cssi/SciSciGPT-SciSciNet huggingface dataset:
Quantum Lightning Never Strikes the Same State Twice

Public key quantum money can be seen as a version of the quantum no-cloning theorem that holds even when the quantum states can be verified by the adversary. In this work, we investigate quantum lightning where no-cloning holds even when the adversary herself generates the quantum state to be cloned. We then study quantum money and quantum lightning, showing the following results: We demonstrate the usefulness of quantum lightning beyond quantum money by showing several potential applications, such as generating random strings with a proof of entropy, to completely decentralized cryptocurrency without a block-chain, where transactions is instant and local. We give Either/Or results for quantum money/lightning, showing that either signatures/hash functions/commitment schemes meet very strong recently proposed notions of security, or they yield quantum money or lightning. Given the difficulty in constructing public key quantum money, this suggests that natural schemes do attain strong security guarantees. We show that instantiating the quantum money scheme of Aaronson and Christiano [STOC’12] with indistinguishability obfuscation that is secure against quantum computers yields a secure quantum money scheme. This construction can be seen as an instance of our Either/Or result for signatures, giving the first separation between two security notions for signatures from the literature. Finally, we give a plausible construction for quantum lightning, which we prove secure under an assumption related to the multi-collision resistance of degree-2 hash functions. Our construction is inspired by our Either/Or result for hash functions, and yields the first plausible standard model instantiation of a non-collapsing collision resistant hash function. This improves on a result of Unruh [Eurocrypt’16] which is relative to a quantum oracle.
Rational Cohomology and Supports for Linear Algebraic Groups

What follows are rough “notes” based upon four lectures given by the author at PIMS in Vancouver over the period June 27–June 30, 2016.
Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression

In this paper, we propose a random projection approach to estimate variance in kernel ridge regression. Our approach leads to a consistent estimator of the true variance, while being computationally more efficient. Our variance estimator is optimal for a large family of kernels, including cubic splines and Gaussian kernels. Simulation analysis is conducted to support our theory.
Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization

Abstract We study the question of reconstructing two signals f and g from their convolution y = f ⁎ g . This problem, known as blind deconvolution, pervades many areas of science and technology, including astronomy, medical imaging, optics, and wireless communications. A key challenge of this intricate non-convex optimization problem is that it might exhibit many local minima. We present an efficient numerical algorithm that is guaranteed to recover the exact solution, when the number of measurements is (up to log-factors) slightly larger than the information-theoretical minimum, and under reasonable conditions on f and g. The proposed regularized gradient descent algorithm converges at a geometric rate and is provably robust in the presence of noise. To the best of our knowledge, our algorithm is the first blind deconvolution algorithm that is numerically efficient, robust against noise, and comes with rigorous recovery guarantees under certain subspace conditions. Moreover, numerical experiments do not only provide empirical verification of our theory, but they also demonstrate that our method yields excellent performance even in situations beyond our theoretical framework.
MegaDepth: Learning Single-View Depth Prediction from Internet Photos

Single-view depth prediction is a fundamental problem in computer vision. Recently, deep learning methods have led to significant progress, but such methods are limited by the available training data. Current datasets based on 3D sensors have key limitations, including indoor-only images (NYU), small numbers of training examples (Make3D), and sparse sampling (KITTI). We propose to use multi-view Internet photo collections, a virtually unlimited data source, to generate training data via modern structure-from-motion and multi-view stereo (MVS) methods, and present a large depth dataset called MegaDepth based on this idea. Data derived from MVS comes with its own challenges, including noise and unreconstructable objects. We address these challenges with new data cleaning methods, as well as automatically augmenting our data with ordinal depth relations generated using semantic segmentation. We validate the use of large amounts of Internet data by showing that models trained on MegaDepth exhibit strong generalization-not only to novel scenes, but also to other diverse datasets including Make3D, KITTI, and DIW, even when no images from those datasets are seen during training.1
Minimizing closed geodesics via critical points of the uniform energy

In this paper we study 1/k-geodesics, those closed geodesics that minimize on any subinterval of length $l(\gamma)/k$. We employ energy methods to provide a relationship between the 1/k-geodesics and what we define as the balanced points of the uniform energy. We show that classes of balanced points of the uniform energy persist under the Gromov-Hausdorff convergence of Riemannian manifolds. Additionally, we relate half-geodesics (1/2-geodesics) to the Grove-Shiohama critical points of the distance function. This relationship affords us the ability to study the behavior of half-geodesics via the well developed field of critical point theory. Along the way we provide a complete characterization of the differentiability of the Riemannian distance function.
Piercing numbers in approval voting

Abstract We survey a host of results from discrete geometry that have bearing on the analysis of geometric models of approval voting. Such models view the political spectrum as a geometric space, with geometric constraints on voter preferences. Results on piercing numbers then have a natural interpretation in voting theory, and we survey their implications for various classes of geometric constraints on voter approval sets.
A Canonical Hidden-Variable Space

