The transformations from the primitive cells of the centered Bravais lattices to the corresponding centered cells have conventionally been listed as three-by-three matrices that transform three-space lattice vectors. Using those three-by-three matrices when working in the six-dimensional space of lattices represented as Selling scalars as used in Delone (Delaunay) reduction, one could transform to the three-space representation, apply the three-by-three matrices and then back-transform to the six-space representation, but it is much simpler to have the equivalent six-by-six matrices and apply them directly. The general form of the transformation from the three-space matrix to the corresponding matrix operating on Selling scalars (expressed in space S6) is derived, and the particular S6matrices for the centered Delone types are listed. (Note: in his later publications, Boris Delaunay used the Russian version of his surname, Delone.)

It's way more efficient, not to mention more polite, for everyone.

Given spherically symmetric characteristic initial data for the Einstein-scalar field system with a positive cosmological constant, we provide a criterion, in terms of the dimensionless size and dimensionless renormalized mass content of an annular region of the data, for the formation of a future trapped surface. This corresponds to an extension of Christodoulou's classical criterion by the inclusion of the cosmological term.

We construct semiglobal singular spacetimes for the Einstein equations coupled to a massless scalar field. Consistent with the heuristic analysis of Belinskii, Khalatnikov, Lifshitz or BKL for this system, there are no oscillations due to the scalar field. (This is much simpler than the oscillatory BKL heuristics for the Einstein vacuum equations.) Prior results are due to Andersson and Rendall in the real analytic case, and Rodnianski and Speck in the smooth near-spatially-flat-FLRW case. Similar to Andersson and Rendall we give asymptotic data at the singularity, which we refer to as final data, but our construction is not limited to real analytic solutions. This paper is a test application of tools (a graded Lie algebra formulation of the Einstein equations and a filtration) intended for the more subtle vacuum case. We use homological algebra tools to construct a formal series solution, then symmetric hyperbolic energy estimates to construct a true solution well-approximated by truncations of the formal one. We conjecture that the image of the map from final data to initial data is an open set of anisotropic initial data.

In this paper we study manifolds $M_{Sigma}$ with fibered singularities, more specifically, a relevant space $Riem^{psc}(X_{Sigma})$ of Riemannian metrics with positive scalar curvature. Our main goal is to prove that the space $Riem^{psc}(X_{Sigma})$ is homotopy invariant under certain surgeries on $M_{Sigma}$.

In this paper, we present three distributed algorithms to solve a class of generalized Nash equilibrium (GNE) seeking problems in strongly monotone games. The first one (SD-GENO) is based on synchronous updates of the agents, while the second and the third (AD-GEED and AD-GENO) represent asynchronous solutions that are robust to communication delays. AD-GENO can be seen as a refinement of AD-GEED, since it only requires node auxiliary variables, enhancing the scalability of the algorithm. Our main contribution is to prove converge to a variational GNE of the game via an operator-theoretic approach. Finally, we apply the algorithms to network Cournot games and show how different activation sequences and delays affect convergence. We also compare the proposed algorithms to the only other in the literature (ADAGNES), and observe that AD-GENO outperforms the alternative.

This disclosure provides a network security architecture that permits installation of different software security products as virtual machines (VMs). By relying on a standardized data format and communication structure, a general architecture can be created and used to dynamically build and reconfigure interaction between both similar and dissimilar security products. Use of an integration scheme having defined message types and specified query response framework provides for real-time response and easy adaptation for cross-vendor communication. Examples are provided where an intrusion detection system (IDS) can be used to detect network threats based on distributed threat analytics, passing detected threats to other security products (e.g., products with different capabilities from different vendors) to trigger automatic, dynamically configured communication and reaction. A network security provider using this infrastructure can provide hosted or managed boundary security to a diverse set of clients, each on a customized basis.

An illustrative embodiment of a computer-implemented process for scalable group synthesis receives a group definition, applies a sub-set of conditions to the group definition to form a conditioned group definition, receives a set of entities and populates group membership using the received set of entities and the conditioned group definition, wherein each member responds in the affirmative to the sub-set of conditions.

A multi-threaded in-order superscalar processor 2 includes an issue stage 12 including issue circuitry 22, 24 for selecting instructions to be issued to execution units 14, 16 in dependence upon a currently selected issue policy. A plurality of different issue policies are provided by associated different policy circuitry 28, 30, 32 and a selection between which of these instances of the policy circuitry 28, 30, 32 is active is made by policy selecting circuitry 34 in dependence upon detected dynamic behavior of the processor 2.

