Obfuscation-resilient Android Malware Analysis Based on Contrastive Learning

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware analysis can be achieved through contrastive learning. We take the Android malware classification as an example to demonstrate our analysis. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and benign apps (or other types of malware). Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps are obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two widely used datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining 98.2% true positive rate on classifying 8,112 obfuscated malware samples

Accidental Light Probes

Recovering lighting in a scene from a single image is a fundamental problem in computer vision. While a mirror ball light probe can capture omnidirectional lighting, light probes are generally unavailable in everyday images. In this work, we study recovering lighting from accidental light probes (ALPs) -- common, shiny objects like Coke cans, which often accidentally appear in daily scenes. We propose a physically-based approach to model ALPs and estimate lighting from their appearances in single images. The main idea is to model the appearance of ALPs by photogrammetrically principled shading and to invert this process via differentiable rendering to recover incidental illumination. We demonstrate that we can put an ALP into a scene to allow high-fidelity lighting estimation. Our model can also recover lighting for existing images that happen to contain an ALP.Comment: CVPR2023. Project website: https://kovenyu.com/ALP

M3PT: A Multi-Modal Model for POI Tagging

POI tagging aims to annotate a point of interest (POI) with some informative tags, which facilitates many services related to POIs, including search, recommendation, and so on. Most of the existing solutions neglect the significance of POI images and seldom fuse the textual and visual features of POIs, resulting in suboptimal tagging performance. In this paper, we propose a novel Multi-Modal Model for POI Tagging, namely M3PT, which achieves enhanced POI tagging through fusing the target POI's textual and visual features, and the precise matching between the multi-modal representations. Specifically, we first devise a domain-adaptive image encoder (DIE) to obtain the image embeddings aligned to their gold tags' semantics. Then, in M3PT's text-image fusion module (TIF), the textual and visual representations are fully fused into the POIs' content embeddings for the subsequent matching. In addition, we adopt a contrastive learning strategy to further bridge the gap between the representations of different modalities. To evaluate the tagging models' performance, we have constructed two high-quality POI tagging datasets from the real-world business scenario of Ali Fliggy. Upon the datasets, we conducted the extensive experiments to demonstrate our model's advantage over the baselines of uni-modality and multi-modality, and verify the effectiveness of important components in M3PT, including DIE, TIF and the contrastive learning strategy.Comment: Accepted by KDD 202

Sharp kinetic acceleration potentials during mediated redox catalysis of insulators

Redox mediators could catalyse otherwise slow and energy-inefficient cycling of Li-S and Li-O 2 batteries by shuttling electrons/holes between the electrode and the solid insulating storage materials. For mediators to work efficiently they need to oxidize the solid with fast kinetics yet the lowest possible overpotential. Here, we found that when the redox potentials of mediators are tuned via, e.g., Li + concentration in the electrolyte, they exhibit distinct threshold potentials, where the kinetics accelerate several-fold within a range as small as 10 mV. This phenomenon is independent of types of mediators and electrolyte. The acceleration originates from the overpotentials required to activate fast Li + /e – extraction and the following chemical step at specific abundant surface facets. Efficient redox catalysis at insulating solids requires therefore carefully considering the surface conditions of the storage materials and electrolyte-dependent redox potentials, which may be tuned by salt concentrations or solvents

VQ3D: Learning a 3D-Aware Generative Model on ImageNet

Recent work has shown the possibility of training generative models of 3D content from 2D image collections on small datasets corresponding to a single object class, such as human faces, animal faces, or cars. However, these models struggle on larger, more complex datasets. To model diverse and unconstrained image collections such as ImageNet, we present VQ3D, which introduces a NeRF-based decoder into a two-stage vector-quantized autoencoder. Our Stage 1 allows for the reconstruction of an input image and the ability to change the camera position around the image, and our Stage 2 allows for the generation of new 3D scenes. VQ3D is capable of generating and reconstructing 3D-aware images from the 1000-class ImageNet dataset of 1.2 million training images. We achieve an ImageNet generation FID score of 16.8, compared to 69.8 for the next best baseline method.Comment: 15 pages. For visual results, please visit the project webpage at http://kylesargent.github.io/vq3

Machine learning method for 12^{12}C event classification and reconstruction in the active target time-projection chamber

Active target time projection chambers are important tools in low energy radioactive ion beams or gamma rays related researches. In this work, we present the application of machine learning methods to the analysis of data obtained from an active target time projection chamber. Specifically, we investigate the effectiveness of Visual Geometry Group (VGG) and the Residual neural Network (ResNet) models for event classification and reconstruction in decays from the excited $2^+_2$ state in $^{12}$C Hoyle rotation band. The results show that machine learning methods are effective in identifying $^{12}$C events from the background noise, with ResNet-34 achieving an impressive precision of 0.99 on simulation data, and the best performing event reconstruction model ResNet-18 providing an energy resolution of $\sigma_E<77$ keV and an angular reconstruction deviation of $\sigma_{\theta}<0.1$ rad. The promising results suggest that the ResNet model trained on Monte Carlo samples could be used for future classifying and predicting experimental data in active target time projection chambers related experiments.Comment: 9 pages, 10 figures, 9 table

ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Real Image

We introduce a 3D-aware diffusion model, ZeroNVS, for single-image novel view synthesis for in-the-wild scenes. While existing methods are designed for single objects with masked backgrounds, we propose new techniques to address challenges introduced by in-the-wild multi-object scenes with complex backgrounds. Specifically, we train a generative prior on a mixture of data sources that capture object-centric, indoor, and outdoor scenes. To address issues from data mixture such as depth-scale ambiguity, we propose a novel camera conditioning parameterization and normalization scheme. Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes, and propose "SDS anchoring" to improve the diversity of synthesized novel views. Our model sets a new state-of-the-art result in LPIPS on the DTU dataset in the zero-shot setting, even outperforming methods specifically trained on DTU. We further adapt the challenging Mip-NeRF 360 dataset as a new benchmark for single-image novel view synthesis, and demonstrate strong performance in this setting. Our code and data are at http://kylesargent.github.io/zeronvs/Comment: 17 page

Composite measure of physiological dysregulation as a predictor of mortality: The Long Life Family Study

Biological aging results in changes in an organism that accumulate over age in a complex fashion across different regulatory systems, and their cumulative effect manifests in increased physiological dysregulation (PD) and declining robustness and resilience that increase risks of health disorders and death. Several composite measures involving multiple biomarkers that capture complex effects of aging have been proposed. We applied one such approach, the Mahalanobis distance (

Dynamic Determinants of Longevity and Exceptional Health

It is well known from epidemiology that values of indices describing physiological state in a given age may influence human morbidity and mortality risks. Studies of connection between aging and life span suggest a possibility that dynamic properties of age trajectories of the physiological indices could also be important contributors to morbidity and mortality risks. In this paper we use data on longitudinal changes in body mass index, diastolic blood pressure, pulse pressure, pulse rate, blood glucose, hematocrit, and serum cholesterol in the Framingham Heart Study participants, to investigate this possibility in depth. We found that some of the variables describing individual dynamics of the age-associated changes in physiological indices influence human longevity and exceptional health more substantially than the variables describing physiological state. These newly identified variables are promising targets for prevention aiming to postpone onsets of common elderly diseases and increase longevity