Foot ulcers are one of the most common and severe complication of diabetes mellitus with significant resultant morbidity and mortality. Multiple factors impair wound healing include skin injury, diabetic neuropathy, ischemia, infection, inadequate glycemic control, poor nutritional status, and severe morbidity. It is currently believed that oxidative stress plays a vital role in diabetic wound healing. An imbalance of free radicals and antioxidants in the body results in overproduction of reactive oxygen species which lead to cell, tissue damage, and delayed wound healing. Therefore, decreasing ROS levels through antioxidative systems may reduce oxidative stress-induced damage to improve healing. In this context, we provide an update on the role of oxidative stress and antioxidants in diabetic wound healing through following four perspectives. We then discuss several therapeutic strategies especially dietary bioactive compounds by targeting oxidative stress to improve wounds healing.

Abstract-Existing approaches to online convex optimization (OCO) make sequential one-slot-ahead decisions, which lead to (possibly adversarial) losses that drive subsequent decision iterates. Their performance is evaluated by the so-called regret that measures the difference of losses between the online solution and the best yet fixed overall solution in hindsight. The present paper deals with online convex optimization involving adversarial loss functions and adversarial constraints, where the constraints are revealed after making decisions, and can be tolerable to instantaneous violations but must be satisfied in the long term. Performance of an online algorithm in this setting is assessed by: i) the difference of its losses relative to the best dynamic solution with one-slot-ahead information of the loss function and the constraint (that is here termed dynamic regret); and, ii) the accumulated amount of constraint violations (that is here termed dynamic fit). In this context, a modified online saddle-point (MOSP) scheme is developed, and proved to simultaneously yield sub-linear dynamic regret and fit, provided that the accumulated variations of perslot minimizers and constraints are sub-linearly growing with time. MOSP is also applied to the dynamic network resource allocation task, and it is compared with the well-known stochastic dual gradient method. Under various scenarios, numerical experiments demonstrate the performance gain of MOSP relative to the stateof-the-art.

In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine workers. The Byzantine workers, during the learning process, may send arbitrary incorrect messages to the master due to data corruptions, communication failures or malicious attacks, and consequently bias the learned model. The key to the proposed methods is a regularization term incorporated with the objective function so as to robustify the learning task and mitigate the negative effects of Byzantine attacks. The resultant subgradient-based algorithms are termed Byzantine-Robust Stochastic Aggregation methods, justifying our acronym RSA used henceforth. In contrast to most of the existing algorithms, RSA does not rely on the assumption that the data are independent and identically distributed (i.i.d.) on the workers, and hence fits for a wider class of applications. Theoretically, we show that: i) RSA converges to a near-optimal solution with the learning error dependent on the number of Byzantine workers; ii) the convergence rate of RSA under Byzantine attacks is the same as that of the stochastic gradient descent method, which is free of Byzantine attacks. Numerically, experiments on real dataset corroborate the competitive performance of RSA and a complexity reduction compared to the state-of-the-art alternatives.

Due to the rapid development of artificial intelligence (AI) and internet of things (IoTs), neuromorphic computing and hardware security are becoming more and more important. The volatile memristors, which feature spontaneous decay of device conductance, own the distinct combination of high similarity to the biological neurons and synapses and unique physical mechanisms. They are excellent candidates for mimicking the synaptic functions and ideal randomness source of the entropy for hardware‐based security. Herein, the recent advances of volatile memristors in devices, mechanisms, and application aspects are summarized. First, a brief introduction is presented to describe the switching type, materials, and temporal response of volatile memristors. Second, the volatile switching mechanisms are discussed and grouped into ion effects, thermal effects, and electrical effects. Third, attention is focused on the applications of volatile memristors for access devices, neuromorphic computing (artificial neurons and synapses), and hardware security (true random number generators and physical unclonable functions). Finally, major challenges and future outlook of volatile memristors for neuromorphic computing and hardware security are discussed.