The present application discloses apparatus and methods for increasing channel utilization for a high-speed serial interface of an integrated circuit (IC). A new circuit architecture is disclosed which provides circuitry that may be programmed flexibly to support a multitude of different channel bonding schemes. In accordance with one aspect of the invention, the new architecture decouples the granularity of control-signal channel bonding from the granularity of data-aggregation channel bonding. This advantageously allows optimization of configurations for both types of channel bonding. In another aspect of the invention, the logical boundaries of bonded user channels are decoupled from the physical boundaries of the PCS modules. This decoupling advantageously eliminates a rigid constraint of previous architectures.

A method for analyzing software with pointer analysis may include obtaining a software program, and determining a first independent program slice of the software program describing a first code segment of the software program. The method may further include determining, using a first pointer analysis objective, a first result from performing a first pointer analysis on the first independent program slice, and determining, using the first result, a first dependent program slice of the software program. The method may further include determining, using a second pointer analysis objective, a second result from performing a second pointer analysis on the first dependent program slice. The method may further include generating a report, using these results, indicating whether the software program satisfies a predetermined criterion.

A computer system for processing of data received from a remote device. The computer system includes a master device and at least one slave device. The master device is communicably coupled to the remote device and has a display and a memory. The master device partitions the data into one or more sub data. The at least one slave device is coupled to the master device. The master device delegates processing of the one or more sub data to one or more of the at least one slave device, and the one or more of the at least one slave device correspondingly to the one or more sub data generate processed sub data. The master device stores the processed sub data and outputs the processed sub data to the display.

Sterilization and cleaning of an escalator hand rail are performed. A sterilization and cleaning device including a hand rail; a plasma source for irradiating the hand rails with ions or radicals or UV light; an enclosure for housing the plasma source; a power source for generating plasma; a fan for generating relatively negative pressure in the enclosure; filter units for removing removed bacteria, viruses, and organic matters such as hand marks; and filter plates located backward and forward of a moving direction of the hand rail in the enclosure along the hand rail is provided.

The present invention provides a truss device for an escalator or moving walk comprising an outer panel and an inner panel facing the outer panel, characterized in that, the inner panel is formed from a single integral panel and comprises a body part and a plurality of connection parts extending from the body part toward the outer panel thus forming corresponding through openings in the inner panel, each connection part is connected to the outer panel so that the outer panel and the inner panel are fixed together, wherein the inner panel further comprises a support part formed on the body part of the inner panel and projecting in a direction away from the outer panel. The present invention also provides an escalator or moving walk having the above truss device.

An intermediate support structure for an escalator or moving walk having a dampening device with slots formed therein allowing a supported escalator truss to be free to move laterally to accommodate lateral displacement caused by story drift during an earthquake or seismic event.

Ambushes and kidnappings targeting Iraqis and foreigners have surged this month while the new government is caught up in power struggles over cabinet positions.

A rise in the number of assaults against local government employees prompts Victorian councils to call for a change in sentencing laws to better protect their staff.

Disability advocates say they are concerned about an increase in anxiety among children whose support services have been stripped back during the coronavirus pandemic.

Alarming footage shows high winds driving a large bushfire overnight on the Sunshine Coast, where 10 homes have been lost at Peregian Springs and Peregian Beach.

Chris Cornell’s widow is firing back at her husband’s Soundgarden bandmates, calling the lawsuit they filed against her Wednesday an attempt to “browbeat” her into giving up “copyrighted works.”

Chris Cornell’s widow is firing back at her husband’s Soundgarden bandmates, calling the lawsuit they filed against her Wednesday an attempt to “browbeat” her into giving up “copyrighted works.”

Chris Cornell’s widow is firing back at her husband’s Soundgarden bandmates, calling the lawsuit they filed against her Wednesday an attempt to “browbeat” her into giving up “copyrighted works.”

Pae White's art installations at the Beverly Center have taken over the walls along the five-story escalators.

strataconf: Building Big Data applications now easier http://t.co/jaATalPL4P Hadoop analytic engines simplify creation of low-cost, scalable apps

Invitation Only Research Event

Event participants

Usama Otman Essed, Libya Center for Strategic & Future Studies
Chair: Tim Eaton, Middle East and North Africa Programme, Chatham House

27 November 2015

Expert

Gregory J. Galloway and Hyun Chul Jang
Proc. Amer. Math. Soc. 148 (2020), 2617-2629.
Abstract, references and article information

Invitation Only Research Event

Event participants

Usama Otman Essed, Libya Center for Strategic & Future Studies
Chair: Tim Eaton, Middle East and North Africa Programme, Chatham House

