Evaluating Information Leakage by Quantitative and Interpretable Measurements

Noninterference, a strong security property for a computation process, informally says that the process output is insensitive to the value of its secret inputs – the secret inputs do not "interfere" with those outputs. This is too strong, however; a degree of interference is necessary in almost all real systems. In this dissertation, we propose a measure of noninterference that is more practical. Based on a model of computations with three types of input (secret, attacker-controlled, and others) and an attacker-observable output, we define a noninterference measure that can assess and explain information leaks in actual codebases. We start with assessing a new defense against cache-based side-channel attacks in a cloud environment, using an experiment-based quantitative measure of leakage against existing attacks. It is not enough to measure leakage through empirical analysis, however, as it fails to identify new interference introduced by a weak defense design. We propose a symbolic execution framework to formally measure interference in simple software procedures, encompassing any interference from a set of secret inputs to observable outputs. Leveraging approximate model counting techniques, we make this framework scalable with parallelization. Unfortunately, this technique does not scale to support analysis of hardware processor designs, in part due to its reliance on symbolic execution to create a logical postcondition of the computation. We thus modified the framework to sidestep symbolic execution when analyzing processor designs. To further tame the complexity due to various sources of interference, we extend our framework to remove, or declassify, certain interference from consideration, so that the framework instead highlights other forms of interference, and to provide human-interpretable rules that explain the conditions under which interference occurs. We demonstrate the practicality of the work through case studies of both software-based leakage and vulnerabilities in the RISC-V BOOM core with different configurations.Doctor of Philosoph

Natural Algae-Inspired Microrobots for Emerging Biomedical Applications and Beyond

Algae-inspired microrobots (AIMs) have attracted intense research over the past decade owing to the abundant desired properties of natural microalgae, such as biocompatibility, autofluorescence, and pharmaceutical activity, which make them ideal candidates for biomedical and related applications. With the deepening and widening of applied research, the functions of AIMs have been greatly enriched and enhanced to meet the needs of demanding application scenarios including targeted drug delivery, anticancer/antibacterial therapy, cell stimulation, wound healing, and biomolecule sensing. Notwithstanding, multiple challenges remain to be tackled for transformative advances and clinical translation. In this review, we aim to provide a comprehensive survey of representative advances in AIMs accompanied by the underlying biological/technological backgrounds. We also highlight existing issues that need to be overcome in AIM developments and suggest future research directions in this field.</p

Activity Cliff Prediction: Dataset and Benchmark

Activity cliffs (ACs), which are generally defined as pairs of structurally similar molecules that are active against the same bio-target but significantly different in the binding potency, are of great importance to drug discovery. Up to date, the AC prediction problem, i.e., to predict whether a pair of molecules exhibit the AC relationship, has not yet been fully explored. In this paper, we first introduce ACNet, a large-scale dataset for AC prediction. ACNet curates over 400K Matched Molecular Pairs (MMPs) against 190 targets, including over 20K MMP-cliffs and 380K non-AC MMPs, and provides five subsets for model development and evaluation. Then, we propose a baseline framework to benchmark the predictive performance of molecular representations encoded by deep neural networks for AC prediction, and 16 models are evaluated in experiments. Our experimental results show that deep learning models can achieve good performance when the models are trained on tasks with adequate amount of data, while the imbalanced, low-data and out-of-distribution features of the ACNet dataset still make it challenging for deep neural networks to cope with. In addition, the traditional ECFP method shows a natural advantage on MMP-cliff prediction, and outperforms other deep learning models on most of the data subsets. To the best of our knowledge, our work constructs the first large-scale dataset for AC prediction, which may stimulate the study of AC prediction models and prompt further breakthroughs in AI-aided drug discovery. The codes and dataset can be accessed by https://drugai.github.io/ACNet/

Interface induced Zeeman-protected superconductivity in ultrathin crystalline lead films

Two dimensional (2D) superconducting systems are of great importance to exploring exotic quantum physics. Recent development of fabrication techniques stimulates the studies of high quality single crystalline 2D superconductors, where intrinsic properties give rise to unprecedented physical phenomena. Here we report the observation of Zeeman-type spin-orbit interaction protected superconductivity (Zeeman-protected superconductivity) in 4 monolayer (ML) to 6 ML crystalline Pb films grown on striped incommensurate (SIC) Pb layers on Si(111) substrates by molecular beam epitaxy (MBE). Anomalous large in-plane critical field far beyond the Pauli limit is detected, which can be attributed to the Zeeman-protected superconductivity due to the in-plane inversion symmetry breaking at the interface. Our work demonstrates that in superconducting heterostructures the interface can induce Zeeman-type spin-orbit interaction (SOI) and modulate the superconductivity

A Three-Dimensional Wireless Indoor Localization System

Indoor localization, an emerging technology in location based service (LBS), is now playing a more and more important role both in commercial and in civilian industry. Global position system (GPS) is the most popular solution in outdoor localization field, and the accuracy is around 10 meter error in positioning. However, with complex obstacles in buildings, problems rise in the “last mile” of localization field, which encourage a momentum of indoor localization. The traditional indoor localization system is either range-based or fingerprinting-based, which requires a lot of time and efforts to do the predeployment. In this paper, we present a 3-dimensional on-demand indoor localization system (3D-ODIL), which can be fingerprint-free and deployed rapidly in a multistorey building. The 3D-ODIL consists of two phases, vertical localization and horizontal localization. On vertical direction, we propose multistorey differential (MSD) algorithm and implement it to fulfill the vertical localization, which can greatly reduce the number of anchors deployed. We use enhanced field division (EFD) algorithm to conduct the horizontal localization. EFD algorithm is a range-free algorithm, the main idea of which is to dynamically divide the field within different signature area and position the target. The accuracy and performance have been validated through our extensive analysis and systematic experiments