Abstract The hidden-variable question is whether or not various properties — randomness or correlation, for example — that are observed in the outcomes of an experiment can be explained via introduction of extra (hidden) variables which are unobserved by the experimenter. The question can be asked in both the classical and quantum domains. In the latter, it is fundamental to the interpretation of the quantum formalism (Bell [1] , Kochen and Specker [9] , and others). In building a suitable mathematical model of an experiment, the physical set-up will guide us on how to model the observable variables — i.e., the measurement and outcome spaces. But, by definition, we cannot know what structure to put on the hidden-variable space. Nevertheless, we show that, under a measure-theoretic condition, the hidden-variable question can be put into a canonical form. The condition is that the σ-algebras on the measurement and outcome spaces are countably generated. An argument using a classical result on isomorphisms of measure algebras then shows that the hidden-variable space can always be taken to be the unit interval equipped with the Lebesgue measure on the Borel sets.
On the Number of Rich Lines in Truly High Dimensional Sets

We prove a new upper bound on the number of r-rich lines (lines with at least r points) in a 'truly' d-dimensional configuration of points v_1,...,v_n over the complex numbers. More formally, we show that, if the number of r-rich lines is significantly larger than n^2/r^d then there must exist a large subset of the points contained in a hyperplane. We conjecture that the factor r^d can be replaced with a tight r^{d+1}. If true, this would generalize the classic Szemeredi-Trotter theorem which gives a bound of n^2/r^3 on the number of r-rich lines in a planar configuration. This conjecture was shown to hold in R^3 in the seminal work of Guth and Katz and was also recently proved over R^4 (under some additional restrictions) by Solomon and Sharir. For the special case of arithmetic progressions (r collinear points that are evenly distanced) we give a bound that is tight up to lower order terms, showing that a d-dimensional grid achieves the largest number of r-term progressions. The main ingredient in the proof is a new method to find a low degree polynomial that vanishes on many of the rich lines. Unlike previous applications of the polynomial method, we do not find this polynomial by interpolation. The starting observation is that the degree r-2 Veronese embedding takes r-collinear points to r linearly dependent images. Hence, each collinear r-tuple of points, gives us a dependent r-tuple of images. We then use the design-matrix method of Barak et al. to convert these 'local' linear dependencies into a global one, showing that all the images lie in a hyperplane. This then translates into a low degree polynomial vanishing on the original set.
Characterization of lemniscate atmospheric aberrations in Gemini Planet Imager data

A semi analytic framework for simulating the effects of atmospheric seeing in Adaptive Optics systems on an 8-m telescope is developed with the intention of understanding the origin of the wind-butterfly, a characteristic twolobed halo in the PSF of AO imaging. Simulations show that errors in the compensated phase on the aperture due to servo-lag have preferential direction orthogonal to the direction of wind propagation which, when Fourier Transformed into the image plane, appear with their characteristic lemniscate shape along the wind direction. We develop a metric to quantify the effect of this aberration with the fractional standard deviation in an annulus centered around the PSF, and use telescope pointing to correlate this effect with data from an atmospheric models, the NOAA GFS. Our results show that the jet stream at altitudes of 100-200 hPa (equivalently 10-15 km above sea level) is highly correlated (13.2σ) with the strong butterfly, while the ground wind and other layers are more or less uncorrelated.
Local cohomology modules supported at determinantal ideals

We provide new results on the vanishing of local cohomology modules supported at ideals of minors of matrices over arbitrary commutative Noetherian rings. In the process, we compute the local cohomology of rings of polynomials with integer coefficients---supported at generic determinantal ideals---and also obtain results on F-modules and D-modules that are likely to be of independent interest.
Robustness and games against nature in molecular programming

Matter, especially DNA, is now programmed to carry out useful processes at the nanoscale. As these programs and processes become more complex and their envisioned safety-critical applications approach deployment, it is essential to develop methods for engineering trustworthiness into molecular programs. Some of this can be achieved by adapting existing software engineering methods, but molecular programming also presents new challenges that will require new methods. This paper presents a method for dealing with one such challenge, namely, the difficulty of ascertaining how robust a molecular program is to perturbations of the relative "clock speeds" of its various reactions. The method proposed here is game-theoretic. The robustness of a molecular program is quantified in terms of its ability to win (achieve its original objective) in games against other molecular programs that manipulate its relative clock speeds. This game-theoretic approach is general enough to quantify the security of a molecular program against malicious manipulations of its relative clock speeds. However, this preliminary report focuses on games against nature, games in which the molecular program's opponent perturbs clock speeds randomly (indifferently) according to the probabilities inherent in chemical kinetics.
Context Models for OOV Word Translation in Low-Resource Languages

