Unveiling Single-Bit-Flip Attacks on DNN Executables

Recent research has shown that bit-flip attacks (BFAs) can manipulate deep neural networks (DNNs) via DRAM Rowhammer exploitations. Existing attacks are primarily launched over high-level DNN frameworks like PyTorch and flip bits in model weight files. Nevertheless, DNNs are frequently compiled into low-level executables by deep learning (DL) compilers to fully leverage low-level hardware primitives. The compiled code is usually high-speed and manifests dramatically distinct execution paradigms from high-level DNN frameworks. In this paper, we launch the first systematic study on the attack surface of BFA specifically for DNN executables compiled by DL compilers. We design an automated search tool to identify vulnerable bits in DNN executables and identify practical attack vectors that exploit the model structure in DNN executables with BFAs (whereas prior works make likely strong assumptions to attack model weights). DNN executables appear more "opaque" than models in high-level DNN frameworks. Nevertheless, we find that DNN executables contain extensive, severe (e.g., single-bit flip), and transferrable attack surfaces that are not present in high-level DNN models and can be exploited to deplete full model intelligence and control output labels. Our finding calls for incorporating security mechanisms in future DNN compilation toolchains.Comment: Fix typ

TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification

Audiovisual data is everywhere in this digital age, which raises higher requirements for the deep learning models developed on them. To well handle the information of the multi-modal data is the key to a better audiovisual modal. We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video. More concretely, such data is inherently multi-modal according to both audio and visual cues, which proceed in a strict chronological order. It indicates that temporal information is important in multi-modal acoustic event modeling for both intra- and inter-modal. However, existing methods deal with each modal feature independently and simply fuse them together, which neglects the mining of temporal relation and thus leads to sub-optimal performance. With this motivation, we propose a Temporal Multi-modal graph learning method for Acoustic event Classification, called TMac, by modeling such temporal information via graph learning techniques. In particular, we construct a temporal graph for each acoustic event, dividing its audio data and video data into multiple segments. Each segment can be considered as a node, and the temporal relationships between nodes can be considered as timestamps on their edges. In this case, we can smoothly capture the dynamic information in intra-modal and inter-modal. Several experiments are conducted to demonstrate TMac outperforms other SOTA models in performance. Our code is available at https://github.com/MGitHubL/TMac.Comment: This work has been accepted by ACM MM 2023 for publicatio

Students’ Writer Identities and Writing Practice in Tertiary English-Medium Instruction in China

This study adopts a case study approach to examine how students write in English-medium instruction contexts. It also explores why they write in this way from the perspective of writer identity. Four Chinese university students’ EMI course essays, as well as their interview and stimulated recall responses were collected. The analysis results presented three patterns of writer identity: (1) a member, as an EMI writer, of the academic community as the dominant self; (2) a student writer meeting the course requirements as the dominant self; (3) struggling between the two selves. Having different types of writer identities, the students wrote their EMI course essays in different ways. Their writings presented different features in terms of discoursal choice, language form and format. Suggestions for EMI teaching, EMI teacher training and curricula at the university level are provided

The effect of microstructure on self-propelled droplet jumping

The coalescence-induced droplet jumping on superhydrophobic surfaces has attracted considerable attention over the past several years. Most of the studies on droplet jumping mainly focus the droplet jumping on almost flat surfaces or ignore the effect of the microstructure. However, the microstructure often exists on superhydrophobic surfaces, and this effect remains little noticed and poorly understood. In this work, a simulation is carried out to investigate the effect of microstructure on droplet jumping. The microstructure with a similar scale to the jumping droplet on superhydrophobic will affect the jumping direction. The microstructure will improve the jumping velocity and change the jumping direction of the droplet. This work will provide effective guidelines for the design of functional SHSs with controlled and enhanced droplet jumping for a wide range of industrial applications

Robust semi-supervised classification based on data augmented online ELMs with deep features

Abstract One important strategy in semi-supervised learning is to utilize the predicted pseudo labels of unlabeled data to relieve the overdependence on the ground truth of supervised learning algorithms. However, the performance of such kinds of semi-supervised methods heavily relies on the quality of pseudo labels. To address this issue, a robust semi-supervised classification method, named data augmented online extreme learning machines (ELMs) with deep features (DF-DAELM) is proposed. This method firstly extracts features and infers labels for unlabeled data through self-training. Then, with the learned features and inferred labels, two noise-robust shallow classifiers based on data augmentation (i.e., SLI-OELM and CR-OELM) are proposed to eliminate the adverse effects of noises on classifier training. Specifically, inspired by label smoothing, a data augmented method, SLI-OELM is designed based on stochastic linear interpolation to improve the robustness of classifiers based on ELMs. Furthermore, based on the smoothing assumption, the proposed CR-OELM utilizes an ℓ₂-norm consistency regularization term to implicitly weight noisy samples. Comprehensive experiments demonstrate that DF-DAELM achieves competitive or even better performance on CIFAR-10/100 and SVHN over the related state-of-the-art methods. Meanwhile, for the proposed classifiers, experimental results on the MNIST dataset with different noise levels and sample scales demonstrate their superior performance, especially when the sample scale is small (≤ 20 K) and the noise is strong (40% ~ 80% )

