric. Using deep learning techniques to automatically predict Republican or Democratic party affiliation solely from the Facebook photographs of the members of the 114th U.S. Congress, we demonstrate that predicted class probabilities from our model function as an accurate proxy of the political ideology of images along a left–right (liberal–conservative) dimension. After controlling for the gender and race of politicians, our method achieves an accuracy of 59.28% from single photographs and 82.35% when aggregating scores from multiple photographs (up to 150) of the same person. To better understand image content distinguishing liberal from conservative images, we also perform in-depth content analyses of the photographs. Our findings suggest that conservatives tend to use more images supporting status quo political institutions and hierarchy maintenance, featuring individuals from dominant social groups, and displaying greater happiness than liberals.
Boceprevir, GC-376, and calpain inhibitors II, XII inhibit SARS-CoV-2 viral replication by targeting the viral main protease.

A new coronavirus SARS-CoV-2, also called novel coronavirus 2019 (2019-nCoV), started to circulate among humans around December 2019, and it is now widespread as a global pandemic. The disease caused by SARS-CoV-2 virus is called COVID-19, which is highly contagious and has an overall mortality rate of 6.35% as of May 26, 2020. There is no vaccine or antiviral available for SARS-CoV-2. In this study, we report our discovery of inhibitors targeting the SARS-CoV-2 main protease (Mpro). Using the FRET-based enzymatic assay, several inhibitors including boceprevir, GC-376, and calpain inhibitors II, and XII were identified to have potent activity with single-digit to submicromolar IC50 values in the enzymatic assay. The mechanism of action of the hits was further characterized using enzyme kinetic studies, thermal shift binding assays, and native mass spectrometry. Significantly, four compounds (boceprevir, GC-376, calpain inhibitors II and XII) inhibit SARS-CoV-2 viral replication in cell culture with EC50 values ranging from 0.49 to 3.37 µM. Notably, boceprevir, calpain inhibitors II and XII represent novel chemotypes that are distinct from known substrate-based peptidomimetic Mpro inhibitors. A complex crystal structure of SARS-CoV-2 Mpro with GC-376, determined at 2.15 A resolution with three protomers per asymmetric unit, revealed two unique binding configurations, shedding light on the molecular interactions and protein conformational flexibility underlying substrate and inhibitor binding by Mpro. Overall, the compounds identified herein provide promising starting points for the further development of SARS-CoV-2 therapeutics.
Lifting sum-of-squares lower bounds: degree-2 to degree-4

The degree-4 Sum-of-Squares (SoS) SDP relaxation is a powerful algorithm that captures the best known polynomial time algorithms for a broad range of problems including MaxCut, Sparsest Cut, all MaxCSPs and tensor PCA. Despite being an explicit algorithm with relatively low computational complexity, the limits of degree-4 SoS SDP are not well understood. For example, existing integrality gaps do not rule out a (2−)-algorithm for Vertex Cover or a (0.878+)-algorithm for MaxCut via degree-4 SoS SDPs, each of which would refute the notorious Unique Games Conjecture. We exhibit an explicit mapping from solutions for degree-2 Sum-of-Squares SDP (Goemans-Williamson SDP) to solutions for the degree-4 Sum-of-Squares SDP relaxation on boolean variables. By virtue of this mapping, one can lift lower bounds for degree-2 SoS SDP relaxation to corresponding lower bounds for degree-4 SoS SDPs. We use this approach to obtain degree-4 SoS SDP lower bounds for MaxCut on random d-regular graphs, Sherington-Kirkpatrick model from statistical physics and PSD Grothendieck problem. Our constructions use the idea of pseudocalibration towards candidate SDP vectors, while it was previously only used to produce the candidate matrix which one would show is PSD using much technical work. In addition, we develop a different technique to bound the spectral norms of graphical matrices that arise in the context of SoS SDPs. The technique is much simpler and yields better bounds in many cases than the trace method – which was the sole technique for this purpose.
A Risk-Security Tradeoff in Graphical Coordination Games

A system relying on the collective behavior of decision makers can be vulnerable to a variety of attacks. How well can a system operator protect performance in the face of these risks? We frame this question in the context of graphical coordination games, where the agents in a network choose between two conventions and derive benefits from coordinating neighbors, and system performance is measured in terms of the agents’ welfare. In this article, we assess an operator's ability to mitigate two types of attacks: 1) broad attacks, where the adversary distributes targeted incentives to all agents in the network; and 2) focused attacks, where the adversary can force a selected subset of the agents to commit to a prescribed convention. The system operator can among a class of distributed algorithms that define the local interactions. Our main contribution characterizes the operator's fundamental tradeoff between security against worst-case broad attacks and vulnerability from focused attacks. We show that this tradeoff significantly improves when the operator selects a decision-making process at random. This article highlights the design challenges a system operator faces in maintaining resilience of networked distributed systems.
Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems

Reinforcement learning is a promising approach to learning robot controllers. It has recently been shown that algorithms based on finite-difference estimates of the policy gradient are competitive with algorithms based on the policy gradient theorem. We propose a theoretical framework for understanding this phenomenon. Our key insight is that many dynamical systems (especially those of interest in robot control tasks) are \emph{nearly deterministic}---i.e., they can be modeled as a deterministic system with a small stochastic perturbation. We show that for such systems, finite-difference estimates of the policy gradient can have substantially lower variance than estimates based on the policy gradient theorem. We interpret these results in the context of counterfactual estimation. Finally, we empirically evaluate our insights in an experiment on the inverted pendulum.
Laser velocimetry in the low-speed wind tunnels at Ames Research Center

The historical development of laser velocimetry and its application to low-speed (less than 100 m/sec) aerodynamic flows in the subsonic wind tunnels at Ames Research Center is reviewed. A fully three dimensional velocimeter for the Ames 7- by 10-Foot Wind Tunnel is described, and its capabilities are presented through sample data from a recent experiment. Finally, a long-range (2.6 to 10 m) velocimeter that is designed to be installed within the test section of the Ames 40- by 80-Foot Wind Tunnel is described and sample data are presented.
Machine Learning Instrument Variables for Causal Inference

Instrumental variables (IVs) are a commonly used technique for causal inference from observational data. However, in the recent years, the use of the IV method has come under much criticism across multiple disciplines (e.g. [1] and [6] in Economics; [3] in Marketing; [5], [4], and [2] in Finance). This is because, in practice, the variation induced by IVs can be limited, which yields imprecise or biased estimates of causal effects and renders the approach ineffective for policy decisions. In this paper, we confront these challenges by formulating the problem of constructing instrumental variables from candidate exogenous information as a (supervised) machine learning problem that is amenable to the learning approach. We extend the standard learning framework to develop an algorithm we term MLIV (machine-learned instrumental variables), which allows training of instruments and causal inference to be simultaneously performed from sample data. We provide formal asymptotic theory and show O(√n) consistency and asymptotic normality for machine-learned instrument variables. We illustrate the effectiveness of the MLIVs in empirical environments consisting of both linear and nonlinear model parameters. Simulations and application to real-world data demonstrate that the algorithm is highly effective and significantly improves the performance of causal inference from observational data. The complete version of this paper can be found at https://ssrn.com/abstract=3352957.
MEASURING MASS VIA COORDINATE CUBES

Inspired by a formula of Stern that relates scalar curvature to harmonic functions, we evaluate the mass of an asymptotically flat 3-manifold along faces and edges of a large coordinate cube. In terms of the mean curvature and dihedral angle, the resulting mass formula relates to Gromov’s scalar curvature comparison theory for cubic Riemannian polyhedra. In terms of the geodesic curvature and turning angle of slicing curves, the formula realizes the mass as integration of the angle defect detected by the boundary term in the Gauss–Bonnet theorem.
Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (DRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions in the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DRL environment. The proposed method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.
Flexibility from networks of data centers: A market clearing formulation with virtual links

Abstract Data centers owned and operated by large companies have a high power consumption that is expected to increase in the future. However, the ability to shift computing loads geographically and in time can provide flexibility to the power grid. We introduce the concept of virtual links to capture space-time load flexibility provided by geographically-distributed data centers in market clearing procedures. We show that the virtual link abstraction fits well into existing market clearing frameworks and can help analyze and establish market design properties. This is demonstrated using illustrative case studies.
Background Selection Does Not Mimic the Patterns of Genetic Diversity Produced by Selective Sweeps

