cssi/SciSciGPT-SciSciNet huggingface dataset:
Integrands for QCD rational terms and N = 4 SYM from massive CSW rules

We use massive CSW rules to derive explicit compact expressions for integrands of rational terms in QCD with any number of external legs. Specifically, we present all-n integrands for the one-loop all-plus and one-minus gluon amplitudes in QCD. We extract the finite part of spurious external-bubble contributions systematically; this is crucial for the application of integrand-level CSW rules in theories without supersymmetry. Our approach yields integrands that are independent of the choice of CSW reference spinor even before integration. Furthermore, we present a recursive derivation of the recently proposed massive CSW-style vertex expansion for massive tree amplitudes and loop integrands on the Coulomb-branch of $ \mathcal{N} = {4} $ SYM. The derivation requires a careful study of boundary terms in all-line shift recursion relations, and provides a rigorous (albeit indirect) proof of the recently proposed construction of massive amplitudes from soft-limits of massless on-shell amplitudes. We show that the massive vertex expansion manifestly preserves all holomorphic and half of the anti-holomorphic supercharges, diagram-by-diagram, even off-shell.
Photoinduced Electron-Transfer Degenerate Cope Rearrangement of 2,5-Diaryl-1,5-hexadienes: A Cation-Radical Cyclization−Diradical Cleavage Mechanism

Under the 9,10-dicyanoanthracene (DCA)-sensitized photoinduced electron-transfer (PET) conditions, 2,5-diaryl-3,3,4,4-tetradeuterio-1,5-hexadiene (d4-1) undergoes a degenerate Cope rearrangement to give a photostationary mixture of d4-1 and its 1,1,6,6-tetradeuterio analogue d4-1‘ in 52:48. This reaction involves 1,4-diaryl-2,2,3,3-tetradeuteriocyclohexane-1,4-diyl cation radical (d4-2•+), which can be captured by molecular oxygen as 1,4-diaryl-2,3-dioxabicyclo[2.2.2]octane (3). In contrast, nonphotoinduced electron-transfer (non-PET) reaction of d4-1 with cerium(IV) ammonium nitrate similarly forms d4-2•+, but the degenerate Cope does not take place. This observed striking contrast between PET and non-PET was ascribed to the formation of 1,4-diaryl-2,2,3,3-tetradeuteriocyclohexane-1,4-diyl (d4-2) in the PET process by back-electron transfer from DCA•- to d4-2•+. The results of similar PET and non-PET reactions of 1,4-diphenyl-2,3-diazabicyclo[2.2.2]oct-2-ene (4c) and its 5,5,6,6-tetradeuterio analogue (d...
Opening of cyclopropyl ketones with SmI2. Synthesis of spirocyclic and bicyclic ketones by intramolecular trapping of an electrophile

Abstract A method for the opening of cyclopropyl ketones with samarium(II) iodide followed by the intramolecular trapping of an electrophile is described. This process leads to the obtainment of functionalized spirocyclic, bicyclic, and tricyclic ketones in moderate to good yields.
Structures of dimeric hydrolysis products of thorium.

Three unique thorium dimeric compounds have been crystallized from either direct hydrolysis of Th4+(aq)/HCl or titration of Th(OH)4(am) with Th(NO3)4(aq) and their structures determined using single-crystal X-ray diffraction. The compound [Th2(μ2-OH)2(NO3)6(H2O)6]H2O (1) is identical to that identified previously by Johansson. Two additional unreported compounds have been identified, [Th2(μ2-OH)2(NO3)4(H2O)8](NO3)2 (2) and [Th2(μ2-OH)2Cl2(H2O)12]Cl4·2H2O (3). 1 crystallizes in the monoclinic space group P21/c, with a = 6.792(2) A, b = 11.710(4) A, c = 13.778(5) A, and β = 102.714(5)° and 2 crystallizes in the monoclinic space group P21/n, with a = 6.926(5) A, b = 7.207(1) A, c = 21.502(1) A, and β = 96.380(1)°. The chloride-containing dimer, 3, crystallizes in triclinic P1, with a = 8.080(2) A, b = 8.880(2) A, c = 9.013(2) A, α = 97.41(3)°, β = 91.00(3), and γ = 116.54(3)°. We also present high-energy X-ray scattering data demonstrating the presence of the hydroxo-bridged moiety in solution and discuss o...
For a 'sociology as a team sport'.