Out-of-vocabulary word translation is a major problem for the translation of low-resource languages that suffer from a lack of parallel training data. This paper evaluates the contributions of target-language context models towards the translation of OOV words, specifically in those cases where OOV translations are derived from external knowledge sources, such as dictionaries. We develop both neural and non-neural context models and evaluate them within both phrase-based and self-attention based neural machine translation systems. Our results show that neural language models that integrate additional context beyond the current sentence are the most effective in disambiguating possible OOV word translations. We present an efficient second-pass lattice-rescoring method for wide-context neural language models and demonstrate performance improvements over state-of-the-art self-attention based neural MT systems in five out of six low-resource language pairs.
Learning Structural Weight Uncertainty for Sequential Decision-Making

Learning probability distributions on the weights of neural networks (NNs) has recently proven beneficial in many applications. Bayesian methods, such as Stein variational gradient descent (SVGD), offer an elegant framework to reason about NN model uncertainty. However, by assuming independent Gaussian priors for the individual NN weights (as often applied), SVGD does not impose prior knowledge that there is often structural information (dependence) among weights. We propose efficient posterior learning of structural weight uncertainty, within an SVGD framework, by employing matrix variate Gaussian priors on NN parameters. We further investigate the learned structural uncertainty in sequential decision-making problems, including contextual bandits and reinforcement learning. Experiments on several synthetic and real datasets indicate the superiority of our model, compared with state-of-the-art methods.
Top-N recommender system via matrix completion

Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.
Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments

We present P lume L ocalization under U ncertainty using M aximum-Valu E information and S earch (PLUMES), a planner for localizing and collecting samples at the global maximum of an a priori unknown and partially observable continuous environment. This “maximum seek-and-sample” (MSS) problem is pervasive in the environmental and earth sciences. Experts want to collect scientifically valuable samples at an environmental maximum (e.g., an oil-spill source), but do not have prior knowledge about the phenomenon's distribution. We formulate the MSS problem as a partially-observable Markov decision process (POMDP) with continuous state and observation spaces, and a sparse reward signal. To solve the MSS POMDP, PLUMES uses an information-theoretic reward heuristic with continuous-observation Monte Carlo Tree Search to efficiently localize and sample from the global maximum. In simulation and field experiments, PLUMES collects more scientifically valuable samples than state-of-the-art planners in a diverse set of environments, with various platforms, sensors, and challenging real-world conditions.
Narrowband Channel Estimation for Hybrid Beamforming Millimeter Wave Communication Systems with One-Bit Quantization

Millimeter wave (mmWave) spectrum has drawn attention due to its tremendous available bandwidth. The high propagation losses in the mmWave bands necessitate beamforming with a large number of antennas. Traditionally each antenna is paired with a high-speed analog-to-digital converter (ADC), which results in high power consumption. A hybrid beamforming architecture and one-bit resolution ADCs have been proposed to reduce power consumption. However, analog beamforming and one-bit quantization make channel estimation more challenging. In this paper, we propose a narrowband channel estimation algorithm for mmWave communication systems with one-bit ADCs and hybrid beamforming based on generalized approximate message passing (GAMP). We show through simulation that 1) GAMP variants with one-bit ADCs have better performance than do least-squares estimation methods without quantization, 2) the proposed one-bit GAMP algorithm achieves the lowest estimation error among the GAMP variants, and 3) exploiting more frames and RF chains enhances the channel estimation performance.
Presupernova neutrinos: realistic emissivities from stellar evolution

We present a new calculation of neutrino emissivities and energy spectra from a massive star going through the advanced stages of nuclear burning (presupernova) in the months before becoming a supernova. The contributions from β decay and electron capture, pair annihilation, plasmon decay, and the photoneutrino process are modeled in detail, using updated tabulated nuclear rates. We also use realistic conditions of temperature, density, electron fraction, and nuclear isotopic composition of the star from the state-of-the-art stellar evolution code MESA. Results are presented for a set of progenitor stars with mass between 15 M and 30 M. It is found that β processes contribute substantially to the neutrino emissivity above realistic detection thresholds of few MeV, at selected positions and times in the evolution of the star.
Trajectory Optimization in UAV-Assisted Cellular Networks under Mission Duration Constraint

