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High-grade serous ovarian carcinoma (HGSOC) is genetically unstable and characterised by the presence of subclones with distinct genotypes. Intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and poor prognosis. Here, we use spatial transcriptomics to identify HGSOC subclones and study their association with infiltrating cell populations. Visium spatial transcriptomics reveals multiple tumour subclones with different copy number alterations present within individual tumour sections. These subclones differentially express various ligands and receptors and are predicted to differentially associate with different stromal and immune cell populations. In one sample, CosMx single molecule imaging reveals subclones differentially associating with immune cell populations, fibroblasts, and endothelial cells. Cell-to-cell communication analysis identifies subclone-specific signalling to stromal and immune cells and multiple subclone-specific autocrine loops. Our study highlights the high degree of subclonal heterogeneity in HGSOC and suggests that subclone-specific ligand and receptor expression patterns likely modulate how HGSOC cells interact with their local microenvironment.

Fully Homomorphic Encryption (FHE), a novel cryptographic theory enabling computation directly on ciphertext data, offers significant security benefits but is hampered by substantial performance overhead. In recent years, a series of accelerator designs have significantly enhanced the performance of FHE applications, bringing them closer to real-world applicability. However, these accelerators face challenges related to large on-chip memory and area. Additionally, FHE algorithms undergo rapid development, rendering the previous accelerator designs less perfectly adapted to the evolving landscape of optimized FHE applications. In this paper, we conducted a detailed analysis of existing applications with the new FHE method, making two key observations: 1) the bottleneck of FHE applications shifts from NTT to the inner-product operation, and 2) the optimal {\alpha} of KeySwitch changes with the decrease in multiplicative level. Based on these observations, we designed an accelerator named Taiyi, which includes specific hardware for the inner-product operation and optimizes the NTT and BConv operations through algorithmic derivation. A comparative evaluation of Taiyi against previous state-of-the-art designs reveals an average performance improvement of 1.5x and reduces the area overhead by 15.7%., Comment: 14 pages, 15 figures

Recent years have witnessed e-commerce platforms that voluntarily invest in new digital technologies to help their suppliers reduce production costs. To examine its impact on channel structure, we develop a supply chain model consisting of a supplier and an e-commerce platform who purchases products from the supplier at a wholesale price and sells them to end markets. In addition, the supplier has the option to accept the marketplace by paying a commission fee charged by the platform. We show that the presence of voluntary investment on production improvement can overturn some classical insights from prior studies. For example, a higher marketplace commission fee can lead to a lower selling quantity in the marketplace, but a higher order quantity in the reselling channel under certain conditions. This stands in strict contrast to the opposite finding when the platform invests instead on demand enhancement. In addition, previous studies suggest that offering the supplier the marketplace option is beneficial to the supplier but is detrimental to the platform. However, with voluntary investment, the platform can benefit from allowing the supplier to encroach via the marketplace, and in some situations the supplier suffers from having such an option. We find that the commission fee and the production cost are two interacting forces steering equilibrium decisions for supply chain members, and we characterize the concise operating regimes for the channel structure choices on behalves of the supplier and the platform. A number of extensions are discussed, including product substitution level, investment cost structure, whether the quantity decisions are made simultaneously or sequentially, voluntary investment timing, and endogenous commission fee.

Integrity is critical for maintaining system security, as it ensures that only genuine software is loaded onto a machine. Although confidential virtual machines (CVMs) function within isolated environments separate from the host, it is important to recognize that users still encounter challenges in maintaining control over the integrity of the code running within the trusted execution environments (TEEs). The presence of a sophisticated operating system (OS) raises the possibility of dynamically creating and executing any code, making user applications within TEEs vulnerable to interference or tampering if the guest OS is compromised. To address this issue, this paper introduces NestedSGX, a framework which leverages virtual machine privilege level (VMPL), a recent hardware feature available on AMD SEV-SNP to enable the creation of hardware enclaves within the guest VM. Similar to Intel SGX, NestedSGX considers the guest OS untrusted for loading potentially malicious code. It ensures that only trusted and measured code executed within the enclave can be remotely attested. To seamlessly protect existing applications, NestedSGX aims for compatibility with Intel SGX by simulating SGX leaf functions. We have also ported the SGX SDK and the Occlum library OS to NestedSGX, enabling the use of existing SGX toolchains and applications in the system. Performance evaluations show that context switches in NestedSGX take about 32,000 -- 34,000 cycles, approximately $1.33\times$ -- $1.54\times$ higher than that of Intel SGX. NestedSGX incurs minimal overhead in most real-world applications, with an average overhead below 2% for computation and memory intensive workloads and below 15.68% for I/O intensive workloads.