The present paper deals with online convex optimization involving both time-varying loss functions, and timevarying constraints. The loss functions are not fully accessible to the learner, and instead only the function values (a.k.a. bandit feedback) are revealed at queried points. The constraints are revealed after making decisions, and can be instantaneously violated, yet they must be satisfied in the long term. This setting fits nicely the emerging online network tasks such as fog computing in the Internet-of-Things (IoT), where online decisions must flexibly adapt to the changing user preferences (loss functions), and the temporally unpredictable availability of resources (constraints). Tailored for such human-in-the-loop systems where the loss functions are hard to model, a family of bandit online saddlepoint (BanSaP) schemes are developed, which adaptively adjust the online operations based on (possibly multiple) bandit feedback of the loss functions, and the changing environment. Performance here is assessed by: i) dynamic regret that generalizes the widely used static regret; and, ii) fit that captures the accumulated amount of constraint violations. Specifically, BanSaP is proved to simultaneously yield sub-linear dynamic regret and fit, provided that the best dynamic solutions vary slowly over time. Numerical tests in fog computation offloading tasks corroborate that our proposed BanSaP approach offers competitive performance relative to existing approaches that are based on gradient feedback.

performance, superior flexibility, vivid colors, unique transparency, potential lowcost production with solution processing, etc. [1][2][3][4][5][6][7][8][9] The recent years have witnessed a great leap in device performance and device stability, which are attributed to the delicate molecular structure design, advanced morphology manipulation technology, and device structure evolution. [10][11][12][13][14][15][16][17][18][19] Currently, the best-performed OPVs exhibit a certified efficiency of 19.3% for single junction device and 20.0% for tandem structure, as well as an extrapolated device stability with T 80 over 30 years. [20][21][22] While compared to their inorganic counterparts (e.g., for siliconbased PVs, the efficiency is over 26%), the OPVs are still inferior in efficiency. [23] Therefore, it would be urgent to further improve the device efficiency of OPV, which will require an in-depth understanding on the working principles of OPV, as well as the development of effective strategies to balance the charge generation, transport, and recombination. Among all the strategies, it is generally observed that adding a third component to construct the ternary blend is a very simple but effective method to further boost the device performance of OPVs. [24][25][26][27][28][29][30][31][32] A bunch of benefits have been demonstrated with the multicomponentThe ternary blend is demonstrated as an effective strategy to promote the device performance of organic photovoltaics (OPVs) due to the dilution effect. While the compromise between the charge generation and recombination remains a challenge. Here, a mixed diluent strategy for further improving the device efficiency of OPV is proposed. Specifically, the high-performance OPV system with a polymer donor, i.e., PM6, and a nonfullerene acceptor (NFA), i.e., BTP-eC9, is diluted by the mixed diluents, which involve a high bandgap NFA of BTP-S17 and a low bandgap NFA of BTP-S16 (similar with that of the BTP-eC9). The BTP-S17 of better miscibility with BTP-eC9 can dramatically enhance the open-circuit voltage (V OC ), while the BTP-S16 maximizes the charge generation or the short-circuit current density (J SC ). The interplay of BTP-17 and BTP-S16 enables better compromise between charge generation and recombination, thus leading to a high device performance of 19.76% (certified 19.41%), which is the best among single-junction OPVs. Further analysis on carrier dynamics validates the efficacy of mixed diluents for balancing charge generation and recombination, which can be further attributed to the more diverse energetic landscapes and improved morphology. Therefore, this work provides an effective strategy for highperformance OPV for further commercialization.

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Stochastic bilevel optimization generalizes the classic stochastic optimization from the minimization of a single objective to the minimization of an objective function that depends the solution of another optimization problem. Recently, stochastic bilevel optimization is regaining popularity in emerging machine learning applications such as hyper-parameter optimization and model-agnostic meta learning. To solve this class of stochastic optimization problems, existing methods require either double-loop or two-timescale updates, which are sometimes less efficient. This paper develops a new optimization method for a class of stochastic bilevel problems that we term Single-Timescale stochAstic BiLevEl optimization (STABLE) method. STABLE runs in a single loop fashion, and uses a single-timescale update with a fixed batch size. To achieve an -stationary point of the bilevel problem, STABLE requires O( −2 ) samples in total; and to achieve an -optimal solution in the strongly convex case, STABLE requires O( −1 ) samples. To the best of our knowledge, this is the first bilevel optimization algorithm achieving the same order of sample complexity as the stochastic gradient descent method for the single-level stochastic optimization.