Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct query-adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and results are interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of this architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. Through direct cross-dataset evaluation, the proposed Query-Adaptive Convolution (QAConv) method gains large improvements over popular learning methods (about 10%+ mAP), and achieves comparable results to many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method called TLift is proposed, which improves the performance to a further extent, achieving state-of-the-art results in cross-dataset person re-identification. Code is available at https://github.com/ShengcaiLiao/QAConv.Comment: This is the ECCV 2020 version, including the appendi

Linux Kernel Vulnerabilities: State-of-the-Art Defenses and Open Problems

Avoiding kernel vulnerabilities is critical to achieving security of many systems, because the kernel is often part of the trusted computing base. This paper evaluates the current state-of-the-art with respect to kernel protection techniques, by presenting two case studies of Linux kernel vulnerabilities. First, this paper presents data on 141 Linux kernel vulnerabilities discovered from January 2010 to March 2011, and second, this paper examines how well state-of-the-art techniques address these vulnerabilities. The main findings are that techniques often protect against certain exploits of a vulnerability but leave other exploits of the same vulnerability open, and that no effective techniques exist to handle semantic vulnerabilities---violations of high-level security invariants.United States. Defense Advanced Research Projects Agency. Clean-slate design of Resilient, Adaptive, Secure Hosts (Contract #N66001-10-2-4089

Software Fault Isolation with Api Integrity and Multi-Principal Modules

The security of many applications relies on the kernel being secure, but history suggests that kernel vulnerabilities are routinely discovered and exploited. In particular, exploitable vulnerabilities in kernel modules are common. This paper proposes LXFI, a system which isolates kernel modules from the core kernel so that vulnerabilities in kernel modules cannot lead to a privilege escalation attack. To safely give kernel modules access to complex kernel APIs, LXFI introduces the notion of API integrity, which captures the set of contracts assumed by an interface. To partition the privileges within a shared module, LXFI introduces module principals. Programmers specify principals and API integrity rules through capabilities and annotations. Using a compiler plugin, LXFI instruments the generated code to grant, check, and transfer capabilities between modules, according to the programmer's annotations. An evaluation with Linux shows that the annotations required on kernel functions to support a new module are moderate, and that LXFI is able to prevent three known privilege-escalation vulnerabilities. Stress tests of a network driver module also show that isolating this module using LXFI does not hurt TCP throughput but reduces UDP throughput by 35%, and increases CPU utilization by 2.2-3.7x.United States. Defense Advanced Research Projects Agency. Clean-slate design of Resilient, Adaptive, Secure Hosts (Contract number N66001-10-2-4089)National Science Foundation (U.S.). (Grant number CNS-1053143)National Basic Research Program of China (973 Program) (2007CB807901)National Natural Science Foundation (China) (61033001

Frequency Coupling Admittance Modeling of Quasi-PR Controlled Inverter and Its Stability Comparative Analysis under the Weak Grid

This paper intends to comparatively study the stabilities of grid-connected inverters with three closely related controllers: quasi-proportional resonance (quasi-PR), proportional integral (PI), and proportional resonance (PR) under the weak grid. Firstly, considering the influence of frequency coupling characteristic, a frequency coupling admittance model of quasi-PR controlled inverter is established. Then, the admittance characteristics of the quasi-PR, PI and PR controlled inverters are compared. Admittance characteristics of the PI and PR controlled inverters are similar while the quasi-PR controlled inverter is quite different: the amplitude of the quasi-PR controlled inverter is larger than that of the PI controlled inverter and the phase difference between the two inverters is obvious in the mid-high frequency areas, which are mainly caused by the resonance bandwidth of the quasi-PR controller. Furthermore, the stabilities of the quasi-PR, PI and PR controlled inverters are analyzed. The stabilities of the PI and PR controlled inverters are similar but the quasi-PR controlled inverter is more sensitive to weak grid and high inverter output power. To achieve the same system stability, the voltage outer-loop bandwidth of the quasi-PR controlled inverter should be designed narrower than that of the PI and PR controlled inverters. Finally, experiments verify the correctness of the analyses