The nanoconfinement of water can result in dramatic differences in its physical and chemical properties compared to bulk water. However, a detailed molecular-level understanding of these properties is still lacking. Vibrational spectroscopy, such as Raman and infrared, is a popular experimental tool for studying the structure and dynamics of water, and is often complemented by atomistic simulations to interpret experimental spectra, but there have been few theoretical spectroscopy studies of nanoconfined water using first-principles methods at ambient conditions, let alone under extreme pressure-temperature conditions. Here, we compute the Raman and IR spectra of water nanoconfined by graphene at ambient and extreme pressure-temperature conditions using ab initio simulations. Our results revealed alterations in the Raman stretching and low-frequency bands due to the graphene confinement. We also found spectroscopic evidence indicating that nanoconfinement considerably changes the tetrahedral hydrogen bond network, which is typically found in bulk water. Furthermore, we observed an unusual bending band in the Raman spectrum at similar to 10 GPa and 1000 K, which is attributed to the unique molecular structure of confined ionic water. Additionally, we found that at similar to 20 GPa and 1000 K, confined water transformed into a superionic fluid, making it challenging to identify the IR stretching band. Finally, we computed the ionic conductivity of confined water in the ionic and superionic phases. Our results highlight the efficacy of Raman and IR spectroscopy in studying the structure and dynamics of nanoconfined water in a large pressure-temperature range. Our predicted Raman and IR spectra can serve as a valuable guide for future experiments. We compute the Raman and IR spectra of water nanoconfined by graphene at ambient and extreme pressure-temperature conditions using ab initio simulations.

Junli Ding,1,* Hao Wang,1,* Rui Hou,1,* Yuxin Shi,1 Honghong Fan,1 Yuting Li,1 Jie Mei,1 Qinglin Zhang,2 Tingyan Ruan,1 Junying Xu1 1Department of Oncology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, 214023, People’s Republic of China; 2Department of Gastroenterology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, 214023, People’s Republic of China*These authors contributed equally to this workCorrespondence: Junying Xu; Tingyan Ruan, Department of Oncology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, Jiangsu, 214023, People’s Republic of China, Tel/Fax +86-510-82700775, Email doctorxjy123@163.com; ruantingyan165@163.comBackground: Immune checkpoint inhibitors have achieved limited clinical effectiveness in colon cancer. Stem memory T cells (TSCMs) and in-situ cytotoxic T cells are dominant contributors to host immunity. Currently, data on the correlation between TSCM and T cell abundance and clinicopathological characteristics in colon cancer are largely unavailable.Methods: In-situ cytotoxic T cells are identified based on the quantification of CD3+ and CD8+ markers using immunohistochemistry (IHC) in the core of the tumor and the invasive margin of the tumor. The expression of representative markers of TSCMs, CD27 and CD95, was assayed using IHC in colon cancer tissues. Correlations between the levels of each marker and the clinicopathological characteristics as well as prognosis were evaluated.Results: High densities of CD3+ and CD8+ T cells correlated with stage I-II tumors, whereas a lower infiltration of cytotoxic T cells correlated with advanced-stage tumors. CD27 and CD95 were both expressed in the membrane of T cells present in the tumor stroma and their levels showed a negative correlation with the TNM stage. CD3, CD8, and CD27 were expressed at the same locations simultaneously, indicating their coo

Wei Huang,1,* Zhengtao Qian,2,* Yuxin Shi,1,3,* Zheming Zhang,3 Rui Hou,1,3 Jie Mei,1,3 Junying Xu,1 Junli Ding1 1Department of Oncology, the Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, 214023, People’s Republic of China; 2Department of Clinical Laboratory, Changshu Medicine Examination Institute, Changshu, 215500, People’s Republic of China; 3Wuxi School of Clinical Medicine, Nanjing Medical University, Wuxi, 214023, People’s Republic of China*These authors contributed equally to this workCorrespondence: Junli Ding; Junying Xu, Email dingjunliletters@163.com; doctorxjy123@163.com; xujunying@wuxiph.comBackground: Proteasome 26S subunit ATPase 2 (PSMC2) is a part of the 19S regulatory complex, which catalyzes the unfolding and transport of substrates into the 20S proteasome. Our previous research demonstrated that PSMC2 participates in the tumorigenesis and progression of pancreatic cancer (PC). However, no systematic analysis has been conducted to conclude its expression pattern and correlation with tumor immunity.Aim: To investigate the expression level of PSMC2 in PC, its prognostic value and its relationship with tumor immunity.Methods: In numerous public and internal cohorts, the expression, prognostic significance, and immunological connections of PSMC2 in PC were investigated. Additionally, using data from The Cancer Genome Atlas (TCGA), a pan-cancer analysis was carried out to examine PSMC2’s immunological assocaition, and the predictive power of PSMC2 for immunotherapy was also evaluated in numerous public cohorts.Results: PSMC2 was overexpressed in tumor tissues and linked to unfavorable prognosis in PC. PSMC2 was not only positively correlated with TIICs, also positively correlated with immune checkpoints in PC. In addition to PC, PSMC2 was expected to be an indicator of high immunogenicity in most cancer types. Importantly, PSMC2 could predict the immunotherapeutic responses in various c