The intellectual community of sociologists exists through engagement and debate. I am grateful to seven generous colleagues from a range of backgrounds and standpoints for taking on the challenge of responding to my 2018 British Journal of Sociology Annual Lecture – Andrew Cherlin, Claude Fischer, Margaret Frye, Eva Illouz, Giselinde Kuipers, Mike Savage and Adia Wingfield. Each of their commentaries contributes to laying out a more comprehensive and multidimensional blueprint for the study of, and solutions to, the current challenges American society faces than the one I proposed. In this sense, they all contribute de facto to the kind of ‘sociology as a team sport’ proposed by Giselinde Kuipers in her essay (in reference to ‘sociology as a combat sport’ proposed by Pierre Bourdieu). ‘Sociology as a team sport’ is one where our complementary strengths and areas of expertise define a vision for the path ahead – a programme which I wholeheartedly embrace.
Structure revision and syntheses of epohelmins A and B

[structures: see text] Epohelmins A (24) and B (26) have been reassigned as pyrrolizidin-1-ols, rather than the proposed 9-oxa-4-azabicyclo[6.1.0]nonane structures 1 and 2, respectively. Syntheses of epohelmin A (24) (eight steps, 52% overall yield) and epohelmin B (26) (11 steps, 43% overall yield) have been achieved starting from N-Cbz-(S)-prolinal (9) and ortho ester ketone 17 using a stereoselective aldol reaction and a stereoselective reductive cyclization as the key steps.
Preparation of enol ester epoxides and their ring-opening to α-silyloxyaldehydes

Abstract The Z-selective ruthenium-catalyzed addition of aromatic carboxylic acids to alkynes was followed by dioxirane epoxidation to furnish enol ester epoxides with cis configuration. Upon treatment of enol ester epoxides with tert -butyldimethylsilyl triflate in the presence of 2,6-lutidine, synthetically useful α-silyloxyaldehydes were obtained. This novel transformation was facilitated by microwave irradiation.
Cyclization of α-oxo-oximes to 2-substituted benzoxazoles

Reactions of oximes 9, 17, and 19 with electrophiles 15a-f and 24 in the presence of anhydrous potassium carbonate or triethylamine give 2-substituted condensed ring oxazoles 10, 16a-c, 18a-d, 20a-c, and 25 in a new general route to these compounds.
Using degradation-with-jump measures to estimate life characteristics of lithium-ion battery

Abstract Degradation-with-jump measures are time series data sets containing the information of both continuous and randomly jumping degradation evolution of a system. Traditional maximum likelihood estimation and Bayesian estimation are not convenient for such general jump processes without closed-form distributions. Based on general degradation models derived using Levy driven non-Gaussian Ornstein-Uhlenbeck (OU) processes, we propose a systematic statistical method using linear programing estimators and empirical characteristic functions. The point estimates of reliability function and lifetime moments are obtained by deriving their explicit expressions. We also construct bootstrap procedures for the confidence intervals. Simulation studies for a stable process and a stable driven OU process are performed. In the case study, we use a general Levy process to fit the Li-ion battery life data, and then estimate the reliability and lifetime moments of the battery. By integrally analyzing degradation data series embedded with jump measures, our work provides the efficient and precise estimation for life characteristics.
Salvinorin C, a new neoclerodane diterpene from a bioactive fraction of the hallucinogenic Mexican mint Salvia divinorum.

Salvinorin C (1), a minor component from a biologically active TLC fraction, was isolated from the leaves of the Mexican mint Salvia divinorum. Its structure was elucidated on the basis of extensive proton and C-13 NMR experiments, as well as by comparison of the NMR data with those of the mono- and diacetate derivatives 5−7 of the major NaBH4-reduction product of salvinorin A (2).
Rule of Law with Asian Characteristics: cultural insights from the Occupy Central Movement in Hong Kong

This article uses the Occupy Central social movement in Hong Kong as a natural experiment to consider whether Asian culture influences the understanding and exercise of fundamental rights. In an earlier article, the author explored the relationship between Chinese culture and the rule of law as measured by the World Justice Project Rule of Law Index. This article relies on the earlier work but expands the analysis to consider Asian culture while at the same time focusing specifically on the fundamental rights aspect of the rule of law index. This article shows a strong correlation between lower scores for Asian countries on Individualism, as measured by the Hofstede Dimensions of Culture, and scores on fundamental rights, protection for freedom expression, and protection for freedom of association, as measured by the World Justice Project Index. This correlation is reflected by the Occupy Central movement. Although the movement was an exercise in fundamental rights, its purpose was to promote the rights of the community and the protests were carried out in a manner to reduce the impact on community rights When the movement ended, the negative consequences for the community were a significant reason, and the leaders of the movement were ultimately prosecuted for inciting a public nuisance.
Predicting neighborhoods' socioeconomic attributes using restaurant data.

Accessing high-resolution, timely socioeconomic data such as data on population, employment, and enterprise activity at the neighborhood level is critical for social scientists and policy makers to design and implement location-based policies. However, in many developing countries or cities, reliable local-scale socioeconomic data remain scarce. Here, we show an easily accessible and timely updated location attribute—restaurant—can be used to accurately predict a range of socioeconomic attributes of urban neighborhoods. We merge restaurant data from an online platform with 3 microdatasets for 9 Chinese cities. Using features extracted from restaurants, we train machine-learning models to estimate daytime and nighttime population, number of firms, and consumption level at various spatial resolutions. The trained model can explain 90 to 95% of the variation of those attributes across neighborhoods in the test dataset. We analyze the tradeoff between accuracy, spatial resolution, and number of training samples, as well as the heterogeneity of the predicted results across different spatial locations, demographics, and firm industries. Finally, we demonstrate the cross-city generality of this method by training the model in one city and then applying it directly to other cities. The transferability of this restaurant model can help bridge data gaps between cities, allowing all cities to enjoy big data and algorithm dividends.
A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion

We present a computational framework for estimating the uncertainty in the numerical solution of linearized infinite-dimensional statistical inverse problems. We adopt the Bayesian inference formulation: given observational data and their uncertainty, the governing forward problem and its uncertainty, and a prior probability distribution describing uncertainty in the parameter field, find the posterior probability distribution over the parameter field. The prior must be chosen appropriately in order to guarantee well-posedness of the infinite-dimensional inverse problem and facilitate computation of the posterior. Furthermore, straightforward discretizations may not lead to convergent approximations of the infinite-dimensional problem. And finally, solution of the discretized inverse problem via explicit construction of the covariance matrix is prohibitive due to the need to solve the forward problem as many times as there are parameters. Our computational framework builds on the infinite-dimensional form...
Stein Variational Gradient Descent as Gradient Flow

Stein variational gradient descent (SVGD) is a deterministic sampling algorithm that iteratively transports a set of particles to approximate given distributions, based on a gradient-based update constructed to optimally decrease the KL divergence within a function space. This paper develops the first theoretical analysis on SVGD. We establish that the empirical measures of the SVGD samples weakly converge to the target distribution, and show that the asymptotic behavior of SVGD is characterized by a nonlinear Fokker-Planck equation known as Vlasov equation in physics. We develop a geometric perspective that views SVGD as a gradient flow of the KL divergence functional under a new metric structure on the space of distributions induced by Stein operator.
Comparison estimates for linear forms in additive number theory

Abstract Let R be a commutative ring R with 1 R and with group of units R × . Let Φ = Φ ( t 1 , … , t h ) = ∑ i = 1 h φ i t i be an h-ary linear form with nonzero coefficients φ 1 , … , φ h ∈ R . Let M be an R-module. For every subset A of M, the image of A under Φ is Φ ( A ) = { Φ ( a 1 , … , a h ) : ( a 1 , … , a h ) ∈ A h } . For every subset I of { 1 , 2 , … , h } , there is the subset sum s I = ∑ i ∈ I φ i . Let S ( Φ ) = { s I : ∅ ≠ I ⊆ { 1 , 2 , … , h } } . Theorem Let ϒ ( t 1 , … , t g ) = ∑ i = 1 g υ i t i and Φ ( t 1 , … , t h ) = ∑ i = 1 h φ i t i be linear forms with nonzero coefficients in the ring R. If { 0 , 1 } ⊆ S ( ϒ ) and S ( Φ ) ⊆ R × , then for every e > 0 and c > 1 there exist a finite R-module M with | M | > c and a subset A of M such that ϒ ( A ∪ { 0 } ) = M and | Φ ( A ) | e | M | .
Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition

The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets. Nevertheless, unsupervised image categorization (i.e., without the ground-truth labeling) is much less investigated, yet critically important and difficult when annotations are extremely hard to obtain in the conventional way of "Google Search" and crowd sourcing. We address this problem by presenting a looped deep pseudo-task optimization (LDPO) framework for joint mining of deep CNN features and image labels. Our method is conceptually simple and rests upon the hypothesized "convergence" of better labels leading to better trained CNN models which in turn feed more discriminative image representations to facilitate more meaningful clusters/labels. Our proposed method is validated in tackling two important applications: 1) Large-scale medical image annotation has always been a prohibitively expensive and easily-biased task even for well-trained radiologists. Significantly better image categorization results are achieved via our proposed approach compared to the previous state-of-the-art method. 2) Unsupervised scene recognition on representative and publicly available datasets with our proposed technique is examined. The LDPO achieves excellent quantitative scene classification results. On the MIT indoor scene dataset, it attains a clustering accuracy of 75:3%, compared to the state-of-the-art supervised classification accuracy of 81:0% (when both are based on the VGG-VD model).
Lower bounds for two-sample structural change detection in ising and Gaussian models

The change detection problem is to determine if the Markov network structures of two Markov random fields differ from one another given two sets of samples drawn from the respective underlying distributions. We study the trade-off between the sample sizes and the reliability of change detection, measured as a minimax risk, for the important cases of the Ising models and the Gaussian Markov random fields restricted to the models which have network structures with p nodes and degree at most d, and obtain information-theoretic lower bounds for reliable change detection over these models. We show that for the Ising model, Ω(d2/(log d)2 log p) samples are required from each dataset to detect even the sparsest possible changes, and that for the Gaussian, Q (γ−2 log(p)) samples are required from each dataset to detect change, where γ is the smallest ratio of off-diagonal to diagonal terms in the precision matrices of the distributions. These bounds are compared to the corresponding results in structure learning, and closely match them under mild conditions on the model parameters. Thus, our change detection bounds inherit partial tightness from the structure learning schemes in previous literature, demonstrating that in certain parameter regimes, the naive structure learning based approach to change detection is minimax optimal up to constant factors.
Universality of local eigenvalue statistics in random matrices with external source