In this paper, we address the problem of finding the optimal trajectory for an unmanned aerial vehicle (UAV) for improving the wireless coverage of a terrestrial cellular network. In particular, we consider a UAV that is tasked to travel from one point to another within a given time constraint, and it can also simultaneously assist the cellular network by providing wireless coverage during its mission. Considering an interference limited downlink of a cellular network, we formulate an optimization problem for maximizing the proportional-fair (PF) data rate of the cellular network and explore dynamic programming (DP) technique for finding the optimum UAV trajectory. We also explore the optimal UAV trajectories associated with maximum sum-rate and 5th percentile spectral efficiency (5pSE) rate and compare the capacity and coverage performance of the three approaches. Numerical simulations show that the maximum sum-rate trajectory provides the best per user capacity whereas, the optimal PF trajectory yields higher coverage probability than the other two trajectories. The optimal trajectories are generally infeasible to follow exactly as the UAVs can not take sharp turns due to kinematic constraints. Hence, we generate smooth trajectories using Bezier curve.
Images of 2-adic representations associated to hyperelliptic Jacobians

Abstract Text Let k be a subfield of C which contains all 2-power roots of unity, and let K = k ( α 1 , α 2 , … , α 2 g + 1 ) , where the α i 's are independent and transcendental over k , and g is a positive integer. We investigate the image of the 2-adic Galois action associated to the Jacobian J of the hyperelliptic curve over K given by y 2 = ∏ i = 1 2 g + 1 ( x − α i ) . Our main result states that the image of Galois in Sp ( T 2 ( J ) ) coincides with the principal congruence subgroup Γ ( 2 ) ◁ Sp ( T 2 ( J ) ) . As an application, we find generators for the algebraic extension K ( J [ 4 ] ) / K generated by coordinates of the 4-torsion points of J . Video For a video summary of this paper, please visit http://youtu.be/VXEGYxA6N8w .
Analysis of convergence rates of some Gibbs samplers on continuous state spaces

Abstract We use a non-Markovian coupling and small modifications of techniques from the theory of finite Markov chains to analyze some Markov chains on continuous state spaces. The first is a generalization of a sampler introduced by Randall and Winkler, and the second a Gibbs sampler on narrow contingency tables.
Space-time FLAVORS: finite difference, multisymlectic, and pseudospectral integrators for multiscale PDEs

We present a new class of integrators for stiff PDEs. These integrators are generalizations of FLow AVeraging integratORS (FLAVORS) for stiff ODEs and SDEs introduced in [32] with the following properties: (i) Multiscale: they are based on flow averaging and have a computational cost determined by mesoscopic steps in space and time instead of microscopic steps in space and time; (ii) Versatile: the method is based on averaging the flows of the given PDEs (which may have hidden slow and fast processes). This bypasses the need for identifying explicitly (or numerically) the slow variables or reduced effective PDEs; (iii) Nonintrusive: A pre-existing numerical scheme resolving the microscopic time scale can be used as a black box and easily turned into one of the integrators in this paper by turning the large coefficients on over a microscopic timescale and off during a mesoscopic timescale; (iv) Convergent over two scales: strongly over slow processes and in the sense of measures over fast ones; (v) Structure-preserving: for stiff Hamiltonian PDEs (possibly on manifolds), they can be made to be multi-symplectic, symmetry-preserving (symmetries are group actions that leave the system invariant) in all variables and variational.
Securing Infrastructure Facilities: When Does Proactive Defense Help?

Infrastructure systems are increasingly facing new security threats due to the vulnerabilities of cyber-physical components that support their operation. In this article, we investigate how the infrastructure operator (defender) should prioritize the investment in securing a set of facilities in order to reduce the impact of a strategic adversary (attacker) who can target a facility to increase the overall usage cost of the system. We adopt a game-theoretic approach to model the defender-attacker interaction and study two models: normal form game—where both players move simultaneously—and sequential game—where attacker moves after observing the defender’s strategy. For each model, we provide a complete characterization of how the set of facilities that are secured by the defender in equilibrium vary with the costs of attack and defense. Importantly, our analysis provides a sharp condition relating the cost parameters for which the defender has the first-mover advantage. Specifically, we show that to fully deter the attacker from targeting any facility, the defender needs to proactively secure all “vulnerable facilities” at an appropriate level of effort. We illustrate the outcome of the attacker–defender interaction on a simple transportation network. We also suggest a dynamic learning setup to understand how this outcome can affect the ability of imperfectly informed users to make their decisions about using the system in the post-attack stage.
The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition

Negators, modals, and degree adverbs can significantly affect the sentiment of the words they modify. Often, their impact is modeled with simple heuristics; although, recent work has shown that such heuristics do not capture the true sentiment of multi-word phrases. We created a dataset of phrases that include various negators, modals, and degree adverbs, as well as their combinations. Both the phrases and their constituent content words were annotated with real-valued scores of sentiment association. Using phrasal terms in the created dataset, we analyze the impact of individual modifiers and the average effect of the groups of modifiers on overall sentiment. We find that the effect of modifiers varies substantially among the members of the same group. Furthermore, each individual modifier can affect sentiment words in different ways. Therefore, solutions based on statistical learning seem more promising than fixed hand-crafted rules on the task of automatic sentiment prediction.
Grothendieck-type inequalities in combinatorial optimization