It is increasingly evident that natural selection plays a prominent role in shaping patterns of diversity across the genome. The most commonly studied modes of natural selection are positive selection and negative selection, which refer to directional selection for and against derived mutations, respectively. Positive selection can result in hitchhiking events, in which a beneficial allele rapidly replaces all others in the population, creating a valley of diversity around the selected site along with characteristic skews in allele frequencies and linkage disequilibrium among linked neutral polymorphisms. Similarly, negative selection reduces variation not only at selected sites but also at linked sites, a phenomenon called background selection (BGS). Thus, discriminating between these two forces may be difficult, and one might expect efforts to detect hitchhiking to produce an excess of false positives in regions affected by BGS. Here, we examine the similarity between BGS and hitchhiking models via simulation. First, we show that BGS may somewhat resemble hitchhiking in simplistic scenarios in which a region constrained by negative selection is flanked by large stretches of unconstrained sites, echoing previous results. However, this scenario does not mirror the actual spatial arrangement of selected sites across the genome. By performing forward simulations under more realistic scenarios of BGS, modeling the locations of protein-coding and conserved noncoding DNA in real genomes, we show that the spatial patterns of variation produced by BGS rarely mimic those of hitchhiking events. Indeed, BGS is not substantially more likely than neutrality to produce false signatures of hitchhiking. This holds for simulations modeled after both humans and Drosophila, and for several different demographic histories. These results demonstrate that appropriately designed scans for hitchhiking need not consider BGS's impact on false-positive rates. However, we do find evidence that BGS increases the false-negative rate for hitchhiking, an observation that demands further investigation.
Isogonal weavings on the sphere: knots, links, polycatenanes.

Mathematical knots and links are described as piecewise linear – straight, non-intersecting sticks meeting at corners. Isogonal structures have all corners related by symmetry (`vertex'-transitive). Corner- and stick-transitive structures are termed regular. No regular knots are found. Regular links are cubic or icosahedral and a complete account of these (36 in number) is given, including optimal (thickest-stick) embeddings. Stick 2-transitive isogonal structures are again cubic and icosahedral and also encompass the infinite family of torus knots and links. The major types of these structures are identified and reported with optimal embeddings. The relevance of this work to materials chemistry and biochemistry is noted.
InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity

The skip-gram model for learning word embeddings (Mikolov et al. 2013) has been widely popular, and DeepWalk (Perozzi et al. 2014), among other methods, has extended the model to learning node representations from networks. Recent work of Qiu et al. (2018) provides a closed-form expression for the DeepWalk objective, obviating the need for sampling for small datasets and improving accuracy. In these methods, the "window size" T within which words or nodes are considered to co-occur is a key hyperparameter. We study the objective in the limit as T goes to infinity, which allows us to simplify the expression of Qiu et al. We prove that this limiting objective corresponds to factoring a simple transformation of the pseudoinverse of the graph Laplacian, linking DeepWalk to extensive prior work in spectral graph embeddings. Further, we show that by a applying a simple nonlinear entrywise transformation to this pseudoinverse, we recover a good approximation of the finite-T objective and embeddings that are competitive with those from DeepWalk and other skip-gram methods in multi-label classification. Surprisingly, we find that even simple binary thresholding of the Laplacian pseudoinverse is often competitive, suggesting that the core advancement of recent methods is a nonlinearity on top of the classical spectral embedding approach.
On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems

We consider the problem of recovering a complex vector x ∈ ℂn from m quadratic measurements $\left\{ {\left\langle {{A_i}{\mathbf{x}},{\mathbf{x}}} \right\rangle } \right\}_{i = 1}^m$. This problem, known as quadratic feasibility, encompasses the well known phase retrieval problem and has applications in a wide range of important areas including power system state estimation and x-ray crystallography. In general, not only is the the quadratic feasibility problem NP-hard to solve, but it may in fact be unidentifiable. In this paper, we establish conditions under which this problem becomes identifiable, and further prove isometry properties in the case when the matrices $\left\{ {{A_i}} \right\}_{i = 1}^m$ are Hermitian matrices sampled from a complex Gaussian distribution. Moreover, we explore a nonconvex optimization formulation of this problem, and establish salient features of the associated optimization landscape that enables gradient algorithms with an arbitrary initialization to converge to a globally optimal point with a high probability. Our results also reveal sample complexity requirements for successfully identifying a feasible solution in these contexts.
SPORES: sum-product optimization via relational equality saturation for large scale linear algebra