Consider a random matrix of the form , where Mn is a Wigner matrix and Dn is a real deterministic diagonal matrix (Dn is commonly referred to as an external source in the mathematical physics literature). We study the universality of the local eigenvalue statistics of Wn for a general class of Wigner matrices Mn and diagonal matrices Dn. Unlike the setting of many recent results concerning universality, the global semicircle law fails for this model. However, we can still obtain the universal sine kernel formula for the correlation functions. This demonstrates the remarkable phenomenon that local laws are more resilient than global ones. The universality of the correlation functions follows from a four moment theorem, which we prove using a variant of the approach used earlier by Tao and Vu.
HOMOTOPICAL COMPLEXITY OF 2D BILLIARD ORBITS

Traditionally, rotation numbers for toroidal billiard flows are defined as the limiting vectors of average displacements per time on trajectory segments. Naturally, these creatures live in the (commutative) vector space ℝn, if the toroidal billiard is given on the flat n-torus. The billiard trajectories, being curves, often getting very close to closed loops, quite naturally define elements of the fundamental group of the billiard table. The simplest non-trivial fundamental group obtained this way belongs to the classical Sinai billiard, i.e. the billiard flow on the 2-torus with a single, strictly convex obstacle (with smooth boundary) removed. This fundamental group is known to be the group F2 freely generated by two elements, which is a heavily noncommutative, hyperbolic group in Gromov’s sense. We define the homotopical rotation number and the homotopical rotation set for this model, and provide lower and upper estimates for the latter one, along with checking the validity of classically expected prop...
Gemini planet imager observational calibration XIII: wavelength calibration improvements, stability, and nonlinearity

We present improvements to the wavelength calibration for the lenslet-based Integral Field Spectrograph (IFS), that serves as the science instrument for the Gemini Planet Imager (GPI). The GPI IFS features a 2.7”×2.7” field of view and a 190 x 190 lenslet array (14.1 mas/lenslet) with spectral resolving power ranging from R ~ 35 to 78. A unique wavelength solution is determined for each lenslet characterized by a two-dimensional position, an n-dimensional polynomial describing the spectral dispersion, and the rotation of the spectrum with respect to the detector axis. We investigate the non-linearity of the spectral dispersion across all Y, J, H, and K bands through both on-sky arc lamp images and simulated IFS images using a model of the optical path. Additionally, the 10-hole non-redundant masking mode on GPI provides an alternative measure of wavelength dispersion within a datacube by cross-correlating reference PSFs with science images. This approach can be used to confirm deviations from linear dispersion in the reduced datacubes. We find that the inclusion of a quadratic term provides a factor of 10 improvement in wavelength solution accuracy over the linear solution and is necessary to achieve uncertainties of a few hundredths of a pixel in J band to a few thousands of a pixel in the K bands. This corresponds to a wavelength uncertainty of ~ 0.2 nm across all filters.
Inf-sup stability of geometrically unfitted Stokes finite elements

The paper shows an inf-sup stability property for several well-known 2D and 3D Stokes elements on triangulations which are not fitted to a given smooth or polygonal domain. The property implies stability and optimal error estimates for a class of unfitted finite element methods for the Stokes and Stokes interface problems, such as Nitsche-XFEM or cutFEM. The error analysis is presented for the Stokes problem. All assumptions made in the paper are satisfied once the background mesh is shape-regular and fine enough.
On the approximate shape of degree sequences that are not potentially $H$-graphic

A sequence of nonnegative integers $\pi$ is {\it graphic} if it is the degree sequence of some graph $G$. In this case we say that $G$ is a \textit{realization} of $\pi$, and we write $\pi=\pi(G)$. A graphic sequence $\pi$ is {\it potentially $H$-graphic} if there is a realization of $\pi$ that contains $H$ as a subgraph. Given nonincreasing graphic sequences $\pi_1=(d_1,\ldots,d_n)$ and $\pi_2 = (s_1,\ldots,s_n)$, we say that $\pi_1$ {\it majorizes} $\pi_2$ if $d_i \geq s_i$ for all $i$, $1 \leq i \leq n$. In 1970, Erd\H{o}s showed that for any $K_{r+1}$-free graph $H$, there exists an $r$-partite graph $G$ such that $\pi(G)$ majorizes $\pi(H)$. In 2005, Pikhurko and Taraz generalized this notion and showed that for any graph $F$ with chromatic number $r+1$, the degree sequence of an $F$-free graph is, in an appropriate sense, nearly majorized by the degree sequence of an $r$-partite graph. In this paper, we give similar results for degree sequences that are not potentially $H$-graphic. In particular, there is a graphic sequence $\pi^*(H)$ such that if $\pi$ is a graphic sequence that is not potentially $H$-graphic, then $\pi$ is close to being majorized by $\pi^*(H)$. Similar to the role played by complete multipartite graphs in the traditional extremal setting, the sequence $\pi^*(H)$ asymptotically gives the maximum possible sum of a graphic sequence $\pi$ that is not potentially $H$-graphic.
Understanding finite dimensional representations generically

