Existing approaches to online convex optimization 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 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 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 sublinear dynamic regret and fit, provided that the accumulated variations of per-slot minimizers and constraints are sublinearly 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. Numerical experiments demonstrate the performance gain of MOSP relative to the state of the art.
Instrumented nanoindentation was used to investigate the hardness, elastic modulus, and creep behavior of an austenitic Fe-20Cr-25Ni model alloy at room temperature, with the indented grain ...orientation being the variant. The samples indented close to the {111} surfaces exhibited the highest hardness and modulus. However, nanoindentation creep tests showed the greatest tendency for creep in the {111} indented samples, compared with the samples indented close to the {001} and {101} surfaces. Scanning electron microscopy and cross-sectional transmission electron microscopy revealed slip bands and dislocations in all samples. The slip band patterns on the indented surfaces were influenced by the grain orientations. Deformation twinning was observed only under the {001} indented surfaces. Microstructural analysis and molecular dynamics modeling correlated the anisotropic nanoindentation-creep behavior with the different dislocation substructures formed during indentation, which resulted from the dislocation reactions of certain active slip systems that are determined by the indented grain orientations.
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Abstract
TiO
2
-based powder materials have been widely studied as efficient photocatalysts for water splitting due to their low cost, photo-responsivity, earthly abundance, chemical and thermal ...stability, etc. In particular, the recent breakthrough of nitrogen-doped TiO
2
, which enhances the presence of structural defects and dopant impurities at elevated temperatures, exhibits an impressive visible-light absorption for photocatalytic activity. Although their electronic and optical properties have been extensively studied, the structure-activity relationship and photocatalytic mechanism remain ambiguous. Herein, we report an in-depth structural study of rutile, anatase and mixed phases (commercial P25) with and without nitrogen-doping by variable-temperature synchrotron X-ray powder diffraction. We report that an unusual anisotropic thermal expansion of the anatase phase can reveal the intimate relationship between sub-surface oxygen vacancies, nitrogen-doping level and photocatalytic activity. For highly doped anatase, a new cubic titanium oxynitride phase is also identified which provides important information on the fundamental shift in absorption wavelength, leading to excellent photocatalysis using visible light.
Lane-changing (LC) is an essential driving maneuver on roadways, and risky LC maneuvers account for a large number of crash accidents. This study investigates the LC risk profile during an LC ...process. A risk indicator based on driving safety field theory is employed to measure the instantaneous LC risk at each timestamp during an LC process and generate the LC risk profile. Then, Dynamic Time Warping (DTW) k-means clustering, as a time-series clustering method, is applied to partition the LC risk profiles into several categories. The Next Generation Simulation (NGSIM) US-101 dataset, which contains detailed records of vehicles’ trajectories, is used for case study. In the case study, the LC risk profiles are categorized into “uphill” shape, “bell” shape, and “downhill” shape. The LC risk profiles with “uphill” shape account for the majority of the LC risk profiles. Besides, we find that the LC process with “uphill” shaped risk profile generally has higher LC risk, and the crash risk between LC car and its preceding cars are more relevant to the LC risk. Those findings are likely due to the LC maneuver with the purpose to overtake the preceding car in the original lane. The risk indicator based on driving safety field theory can measure LC risk more comprehensively, compared to the conventional surrogate measures. The DTW k-means clustering method offers a promising approach to investigate the causation of risky LC maneuver based on the risk profile during an LC process.
This paper focuses on exploring the application possibilities and optimization problems of Generative Adversarial Networks (GANs) in spatial computing to improve design efficiency and creativity and ...achieve a more intelligent design process. A method for icon generation is proposed, and a basic architecture for icon generation is constructed. A system with generation and optimization capabilities is constructed to meet various requirements in spatial design by introducing the concept of interactive design and the characteristics of requirement conditions. Next, the generated icons can effectively maintain diversity and innovation while meeting the conditional features by integrating multi-feature recognition modules into the discriminator and optimizing the structure of conditional features. The experiment uses publicly available icon datasets, including LLD-Icon and Icons-50. The icon shape generated by the model proposed here is more prominent, and the color of colored icons can be more finely controlled. The Inception Score (IS) values under different models are compared, and it is found that the IS value of the proposed model is 7.05, which is higher than that of other GAN models. The multi-feature icon generation model based on Auxiliary Classifier GANs performs well in presenting multiple feature representations of icons. After introducing multi-feature recognition modules into the network model, the peak error of the recognition network is only 2.000 in the initial stage, while the initial error of the ordinary GAN without multi-feature recognition modules is as high as 5.000. It indicates that the improved model effectively helps the discriminative network recognize the core information of icon images more quickly. The research results provide a reference basis for achieving more efficient and innovative interactive space design.