Machine learning algorithms are commonly specified in linear algebra (LA). LA expressions can be rewritten into more efficient forms, by taking advantage of input properties such as sparsity, as well as program properties such as common subexpressions and fusible operators. The complex interaction among these properties' impact on the execution cost poses a challenge to optimizing compilers. Existing compilers resort to intricate heuristics that complicate the codebase and add maintenance cost, but fail to search through the large space of equivalent LA expressions to find the cheapest one. We introduce a general optimization technique for LA expressions, by converting the LA expressions into Relational Algebra (RA) expressions, optimizing the latter, then converting the result back to (optimized) LA expressions. The rewrite rules we design in this approach are complete, meaning that any equivalent LA expression is covered in the search space. The challenge is the major size of the search space, and we address this by adopting and extending a technique used in compilers, called equality saturation. Our optimizer, SPORES, uses rule sampling to quickly cover vast portions of the search space; it then uses a constraint solver to extract the optimal plan from the covered space, or alternatively uses a greedy algorithm to shorten compile time. We integrate SPORES into SystemML and validate it empirically across a spectrum of machine learning tasks; SPORES can derive all existing hand-coded optimizations in SystemML, and perform new optimizations that lead to up to 10X speedup.
Rademacher type and Enflo type coincide

A nonlinear analogue of the Rademacher type of a Banach space was introduced in classical work of Enflo. The key feature of Enflo type is that its definition uses only the metric structure of the Banach space, while the definition of Rademacher type relies on its linear structure. We prove that Rademacher type and Enflo type coincide, settling a long-standing open problem in Banach space theory. The proof is based on a novel dimension-free analogue of Pisier's inequality on the discrete cube.
Error-Detected State Transfer and Entanglement in a Superconducting Quantum Network

The challenge of linking and operating physically independent modular quantum computers is tackled: Several key capabilities are experimentally demonstrated, including error correction.
Should a materialist believe in qualia

(1995). Should a materialist believe in Qualia? Australasian Journal of Philosophy: Vol. 73, No. 1, pp. 140-144.
Learning Speaker Embedding from Text-to-Speech.

Zero-shot multi-speaker Text-to-Speech (TTS) generates target speaker voices given an input text and the corresponding speaker embedding. In this work, we investigate the effectiveness of the TTS reconstruction objective to improve representation learning for speaker verification. We jointly trained end-to-end Tacotron 2 TTS and speaker embedding networks in a self-supervised fashion. We hypothesize that the embeddings will contain minimal phonetic information since the TTS decoder will obtain that information from the textual input. TTS reconstruction can also be combined with speaker classification to enhance these embeddings further. Once trained, the speaker encoder computes representations for the speaker verification task, while the rest of the TTS blocks are discarded. We investigated training TTS from either manual or ASR-generated transcripts. The latter allows us to train embeddings on datasets without manual transcripts. We compared ASR transcripts and Kaldi phone alignments as TTS inputs, showing that the latter performed better due to their finer resolution. Unsupervised TTS embeddings improved EER by 2.06\% absolute with regard to i-vectors for the LibriTTS dataset. TTS with speaker classification loss improved EER by 0.28\% and 0.73\% absolutely from a model using only speaker classification loss in LibriTTS and Voxceleb1 respectively.
End-to-End Spoken Language Understanding Without Full Transcripts.

An essential component of spoken language understanding (SLU) is slot filling: representing the meaning of a spoken utterance using semantic entity labels. In this paper, we develop end-to-end (E2E) spoken language understanding systems that directly convert speech input to semantic entities and investigate if these E2E SLU models can be trained solely on semantic entity annotations without word-for-word transcripts. Training such models is very useful as they can drastically reduce the cost of data collection. We created two types of such speech-to-entities models, a CTC model and an attention-based encoder-decoder model, by adapting models trained originally for speech recognition. Given that our experiments involve speech input, these systems need to recognize both the entity label and words representing the entity value correctly. For our speech-to-entities experiments on the ATIS corpus, both the CTC and attention models showed impressive ability to skip non-entity words: there was little degradation when trained on just entities versus full transcripts. We also explored the scenario where the entities are in an order not necessarily related to spoken order in the utterance. With its ability to do re-ordering, the attention model did remarkably well, achieving only about 2% degradation in speech-to-bag-of-entities F1 score.
Double-diffusive Erosion of the Core of Jupiter

We present direct numerical simulations of the transport of heat and heavy elements across a double-diffusive interface or a double-diffusive staircase, in conditions that are close to those one might expect to find near the boundary between the heavy-element-rich core and the hydrogen–helium envelope of giant planets such as Jupiter. We find that the nondimensional ratio of the buoyancy flux associated with heavy-element transport to the buoyancy flux associated with heat transport lies roughly between 0.5 and 1, which is much larger than previous estimates derived by analogy with geophysical double-diffusive convection. Using these results in combination with a core erosion model proposed by Guillot et al., we find that the entire core of Jupiter would be eroded within less than 1 Myr, assuming that the core–envelope boundary is composed of a single interface. We also propose an alternative model that is more appropriate in the presence of a well-established double-diffusive staircase, and find that in this limit a large fraction of the core could be preserved. These findings are interesting in the context of Juno's recent results, but call for further modeling efforts to better understand the process of core erosion from first principles.
Time-dependent Diffusive Shock Acceleration in Slow Supernova Remnant Shocks

