Optimal Attack against Autoregressive Models by Manipulating the Environment

We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). In this threat model, the environment evolves according to a dynamical system; an autoregressive model observes the current environment state and predicts its future values; an attacker has the ability to modify the environment state in order to manipulate future autoregressive forecasts. The attacker's goal is to force autoregressive forecasts into tracking a target trajectory while minimizing its attack expenditure. In the white-box setting where the attacker knows the environment and forecast models, we present the optimal attack using LQR for linear models, and Model Predictive Control (MPC) for nonlinear models. In the black-box setting, we combine system identification and MPC. Experiments demonstrate the effectiveness of our attacks

Transition in Hypersonic Boundary Layers: Role of Dilatational Waves

Transition and turbulence production in a hypersonic boundary layer is investigated in a Mach 6 quiet wind tunnel using Rayleigh-scattering visualization, fast-response pressure measurements, and particle image velocimetry. It is found that the second instability acoustic mode is the key modulator of the transition process. The second mode experiences a rapid growth and a very fast annihilation due to the effect of bulk viscosity. The second mode interacts strongly with the first vorticity mode to directly promote a fast growth of the latter and leads to immediate transition to turbulence.Comment: 5 pages, 6 figure

A New World for Chemical Synthesis?

This perspective seeks to provide an overarching vision of the current state of chemical synthesis methodology using machinery as enabling tools. It highlights current capabilities and limitations in this highly digitally-connected world and suggests areas where new opportunities may arise in the future by going well beyond our present levels of innovation and automation. There is a new need for improved downstream processing tools, advanced reactor design, computational predictive algorithms and integration of robotic systems to maximise the human resource to facilitate a new era in the assembly of our functional materials.The authors gratefully acknowledge financial support from Pfizer (Y.C.), DSTL (O. M.) and the H2020-FET OPEN-2016-2017 Programme of the European commission (D.E.F., S.V.L.;grant agreement number:737266-ONE FLOW

A Practical Method for Continuous Production of sp3-Rich Compounds from (Hetero)Aryl Halides and Redox-Active Esters.

A practically useful coupling reaction between aromatic halides and redox-active esters was realized by nickel catalysis through the use of a packed zinc bed column in continuous flow. Multiple reuse of the column showed a negligible decrease in efficiency, affording high space/time yields. A wide range of substrates, including a number of heteroaryl halides and polyfunctional materials were coupled in generally good yields. Longer-time and larger-scale experiments further demonstrates the robustness of the system

A Photoredox Coupling Reaction of Benzylboronic Esters and Carbonyl Compounds in Batch and Flow.

Mild cross-coupling reaction between benzylboronic esters with carbonyl compounds and some imines was achieved under visible-light-induced iridium-catalyzed photoredox conditions. Functional group tolerance was demonstrated by 51 examples, including 13 heterocyclic compounds. Gram-scale reaction was realized through the use of computer-controlled continuous flow photoreactors.Uniqsis Ltd and Mark Ladlow for the generous loan 236 of a Photosyn reactor. Y.C. thanks Pfizer for funding the 237 postdoctoral fellowship. The authors also gratefully acknowl- 238 edge financial support from H2020-FETOPEN-2016-2017 239 program of European commission (S.V.L.; grant agreement 240 no.: 737266-ONE FLOW)

MaxMin-L2-SVC-NCH: A New Method to Train Support Vector Classifier with the Selection of Model's Parameters

The selection of model's parameters plays an important role in the application of support vector classification (SVC). The commonly used method of selecting model's parameters is the k-fold cross validation with grid search (CV). It is extremely time-consuming because it needs to train a large number of SVC models. In this paper, a new method is proposed to train SVC with the selection of model's parameters. Firstly, training SVC with the selection of model's parameters is modeled as a minimax optimization problem (MaxMin-L2-SVC-NCH), in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal model's parameters. A lower time complexity can be expected in MaxMin-L2-SVC-NCH because CV is abandoned. A gradient-based algorithm is then proposed to solve MaxMin-L2-SVC-NCH, in which L2-SVC-NCH is solved by a projected gradient algorithm (PGA) while the maximization problem is solved by a gradient ascent algorithm with dynamic learning rate. To demonstrate the advantages of the PGA in solving L2-SVC-NCH, we carry out a comparison of the PGA and the famous sequential minimal optimization (SMO) algorithm after a SMO algorithm and some KKT conditions for L2-SVC-NCH are provided. It is revealed that the SMO algorithm is a special case of the PGA. Thus, the PGA can provide more flexibility. The comparative experiments between MaxMin-L2-SVC-NCH and the classical parameter selection models on public datasets show that MaxMin-L2-SVC-NCH greatly reduces the number of models to be trained and the test accuracy is not lost to the classical models. It indicates that MaxMin-L2-SVC-NCH performs better than the other models. We strongly recommend MaxMin-L2-SVC-NCH as a preferred model for SVC task