Purpose: This study is designed to assess the safety and therapeutic response to ¹⁷⁷Lu-EB-PSMA treatment with escalating doses in patients with metastatic castration-resistant prostate cancer (mCRPC). Methods: With institutional review board approval and informed consent, patients were randomly divided into three groups: Group A (n = 10) were treated with 1.18 ± 0.09 GBq/dose of ¹⁷⁷Lu-EB-PSMA. Group B (n = 10) were treated with 2.12 ± 0.19 GBq/dose of ¹⁷⁷Lu-EB-PSMA. Group C (n = 8) were treated with 3.52 ± 0.58 GBq/dose of ¹⁷⁷Lu-EB-PSMA. Eligible patients received up to three cycles of ¹⁷⁷Lu-EB-PSMA therapy, at eight-week intervals. Results: Due to disease progression or bone marrow suppression, 4 out of 10, 5 out of 10, and 5 out of 10 patients completed three cycles therapy as planned in Groups A, B, and C, respectively. The prostate-specific antigen (PSA) response was correlated with treatment dose, with PSA disease control rates in Group B (70%) and C (75%) being higher than that in Group A (10%) (P = 0.007), but no correlation between Group B and Group C was found. ⁶⁸Ga-PSMA PET/CT showed response in all the treatment groups, however, there was no significant difference between the three groups. Hematologic toxicity study found that platelets in Group B and Group C decreased more than those in Group A, and that Grade 4 thrombocytopenia occurred in 2 (25.0%) patients in Group C. No serious nephritic or hepatic side effects were observed. Conclusion: This study demonstrates that 2.12 GBq/dose of ¹⁷⁷Lu-EB-PSMA seems to be safe and adequate in tumor treatment. Further investigations with increased number of patients are warranted.

WASHINGTON – A pesar de que los gobiernos de América Latina y el Caribe han tomado medidas generosas e innovadoras para lidiar con el desplazamiento forzado desde Venezuela y más recientemente desde Nicaragua, la cálida bienvenida se ha enfriado en algunos lugares a medida que el número de entradas, la presión sobre los servicios públicos y la preocupación del público aumenta.

This paper considers a Bayesian approach to graph-based semi-supervised learning. We show that if the graph parameters are suitably scaled, the graph-posteriors converge to a continuum limit as the size of the unlabeled data set grows. This consistency result has profound algorithmic implications: we prove that when consistency holds, carefully designed Markov chain Monte Carlo algorithms have a uniform spectral gap, independent of the number of unlabeled inputs. Numerical experiments illustrate and complement the theory.

The horseshoe prior is frequently employed in Bayesian analysis of high-dimensional models, and has been shown to achieve minimax optimal risk properties when the truth is sparse. While optimization-based algorithms for the extremely popular Lasso and elastic net procedures can scale to dimension in the hundreds of thousands, algorithms for the horseshoe that use Markov chain Monte Carlo (MCMC) for computation are limited to problems an order of magnitude smaller. This is due to high computational cost per step and growth of the variance of time-averaging estimators as a function of dimension. We propose two new MCMC algorithms for computation in these models that have significantly improved performance compared to existing alternatives. One of the algorithms also approximates an expensive matrix product to give orders of magnitude speedup in high-dimensional applications. We prove guarantees for the accuracy of the approximate algorithm, and show that gradually decreasing the approximation error as the chain extends results in an exact algorithm. The scalability of the algorithm is illustrated in simulations with problem size as large as $N=5,000$ observations and $p=50,000$ predictors, and an application to a genome-wide association study with $N=2,267$ and $p=98,385$. The empirical results also show that the new algorithm yields estimates with lower mean squared error, intervals with better coverage, and elucidates features of the posterior that were often missed by previous algorithms in high dimensions, including bimodality of posterior marginals indicating uncertainty about which covariates belong in the model.

John J. Dziak, Donna L. Coffman, Matthew Reimherr, Justin Petrovich, Runze Li, Saul Shiffman, Mariya P. Shiyko.

Source: Statistics Surveys, Volume 13, 150--180.

Abstract:
Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametric way via scalar-on-function regression. However, the resulting fitted coefficient regression function requires special care for correct interpretation, as it represents the joint relationship of time points to the outcome, rather than a marginal or cross-sectional relationship. We provide practical guidelines, based on experience with scientific applications, for helping practitioners interpret their results and illustrate these ideas using data from a smoking cessation study.