We survey the development and status quo of a subject best described as “generic representation theory of finite dimensional algebras”, which started taking shape in the early 1980s. Let \({\Lambda }\) be a finite dimensional algebra over an algebraically closed field. Roughly, the theory aims at (a) pinning down the irreducible components of the standard parametrizing varieties for the \({\Lambda }\)-modules with a fixed dimension vector, and (b) assembling generic information on the modules in each individual component, that is, assembling data shared by all modules in a dense open subset of that component. We present an overview of results spanning the spectrum from hereditary algebras through the tame non-hereditary case to wild non-hereditary algebras.
PT Symmetry, Conformal Symmetry, and the Metrication of Electromagnetism

We present some interesting connections between PT symmetry and conformal symmetry. We use them to develop a metricated theory of electromagnetism in which the electromagnetic field is present in the geometric connection. However, unlike Weyl who first advanced this possibility, we do not take the connection to be real but to instead be PT symmetric, with it being \(iA_{\mu }\) rather than \(A_{\mu }\) itself that then appears in the connection. With this modification the standard minimal coupling of electromagnetism to fermions is obtained. Through the use of torsion we obtain a metricated theory of electromagnetism that treats its electric and magnetic sectors symmetrically, with a conformal invariant theory of gravity being found to emerge. An extension to the non-Abelian case is provided.
The Effect of Communication on Noncooperative Multiplayer Multi-armed Bandit Problems

We consider decentralized stochastic multi-armed bandit problem with multiple players in the case of different communication probabilities between players. Each player makes a decision of pulling an arm without cooperation while aiming to maximize his or her reward but informs his or her neighbors in the end of every turn about the arm he or she pulled and the reward he or she got. Neighbors of players are determined according to an Erd?s-Renyi graph with connectivity ? which is reproduced in the beginning of every turn. We consider i.i.d. rewards generated by a Bernoulli distribution and assume that players are unaware about the arms’ probability distributions and their mean values. In case of a collision, we assume that only one of the players who is randomly chosen gets the reward where the others get zero reward. We study the effects of ?, the degree of communication between players, on the cumulative regret using well-known algorithms UCB1, ?-Greedy and Thompson Sampling.
Switching to learn

A network of agents attempt to learn some unknown state of the world drawn by nature from a finite set. Agents observe private signals conditioned on the true state, and form beliefs about the unknown state accordingly. Each agent may face an identification problem in the sense that she cannot distinguish the truth in isolation. However, by communicating with each other, agents are able to benefit from side observations to learn the truth collectively. Unlike many distributed algorithms which rely on all-time communication protocols, we propose an efficient method by switching between Bayesian and non-Bayesian regimes. In this model, agents exchange information only when their private signals are not informative enough; thence, by switching between the two regimes, agents efficiently learn the truth using only a few rounds of communications. The proposed algorithm preserves learnability while incurring a lower communication cost. We also verify our theoretical findings by simulation examples.
Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems

Voice Processing Systems (VPSes), now widely deployed, have been made significantly more accurate through the application of recent advances in machine learning. However, adversarial machine learning has similarly advanced and has been used to demonstrate that VPSes are vulnerable to the injection of hidden commands - audio obscured by noise that is correctly recognized by a VPS but not by human beings. Such attacks, though, are often highly dependent on white-box knowledge of a specific machine learning model and limited to specific microphones and speakers, making their use across different acoustic hardware platforms (and thus their practicality) limited. In this paper, we break these dependencies and make hidden command attacks more practical through model-agnostic (blackbox) attacks, which exploit knowledge of the signal processing algorithms commonly used by VPSes to generate the data fed into machine learning systems. Specifically, we exploit the fact that multiple source audio samples have similar feature vectors when transformed by acoustic feature extraction algorithms (e.g., FFTs). We develop four classes of perturbations that create unintelligible audio and test them against 12 machine learning models, including 7 proprietary models (e.g., Google Speech API, Bing Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful attacks against all targets. Moreover, we successfully use our maliciously generated audio samples in multiple hardware configurations, demonstrating effectiveness across both models and real systems. In so doing, we demonstrate that domain-specific knowledge of audio signal processing represents a practical means of generating successful hidden voice command attacks.
Comparison of dislocation density tensor fields derived from discrete dislocation dynamics and crystal plasticity simulations of torsion

