Renormalized solutions of a nonlinear parabolic equation with double degeneracy

In this paper, we consider the initial-boundary value problem of a nonlinear parabolic equation with double degeneracy, and establish the existence and uniqueness theorems of renormalized solutions which are stronger than $BV$ solutions

Synergy of Multiple Satellite Observations in the Study of Cloud Thermodynamics of Tropical Deep Convection.

Tropical convection lies at the heart of atmospheric research, especially for global weather and climate predictions; satellite measurements with large spatial coverage provide valuable information to deepen and broaden our scientific understandings of this subject. This thesis is motivated to utilize satellite measurements with assistance of modeling tools in a synergistic way to study tropical deep convection. First a generic parallax correction method is proposed to remove the biases resulting from the mismatch of satellite footprints due to different sensor viewing angles targeting the same object. Second a non-blackbody correction is proposed to better estimate cloud top temperature utilizing the vertical structure within the cloud top layer probed by CloudSat and CALIPSO. The distance between the physical cloud top and the effective emission level is shown to have a linear dependence on cloud top fuzziness (CTF; difference between cloud top and 10dBz radar echo) when CTF is less than ~2km. Beyond this threshold, the effective emission level remains 0.74km below the cloud top due to the saturation of IR absorption and emission. This relationship clearly improves simulated MODIS radiances comparing with the observed counterparts. The distribution of cloud top buoyancy for tropical deep convections derived using cloud top and ambient condition indicates that convective development is sensitive to both land-ocean contrast and diurnal cycle. Under certain assumptions, vertical velocity inside the convective core is derived and the result is consistent with typical vertical velocity profiles observed by air-bone Doppler radars for tropical deep convections, such as the altitude for the maximum vertical velocity and the existence of a weak detrainment layer in the mid-troposphere. GCM simulations indicate that overshooting deep convection could be responsible for the vertical transport of black carbon into the stratosphere especially over the India subcontinent during South Asia summer monsoon, and that black carbon in the stratosphere is transported upward at as large as twice the speed of water vapor transport. To explore a possible observational strategy for such injection of black carbon into the stratosphere, a limb-view infrared detection method is proposed based on forward modeling of radiative transfer and the simulated profiles.PhDAtmospheric, Oceanic and Space SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/109016/1/cpwang_1.pd

Public key encryption with keyword search secure against keyword guessing attacks without random oracle

The notion of public key encryption with keyword search (PEKS) was put forth by Boneh et al. to enable a server to search from a collection of encrypted emails given a “trapdoor” (i.e., an encrypted keyword) provided by the receiver. The nice property in this scheme allows the server to search for a keyword, given the trapdoor. Hence, the verifier can merely use an untrusted server, which makes this notion very practical. Following Boneh et al.’s work, there have been subsequent works that have been proposed to enhance this notion. Two important notions include the so-called keyword guessing attack and secure channel free, proposed by Byun et al. and Baek et al., respectively. The former realizes the fact that in practice, the space of the keywords used is very limited, while the latter considers the removal of secure channel between the receiver and the server to make PEKS practical. Unfortunately, the existing construction of PEKS secure against keyword guessing attack is only secure under the random oracle model, which does not reflect its security in the real world. Furthermore, there is no complete definition that captures secure channel free PEKS schemes that are secure against chosen keyword attack, chosen ciphertext attack, and against keyword guessing attacks, even though these notions seem to be the most practical application of PEKS primitives. In this paper, we make the following contributions. First, we define the strongest model of PEKS which is secure channel free and secure against chosen keyword attack, chosen ciphertext attack, and keyword guessing attack. In particular, we present two important security notions namely IND-SCF-CKCA and IND-KGA. The former is to capture an inside adversary, while the latter is to capture an outside adversary. Intuitively, it should be clear that IND-SCF-CKCA captures a more stringent attack compared to IND-KGA. Second, we present a secure channel free PEKS scheme secure without random oracle under the well known assumptions, namely DLP, DBDH, SXDH and truncated q-ABDHE assumption. Our contributions fill the gap in the literature and hence, making the notion of PEK

Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweighting

Recently, many semi-supervised object detection (SSOD) methods adopt teacher-student framework and have achieved state-of-the-art results. However, the teacher network is tightly coupled with the student network since the teacher is an exponential moving average (EMA) of the student, which causes a performance bottleneck. To address the coupling problem, we propose a Cycle Self-Training (CST) framework for SSOD, which consists of two teachers T1 and T2, two students S1 and S2. Based on these networks, a cycle self-training mechanism is built, i.e., S1${\rightarrow}$T1${\rightarrow}$S2${\rightarrow}$T2${\rightarrow}$S1. For S${\rightarrow}$T, we also utilize the EMA weights of the students to update the teachers. For T${\rightarrow}$S, instead of providing supervision for its own student S1(S2) directly, the teacher T1(T2) generates pseudo-labels for the student S2(S1), which looses the coupling effect. Moreover, owing to the property of EMA, the teacher is most likely to accumulate the biases from the student and make the mistakes irreversible. To mitigate the problem, we also propose a distribution consistency reweighting strategy, where pseudo-labels are reweighted based on distribution consistency across the teachers T1 and T2. With the strategy, the two students S2 and S1 can be trained robustly with noisy pseudo labels to avoid confirmation biases. Extensive experiments prove the superiority of CST by consistently improving the AP over the baseline and outperforming state-of-the-art methods by 2.1% absolute AP improvements with scarce labeled data.Comment: ACM Multimedia 202