Data-driven modelling and computational predictions based on maximum entropy principle (MaxEnt-principle) aim at finding as-simple-as-possible - but not simpler then necessary - models that allow to avoid the data overfitting problem. We derive a multivariate non-parametric and non-stationary formulation of the MaxEnt-principle and show that its solution can be approximated through a numerical maximisation of the sparse constrained optimization problem with regularization. Application of the resulting algorithm to popular financial benchmarks reveals memoryless models allowing for simple and qualitative descriptions of the major stock market indexes data. We compare the obtained MaxEnt-models to the heteroschedastic models from the computational econometrics (GARCH, GARCH-GJR, MS-GARCH, GARCH-PML4) in terms of the model fit, complexity and prediction quality. We compare the resulting model log-likelihoods, the values of the Bayesian Information Criterion, posterior model probabilities, the quality of the data autocorrelation function fits as well as the Value-at-Risk prediction quality. We show that all of the considered seven major financial benchmark time series (DJI, SPX, FTSE, STOXX, SMI, HSI and N225) are better described by conditionally memoryless MaxEnt-models with nonstationary regime-switching than by the common econometric models with finite memory. This analysis also reveals a sparse network of statistically-significant temporal relations for the positive and negative latent variance changes among different markets. The code is provided for open access.

Dimitris Bertsimas, Bart Van Parys.

Source: The Annals of Statistics, Volume 48, Number 1, 300--323.

Abstract:
We present a novel binary convex reformulation of the sparse regression problem that constitutes a new duality perspective. We devise a new cutting plane method and provide evidence that it can solve to provable optimality the sparse regression problem for sample sizes $n$ and number of regressors $p$ in the 100,000s, that is, two orders of magnitude better than the current state of the art, in seconds. The ability to solve the problem for very high dimensions allows us to observe new phase transition phenomena. Contrary to traditional complexity theory which suggests that the difficulty of a problem increases with problem size, the sparse regression problem has the property that as the number of samples $n$ increases the problem becomes easier in that the solution recovers 100% of the true signal, and our approach solves the problem extremely fast (in fact faster than Lasso), while for small number of samples $n$, our approach takes a larger amount of time to solve the problem, but importantly the optimal solution provides a statistically more relevant regressor. We argue that our exact sparse regression approach presents a superior alternative over heuristic methods available at present.

Seth Flaxman, Michael Chirico, Pau Pereira, Charles Loeffler.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2564--2585.

Abstract:
We propose a generic spatiotemporal event forecasting method which we developed for the National Institute of Justice’s (NIJ) Real-Time Crime Forecasting Challenge (National Institute of Justice (2017)). Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. While the smoothing kernels capture the two main approaches in current use in the field of crime forecasting, kernel density estimation (KDE) and self-exciting point process (SEPP) models, the RKHS component of the model can be understood as an approximation to the popular log-Gaussian Cox Process model. For inference, we discretize the spatiotemporal point pattern and learn a log-intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales, number of autoregressive lags and bandwidths for smoothing kernels as well as cell shape, size and rotation, were learned using cross validation. Resulting predictions significantly exceeded baseline KDE estimates and SEPP models for sparse events.

Matthew Moores, Geoff Nicholls, Anthony Pettitt, Kerrie Mengersen.

Source: Bayesian Analysis, Volume 15, Number 1, 1--27.

Abstract:
The inverse temperature parameter of the Potts model governs the strength of spatial cohesion and therefore has a major influence over the resulting model fit. A difficulty arises from the dependence of an intractable normalising constant on the value of this parameter and thus there is no closed-form solution for sampling from the posterior distribution directly. There is a variety of computational approaches for sampling from the posterior without evaluating the normalising constant, including the exchange algorithm and approximate Bayesian computation (ABC). A serious drawback of these algorithms is that they do not scale well for models with a large state space, such as images with a million or more pixels. We introduce a parametric surrogate model, which approximates the score function using an integral curve. Our surrogate model incorporates known properties of the likelihood, such as heteroskedasticity and critical temperature. We demonstrate this method using synthetic data as well as remotely-sensed imagery from the Landsat-8 satellite. We achieve up to a hundredfold improvement in the elapsed runtime, compared to the exchange algorithm or ABC. An open-source implementation of our algorithm is available in the R package bayesImageS .

Escalator

Pediatric obesity is a prevalent public health issue that is associated with medical and physical consequences. Clinic-based interventions for pediatric obesity are effective, but they have limited reach and are costly.

This is the first examination of an empirically informed, scalable treatment of pediatric overweight and obesity delivered in YMCAs. The results indicate that a scalable, community-based pediatric obesity intervention can produce clinically meaningful changes in weight and quality of life. (Read the full article)

The pharmaceutical retailer says it was forced to increase prices of surgical masks because of suppliers' inflated quotes and to match competitors, while the commission says retailers could have been following Dis-Chem's lead.

A merit-based immigration plan being put together by Jared Kushner, White House senior adviser it could lead to an escalation in US visas for extremely skilled workers, sources revealed on 24 Apr 2019.Merit-based Immigration ProposalKushner is assumed…