The importance of accurate simulation of the plastic deformation of ductile metals to the design of structures and components is well-known. Many techniques exist that address the length scales relevant to deformation processes, including dislocation dynamics (DD), which models the interaction and evolution of discrete dislocation line segments, and crystal plasticity (CP), which incorporates the crystalline nature and restricted motion of dislocations into a higher scale continuous field framework. While these two methods are conceptually related, there have been only nominal efforts focused on the system-level material response that use DD-generated information to enhance the fidelity of plasticity models. To ascertain to what degree the predictions of CP are consistent with those of DD, we compare their global and microstructural response in a number of deformation modes. After using nominally homogeneous compression and shear deformation dislocation dynamics simulations to calibrate crystal plasticity flow rule parameters, we compare not only the system-level stress-strain response of prismatic wires in torsion but also the resulting geometrically necessary dislocation density tensor fields. To establish a connection between explicit description of dislocations and the continuum assumed with crystal plasticity simulations, we ascertain the minimum length-scale at which meaningful dislocation density fields appear. Our results show that, for the case of torsion, the two material models can produce comparable spatial dislocation density distributions.
Robust distributed averaging on networks with adversarial intervention

We study the interaction between a network designer and an adversary over a dynamical network. The network consists of nodes performing continuous-time distributed averaging. The goal of the network designer is to assist the nodes reach consensus by changing the weights of a limited number of links in the network. Meanwhile, an adversary strategically disconnects a set of links to prevent the nodes from converging. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. We utilize Pontryagin's Maximum Principle (MP) to tackle both problems and derive the optimal strategies. Although the canonical equations provided by the MP are intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Finally, we provide a sufficient condition for the existence of a saddle-point equilibrium for the underlying zero-sum game.
Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels

Nonlinear kernels can be approximated using finite-dimensional feature maps for efficient risk minimization. Due to the inherent trade-off between the dimension of the (mapped) feature space and the approximation accuracy, the key problem is to identify promising (explicit) features leading to a satisfactory out-of-sample performance. In this work, we tackle this problem by efficiently choosing such features from multiple kernels in a greedy fashion. Our method sequentially selects these explicit features from a set of candidate features using a correlation metric. We establish an out-of-sample error bound capturing the trade-off between the error in terms of explicit features (approximation error) and the error due to spectral properties of the best model in the Hilbert space associated to the combined kernel (spectral error). The result verifies that when the (best) underlying data model is sparse enough, i.e., the spectral error is negligible, one can control the test error with a small number of explicit features, that can scale poly-logarithmically with data. Our empirical results show that given a fixed number of explicit features, the method can achieve a lower test error with a smaller time cost, compared to the state-of-the-art in data-dependent random features.
Top-stable degenerations of finite dimensional representations II

Abstract Let Λ be a finite dimensional algebra over an algebraically closed field. We exhibit slices of the representation theory of Λ that are always classifiable in stringent geometric terms. Namely, we prove that, for any semisimple object T ∈ Λ - mod , the class of those Λ-modules with fixed dimension vector (say d) and top T which do not permit any proper top-stable degenerations possesses a fine moduli space. This moduli space, ModuliMax d T , is a projective variety. Despite classifiability up to isomorphism, the targeted collections of modules are representation-theoretically rich: indeed, any projective variety arises as ModuliMax d T for suitable choices of Λ, d, and T. In tandem, we give a structural characterization of the finite dimensional representations that have no proper top-stable degenerations.
Simple Recurrent Units for Highly Parallelizable Recurrence.

Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5—9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model (Vaswani et al., 2017) on translation by incorporating SRU into the architecture.
Deep Learning Based Speech Beamforming

Multi-channel speech enhancement with ad-hoc sensors has been a challenging task. Speech model guided beamforming algorithms are able to recover natural sounding speech, but the speech models tend to be oversimplified or the inference would otherwise be too complicated. On the other hand, deep learning based enhancement approaches are able to learn complicated speech distributions and perform efficient inference, but they are unable to deal with variable number of input channels. Also, deep learning approaches introduce a lot of errors, particularly in the presence of unseen noise types and settings. We have therefore proposed an enhancement framework called DEEPBEAM, which combines the two complementary classes of algorithms. DEEPBEAM introduces a beamforming filter to produce natural sounding speech, but the filter coefficients are determined with the help of a monaural speech enhancement neural network. Experiments on synthetic and real-world data show that Deepbeam is able to produce clean, dry and natural sounding speech, and is robust against unseen noise.
Determinant Codes With Helper-Independent Repair for Single and Multiple Failures

Determinant codes are a class of exact-repair regenerating codes for distributed storage systems with parameters $(n,k=d,d)$ . These codes cover the entire trade-off between per-node storage and repair-bandwidth. In an earlier work of the authors, the repair data of the determinant code sent by a helper node to repair a failed node depends on the identity of the other helper nodes participating in the process, which is practically undesired. In this paper, a new repair mechanism is proposed for determinant codes, which relaxes this dependency, while preserving all other properties of the code. Moreover, it is shown that the determinant codes are capable of repairing multiple failures, with a per-node repair-bandwidth which scales sub-linearly with the number of failures.
Classification of scalar third order ordinary differential equations linearizable via generalized contact transformations