We survey connections of the Grothendieck inequality and its variants to combinatorial optimization and computational complexity. © 2011 Wiley Periodicals, Inc.
Towards provably safe mixed transportation systems with human-driven and automated vehicles

Currently we are in an environment where the fraction of automated vehicles is negligibly small. We anticipate that this fraction will increase in the coming decades before, if ever, we have a fully automated transportation system. Motivated by this we address the problem of provable safety of mixed traffic consisting of both intelligent vehicles (IVs) as well as human driven vehicles (HVs). An important issue that arises is that such mixed systems may well have lesser throughput than all-human traffic systems, if the automated vehicles are expected to remain provably safe with respect to human traffic. This necessitates the consideration of strategies such as platooning of automated vehicles in order to increase the throughput. In this paper we address the design of provably safe systems consisting of a mix of automated and human-driven vehicles including the use of platooning by automated vehicles. We design motion planning policies and coordination rules for participants in this novel mixed system. HVs are considered as nearsighted and modeled with relatively loose constraints, while IVs are considered as capable of following much tighter constraints. HVs are expected to follow reasonable and simple rules. IVs are designed to move under a model predictive control (MPC) based motion plans and coordination protocols. Our contribution in this paper is in showing how to integrate these two types of models safely into a mixed system. System safety is proved in single lane scenarios, as well as in multi-lane situations allowing lane changes.
A note on surgery obstructions and hyperbolic integer homology spheres

Auckly gave two examples of irreducible integer homology spheres (one toroidal and one hyperbolic) which are not surgery on a knot in the three-sphere. Using Heegaard Floer homology, the authors and Karakurt provided infinitely many small Seifert fibered examples. In this note, we extend those results to give infinitely many hyperbolic examples, as well as infinitely many examples with arbitrary JSJ decomposition.
Mechanisms of multi-strain coexistence in host-phage systems with nested infection networks

Abstract Bacteria and their viruses (bacteriophages) coexist in natural environments forming complex infection networks. Recent empirical findings suggest that phage–bacteria infection networks often possess a nested structure such that there is a hierarchical relationship among who can infect whom. Here we consider how nested infection networks may affect phage and bacteria dynamics using a multi-type Lotka–Volterra framework with cross-infection. Analysis of similar models has, in the past, assumed simpler interaction structures as a first step towards tractability. We solve the proposed model, finding trade-off conditions on the life-history traits of both bacteria and viruses that allow coexistence in communities with nested infection networks. First, we find that bacterial growth rate should decrease with increasing defense against infection. Second, we find that the efficiency of viral infection should decrease with host range. Next, we establish a relationship between relative densities and the curvature of life history trade-offs. We compare and contrast the current findings to the “Kill-the-Winner” model of multi-species phage–bacteria communities. Finally, we discuss a suite of testable hypotheses stemming from the current model concerning relationships between infection range, life history traits and coexistence in complex phage–bacteria communities.
adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs

Recurrent Neural Networks, or RNNs, are powerful models that achieve exceptional performance on a plethora pattern recognition problems. However, the training of RNNs is a computationally difficult task owing to the well-known "vanishing/exploding" gradient problem. Algorithms proposed for training RNNs either exploit no or limited curvature information and have cheap per-iteration complexity, or attempt to gain significant curvature information at the cost of increased per-iteration cost. The former set includes diagonally-scaled first-order methods such as Adagrad and Adam, while the latter consists of second-order algorithms like Hessian-Free Newton and K-FAC. In this paper, we present adaQN, a stochastic quasi-Newton algorithm for training RNNs. Our approach retains a low per-iteration cost while allowing for non-diagonal scaling through a stochastic L-BFGS updating scheme. The method uses a novel L-BFGS scaling initialization scheme and is judicious in storing and retaining L-BFGS curvature pairs. We present numerical experiments on two language modeling tasks and show that adaQN is competitive with popular RNN training algorithms.
The Kauffman bracket skein module of two-bridge links

We calculate the Kauffman bracket skein module (KBSM) of the complement of all two-bridge links. For a two-bridge link, we show that the KBSM of its complement is free over the ring $\BC[t^{\pm 1}]$ and when reducing $t=-1$, it is isomorphic to the ring of regular functions on the character variety of the link group.
Optimal Design and Analysis of the Exponentially Weighted Moving Average Chart for Exponential Data