We introduce Progressive Prompts - a simple and efficient approach for continual learning in language models. Our method allows forward transfer and resists catastrophic forgetting, without relying on data replay or a large number of task-specific parameters. Progressive Prompts learns a new soft prompt for each task and sequentially concatenates it with the previously learned prompts, while keeping the base model frozen. Experiments on standard continual learning benchmarks show that our approach outperforms state-of-the-art methods, with an improvement >20% in average test accuracy over the previous best-preforming method on T5 model. We also explore a more challenging continual learning setup with longer sequences of tasks and show that Progressive Prompts significantly outperforms prior methods.

Large multilingual language models typically rely on a single vocabulary shared across 100+ languages. As these models have increased in parameter count and depth, vocabulary size has remained largely unchanged. This \textit{vocabulary bottleneck} limits the representational capabilities of multilingual models like XLM-R. In this paper, we introduce a new approach for scaling to very large multilingual vocabularies by de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity to achieve sufficient coverage for each individual language. Tokenizations using our vocabulary are typically more semantically meaningful and shorter compared to XLM-R. Leveraging this improved vocabulary, we train XLM-V, a multilingual language model with a one million token vocabulary. XLM-V outperforms XLM-R on every task we tested on ranging from natural language inference (XNLI), question answering (MLQA, XQuAD, TyDiQA), to named entity recognition (WikiAnn). XLM-V is particularly effective on low-resource language tasks and outperforms XLM-R by 11.2% and 5.8% absolute on MasakhaNER and Americas NLI, respectively., Comment: EMNLP 2023

Distillation from Weak Teacher (DWT) is a method of transferring knowledge from a smaller, weaker teacher model to a larger student model to improve its performance. Previous studies have shown that DWT can be effective in the vision domain and natural language processing (NLP) pre-training stage. Specifically, DWT shows promise in practical scenarios, such as enhancing new generation or larger models using pre-trained yet older or smaller models and lacking a resource budget. However, the optimal conditions for using DWT have yet to be fully investigated in NLP pre-training. Therefore, this study examines three key factors to optimize DWT, distinct from those used in the vision domain or traditional knowledge distillation. These factors are: (i) the impact of teacher model quality on DWT effectiveness, (ii) guidelines for adjusting the weighting value for DWT loss, and (iii) the impact of parameter remapping as a student model initialization technique for DWT., Comment: Findings of ACL 2023

Many real-world applications require making multiple predictions from the same text. Fine-tuning a large pre-trained language model for each downstream task causes computational burdens in the inference time due to several times of forward passes. To amortize the computational cost, freezing the language model and building lightweight models for downstream tasks based on fixed text representations are common solutions. Accordingly, how to learn fixed but general text representations that can generalize well to unseen downstream tasks becomes a challenge. Previous works have shown that the generalizability of representations can be improved by fine-tuning the pre-trained language model with some source tasks in a multi-tasking way. In this work, we propose a prefix-based method to learn the fixed text representations with source tasks. We learn a task-specific prefix for each source task independently and combine them to get the final representations. Our experimental results show that prefix-based training performs better than multi-tasking training and can update the text representations at a smaller computational cost than multi-tasking training., Comment: Paper accepted by EMNLP 2023 Findings

Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft prompts constitute <0.1% of total parameters), it typically performs worse than other efficient tuning methods and is quite sensitive to hyper-parameters. In this work, we introduce Residual Prompt Tuning - a simple and efficient method that significantly improves the performance and stability of prompt tuning. We propose to reparameterize soft prompt embeddings using a shallow network with a residual connection. Our experiments show that Residual Prompt Tuning significantly outperforms prompt tuning on SuperGLUE benchmark. Notably, our method reaches +7 points improvement over prompt tuning with T5-Base and allows to reduce the prompt length by 10x without hurting performance. In addition, we show that our approach is robust to the choice of learning rate and prompt initialization, and is effective in few-shot settings., Comment: ACL Findings 2023

We present Multiscale Multiview Vision Transformers (MMViT), which introduces multiscale feature maps and multiview encodings to transformer models. Our model encodes different views of the input signal and builds several channel-resolution feature stages to process the multiple views of the input at different resolutions in parallel. At each scale stage, we use a cross-attention block to fuse information across different views. This enables the MMViT model to acquire complex high-dimensional representations of the input at different resolutions. The proposed model can serve as a backbone model in multiple domains. We demonstrate the effectiveness of MMViT on audio and image classification tasks, achieving state-of-the-art results.