AbstractWhereas Lie had linearized scalar second order ordinary differential equations (ODEs) by point transformations, and later Chern had extended to the third order by using contact transformation, till recently no work had been done for higher order (or systems) of ODEs. Lie had found a unique class defined by the number of infinitesimal symmetry generators but the more general ODEs were not so classified. Recently, classifications of higher order and systems of ODEs were provided. In this paper we relate contact symmetries of scalar ODEs with point symmetries of reduced systems. We define a new type of transformation that builds upon this relation and obtain equivalence classes of scalar third order ODEs linearizable via these transformations. Four equivalence classes of such equations are seen to exist.
A generalized Cuntz-Krieger uniqueness theorem for higher rank graphs

We present a uniqueness theorem for k-graph C ∗ -algebras that requires neither an aperiodicity nor a gauge invariance assumption. Specifically, we prove that for the injectivity of a representation of a k-graph C ∗ -algebra, it is sufficient that the representation be injective on a distinguished abelian C ∗ -subalgebra. A crucial part of the proof is the application of an abstract uniqueness theorem, which says that such a uniqueness property follows from the existence of a jointly faithful collection of states on the ambient C ∗ -algebra, each of which is the unique extension of a state on the distinguished abelian C ∗ -subalgebra.
Event excitation for event-driven control and optimization of multi-agent systems

We consider event-driven methods in a general framework for the control and optimization of multi-agent systems, viewing them as stochastic hybrid systems. Such systems often have feasible realizations in which the events needed to excite an on-line event-driven controller cannot occur, rendering the use of such controllers ineffective. We show that this commonly happens in environments which contain discrete points of interest which the agents must visit. To address this problem in event-driven gradient-based optimization problems, we propose a new metric for the objective function which creates a potential field guaranteeing non-zero gradient values when no events are present and which results in eventual event excitation. We apply this approach to the class of cooperative multi-agent data collection problems using the event-driven Infinitesimal Perturbation Analysis (IPA) methodology and include numerical examples illustrating its effectiveness.
OPEB: Open physical environment benchmark for artificial intelligence

Artificial Intelligence methods to solve continuous-control tasks have made significant progress in recent years. However, these algorithms have important limitations and still need significant improvement to be used in industry and real-world applications. This means that this area is still in an active research phase. To involve a large number of research groups, standard benchmarks are needed to evaluate and compare proposed algorithms. In this paper, we propose a physical environment benchmark framework to facilitate collaborative research in this area by enabling different research groups to integrate their designed benchmarks in a unified cloud-based repository and also share their actual implemented benchmarks via the cloud. We demonstrate the proposed framework using an actual implementation of the classical mountain-car example and present the results obtained using a Reinforcement Learning algorithm.
Noncommutative marked surfaces

Abstract The aim of the paper is to attach a noncommutative cluster-like structure to each marked surface Σ. This is a noncommutative algebra A Σ generated by “noncommutative geodesics” between marked points subject to certain triangle relations and noncommutative analogues of Ptolemy–Plucker relations. It turns out that the algebra A Σ exhibits a noncommutative Laurent Phenomenon with respect to any triangulation of Σ, which confirms its “cluster nature”. As a surprising byproduct, we obtain a new topological invariant of Σ, which is a free or a 1-relator group easily computable in terms of any triangulation of Σ. Another application is the proof of Laurentness and positivity of certain discrete noncommutative integrable systems.
Finite horizon robustness analysis of LTV systems using integral quadratic constraints

The goal of this paper is to assess the robustness of an uncertain linear time-varying (LTV) system on a finite time horizon. The uncertain system is modeled as an interconnection of a known LTV system and a perturbation. The input/output behavior of the perturbation is described by time-domain, integral quadratic constraints (IQCs). Typical notions of robustness, e.g. nominal stability and gain/phase margins, can be insufficient for finite-horizon analysis. Instead, this paper focuses on robust induced gains and bounds on the reachable set of states. Sufficient conditions to compute robust performance bounds are formulated using dissipation inequalities and IQCs. The analysis conditions are provided in two equivalent forms as Riccati differential equations and differential linear matrix inequalities, and an algorithm is developed leveraging both forms.
The James Function

SummaryWe investigate the properties of the James function, associated with Bill James's so-called “log5 method,” which assigns a probability to the result of a game between two teams based on their respective winning percentages. We also introduce and study a class of functions, which we call Jamesian, that satisfy the same a priori conditions that were originally used to describe the James function.
Simultaneous Core Partitions: Parameterizations and Sums

Fix coprime $s,t\ge1$. We re-prove, without Ehrhart reciprocity, a conjecture of Armstrong (recently verified by Johnson) that the finitely many simultaneous $(s,t)$-cores have average size $\frac{1}{24}(s-1)(t-1)(s+t+1)$, and that the subset of self-conjugate cores has the same average (first shown by Chen-Huang-Wang). We similarly prove a recent conjecture of Fayers that the average weighted by an inverse stabilizer - giving the "expected size of the $t$-core of a random $s$-core" - is $\frac{1}{24}(s-1)(t^2-1)$. We also prove Fayers' conjecture that the analogous self-conjugate average is the same if $t$ is odd, but instead $\frac{1}{24}(s-1)(t^2+2)$ if $t$ is even. In principle, our explicit methods - or implicit variants thereof - extend to averages of arbitrary powers. The main new observation is that the stabilizers appearing in Fayers' conjectures have simple formulas in Johnson's $z$-coordinates parameterization of $(s,t)$-cores. We also observe that the $z$-coordinates extend to parameterize general $t$-cores. As an example application with $t := s+d$, we count the number of $(s,s+d,s+2d)$-cores for coprime $s,d\ge1$, verifying a recent conjecture of Amdeberhan and Leven.
Contexts of Diffusion: Adoption of Research Synthesis in Social Work and Women’s Studies