We study optimal design of the ExponentiallyWeighted Moving Average (EWMA) chart by a proper choice of the smoothing factor and the initial value (headstart) of the decision statistic. The particular problem addressed is that of quickest detection of an abrupt change in the parameter of a discrete-time exponential model. Both pre- and post-change parameter values are assumed known, but the change-point is not known. For this change-point detection scenario, we examine the performance of the conventional one-sided EWMA chart with respect to two optimality criteria: Pollak’s minimax criterion associated with the maximal conditional expected delay to detection and Shiryaev’s multi-cyclic setup associated with the stationary expected delay to detection. Using the integral-equations approach, we derive the exact closed-form formulae for all of the required performance measures. Based on these formulae we find the optimal smoothing factor and headstart by solving the corresponding two bivariate constraint optimization problems. Finally, the performance of the optimized EWMA chart is compared against that of the Shiryaev–Roberts–r procedure in the minimax setting, and against that of the original Shiryaev–Roberts procedure in the multi-cyclic setting. The main conclusion is that the EWMA chart, when fully optimized, turns out to be a very competitive procedure, with performance nearly indistinguishable from that of the known-to-be best Shiryaev–Roberts–r and Shiryaev–Roberts procedures. DOI: http://dx.doi.org/10.4038/sljastats.v5i4.7784
Thurston norms of tunnel number-one manifolds

The Thurston norm of a three-manifold measures the complexity of surfaces representing two-dimensional homology classes. We study the possible unit balls of Thurston norms of three-manifolds M with...
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGRAPHEMB, is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner. The learned neural network can be considered as a function that receives any graph as input, either seen or unseen in the training set, and transforms it into an embedding. A novel graph-level embedding generation mechanism called Multi-Scale Node Attention (MSNA), is proposed. Experiments on five real graph datasets show that UGRAPHEMB achieves competitive accuracy in the tasks of graph classification, similarity ranking, and graph visualization.
Simulation of Programmable Matter Systems Using Active Tile-Based Self-Assembly

Self-assembly refers to the process by which small, simple components mix and combine to form complex structures using only local interactions. Designed as a hybrid between tile assembly models and cellular automata, the Tile Automata (TA) model was recently introduced as a platform to help study connections between various models of self-assembly. However, in this paper we present a result in which we use TA to simulate arbitrary systems within the amoebot model, a theoretical model of programmable matter in which the individual components are relatively simple state machines that are able to sense the states of their neighbors and to move via series of expansions and contractions.
β-Fluorofentanyls Are pH-Sensitive Mu Opioid Receptor Agonists.

The concept recently postulated by Stein and co-workers (Science2017, 355, 966) that mu opioid receptor (MOR) agonists possessing amines with attenuated basicity show pH-dependent activity and can selectively act at damaged, low pH tissues has been additionally supported by in vitro studies reported here. We synthesized and tested analogs of fentanyl possessing one or two fluorine atoms at the beta position of the phenethylamine side chain, with additional fluorines optionally added to the benzene ring of the side chain. These compounds were synthesized in 1 to 3 steps from commercial building blocks. The novel bis-fluorinated analog RR-49 showed superior pH sensitivity, with full efficacy relative to DAMGO, but with 19-fold higher potency (IC50) in a MOR cAMP assay at pH 6.5 versus 7.4. Such compounds hold significant promise as analgesics for inflammatory pain with reduced abuse potential.
Fiscal Rules for the Western Balkans

Policy toward fiscal rules is an important issue in the countries of the Western Balkans (Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia, and Serbia). According to a rough estimate, the countries with rules (all but North Macedonia) have complied with their debt and overall-deficit rules a little more than half the time. An online survey, conducted for this paper, suggests that public understanding of the rules is limited, which may reduce the political pressure for compliance. To get debt down to prudent levels, Albania and Montenegro will need a strong commitment to complying with their fiscal rules and will often have to do more than their deficit rules require. The following principles should guide future policy toward fiscal rules: more emphasis should be given to ensuring that fiscal rules are widely understood and enjoy the support of a broad range of stakeholders; policy toward the rules should be consistent with accession to the European Union, but the rules should be simpler than the European Union's and the debt limits lower; limits in rules should not be mistaken for targets; and public financial management should be improved to support the implementation of rules.
Cross-Attention End-to-End ASR for Two-Party Conversations.

We present an end-to-end speech recognition model that learns interaction between two speakers based on the turn-changing information. Unlike conventional speech recognition models, our model exploits two speakers' history of conversational-context information that spans across multiple turns within an end-to-end framework. Specifically, we propose a speaker-specific cross-attention mechanism that can look at the output of the other speaker side as well as the one of the current speaker for better at recognizing long conversations. We evaluated the models on the Switchboard conversational speech corpus and show that our model outperforms standard end-to-end speech recognition models.
Combinatorial design of chemical-dependent protein switches for controlling intracellular electron transfer

