Probabilistic Generative Transformer Language models for Generative Design of Molecules

Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the "molecules grammars" with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformerComment: 13 page

A New Switched State Jump Observer for Traffic Density Estimation in Expressways Based on Hybrid-Dynamic-Traffic-Network-Model

When faced with problems such as traffic state estimation, state prediction, and congestion identification for the expressway network, a novel switched observer design strategy with jump states is required to reconstruct the traffic scene more realistically. In this study, the expressway network is firstly modeled as the special discrete switched system, which is called the piecewise affine system model, a partition of state subspace is introduced, and the convex polytopes are utilized to describe the combination modes of cells. Secondly, based on the hybrid dynamic traffic network model, the corresponding switched observer (including state jumps) is designed. Furthermore, by applying multiple Lyapunov functions and S-procedure theory, the observer design problem can be converted into the existence issue of the solutions to the linear matrix inequality. As a result, a set of gain matrices can be obtained. The estimated states start to jump when the mode changes occur, and the updated value of the estimated state mainly depends on the estimated and the measured values at the previous time. Lastly, the designed state jump observer is applied to the Beijing Jingkai expressway, and the superiority and the feasibility are demonstrated in the application results

Electron dynamics in topological insulator based semiconductor-metal interfaces (topological p-n interface based on Bi2Se3 class)

Single-Dirac-cone topological insulators (TI) are the first experimentally discovered class of three dimensional topologically ordered electronic systems, and feature robust, massless spin-helical conducting surface states that appear at any interface between a topological insulator and normal matter that lacks the topological insulator ordering. This topologically defined surface environment has been theoretically identified as a promising platform for observing a wide range of new physical phenomena, and possesses ideal properties for advanced electronics such as spin-polarized conductivity and suppressed scattering. A key missing step in enabling these applications is to understand how topologically ordered electrons respond to the interfaces and surface structures that constitute a device. Here we explore this question by using the surface deposition of cathode (Cu/In/Fe) and anode materials (NO$_2$) and control of bulk doping in Bi$_2$Se$_3$ from P-type to N-type charge transport regimes to generate a range of topological insulator interface scenarios that are fundamental to device development. The interplay of conventional semiconductor junction physics and three dimensional topological electronic order is observed to generate novel junction behaviors that go beyond the doped-insulator paradigm of conventional semiconductor devices and greatly alter the known spin-orbit interface phenomenon of Rashba splitting. Our measurements for the first time reveal new classes of diode-like configurations that can create a gap in the interface electron density near a topological Dirac point and systematically modify the topological surface state Dirac velocity, allowing far reaching control of spin-textured helical Dirac electrons inside the interface and creating advantages for TI superconductors as a Majorana fermion platform over spin-orbit semiconductors.Comment: 14 pages, 4 Figure

Vertical Stress and Deformation Characteristics of Roadside Backfilling Body in Gob-Side Entry for Thick Coal Seams with Different Pre-Split Angles

Retained gob-side entry (RGE) is a significant improvement for fully-mechanized longwall mining. The environment of surrounding rock directly affects its stability. Roadside backfilling body (RBB), a man-made structure in RGE plays the most important role in successful application of the technology. In the field, however, the vertical deformation of RBB is large during the panel extraction, which leads to malfunction of the RGE. In order to solve the problem, roof pre-split is employed. According to geological conditions as well as the physical modeling of roof behavior and deformation of surrounding rock, the support resistance of RBB is calculated. The environment of surrounding rock, vertical stress and vertical deformation of the RBB in the RGE with different roof pre-split angles are analyzed using FLAC3D software. With the increase of roof pre-split angle, the vertical stresses both in the coal wall and RBB are minimum, and the vertical deformation of RBB also decreases from 110.51 mm to 6.1 mm. Therefore, based on the results of numerical modeling and field observation, roof pre-split angle of 90° is more beneficial to the maintenance of the RGE

Effect of Heat Treatment on the Microstructure and Mechanical Properties of Additive Manufactured Ti-6.5Al-2Zr-1Mo-1V Alloy

Ti-6.5Al-2Zr-1Mo-1V (TA15), widely used in the aerospace industry, is a medium- to high-strength, near-α titanium alloy with high aluminium equivalent value. The TA15 fabricated via laser powder bed fusion (L-PBF) normally presents a typical brittle appearance in as-built status, with high strength and low ductility. In this study, the microstructure and properties of L-PBF TA15 were engineered by various heat treatments below the β-transus temperature (1022 °C). After heat treatment, the original acicular martensite gradually transforms into a typical lamellar α + β dual-phase structure. Withannealing temperature increases, the lamellar α phase thickened with a decreased aspect ratio. Globularisation of the α grain can be noticed when annealing above 800 °C, which leads to a balance between strength and ductility. After heat treatment between 800–900 °C, the desired combination of strength and ductility can be achieved, with elongation of about 12.5% and ultimate tensile strength of about 1100 Mpa

Dynamic Budget Throttling in Repeated Second-Price Auctions

Throttling is one of the most popular budget control methods in today's online advertising markets. When a budget-constrained advertiser employs throttling, she can choose whether or not to participate in an auction after the advertising platform recommends a bid. This paper focuses on the dynamic budget throttling process in repeated second-price auctions from a theoretical view. An essential feature of the underlying problem is that the advertiser does not know the distribution of the highest competing bid upon entering the market. To model the difficulty of eliminating such uncertainty, we consider two different information structures. The advertiser could obtain the highest competing bid in each round with full-information feedback. Meanwhile, with partial information feedback, the advertiser could only have access to the highest competing bid in the auctions she participates in. We propose the OGD-CB algorithm, which involves simultaneous distribution learning and revenue optimization. In both settings, we demonstrate that this algorithm guarantees an $O(\sqrt{T\log T})$ regret with probability $1 - O(1/T)$ relative to the fluid adaptive throttling benchmark. By proving a lower bound of $\Omega(\sqrt{T})$ on the minimal regret for even the hindsight optimum, we establish the near optimality of our algorithm. Finally, we compare the fluid optimum of throttling to that of pacing, another widely adopted budget control method. The numerical relationship of these benchmarks sheds new light on the understanding of different online algorithms for revenue maximization under budget constraints.Comment: 29 pages, 1 tabl

Simulation study of Ferricyanide/Ferrocyanide concentric annulus thermocell with different electrode spacing and cell direction

Thermogalvanic cell also named as thermocell is a new type of technology converting low-grade thermal energy to electricity. In this study, we establish an one-dimensional model of a Fe(CN)63-/4- concentric annulus thermocell and evaluate the influence of electrode spacing and cell direction on the cell performance. Results indicate the ratio of electrolyte thermal resistance to total thermal resistance plays a crucial role in cell performance while electric resistance has relatively less influence. The power of thermocell rises significantly as the electrode spacing increases, from about 0.75mW in both directions to 1.75 mW in horizontal direction and 2.75 mW in vertical direction. Convection of electrolyte is unfavorable to cell performance and the critical electrode spacing where convection begins to affect heat transfer is predicted to be the optimized spacing. At all values of electrode spacing in this study, thermocell in vertical direction performs better than that of horizontal direction