Recent gamma-ray observations show that middle-aged supernova remnants (SNRs) interacting with molecular clouds can be sources of both GeV and TeV emission. Models involving reacceleration of preexisting cosmic rays (CRs) in the ambient medium and direct interaction between SNR and molecular clouds have been proposed to explain the observed gamma-ray emission. For the reacceleration process, standard diffusive shock acceleration (DSA) theory in the test particle limit produces a steady-state particle spectrum that is too flat compared to observations, which suggests that the high-energy part of the observed spectrum has not yet reached a steady state. We derive a time-dependent DSA solution in the test particle limit for situations involving reacceleration of preexisting CRs in the preshock medium. Simple estimates with our time-dependent DSA solution plus a molecular cloud interaction model can reproduce the overall shape of the spectra of IC 443 and W44 from GeV to TeV energies through pure π0-decay emission. We allow for a power-law momentum dependence of the diffusion coefficient, finding that a power-law index of 0.5 is favored.
Cluster complexes via semi-invariants

We define and study virtual representation spaces for vectors hav- ing both positive and negative dimensions at the vertices of a quiver without oriented cycles. We consider the natural semi-invariants on these spaces which we call virtual semi-invariants and prove that they satisfy the three basic theorems: the First Fundamental Theorem, the Saturation Theorem and the Canonical Decomposition Theorem. In the special case of Dynkin quivers with n vertices this gives the fun- damental interrelationship between supports of the semi-invariants and the Tilting Triangulation of the (n 1)-sphere.
Study on initial geometry fluctuations via participant plane correlations in heavy ion collisions: part II

Further investigation of the participant plane correlations within a Glauber model framework is presented, focusing on correlations between three or four participant planes of different order. A strong correlation is observed for \(\cos(2\varPhi_{2}^{*}+3\varPhi _{3}^{*}-5\varPhi_{5}^{*})\) which is a reflection of the elliptic shape of the overlap region. The correlation between the corresponding experimental event plane angles can be easily measured. Strong correlations of similar geometric origin are also observed for \(\cos(2\varPhi_{2}^{*}+4\varPhi _{4}^{*}- 6\varPhi_{6}^{*})\), \(\cos(2\varPhi_{2}^{*}-3\varPhi_{3}^{*}-4\varPhi_{4}^{*}+5\varPhi_{5}^{*})\), \(\cos(6\varPhi_{2}^{*}+3\varPhi_{3}^{*}- 4\varPhi_{4}^{*}- 5\varPhi_{5}^{*})\), \(\cos(\varPhi_{1}^{*}-2\varPhi_{2}^{*}-3\varPhi_{3}^{*}+4\varPhi _{4}^{*})\), \(\cos(\varPhi_{1}^{*}+6\varPhi_{2}^{*}-3\varPhi _{3}^{*}-4\varPhi_{4}^{*})\), and \(\cos(\varPhi_{1}^{*}+2\varPhi_{2}^{*}+3\varPhi_{3}^{*}-6\varPhi _{6}^{*})\), which are also measurable. Experimental measurements of the corresponding event plane correlators in heavy ion collisions at RHIC and the LHC may improve our understanding of the physics underlying the measured higher order flow harmonics.
Nuclear Interactions from the Renormalization Group

We discuss how the renormalization group can be used to derive effective nuclear interactions. Starting from the model-independent low-momentum interaction Vlow k, we successively integrate out high-lying particle and hole states from momentum shells around the Fermi surface as proposed by Shankar. The renormalization group approach allows for a systematic calculation of induced interactions and yields similar contributions to the scattering amplitude as the two-body parquet equations. We review results for the 1S0 and 3P2 superfluid pairing gaps as well as the spin dependence of effective interactions in neutron matter. Implications for the cooling of neutron stars are discussed.
CHANDRA OBSERVATION OF THE RELATIVISTIC BINARY J1906+0746