Existing resource allocation approaches for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances ...to facilitate online resource allocation tasks. By recognizing the central role of Lagrange multipliers, the underlying constrained optimization problem is formulated as a machine learning task involving both training and operational modes, with the goal of learning the sought multipliers in a fast and efficient manner. To this end, an order-optimal offline learning approach is developed first for batch training, and it is then generalized to the online setting with a procedure termed learn-and-adapt. The novel resource allocation protocol permeates benefits of stochastic approximation and statistical learning to obtain low-complexity online updates with learning errors close to the statistical accuracy limits, while still preserving adaptation performance, which in the stochastic network optimization context guarantees queue stability. Analysis and simulated tests demonstrate that the proposed data-driven approach improves the delay and convergence performance of existing resource allocation schemes.
A large number of geo-distributed data centers begin to surge in the era of data deluge and information explosion. To meet the growing demand in massive data processing, the infrastructure of future ...data centers must be energy-efficient and sustainable. Facing this challenge, a systematic framework is put forth in this paper to integrate renewable energy sources (RES), distributed storage units, cooling facilities, as well as dynamic pricing into the workload and energy management tasks of a data center network. To cope with RES uncertainty, the resource allocation task is formulated as a robust optimization problem minimizing the worst-case net cost. Compared with existing stochastic optimization methods, the proposed approach entails a deterministic uncertainty set where generated RES reside, thus can be readily obtained in practice. It is further shown that the problem can be cast as a convex program, and then solved in a distributed fashion using the dual decomposition method. By exploiting the spatio-temporal diversity of local temperature, workload demand, energy prices, and renewable availability, the proposed approach outperforms existing alternatives, as corroborated by extensive numerical tests performed using real data.
SUMMARY
Multichannel singular spectrum analysis (MSSA) is a powerful tool to extract spatiotemporal signals and filter errors from the noisy time-series of monthly gravity field models from the ...satellite data of gravity recovery and climate experiment (GRACE). Since the GRACE monthly gravity models are missed about 17 months, we develop an improved MSSA approach, which can directly process the incomplete time-series without either data interpolation or iteration. The time-series of 14-yr (2002.04–2016.08) monthly gravity field models of CSR-RL06 up to degree and order 60 are analysed with improved MSSA compared to the MSSA with linear data interpolation and iteration MSSA. By using our improved MSSA approach, the first 11 principal components derived can capture 91.18 per cent of the total variance, higher than 85.80 and 86.44 per cent of the total variance, derived by linear interpolation MSSA and iteration MSSA, respectively. The ratios of the latitude weighted RMS over the land and ocean signals are used to evaluate the efficiency of eliminating noise by the MSSA approach. For improved MSSA, the mean RMS ratio of land and ocean signals of all available months is higher than linear interpolation and iteration MSSA, which indicates that improved MSSA can suppress noise more efficiently and extract more geophysical signals from real GRACE data. Furthermore, the 50 repeated experiments show that all the root mean squared errors and mean absolute errors derived by our improved MSSA are smaller than other MSSA approaches. Moreover, the improved MSSA performs still better than other MSSA based approaches for the cases of large data gaps.
Secondary organic aerosol (SOA) created from the photooxidation of a mixture of isoprene and dimethyl sulfide (DMS) was studied at different NO
x
concentrations (40–220 ppb) and humidities (12%, 42% ...and 80%) using a Teflon film indoor chamber. To study the effect of isoprene on DMS products, the major DMS photooxidation products, such as sulfuric acid, methanesulfonic acid (MSA) and methanesulfinic acid (MSIA), were quantified in both the presence and the absence of isoprene using a Particle-Into-Liquid-Sampler coupled with Ion Chromatography (PILS-IC). The resulting PILS-IC data showed that the DMS aerosol yield significantly decreased due to the photooxidation of isoprene. A 35.2% DMS aerosol yield reduction was observed due to 800 ppb isoprene in 185 ppb NO
x
and 140 ppb DMS. Among the aerosol-phase DMS oxidation products, MSA was the most sensitive to the presence of isoprene (e.g., 46% reduction). The DMS aerosol product analysis indicates that isoprene oxidation affects the pathways of MSA formation on the aerosol surface. Using a new approach that implements an Organic Carbon (OC) analyzer, the isoprene SOA yield (
Y
iso) in the DMS/isoprene/NO
x
system was also estimated. The OC data showed that
Y
iso increased significantly with DMS compared to the
Y
iso without DMS. For example,
Y
iso with 80 ppb NO
x
and 840 ppb isoprene was increased by 124.6% due to 100 ppb DMS at RH = 42%. Our study suggests that the heterogeneous reactions of isoprene oxidation products with the highly acidic products (e.g., MSA and sulfuric acid) from DMS photooxidation can considerably contribute to the
Y
iso increase.
► The impact of DMS on isoprene SOA yields at different humidity and NO
x
conditions. ► DMS aerosol products quantified using a PILS-IC. ► Isoprene SOA yields and DMS aerosol yields from photooxidation of isoprene and DMS.
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