Nivolumab and ipilimumab population pharmacokinetics in support of pediatric dose recommendations—Going beyond the body‐size effect

Abstract Body size has historically been considered the primary source of difference in the pharmacokinetics (PKs) of monoclonal antibodies (mAbs) between children aged greater than or equal to 2 years and adults. The contribution of age‐associated differences (e.g., ontogeny) beyond body‐size differences in the pediatric PKs of mAbs has not been comprehensively evaluated. In this study, the population PK of two mAbs (nivolumab and ipilimumab) in pediatric oncology patients were characterized. The effects of age‐related covariates on nivolumab or ipilimumab PKs were assessed using data from 13 and 10 clinical studies, respectively, across multiple tumor types, including melanoma, lymphoma, central nervous system tumors (CNSTs), and other solid tumors. Clearance was lower in pediatric patients (aged 1–17 years) with solid tumors or CNST than in adults after adjusting for other covariates, including the effect of body size. In contrast, clearance was similar in pediatric patients with lymphoma to that in adults with lymphoma. The pediatric effects characterized have increased the accuracy of the predictions of the model, facilitating its use in subsequent exposure comparisons between pediatric and adult patients, as well as for exposure–response analyses to inform pediatric dosing. This study approach may be applicable to the optimization of pediatric dosing of other mAbs and possibly other biologics

Enhancing the oxygen reduction activity of PrBaCo2O5+δ double perovskite cathode by tailoring the calcination temperatures

In this study, the oxygen reduction activity of PrBaCoO (PBC) double perovskite is remarkably enhanced by rationally tuning the calcination temperatures of the cathode precursor for solid oxide fuel cells (SOFCs). Effects of the calcination temperatures on the phase structure, microstructure, surface area and oxygen reduction reaction (ORR) activity of PBC cathode is systematically investigated. The cathode with optimized calcination temperature (900 °C, PBC-900) shows excellent activity and stability for ORR at 600 °C in terms of area specific resistances (ASRs). A distinctive low ASR of 0.068 Ω cm is obtained at 600 °C for PBC-900, which is 92.6%, 34.6% and 15.0% lower than PBC-800, PBC-1000 and PBC-1100, respectively. After operating for 250 h in air at 600 °C, the ASR value of PBC-900 is not significantly reduced. Furthermore, a single cell with PBC-900 cathode delivers attractive peak power density of 1.60 W cm at 600 °C. The present study suggests that the ORR activity of PBC cathode can be greatly boosted by rationally tailoring the calcination temperatures, which may bring new avenue for the design of highly active cathodes for SOFCs

The relationship between sleep duration and activities of daily living (ADL) disability in the Chinese oldest-old: A cross-sectional study

Objective To investigate the relationship between sleep duration and activities of daily living (ADL) disability, and to explore the optimal sleep duration among oldest-old Chinese individuals. Methods In this cross-sectional study, 1,798 participants (73.2% female) were recruited from Dongxing and Shanglin in Guangxi Zhuang Autonomous Region, China in 2019. The restricted cubic spline function was used to assess the dose-response relationship between sleep duration and ADL disability, and the odds ratios (ORs) of the associations were estimated by logistic regression models. Results The overall prevalence of ADL disability was 63% (64% in females and 58% in males). The prevalence was 71% in the Han population (72% in females and 68% in males), 60% in the Zhuang population (62% in females and 54% in males) and 53% in other ethnic population (53% in females and 53% in males). A nonlinear relationship between sleep duration and ADL disability was observed. Sleep duration of 8-10 hours was associated with the lowest risk of ADL disability. Sleep duration (≥12 hours) was associated with the risk of ADL disability among the oldest-old individuals after adjusting for confounding factors (OR = 1.47, 95% CI [1.02, 2.10], p < 0.05). Conclusion Sleep duration more than 12 hours may be associated with an increased risk of ADL disability in the oldest-old individuals, and the optimal sleep duration among this population could be 8–10 h