Whether by processing videos with fixed resolution from start to end or incorporating pooling and down-scaling strategies, existing video transformers process the whole video content throughout the network without specially handling the large portions of redundant information. In this paper, we present a Supertoken Video Transformer (SVT) that incorporates a Semantic Pooling Module (SPM) to aggregate latent representations along the depth of visual transformer based on their semantics, and thus, reduces redundancy inherent in video inputs.~Qualitative results show that our method can effectively reduce redundancy by merging latent representations with similar semantics and thus increase the proportion of salient information for downstream tasks.~Quantitatively, our method improves the performance of both ViT and MViT while requiring significantly less computations on the Kinectics and Something-Something-V2 benchmarks.~More specifically, with our SPM, we improve the accuracy of MAE-pretrained ViT-B and ViT-L by 1.5% with 33% less GFLOPs and by 0.2% with 55% less FLOPs, respectively, on the Kinectics-400 benchmark, and improve the accuracy of MViTv2-B by 0.2% and 0.3% with 22% less GFLOPs on Kinectics-400 and Something-Something-V2, respectively.

Fine-tuning pre-trained language models (LMs) has become the de facto standard in many NLP tasks. Nevertheless, fine-tuned LMs are still prone to robustness issues, such as adversarial robustness and model calibration. Several perspectives of robustness for LMs have been studied independently, but lacking a unified consideration in multiple perspectives. In this paper, we propose Robustifying LMs via Adversarial perturbation with Selective Training (ROAST), a simple yet effective fine-tuning technique to enhance the multi-perspective robustness of LMs in a unified way. ROAST effectively incorporates two important sources for the model robustness, robustness on the perturbed inputs and generalizable knowledge in pre-trained LMs. To be specific, ROAST introduces adversarial perturbation during fine-tuning while the model parameters are selectively updated upon their relative importance to minimize unnecessary deviation. Under a unified evaluation of fine-tuned LMs by incorporating four representative perspectives of model robustness, we demonstrate the effectiveness of ROAST compared to state-of-the-art fine-tuning methods on six different types of LMs, which indicates its usefulness in practice. © 2023 Association for Computational Linguistics.

The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems, unifying various vision-language (VL) tasks by instruction tuning. However, due to the enormous diversity in input-output formats in the vision domain, existing general-purpose models fail to successfully integrate segmentation and multi-image inputs with coarse-level tasks into a single framework. In this work, we introduce VistaLLM, a powerful visual system that addresses coarse- and fine-grained VL tasks over single and multiple input images using a unified framework. VistaLLM utilizes an instruction-guided image tokenizer that filters global embeddings using task descriptions to extract compressed and refined features from numerous images. Moreover, VistaLLM employs a gradient-aware adaptive sampling technique to represent binary segmentation masks as sequences, significantly improving over previously used uniform sampling. To bolster the desired capability of VistaLLM, we curate CoinIt, a comprehensive coarse-to-fine instruction tuning dataset with 6.8M samples. We also address the lack of multi-image grounding datasets by introducing a novel task, AttCoSeg (Attribute-level Co-Segmentation), which boosts the model's reasoning and grounding capability over multiple input images. Extensive experiments on a wide range of V- and VL tasks demonstrate the effectiveness of VistaLLM by achieving consistent state-of-the-art performance over strong baselines across all downstream tasks. Our project page can be found at https://shramanpramanick.github.io/VistaLLM/., Comment: 24 pages including references and supplementary

Few-shot Relation Extraction (FSRE) aims to extract relational facts from a sparse set of labeled corpora. Recent studies have shown promising results in FSRE by employing Pre-trained Language Models (PLMs) within the framework of supervised contrastive learning, which considers both instances and label facts. However, how to effectively harness massive instance-label pairs to encompass the learned representation with semantic richness in this learning paradigm is not fully explored. To address this gap, we introduce a novel synergistic anchored contrastive pre-training framework. This framework is motivated by the insight that the diverse viewpoints conveyed through instance-label pairs capture incomplete yet complementary intrinsic textual semantics. Specifically, our framework involves a symmetrical contrastive objective that encompasses both sentence-anchored and label-anchored contrastive losses. By combining these two losses, the model establishes a robust and uniform representation space. This space effectively captures the reciprocal alignment of feature distributions among instances and relational facts, simultaneously enhancing the maximization of mutual information across diverse perspectives within the same relation. Experimental results demonstrate that our framework achieves significant performance enhancements compared to baseline models in downstream FSRE tasks. Furthermore, our approach exhibits superior adaptability to handle the challenges of domain shift and zero-shot relation extraction. Our code is available online at https://github.com/AONE-NLP/FSRE-SaCon.