PSR J1906+0746 is a young radio pulsar ({tau} = 112 kyr, P = 144 ms) in a tight binary (P{sub orb} = 3.98 hr) with a compact high-mass companion (M{sub comp} {approx_equal} 1.36 M {sub sun}), at the distance of about 5 kpc. We observed this unique relativistic binary with the Chandra Advanced CCD Imaging Spectrometer detector for 31.6 ks. Surprisingly, not a single photon was detected within the 3'' radius from the J1906+0746 radio position. For a plausible range of hydrogen column densities, n {sub H} = (0.5-1) x 10{sup 22} cm{sup -2}, the nondetection corresponds to the 90% upper limit of (3-5) x 10{sup 30} erg s{sup -1} on the unabsorbed 0.5-8 keV luminosity for the power-law model with {gamma} = 1.0-2.0, and {approx}10{sup 32} erg s{sup -1} on the bolometric luminosity of the thermal emission from the neutrons star surface. The inferred limits are the lowest known for pulsars with spin-down properties similar to those of PSR J1906+0746. We have also tentatively detected a puzzling extended structure which looks like a tilted ring with a radius of 1.'6 centered on the pulsar. The measured 0.5-8 keV flux of the feature, {approx}3.1 x 10{sup -14} erg cm{sup -2}more » s{sup -1}, implies an unabsorbed luminosity of 1.2 x 10{sup 32} erg s{sup -1} (4.5 x 10{sup -4} of the pulsar's E-dot) for n {sub H} = 0.7 x 10{sup 22} cm{sup -2}. If the ring is not a peculiar noise artifact, the pulsar wind nebula around an unusually underluminous pulsar would be the most plausible interpretation.« less
VISCOUS BOUNDARY LAYERS OF RADIATION-DOMINATED, RELATIVISTIC JETS. II. THE FREE-STREAMING JET MODEL

We analyze the interaction of a radiation-dominated jet and its surroundings using the equations of radiation hydrodynamics in the viscous limit. In a previous paper we considered the two-stream scenario, which treats the jet and its surroundings as distinct media interacting through radiation viscous forces. Here we present an alternative boundary layer model, known as the free-streaming jet model—where a narrow stream of fluid is injected into a static medium—and present solutions where the flow is ultrarelativistic and the boundary layer is dominated by radiation. It is shown that these jets entrain material from their surroundings and that their cores have a lower density of scatterers and a harder spectrum of photons, leading to observational consequences for lines of sight that look "down the barrel of the jet." These jetted outflow models may be applicable to the jets produced during long gamma-ray bursts and super-Eddington phases of tidal disruption events.
Implicit Asymptotic Preserving Method for Linear Transport Equations

The computation of the radiative transfer equation is expensive mainly due to two stiff terms: the transport term and the collision operator. The stiffness in the former comes from the fact that particles (such as photons) travel at the speed of light, while that in the latter is due to the strong scattering in the optically thick region. We study the fully implicit scheme for this equation to account for the stiffness. The main challenge in the implicit treatment is the coupling between the spacial and angular coordinates that requires the large size of the to-be-inverted matrix, which is also ill-conditioned and not necessarily symmetric. Our main idea is to utilize the spectral structure of the ill-conditioned matrix to construct a pre-conditioner, which, along with an exquisite split of the spatial and angular dependence, significantly improve the condition number and allows a matrix-free treatment. We also design a fast solver to compute this pre-conditioner explicitly in advance. Our method is shown to be efficient in both diffusive and free streaming limit, and the computational cost is comparable to the state-of-the-art method. Various examples including anisotropic scattering and two-dimensional problems are provided to validate the effectiveness of our method.
ON THE SATURATION OF THE MAGNETOROTATIONAL INSTABILITY VIA PARASITIC MODES

We investigate the stability of incompressible, exact, non-ideal magnetorotational (MRI) modes against parasitic instabilities. Both Kelvin-Helmholtz and tearing-mode parasitic instabilities may occur in the dissipative regimes accessible to current numerical simulations. We suppose that a primary MRI mode saturates at an amplitude such that its fastest parasite has a growth rate comparable to its own. The predicted alpha parameter then depends critically on whether the fastest primary and parasitic modes fit within the computational domain and whether non-axisymmetric parasitic modes are allowed. Hence, even simulations that resolve viscous and resistive scales may not saturate properly unless the numerical domain is large enough to allow the free evolution of both MRI and parasitic modes. To minimally satisfy these requirements in simulations with vertical background fields, the vertical extent of the domain should accommodate the fastest growing MRI mode while the radial and azimuthal extents must be twice as large. The fastest parasites have horizontal wavelengths roughly twice as long as the vertical wavelength of the primary.