CCNB1IP1 prevents ubiquitination‐mediated destabilization of MYCN and potentiates tumourigenesis of MYCN‐amplificated neuroblastoma

Abstract Background MYCN amplification as a common genetic alteration that correlates with a poor prognosis for neuroblastoma (NB) patients. However, given the challenge of directly targeting MYCN, indirect strategies to modulate MYCN by interfering with its cofactors are attractive in NB treatment. Although cyclin B1 interacting protein 1 (CCNB1IP1) has been found to be upregulated in MYCN‐driven mouse NB tissues, its regulation with MYCN and collaboration in driving the biological behaviour of NB remains unknown. Methods To evaluate the expression and clinical significance of CCNB1IP1 in NB patients, public datasets, clinical NB samples and cell lines were explored. MTT, EdU incorporation, colony and tumour sphere formation assays, and a mouse xenograft tumour model were utilized to examine the biological function of CCNB1IP1. The reciprocal manipulation of CCNB1IP1 and MYCN and the underlying mechanisms involved were investigated by gain‐ and loss‐of‐function approaches, dual‐luciferase assay, chromatin immunoprecipitation (CHIP) and co‐immunoprecipitation (Co‐IP) experiments. Results CCNB1IP1 was upregulated in MYCN‐amplified (MYCN‐AM) NB cell lines and patients‐derived tumour tissues, which was associated with poor prognosis. Phenotypic studies revealed that CCNB1IP1 facilitated the proliferation and tumourigenicity of NB cells in cooperation with MYCN in vitro and in vivo. Mechanistically, MYCN directly mediates the transcription of CCNB1IP1, which in turn attenuated the ubiquitination and degradation of MYCN protein, thus enhancing CCNB1IP1‐MYCN cooperativity. Moreover, CCNB1IP1 competed with F box/WD‐40 domain protein 7 (FBXW7) for MYCN binding and enabled MYCN‐mediated tumourigenesis in a C‐terminal domain‐dependent manner. Conclusions Our study revealed a previously uncharacterized mechanism of CCNB1IP1‐mediated MYCN protein stability and will provide new prospects for precise treatment of MYCN‐AM NB based on MYCN‐CCNB1IP1 interaction

Turbo Gradient and Spin-Echo BLADE-DWI for Extraocular Muscles in Thyroid-Associated Ophthalmopathy

Purpose: To investigate feasibility and diagnostic performance of turbo gradient and spin-echo BLADE (proprietary name for Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction [PROPELLER] in Siemens MR systems)-diffusion weighted imaging (TGSE-BLADE-DWI) for depicting extraocular muscle (EOM) involvement and activity in thyroid-associated ophthalmopathy (TAO), and to compare TGSE-BLADE-DWI with readout-segmented echo-planar imaging (RESOLVE). Materials and methods: Thirty-five patients with identified TAO underwent the two DWI scans. Two radiologists visually scored the image quality of the two DWIs with respect to the susceptibility artifacts and geometric distortions on a three-point scale. The maximum size (Sizemax) of EOMs and corresponding ADCs (cADCs) of each patient were compared between the active and inactive phases. The clinical activity score (CAS) was used as a reference to assess the diagnostic performance of EOM ADCs for grading TAO activity. ROC analysis, Pearson correlation, and Wilcoxon signed-rank test were used for statistical analyses. Results: For scores of EOMs, the image quality of TGSE-BLADE-DWI was significantly higher than that of RESOLVE. There were no statistically significant differences between the AUCs of the two DWIs, Sizemax, or cADCs between the active and inactive phases. TGSE-BLADE-DWI ADCs were significantly higher than the RESOLVE ADCs in the right superior rectus, right lateral rectus, left superior rectus, and left inferior rectus. There were no statistically significant correlations between the cADC or Sizemax, and CAS. The highest AUC was 0.697 for RESOLVE and 0.657 for TGSE-BLADE-DWI. The best performing ADC threshold was 1.85 × 10−3 mm2/s with 85.7% sensitivity, 58.8% specificity and 66.67% accuracy for RESOLVE and 1.99 × 10−3 mm2/s with 79.0% sensitivity, and 55.6% specificity and 65.27% accuracy for TGSE-BLADE-DWI. Conclusion: Compared to RESOLVE, TGSE-BLADE-DWI provided improved image quality with fewer susceptibility artifacts and geometric distortions for EOM visualization and showed an equivalent performance in detecting active TAO

Blends of Cyanate Ester and Phthalonitrile–Polyhedral Oligomeric Silsesquioxane Copolymers: Cure Behavior and Properties

Blends of cyanate ester and phthalonitrile&ndash;polyhedral oligomeric silsesquioxane copolymers were prepared, and their cure behavior and properties were compared via differential scanning calorimetry (DSC) analysis, thermogravimetric (TG) analysis, dynamic mechanical analysis, Fourier-transform far-infrared (FTIR) spectroscopy, and rheometric studies. The copolymer blends showed high chemical reactivity, low viscosity, and good thermal stability (TG temperatures were above 400 &deg;C). The glass-transition temperature of the blends increased by at least 140 &deg;C compared to cyanate ester resin. The blends are suitable for preparing carbon-fiber-reinforced composite materials via a winding process and a prepreg lay-up process with a molding technique. The FTIR data showed that the polymerization products contained triazine-ring structures that were responsible for the superior thermal properties