One challenge with controlling electron flow in cells is the lack of biomolecules that directly couple the sensing of environmental conditions to electron transfer efficiency. To overcome this protein component limitation, we randomly inserted the ligand binding domain (LBD) from the human estrogen receptor (ER) into a thermostable 2Fe-2S ferredoxin (Fd) from Mastigocladus laminosus and used a bacterial selection to identify Fd-LBD fusion proteins that support electron transfer from a Fd-NADP reductase (FNR) to a Fd-dependent sulfite reductase (SIR). Mapping LBD insertion sites onto structure revealed that Fd tolerates domain insertion adjacent to or within the tetracysteine motif that coordinates the 2Fe-2S metallocluster. With both classes of the fusion proteins, cellular ET was enhanced by the ER antagonist 4-hydroxytamoxifen. In addition, one of Fds arising from ER-LBD insertion within the tetracysteine motif acquires an oxygen-tolerant 2Fe-2S cluster, suggesting that ET is regulated through post-translational ligand binding.
FinTech, BigTech, and the Future of Banks

Banks are unique in that they combine the production of liquid claims with loans. They can replicate most of what FinTech firms can do, but FinTech firms benefit from an uneven playing field in that they are less regulated than banks. The uneven playing field enables non-bank FinTech firms to challenge banks for specific products whose success is not tied to what makes banks unique, but they cannot replace banks as such. In contrast, BigTech firms have unique advantages that banks cannot easily replicate and therefore present a much stronger challenge to established banks in consumer finance and loans to small firms. Both Fintech and BigTech are contributing to a secular trend of banks losing their comparative advantage as they have less access to unique information about parties seeking credit.
Evaluation of omeprazole and lansoprazole as inhibitors of cytochrome P450 isoforms

The human clearance of omeprazole and lansoprazole is conducted primarily by the hepatic cytochrome P450 (CYP) system. Efficacy data indicate few differences between these two drugs, but they may exhibit discrete drug interaction profiles. To compare the potency and specificity of these drugs as inhibitors of CYP isoforms, we performed in vitro studies with human liver microsomal preparations. Both drugs were potent, competitive inhibitors of CYP2C19, as measured by the conversion of S-mephenytoin to 4-hydroxymephenytoin (k(i) = 3.1 +/- 2.2 microM for omeprazole, K(i) = 3.2 +/- 1.3 microM for lansoprazole). For omeprazole, the highest concentration at which >70% inhibition of CYP2C19 was observed with no significant inhibitory effect on other isoforms was at least 20 times greater than K(i). Both drugs were competitive inhibitors of CYP2C9-catalyzed conversion of tolbutamide to 4-hydroxytolbutamide (K(i) = 40.1 +/- 14.8 microM for omeprazole, K(i) = 52.1 +/- 1.4 microM for lansoprazole) and were noncompetitive inhibitors of CYP3A-catalyzed conversion of dextromethorphan to 3-methoxymorphinan (K(i) = 84.4 +/- 4.0 microM for omeprazole, K(i) = 170.4 +/- 7.1 microM for lansoprazole). Lansoprazole was at least 5 times more potent (K(i) = 44.7 +/- 22.0 microM) than omeprazole (k(i) = 240.7 +/- 102.0 microM) as an inhibitor of CYP2D6-mediated conversion of dextromethorphan to dextrorphan. No inhibition of CYP1A2, assessed by measuring the conversion of phenacetin to acetaminophen, was noted. Our data suggest that whereas the inhibitory profiles of these two drugs are similar, lansoprazole may be the more important in vitro inhibitor of CYP2D6. Since its inhibition is very potent and has a broad "window of selectivity," omeprazole seems to be a useful, selective inhibitor of CYP2C19.
Legal professional perspectives on barriers and supports for school-aged students and families during reunification from foster care

Abstract Backgroud Supports following reunification play an important role in successful transitions from out-of-home care to home and school settings for children and their families. There are several stakeholders who play critical roles supporting families during this transition. One key role is tied to legal professionals, who often work closely with the child or family while in care and then continue to monitor family progress during the transition from foster care to the home setting. Although work has been conducted to understand what supports other stakeholders feel children and families need during this transition, little is known about the perspectives of legal professionals. Objective This exploratory study gathered perspectives from legal professionals regarding the challenges these youths and families face and necessary supports to promote successful reunification. Three research questions guided this work: (1) How prepared do legal professionals feel youth and families are for the reunification period? (2) What do legal professionals perceive as existing barriers for youth and families during reunification? and (3) What services do legal professional perceive as necessary to promote successful reunification and the effectiveness of existing supports? Methods Participants (N = 13) completed a brief survey including demographic items and questions on reunification supports following departure from foster care. Participants also engaged in a focus group using the nominal group technique to address two primary questions that addressed challenges faced by families during reunification and necessary supports or services to promote positive youth and family outcomes. Results Participants felt that families were not well prepared for reunification and that current supports were somewhat effective to support this transition period. They also indicated supports in the mental health domain to be most important. A total of 36 independent barriers were generated for the first question and 27 supports were identified for question two. Conclusion Establishing effective and accessible supports for families during reunification is necessary for positive family outcomes. This study highlighted barriers in preventing successful reunification, namely limited access to resources and supports in preventing successful reunification and the importance of mental health support for the entire family in promoting successful outcomes.
Joins in the strong Weihrauch degrees

