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Place recognition is an essential part of simultaneous localization and mapping (SLAM). LiDAR-based place recognition relies almost exclusively on geometric information. However, geometric information may become unreliable when faced with environments dominated by unstructured objects. In this paper, we explore the role of segmentation for extracting key structured information. We propose STV-SC, a novel segmentation and temporal verification enhanced place recognition method for unstructured environments. It contains a range image-based 3D point segmentation algorithm and a three-stage process to detect a loop. The three-stage method consists of a two-stage candidate loop search process and a one-stage segmentation and temporal verification (STV) process. Our STV process utilizes the time-continuous feature of SLAM to determine whether there is an occasional mismatch. We quantitatively demonstrate that the STV process can trigger false detections caused by unstructured objects and effectively extract structured objects to avoid outliers. Comparison with state-of-art algorithms on public datasets shows that STV-SC can run online and achieve improved performance in unstructured environments (Under the same precision, the recall rate is 1.4∼16% higher than Scan context). Therefore, our algorithm can effectively avoid the mismatching caused by the original algorithm in unstructured environment and improve the environmental adaptability of mobile agents. [ABSTRACT FROM AUTHOR]

This work considers the minimization of a sum of an expectation-valued coordinate-wise smooth nonconvex function and a nonsmooth block-separable convex regularizer. We propose an asynchronous variance-reduced algorithm, where in each iteration, a single block is randomly chosen to update its estimates by a proximal variable sample-size stochastic gradient scheme, while the remaining blocks are kept invariant. Notably, each block employs a steplength relying on its block-specific Lipschitz constant while batch-sizes are updated as a function of the number of times that block is selected. We show that every limit point is a stationary point and establish the ergodic non-asymptotic rate O (1 / K). Iteration and oracle complexity to obtain an ε-stationary point are shown to be O (1 / ϵ) and O (1 / ϵ 2) , respectively. Furthermore, under a proximal Polyak–Łojasiewicz condition with batch sizes increasing at a geometric rate, we prove that the suboptimality diminishes at a geometric rate, the optimal deterministic rate while iteration and oracle complexity to obtain an ε-optimal solution are O (ln ⁡ (1 / ϵ)) and O (1 / ϵ) 1 + c with c ≥ 0. In the single block setting, we obtain the optimal oracle complexity O (1 / ϵ). Finally, preliminary numerics suggest that the schemes compare well with competitors reliant on global Lipschitz constants. [ABSTRACT FROM AUTHOR]

In "Asynchronous Schemes for Stochastic and Misspecified Potential Games and Nonconvex Optimization," Lei and Shanbhag consider a class of convex stochastic Nash games, possibly corrupted by parametric misspecification and characterized by a possibly nonconvex potential function. The authors present an asynchronous inexact proximal best-response (BR) scheme in which, at any step, a randomly selected player computes an inexact BR step (via stochastic approximation) and other players keep their strategies invariant. Misspecification is addressed by a simultaneous learning process reliant on an increasing batch size of sampled gradients. Almost-sure convergence guarantees are provided to the set of Nash equilibria, and such claims can be extended to delay-afflicted regimes, generalized potential games (with coupled strategy sets), and weighted potential games. In fact, equilibria of this potential game are stationary points of the potential function and asynchronous inexact BR schemes are, in essence, randomized block-coordinate schemes for a subclass of stochastic nonconvex optimization problems. The distributed computation of equilibria and optima has seen growing interest in a broad collection of networked problems. We consider the computation of Nash equilibria of convex stochastic noncooperative games characterized by a possibly nonconvex potential function. Since any stationary point of the potential function is a Nash equilibrium, there is an equivalence between asynchronous best-response (BR) schemes applied on a noncooperative game and block-coordinate descent (BCD) schemes implemented on the associated potential function. We focus on two classes of such games: (Problem 1): a potential game, in which each player solves a parameterized stochastic convex program, and (Problem 2): a misspecified generalization, in which the player-specific stochastic program is complicated by a parametric misspecification with the unknown parameter being the solution to a stochastic convex optimization problem. In both settings, exact proximal BR solutions are generally unavailable in finite time because they necessitate solving stochastic programs. Consequently, we design two asynchronous inexact proximal BR schemes to solve Problems 1 and 2, respectively, in which in each iteration, a single player is randomly chosen to compute an inexact proximal BR solution (via stochastic approximation) with delayed rival information while the other players keep their strategies invariant. In the misspecified regime (Problem 2), each player possesses an extra estimate of the misspecified parameter by using a projected stochastic gradient algorithm with an increasing batch of sampled gradients. By imposing suitable conditions on the inexactness sequences, we prove that the iterates produced by both schemes converge almost surely to a connected subset of the set of Nash equilibria. When the player-specific problems are strongly convex, an inexact pure BR scheme (without a proximal term) is shown to be convergent. In effect, we provide what we believe is among the first randomized BCD schemes for stochastic nonconvex (but block-wise convex) optimization with almost sure convergence properties. We further show that the associated gap function converges to zero in mean. These statements can be extended to allow for accommodating weighted potential games and generalized potential games. Finally, we present preliminary numerics by applying the proposed schemes to congestion control and Nash–Cournot competition. [ABSTRACT FROM AUTHOR]