Fine-tuning pre-trained language models (LMs) has become the de facto standard in many NLP tasks. Nevertheless, fine-tuned LMs are still prone to robustness issues, such as adversarial robustness and model calibration. Several perspectives of robustness for LMs have been studied independently, but lacking a unified consideration in multiple perspectives. In this paper, we propose Robustifying LMs via Adversarial perturbation with Selective Training (RoAST), a simple yet effective fine-tuning technique to enhance the multi-perspective robustness of LMs in a unified way. RoAST effectively incorporates two important sources for the model robustness, robustness on the perturbed inputs and generalizable knowledge in pre-trained LMs. To be specific, RoAST introduces adversarial perturbation during fine-tuning while the model parameters are selectively updated upon their relative importance to minimize unnecessary deviation. Under a unified evaluation of fine-tuned LMs by incorporating four representative perspectives of model robustness, we demonstrate the effectiveness of RoAST compared to state-of-the-art fine-tuning methods on six different types of LMs, which indicates its usefulness in practice., Comment: 33 pages, accepted at EMNLP 2023 Findings

Red-teaming is a common practice for mitigating unsafe behaviors in Large Language Models (LLMs), which involves thoroughly assessing LLMs to identify potential flaws and addressing them with responsible and accurate responses. While effective, manual red-teaming is costly, and existing automatic red-teaming typically discovers safety risks without addressing them. In this paper, we propose a Multi-round Automatic Red-Teaming (MART) method, which incorporates both automatic adversarial prompt writing and safe response generation, significantly increasing red-teaming scalability and the safety of the target LLM. Specifically, an adversarial LLM and a target LLM interplay with each other in an iterative manner, where the adversarial LLM aims to generate challenging prompts that elicit unsafe responses from the target LLM, while the target LLM is fine-tuned with safety aligned data on these adversarial prompts. In each round, the adversarial LLM crafts better attacks on the updated target LLM, while the target LLM also improves itself through safety fine-tuning. On adversarial prompt benchmarks, the violation rate of an LLM with limited safety alignment reduces up to 84.7% after 4 rounds of MART, achieving comparable performance to LLMs with extensive adversarial prompt writing. Notably, model helpfulness on non-adversarial prompts remains stable throughout iterations, indicating the target LLM maintains strong performance on instruction following.

Knowledge Distillation (KD) compresses computationally expensive pre-trained language models (PLMs) by transferring their knowledge to smaller models, allowing their use in resource-constrained or real-time settings. However, most smaller models fail to surpass the performance of the original larger model, resulting in sacrificing performance to improve inference speed. To address this issue, we propose Co-Training and Co-Distillation (CTCD), a novel framework that improves performance and inference speed together by co-training two models while mutually distilling knowledge. The CTCD framework successfully achieves this based on two significant findings: 1) Distilling knowledge from the smaller model to the larger model during co-training improves the performance of the larger model. 2) The enhanced performance of the larger model further boosts the performance of the smaller model. The CTCD framework shows promise as it can be combined with existing techniques like architecture design or data augmentation, replacing one-way KD methods, to achieve further performance improvement. Extensive ablation studies demonstrate the effectiveness of CTCD, and the small model distilled by CTCD outperforms the original larger model by a significant margin of 1.66 on the GLUE benchmark., Comment: Findings of EMNLP 2023

We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our model series are built through continual pretraining from Llama 2 with longer training sequences and on a dataset where long texts are upsampled. We perform extensive evaluation on language modeling, synthetic context probing tasks, and a wide range of research benchmarks. On research benchmarks, our models achieve consistent improvements on most regular tasks and significant improvements on long-context tasks over Llama 2. Notably, with a cost-effective instruction tuning procedure that does not require human-annotated long instruction data, the 70B variant can already surpass gpt-3.5-turbo-16k's overall performance on a suite of long-context tasks. Alongside these results, we provide an in-depth analysis on the individual components of our method. We delve into Llama's position encodings and discuss its limitation in modeling long dependencies. We also examine the impact of various design choices in the pretraining process, including the data mix and the training curriculum of sequence lengths -- our ablation experiments suggest that having abundant long texts in the pretrain dataset is not the key to achieving strong performance, and we empirically verify that long context continual pretraining is more efficient and similarly effective compared to pretraining from scratch with long sequences.

Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous works have made remarkable progress in leveraging attention mechanism to extract opinion words for different aspects. However, a persistent challenge is the effective management of semantic mismatches, which stem from attention mechanisms that fall short in adequately aligning opinions words with their corresponding aspect in multi-aspect sentences. To address this issue, we propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect. Specifically, we first introduce a neighboring span enhanced module which highlights various compositions of neighboring words and given aspects. In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect. Extensive experiments on three benchmark datasets demonstrate that our model achieves state-of-the-art results. The source code is available at https://github.com/AONE-NLP/ABSA-AOAN., Comment: 8 pages, 5 figure, ECAI 2023

