Higher-order symmetric duality in nondifferentiable multiobjective programming problems

AbstractIn this paper, a pair of nondifferentiable multiobjective programming problems is first formulated, where each of the objective functions contains a support function of a compact convex set in Rn. For a differentiable function h:Rn×Rn→R, we introduce the definitions of the higher-order F-convexity (F-pseudo-convexity, F-quasi-convexity) of function f:Rn→R with respect to h. When F and h are taken certain appropriate transformations, all known other generalized invexity, such as η-invexity, type I invexity and higher-order type I invexity, can be put into the category of the higher-order F-invex functions. Under these the higher-order F-convexity assumptions, we prove the higher-order weak, higher-order strong and higher-order converse duality theorems related to a properly efficient solution

Sensitivity of modeled far‐IR radiation budgets in polar continents to treatments of snow surface and ice cloud radiative properties

While most general circulation models assume spectrally independent surface emissivity and nonscattering clouds in their longwave radiation treatment, spectral variation of the index of refraction of ice indicates that in the far IR, snow surface emissivity can vary considerably and ice clouds can cause nonnegligible scattering. These effects are more important for high‐elevation polar continents where the dry and cold atmosphere is not opaque in the far IR. We carry out sensitivity studies to show that in a winter month over the Antarctic Plateau including snow surface spectral emissivity and ice cloud scattering in radiative transfer calculation reduces net upward far‐IR flux at both top of atmosphere and surface. The magnitudes of such reductions in monthly mean all‐sky far‐IR flux range from 0.72 to 1.47 Wm −2 , with comparable contributions from the cloud scattering and the surface spectral emissivity. The reduction is also sensitive to sizes of both snow grains and cloud particles. Key Points Ice cloud and snow surface radiative properties vary considerably in the far IR Snow surface emissivity and cloud scattering affect far IR comparably Even for far‐IR radiation alone, the impact is nonnegligiblePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109339/1/Auxiliary_material_Aug27.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109339/2/grl52118.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109339/3/TableS01.pd

Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks

Cloud radiative feedback is central to our projection of future climate change. It can be estimated using the cloud radiative kernel (CRK) method or adjustment method. This study, for the first time, examines the contributions of each spectral band to the longwave (LW) cloud radiative feedbacks (CRFs). Simulations of three warming scenarios are analyzed, including +2 K sea surface temperature, 2 × CO2, and 4 × CO2 experiments. While the LW broadband CRFs derived from the CRK and adjustment methods agree with each other, they disagree on the relative contributions from the far‐infrared and window bands. The CRK method provides a consistent band‐by‐band decomposition of LW CRF for different warming scenarios. The simulated and observed short‐term broadband CRFs for the 2003–2013 period are similar to the long‐term counterparts, but their band‐by‐band decompositions are different, which can be further related to the cloud fraction changes in respective simulations and observation.Plain Language SummaryWe studied how the cloud change in response to surface temperature change leads to the changes of radiation at the top of the atmosphere (referred to as cloud radiative feedback) over different frequency ranges in the longwave (referred to as spectral bands). While different methods can provide a similar estimate of broadband cloud radiative feedbacks, the decomposition to different longwave spectral bands can be different from one method to another. The cloud radiative kernel method can provide a more consistent band‐by‐band decomposition of the longwave cloud radiative feedback for different warming scenarios. The decomposition for cloud radiative feedback derived from the warming experiments is considerably different from that derived from decadal‐scale observations and simulations. Such differences in spectral band decomposition can be related to the specific cloud fraction changes for different types of clouds defined with respect to cloud top pressure and cloud opacity.Key PointsThe band‐by‐band decomposition of cloud radiative feedback is studied for the first timeTwo different methods can give similar longwave broadband radiative feedbacks, but their band‐by‐band decompositions are differentSeemingly agreeable broadband cloud radiative feedbacks can have different spectral decompositions, which can be related to cloud changesPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150592/1/grl59162_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150592/2/grl59162.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150592/3/grl59162-sup-0001-2019GL083466-SI.pd

Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model

Frozen and unfrozen surfaces exhibit different longwave surface emissivities with different spectral characteristics, and outgoing longwave radiation and cooling rates are reduced for unfrozen scenes relative to frozen ones. Here physically realistic modeling of spectrally resolved surface emissivity throughout the coupled model components of the Community Earth System Model (CESM) is advanced, and implications for model high‐latitude biases and feedbacks are evaluated. It is shown that despite a surface emissivity feedback amplitude that is, at most, a few percent of the surface albedo feedback amplitude, the inclusion of realistic, harmonized longwave, spectrally resolved emissivity information in CESM1.2.2 reduces wintertime Arctic surface temperature biases from −7.2 ± 0.9 K to −1.1 ± 1.2 K, relative to observations. The bias reduction is most pronounced in the Arctic Ocean, a region for which Coupled Model Intercomparison Project version 5 (CMIP5) models exhibit the largest mean wintertime cold bias, suggesting that persistent polar temperature biases can be lessened by including this physically based process across model components. The ice emissivity feedback of CESM1.2.2 is evaluated under a warming scenario with a kernel‐based approach, and it is found that emissivity radiative kernels exhibit water vapor and cloud cover dependence, thereby varying spatially and decreasing in magnitude over the course of the scenario from secular changes in atmospheric thermodynamics and cloud patterns. Accounting for the temporally varying radiative responses can yield diagnosed feedbacks that differ in sign from those obtained from conventional climatological feedback analysis methods.Plain Language SummaryClimate models have exhibited a persistent cold‐pole bias, whereby they systematically underestimate the average temperature and the amplification of climate change at high latitudes. A number of different explanations have been advanced for cold‐pole biases, which can be broadly divided into radiative and dynamic explanations. Here we explore in detail a relatively novel radiative explanation for the cold‐pole bias: the ice emissivity feedback. Similar to the difference in shortwave reflectivity of unfrozen and frozen surfaces, recent literature has shown that unfrozen surfaces are less emissive than frozen surfaces, which can induce a positive radiative feedback. We first present the highly nontrivial implementation of this feedback in a global circulation model (GCM) and show how to harmonize the disjointed representation of surface emissivity within the radiative transfer calculated by atmospheric and land components of a GCM. With this modified model, we show how this ice emissivity feedback depends on atmospheric water vapor and thus varies on time scales ranging from seasonal to centennial. We also show that the ice emissivity feedback is seasonally complementary to the well‐known ice‐albedo feedback, where the former is most influential during polar night. Finally, we show that including this feedback essentially eliminates the cold‐pole bias on the model we used.Key PointsLW spectral surface emissivity improves CESM Arctic surface temperature bias by 6.1 ± 1.9 degrees KelvinSpectral emissivity kernels computed for 200+ period are nonlinear in timeTemporally and spatially localized atmospheric dynamics show decreased climatological seasonal sea ice emissivity radiative response in ArcticPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142486/1/jgrd54377_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142486/2/jgrd54377.pd

Intelligent gold nanocluster for effective treatment of malignant tumor via tumor-specific photothermal-chemodynamic therapy with AIE guidance.

Precise and efficient therapy of malignant tumors is always a challenge. Herein, gold nanoclusters co-modified by aggregation-induced-emission (AIE) molecules, copper ion chelator (acylthiourea) and tumor-targeting agent (folic acid) were fabricated to perform AIE-guided and tumor-specific synergistic therapy with great spatio-temporal controllability for the targeted elimination and metastasis inhibition of malignant tumors. During therapy, the functional gold nanoclusters (AuNTF) would rapidly accumulate in the tumor tissue due to the enhanced permeability and retention effect as well as folic acid-mediated tumor targeting, which was followed by endocytosis by tumor cells. After that, the overexpressed copper ions in the tumor cells would trigger the aggregation of these intracellular AuNTF via a chelation process that not only generated the photothermal agent in situ to perform the tumor-specific photothermal therapy damaging the primary tumor, but also led to the copper deficiency of tumor cells to inhibit its metastasis. Moreover, the copper ions were reduced to cuprous ions along with the chelation, which further catalysed the excess H2O2 in the tumor cells to produce cytotoxic reactive oxygen species, resulting in additional chemodynamic therapy for enhanced antitumor efficiency. The aggregation of AuNTF also activated the AIE molecules to present fluorescence, which not only imaged the therapeutic area for real-time monitoring of this tumor-specific synergistic therapy, but also allowed us to perform near-infrared radiation at the correct time point and location to achieve optimal photothermal therapy. Both in vitro and in vivo results revealed the strong tumor elimination, effective metastasis inhibition and high survival rate of tumor-bearing mice after treatment using the AuNTF nanoclusters, indicating that this AIE-guided and tumor-specific synergistic strategy could offer a promising approach for tumor therapy