Texts reveal the subjects of interest in research fields, and the values, beliefs, and practices of researchers. In this study, texts are examined through bibliometric mapping and topic modeling to provide a bird’s eye view of the social dynamics associated with the diffusion of research synthesis methods in the contexts of Social Work and Women’s Studies. Research synthesis texts are especially revealing because the methods, which include meta-analysis and systematic review, are reliant on the availability of past research and data, sometimes idealized as objective, egalitarian approaches to research evaluation, fundamentally tied to past research practices, and performed with the goal informing future research and practice. This study highlights the co-influence of past and subsequent research within research fields; illustrates dynamics of the diffusion process; and provides insight into the cultural contexts of research in Social Work and Women’s Studies. This study suggests the potential to further develop bibliometric mapping and topic modeling techniques to inform research problem selection and resource allocation.
Depth-Limited Solving for Imperfect-Information Games

A fundamental challenge in imperfect-information games is that states do not have well-defined values. As a result, depth-limited search algorithms used in single-agent settings and perfect-information games do not apply. This paper introduces a principled way to conduct depth-limited solving in imperfect-information games by allowing the opponent to choose among a number of strategies for the remainder of the game at the depth limit. Each one of these strategies results in a different set of values for leaf nodes. This forces an agent to be robust to the different strategies an opponent may employ. We demonstrate the effectiveness of this approach by building a master-level heads-up no-limit Texas hold'em poker AI that defeats two prior top agents using only a 4-core CPU and 16 GB of memory. Developing such a powerful agent would have previously required a supercomputer.
The Matrix Generalized Inverse Gaussian Distribution: Properties and Applications

While the Matrix Generalized Inverse Gaussian $$\mathcal {MGIG}$$ distribution arises naturally in some settings as a distribution over symmetric positive semi-definite matrices, certain key properties of the distribution and effective ways of sampling from the distribution have not been carefully studied. In this paper, we show that the $$\mathcal {MGIG}$$ is unimodal, and the mode can be obtained by solving an Algebraic Riccati Equation ARE equationi¾ź[7]. Based on the property, we propose an importance sampling method for the $$\mathcal {MGIG}$$ where the mode of the proposal distribution matches that of the target. The proposed sampling method is more efficient than existing approachesi¾ź[32, 33], which use proposal distributions that may have the mode far from the $$\mathcal {MGIG}$$'s mode. Further, we illustrate that the the posterior distribution in latent factor models, such as probabilistic matrix factorization PMFi¾ź[24], when marginalized over one latent factor has the $$\mathcal {MGIG}$$ distribution. The characterization leads to a novel Collapsed Monte Carlo CMC inference algorithm for such latent factor models. We illustrate that CMC has a lower log loss or perplexity than MCMC, and needs fewer samples.
Nonlinear price dynamics of S&P 100 stocks

Abstract The methodology presented provides a quantitative way to characterize investor behavior and price dynamics within a particular asset class and time period. The methodology is applied to a data set consisting of over 250,000 data points of the S&P 100 stocks during 2004–2018. Using a two-way fixed-effects model, we uncover trader motivations including evidence of both under- and overreaction within a unified setting. A nonlinear relationship is found between return and trend suggesting a small, positive trend increases the return, while a larger one tends to decrease it. The shape parameters of the nonlinearity quantify trader motivation to buy into trends or wait for bargains. The methodology allows the testing of any behavioral finance bias or technical analysis concept.
Computation of forward stochastic reach sets: Application to stochastic, dynamic obstacle avoidance

We propose a method to efficiently compute the forward stochastic reach (FSR) set and its probability measure. We consider nonlinear systems with an affine disturbance input, that is stochastic and bounded. This model includes uncontrolled systems and systems with an a priori known controller, and often arises in problems in obstacle avoidance in mobile robotics. When used as a constraint in finite horizon controller synthesis, the FSR set and its probability measure facilitate probabilistic collision avoidance. This is in contrast to the traditional game-theoretic approaches which presume the obstacles are adversaries, generating hard constraints that cannot be violated. We tailor our approach to accommodate the geometry of the rigid body obstacles, and show convexity is assured when the rigid body shape of each obstacle is convex. We extend existing methods for multi-obstacle avoidance through mixed integer programming (with linear robot and obstacle dynamics) to accommodate chance constraints derived using the FSR analysis. We use our method to synthesize a receding horizon controller that drives a robot to a desired goal while avoiding several rigid-body obstacle with stochastic dynamics. Our approach can provide solutions when approaches that presume a worst-case action from the obstacle fail.