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

ARM recently introduced the Confidential Compute Architecture (CCA) as part of the upcoming ARMv9-A architecture. CCA enables the support of confidential virtual machines (cVMs) within a separate world called the Realm world, providing protection from the untrusted normal world. While CCA offers a promising future for confidential computing, the widespread availability of CCA hardware is not expected in the near future, according to ARM's roadmap. To address this gap, we present virtCCA, an architecture that facilitates virtualized CCA using TrustZone, a mature hardware feature available on existing ARM platforms. Notably, virtCCA can be implemented on platforms equipped with the Secure EL2 (S-EL2) extension available from ARMv8.4 onwards, as well as on earlier platforms that lack S-EL2 support. virtCCA is fully compatible with the CCA specifications at the API level. We have developed the entire CCA software and firmware stack on top of virtCCA, including the enhancements to the normal world's KVM to support cVMs, and the TrustZone Management Monitor (TMM) that enforces isolation among cVMs and provides cVM life-cycle management. We have implemented virtCCA on real ARM servers, with and without S-EL2 support. Our evaluation, conducted on micro-benchmarks and macro-benchmarks, demonstrates that the overhead of running cVMs is acceptable compared to running normal-world VMs. Specifically, in a set of real-world workloads, the overhead of virtCCA-SEL2 is less than 29.5% for I/O intensive workloads, while virtCCA-EL3 outperforms the baseline in most cases.

Hydrocarbons are of great importance in carbon-bearing fluids in deep Earth and in ice giant planets at extreme pressure (P)-temperature (T) conditions. Raman spectroscopy is a powerful tool to study the chemical speciation of hydrocarbons. However, it is challenging interpreting Raman data at extreme conditions. Here, we performed ab initio molecular dynamics simulations coupled with the modern theory of polarization to calculate Raman spectra of methane, ethane and propane up to 48 GPa and 2000 K. Our method includes anharmonic temperature effects. We studied the pressure and temperature effects on the Raman bands and identified the characteristic Raman modes for the C-C and C-C-C bonds. To the best of our knowledge, this is the first time that the Raman spectra of hydrocarbons have been calculated at extreme P-T conditions. Our results may help with the interpretation of in situ Raman data of hydrocarbons at extreme P-T conditions, with important implications for studying hydrocarbon reactions in the deep carbon cycle inside Earth and the composition of ice giant planets.

Hydrocarbons are of great importance in carbon-bearing fluids in deep Earth and in ice giant planets at extreme pressure (P)-temperature (T) conditions. Raman spectroscopy is a powerful tool to study the chemical speciation of hydrocarbons. However, it is challenging interpreting Raman data at extreme conditions. Here, we performed ab initio molecular dynamics simulations coupled with the modern theory of polarization to calculate Raman spectra of methane, ethane and propane up to 48 GPa and 2000 K. Our method includes anharmonic temperature effects. We studied the pressure and temperature effects on the Raman bands and identified the characteristic Raman modes for the C-C and C-C-C bonds. To the best of our knowledge, this is the first time that the Raman spectra of hydrocarbons have been calculated at extreme P-T conditions. Our results may help with the interpretation of in situ Raman data of hydrocarbons at extreme P-T conditions, with important implications for studying hydrocarbon reactions in the deep carbon cycle inside Earth and the composition of ice giant planets.

The contact stress of flexible joint is an important factor affecting the safety of ultra-short-radius drilling tools. However, calculating the contact stress accurately is difficult, because the method for solving the multibody collision of the flexible joint is complex. A novel analytical method for the contact stress of ball cage flexible joint is presented in this study. The method was used to establish the relationship between the parameters and contact stress of the flexible joint during the drilling process. The contact stress analytical models of the ball head, ball key, and ball seat were established based on Hertz contact theory, and the finite element method was used to analyse the variations of contact stress under the weight of bit and torque. The influences of borehole curvature and deflection angle on contact stress were discussed for parameter optimization. Results demonstrate that the contact stress values on the ball key, inner raceway, and outer raceway are concentrative. The ball key is the most dangerous part of the flexible joint, and its safety can be improved by increasing its diameter. When the deflection angle between the flexible joint was increased from 0 ° to 18 °, the contact stress value also increased to 220 MPa. However, the contact position was random on the inside of deflection. The proposed method provides a good prospect to optimize the structure parameters of the flexible joint and ultra-short-radius drilling parameters, Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Electronic Components, Technology and Materials

In this paper, we propose TensorFHE, an FHE acceleration solution based on GPGPU for real applications on encrypted data. TensorFHE utilizes Tensor Core Units (TCUs) to boost the computation of Number Theoretic Transform (NTT), which is the part of FHE with highest time-cost. Moreover, TensorFHE focuses on performing as many FHE operations as possible in a certain time period rather than reducing the latency of one operation. Based on such an idea, TensorFHE introduces operation-level batching to fully utilize the data parallelism in GPGPU. We experimentally prove that it is possible to achieve comparable performance with GPGPU as with state-of-the-art ASIC accelerators. TensorFHE performs 913 KOPS and 88 KOPS for NTT and HMULT (key FHE kernels) within NVIDIA A100 GPGPU, which is 2.61x faster than state-of-the-art FHE implementation on GPGPU; Moreover, TensorFHE provides comparable performance to the ASIC FHE accelerators, which makes it even 2.9x faster than the F1+ with a specific workload. Such a pure software acceleration based on commercial hardware with high performance can open up usage of state-of-the-art FHE algorithms for a broad set of applications in real systems., Comment: To be appeared in the 29th IEEE International Symposium on High-Performance Computer Architecture (HPCA-29), 2023