A Physically Based Algorithm for Non-Blackbody Correction of Cloud-Top Temperature and Application to Convection Study

Cloud-top temperature (CTT) is an important parameter for convective clouds and is usually different from the 11-micrometers brightness temperature due to non-blackbody effects. This paper presents an algorithm for estimating convective CTT by using simultaneous passive [Moderate Resolution Imaging Spectroradiometer (MODIS)] and active [CloudSat 1 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements of clouds to correct for the non-blackbody effect. To do this, a weighting function of the MODIS 11-micrometers band is explicitly calculated by feeding cloud hydrometer profiles from CloudSat and CALIPSO retrievals and temperature and humidity profiles based on ECMWF analyses into a radiation transfer model.Among 16 837 tropical deep convective clouds observed by CloudSat in 2008, the averaged effective emission level (EEL) of the 11-mm channel is located at optical depth; approximately 0.72, with a standard deviation of 0.3. The distance between the EEL and cloud-top height determined by CloudSat is shown to be related to a parameter called cloud-top fuzziness (CTF), defined as the vertical separation between 230 and 10 dBZ of CloudSat radar reflectivity. On the basis of these findings a relationship is then developed between the CTF and the difference between MODIS 11-micrometers brightness temperature and physical CTT, the latter being the non-blackbody correction of CTT. Correction of the non-blackbody effect of CTT is applied to analyze convective cloud-top buoyancy. With this correction, about 70% of the convective cores observed by CloudSat in the height range of 6-10 km have positive buoyancy near cloud top, meaning clouds are still growing vertically, although their final fate cannot be determined by snapshot observations

FlashDecoding++: Faster Large Language Model Inference on GPUs

As the Large Language Model (LLM) becomes increasingly important in various domains. However, the following challenges still remain unsolved in accelerating LLM inference: (1) Synchronized partial softmax update. The softmax operation requires a synchronized update operation among each partial softmax result, leading to ~20% overheads for the attention computation in LLMs. (2) Under-utilized computation of flat GEMM. The shape of matrices performing GEMM in LLM inference is flat, leading to under-utilized computation and >50% performance loss after padding zeros in previous designs. (3) Performance loss due to static dataflow. Kernel performance in LLM depends on varied input data features, hardware configurations, etc. A single and static dataflow may lead to a 50.25% performance loss for GEMMs of different shapes in LLM inference. We present FlashDecoding++, a fast LLM inference engine supporting mainstream LLMs and hardware back-ends. To tackle the above challenges, FlashDecoding++ creatively proposes: (1) Asynchronized softmax with unified max value. FlashDecoding++ introduces a unified max value technique for different partial softmax computations to avoid synchronization. (2) Flat GEMM optimization with double buffering. FlashDecoding++ points out that flat GEMMs with different shapes face varied bottlenecks. Then, techniques like double buffering are introduced. (3) Heuristic dataflow with hardware resource adaptation. FlashDecoding++ heuristically optimizes dataflow using different hardware resource considering input dynamics. Due to the versatility of optimizations in FlashDecoding++, FlashDecoding++ can achieve up to 4.86x and 2.18x speedup on both NVIDIA and AMD GPUs compared to Hugging Face implementations. FlashDecoding++ also achieves an average speedup of 1.37x compared to state-of-the-art LLM inference engines on mainstream LLMs