The Weihrauch degrees and strong Weihrauch degrees are partially ordered structures representing degrees of unsolvability of various mathematical problems. Their study has been widely applied in computable analysis, complexity theory, and more recently, also in computable combinatorics. We answer an open question about the algebraic structure of the strong Weihrauch degrees, by exhibiting a join operation that turns these degrees into a lattice. Previously, the strong Weihrauch degrees were only known to form a lower semi-lattice. We then show that unlike the Weihrauch degrees, which are known to form a distributive lattice, the lattice of strong Weihrauch degrees is not distributive. Therefore, the two structures are not isomorphic.
RaGOO: fast and accurate reference-guided scaffolding of draft genomes

We present RaGOO, a reference-guided contig ordering and orienting tool that leverages the speed and sensitivity of Minimap2 to accurately achieve chromosome-scale assemblies in minutes. After the pseudomolecules are constructed, RaGOO identifies structural variants, including those spanning sequencing gaps. We show that RaGOO accurately orders and orients 3 de novo tomato genome assemblies, including the widely used M82 reference cultivar. We then demonstrate the scalability and utility of RaGOO with a pan-genome analysis of 103 Arabidopsis thaliana accessions by examining the structural variants detected in the newly assembled pseudomolecules. RaGOO is available open source at https://github.com/malonge/RaGOO .
Isolation of Cardiomyocytes Undergoing Mitosis With Complete Cytokinesis.

Rationale: Adult human cardiomyocytes do not complete cytokinesis despite passing through the S-phase of the cell cycle. As a result, polyploidization and multinucleation occur. To get a deeper und...
Neurotropism of Enterovirus D68 Isolates Is Independent of Sialic Acid and Is Not a Recently Acquired Phenotype.

ABSTRACT Acute flaccid myelitis (AFM) is a rare but serious illness of the nervous system, specifically affecting the gray matter of the spinal cord, motor-controlling regions of the brain, and cranial nerves. Most cases of AFM are pathogen associated, typically with poliovirus and enterovirus infections, and occur in children under the age of 6 years. Enterovirus D68 (EV-D68) was first isolated from children with pneumonia in 1962, but an association with AFM was not observed until the 2014 outbreak. Organotypic mouse brain slice cultures generated from postnatal day 1 to 10 mice and adult ifnar knockout mice were used to determine if neurotropism of EV-D68 is shared among virus isolates. All isolates replicated in organotypic mouse brain slice cultures, and three isolates replicated in primary murine astrocyte cultures. All four EV-D68 isolates examined caused paralysis and death in adult ifnar knockout mice. In contrast, no viral disease was observed after intracranial inoculation of wild-type mice. Six of the seven EV-D68 isolates, including two from 1962 and four from the 2014 outbreak, replicated in induced human neurons, and all of the isolates replicated in induced human astrocytes. Furthermore, a putative viral receptor, sialic acid, is not required for neurotropism of EV-D68, as viruses replicated within neurons and astrocytes independent of binding to sialic acid. These observations demonstrate that EV-D68 is neurotropic independent of its genetic lineage and can infect both neurons and astrocytes and that neurotropism is not a recently acquired characteristic as has been suggested. Furthermore, the results show that in mice the innate immune response is critical for restricting EV-D68 disease. IMPORTANCE Since 2014, numerous outbreaks of childhood infections with enterovirus D68 (EV-D68) have occurred worldwide. Most infections are associated with flu-like symptoms, but paralysis may develop in young children. It has been suggested that infection only with recent viral isolates can cause paralysis. To address the hypothesis that EV-D68 has recently acquired neurotropism, murine organotypic brain slice cultures, induced human motor neurons and astrocytes, and mice lacking the alpha/beta interferon receptor were infected with multiple virus isolates. All EV-D68 isolates, from 1962 to the present, can infect neural cells, astrocytes, and neurons. Furthermore, our results show that sialic acid binding does not play a role in EV-D68 neuropathogenesis. The study of EV-D68 infection in organotypic brain slice cultures, induced motor neurons, and astrocytes will allow for the elucidation of the mechanism by which the virus infection causes disease.
Estimating Unknown Cycles in Geophysical Data

Examples of cyclic (periodic) behavior in geophysical data abound. In many cases the primary period is known, such as in daily measurements of rain, temperature, and sea level. However, many time series of measurements contain cycles of unknown or varying length. We consider the problem of estimating the unknown period in a time series.We review the basic methods, compare their performance through a simulation studyusing observed sea level data, apply them to an astronomical data set, and discuss generalizations of the methods.