Masked Language Modeling (MLM) has proven to be an essential component of Vision-Language (VL) pretraining. To implement MLM, the researcher must make two design choices: the masking strategy, which determines which tokens to mask, and the masking rate, which determines how many tokens to mask. Previous work has focused primarily on the masking strategy while setting the masking rate at a default of 15\%. In this paper, we show that increasing this masking rate improves downstream performance while simultaneously reducing performance gap among different masking strategies, rendering the uniform masking strategy competitive to other more complex ones. Surprisingly, we also discover that increasing the masking rate leads to gains in Image-Text Matching (ITM) tasks, suggesting that the role of MLM goes beyond language modeling in VL pretraining.

As cache-based side-channel attacks become serious security problems, various defenses have been proposed and deployed in both software and hardware. Consequently, cache-based side-channel attacks on processes co-residing on the same core are becoming extremely difficult. Most of recent attacks then shift their focus to the last-level cache (LLC). Although cache partitioning is currently the most promising defense against the attacks abusing LLC, it is ineffective in thwarting the side-channel attacks that automatically create eviction sets or bypass the user address space layout randomization. In fact, these attacks are largely undefended in current computer systems. We propose Remapped Cache Layout (\textsf{RCL}) -- a pure hardware defense against a broad range of conflict-based side-channel attacks. \textsf{RCL} obfuscates the mapping from address to cache sets; therefore, an attacker cannot accurately infer the location of her data in caches or using a cache set to infer her victim's data. To our best knowledge, it is the first defense to thwart the aforementioned largely undefended side-channel attacks . \textsf{RCL} has been implemented in a superscalar processor and detailed evaluation results show that \textsf{RCL} incurs only small costs in area, frequency and execution time.

We propose a novel system to help fact-checkers formulate search queries for known misinformation claims and effectively search across multiple social media platforms. We introduce an adaptable rewriting strategy, where editing actions for queries containing claims (e.g., swap a word with its synonym; change verb tense into present simple) are automatically learned through offline reinforcement learning. Our model uses a decision transformer to learn a sequence of editing actions that maximizes query retrieval metrics such as mean average precision. We conduct a series of experiments showing that our query rewriting system achieves a relative increase in the effectiveness of the queries of up to 42%, while producing editing action sequences that are human interpretable., Comment: AACL 2023 (long paper)

Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage. It freezes the pre-trained language model and only optimizes a few task-specific prompts. In this paper, we propose a conditional prompt generation method to generate prompts for each input instance, referred to as the Instance-Dependent Prompt Generation (IDPG). Unlike traditional prompt tuning methods that use a fixed prompt, IDPG introduces a lightweight and trainable component to generate prompts based on each input sentence. Extensive experiments on ten natural language understanding (NLU) tasks show that the proposed strategy consistently outperforms various prompt tuning baselines and is on par with other efficient transfer learning methods such as Compacter while tuning far fewer model parameters., Comment: To appear at the NAACL 2022 main conference

The saturating counter is the basic module of the dynamic branch predictor, which involves the core technique to improve instruction level parallelism performance in modern processors. However, most studies focus on the performance improvement and hardware consumption of saturating counters, while ignoring the security problems they may cause. In this paper, we creatively propose to study and design saturating counters from the defense perspective of differential privacy, so that attackers cannot distinguish the states that saturating counters are in and further infer sensitive information. To obtain theoretical guarantees, we use Markov chain to formalize the attack algorithm applied to the saturating counter, investigate into the optimal attack strategy and calculate the probability of successful attack. Furthermore, we find that the attacker is able to accurately guess the branch execution of the victim's process in the existing saturating counters. To avoid this, we design a new probabilistic saturating counter, which generalizes the existing conventional and probabilistic saturating counters. The guarantee of differential privacy is applied to deduce parameters of the new saturating counters so that the security requirement can be satisfied. We also theoretically calculate the misprediction rate when the saturating counter reaches the steady state. The experimental results on testing programs show that the calculated theoretical results agree with the experimental performances. Compared with the existing conventional and probabilistic saturating counters, when the parameters of our designed models are selected appropriately, the new saturating counters can not only ensure similar operational performance, but also establish strict security guarantee.