In this paper, we consider a stochastic Nash game in which each player minimizes a parameterized expectation-valued convex objective function. In deterministic regimes, proximal best-response (BR) schemes have been shown to be convergent under a suitable spectral property associated with the proximal BR map. However, a direct application of this scheme to stochastic settings requires obtaining exact solutions to stochastic optimization problems at each iteration. Instead, we propose an inexact generalization of this scheme in which an inexact solution to the BR problem is computed in an expected-value sense via a stochastic approximation (SA) scheme. On the basis of this framework, we present three inexact BR schemes: (i) First, we propose a synchronous inexact BR scheme where all players simultaneously update their strategies. (ii) Second, we extend this to a randomized setting where a subset of players is randomly chosen to update their strategies while the other players keep their strategies invariant. (iii) Third, we propose an asynchronous scheme, where each player chooses its update frequency while using outdated rival-specific data in updating its strategy. Under a suitable contractive property on the proximal BR map, we proceed to derive almost sure convergence of the iterates to the Nash equilibrium (NE) for (i) and (ii) and mean convergence for (i)–(iii). In addition, we show that for (i)–(iii), the generated iterates converge to the unique equilibrium in mean at a linear rate with a prescribed constant rather than a sublinear rate. Finally, we establish the overall iteration complexity of the scheme in terms of projected stochastic gradient (SG) steps for computing an ɛ -NE2 (or ɛ -NE∞) and note that in all settings, the iteration complexity is O (1 / ɛ 2 (1 + c) + δ) , where c = 0 in the context of (i), and c > 0 represents the positive cost of randomization in (ii) and asynchronicity and delay in (iii). Notably, in the synchronous regime, we achieve a near-optimal rate from the standpoint of solving stochastic convex optimization problems by SA schemes. The schemes are further extended to settings where players solve two-stage stochastic Nash games with linear and quadratic recourse. Finally, preliminary numerics developed on a multiportfolio investment problem and a two-stage capacity expansion game support the rate and complexity statements. [ABSTRACT FROM AUTHOR]

Synthesis of binary electrocatalysts from substrates is ideal for further integration of water‐splitting electrodes. The seamless growth of an electrocatalyst directly from an alloy substrate would provide excellent mechanical behavior and lower interface resistance. Herein, a three‐dimensional (3D) intertwined Ni0.86Fe2.14O4 hexagonal plate electrocatalyst as the active site for the oxygen evolution reaction (OER) was successfully prepared by seamless growth and direct synthesis on Ni−Fe foam (NFF) via a hydrothermal route. Ni0.86Fe2.14O4/NFF exhibited excellent OER electrochemical properties, achieving a low overpotential of 286 mV to reach a current density of 200 mA cm−2 and prominent electrochemical durability. The synergistic effect between Ni and Fe in Ni0.86Fe2.14O4 and the seamless connection between Ni0.86Fe2.14O4 and the substrate make main contribution to excellent OER activity for the electrode system. This synthesis strategy provides a route for the synthesis of binary metal‐oxide electrocatalysts from alloys, which can be extended to other binary electrocatalysts. [ABSTRACT FROM AUTHOR]

• Inexpensive ammonium metatungstate for synthesis of WS 2 is used. • Composite film with microcavity and nanofilm inside is formed. • The graphene-WS 2 composite presented fast lithium insertation/deinsertation. • The structure of the composite can be modified by the ratio of graphene to WS 2. In this study, the water-evaporation-induced assembly of a graphene oxide-ammonium metatungstate hydrate multilayer film was developed. After chemical reduction and high-temperature sulfurization, a graphene-WS 2 composite nanofilm with microcavities was obtained. When evaluated as an anode material in a lithium-ion battery, the graphene-WS 2 sample (precursor graphene oxide/WS 2 mass ratio of 1:4) exhibited high initial specific capacity (∼980 mAh g−1 at 0.1 A g−1), optimal cycling performance (∼450 mAh g−1 after 100 cycles at 0.1 A g−1), and superior rate capability (∼300 mAh g−1 at 2 A g−1). The graphene/WS 2 mass ratio influenced the contact between WS 2 and graphene and the porous nanofilm structure facilitated the kinetics of lithium-ion diffusion. [ABSTRACT FROM AUTHOR]

Neat electro-optic films containing binary self-assembly dendritic chromophores FD2/HD2 have developed to increase the poling efficiency and stability of the materials. • Chromophores functionalized with pentafluorobenzene or benzene rings have been synthesized. • The electro-optic coefficient of film-FD2/HD2 is 275 pm/V at 1.31 µm. • The long-term alignment stability of the electro-optical material FD2/HD2 is greatly improved. Four nonlinear optical chromophores FD1-2 and HD1-2 based on dialkyl aniline donor have been synthesized. The donor and bridge part of the chromophores were substituted with aromatic dendron (HD) or fluoroaromatic dendron (FD) groups. Self-assembly strategy through π-π stacking of HD/FD groups was adopted to increase the poling efficiency and long-term stability of the materials. The film FD2/HD2 shown large EO coefficient value of 275 pm/V. After annealing at room temperature for 1000 h, about 91% of the initial r 33 values could be still maintained for the poled films of FD2/HD2, compare to only 53% left for film FD2. [ABSTRACT FROM AUTHOR]