The reactions of CO2 in water under extreme pressure-temperature conditions are of great importance to the carbon storage and transport below Earth’s surface, which substantially affect the carbon budget in the atmosphere. Previous studies focus on the CO2(aq) solutions in the bulk phase, but underground aqueous solutions are often confined to the nanoscale, and nanoconfinement and solid-liquid interfaces may substantially affect chemical speciation and reaction mechanisms, which are poorly known on the molecular scale. Here, we apply extensive ab initio molecular dynamics simulations to study aqueous carbon solutions nanoconfined by graphene and stishovite (SiO2) at 10 GPa and 1000 ~ 1400 K. We find that CO2(aq) reacts more in nanoconfinement than in bulk. The stishovite-water interface makes the solutions more acidic, which shifts the chemical equilibria, and the interface chemistry also significantly affects the reaction mechanisms. Our findings suggest that CO2(aq) in deep Earth is more active than previously thought, and confining CO2 and water in nanopores may enhance the efficiency of mineral carbonation. © 2022, The Author(s).

Hydrocarbons are of great importance in carbon-bearing fluids in deep Earth and in ice giant planets at extreme pressure (P)-temperature (T) conditions. Raman spectroscopy is a powerful tool to study the chemical speciation of hydrocarbons. However, it is challenging interpreting Raman data at extreme conditions. Here, we performed ab initio molecular dynamics simulations coupled with the modern theory of polarization to calculate Raman spectra of methane, ethane and propane up to 48 GPa and 2000 K. Our method includes anharmonic temperature effects. We studied the pressure and temperature effects on the Raman bands and identified the characteristic Raman modes for the C-C and C-C-C bonds. To the best of our knowledge, this is the first time that the Raman spectra of hydrocarbons have been calculated at extreme P-T conditions. Our results may help with the interpretation of in situ Raman data of hydrocarbons at extreme P-T conditions, with important implications for studying hydrocarbon reactions in the deep carbon cycle inside Earth and the composition of ice giant planets.

Background: Few studies have focused on the prevalence of abdominal obesity in Chinese middle-aged and older adults with a normal body mass index (BMI). Furthermore, it is still unclear whether abdominal obesity is an independent risk factor for type 2 diabetes mellitus (T2DM). Participants with a normal BMI are usually neglected during assessments of abdominal obesity-associated T2DM risk since the current recommendations for medical interventions are mainly focused on overall body mass index rather than fat deposition patterns. Methods: In this study, 7942 normal-BMI participants aged over 45 years from the China Health and Retirement Longitudinal Study were included to assess the prevalence of abdominal obesity defined by waist circumference (WC) or waist-to-height ratio (WHtR). In addition, 4348 normal-BMI individuals with no diabetes at baseline were included to evaluate the association between abdominal obesity and the risk of T2DM with the Cox proportional hazards model. Results: The prevalence (95% confidence interval, CI) of increased WC and substantially increased WC among adults with a normal BMI was 22.0% (21.1%-22.9%) and 18.1% (17.3%-19.0%), respectively. The adjusted hazard ratios and 95% CIs for T2DM incidence were 1.39 (1.05– 1.85) and 1.89 (1.42– 2.53) for those with increased WC and substantially increased WC, respectively, compared to the individuals with a normal WC. Similar HRs were obtained for the association between WHtR and the risk of T2DM. In prediabetic patients, the HRs (95% CIs) for new-onset T2DM for those with increased WC and substantially increased WC were 1.85 (1.27– 2.69) and 2.46 (1.67– 3.61), respectively, when compared with individuals with normal WC. This positive association was observed in women but not in men or adults with normal glucose tolerance (NGT). Conclusion: Abdominal obesity is highly prevalent among middle-aged and older Chinese adults with a normal BMI, and maintaining a normal waist circumference may be benefi

Microbially derived extracellular polymeric substances (EPSs) occupy a large portion of dissolved organic matter (DOM) in surface waters, but the understanding of the photochemical behaviors of EPS is still very limited. In this study, the photochemical characteristics of EPS from different microbial sources (Shewanella oneidensis, Escherichia coli, and sewage sludge flocs) were investigated in terms of the production of reactive species (RS), such as triplet intermediates (3EPS*), hydroxyl radicals (•OH), and singlet oxygen (1O2). The steady-state concentrations of •OH, 3EPS*, and 1O2 varied in the ranges of 2.55–8.73 × 10–17, 3.01–4.56 × 10–15, and 2.08–2.66 × 10–13 M, respectively, which were within the range reported for DOM from other sources. The steady-state concentrations of RS varied among different EPS isolates due to the diversity of their composition. A strong photochemical degradation of the protein-like components in EPS isolates was identified by excitation emission matrix fluorescence with parallel factor analysis, but relatively, humic-like components remained stable. Fourier-transform ion cyclotron resonance mass spectrometry further revealed that the aliphatic portion of EPS was resistant to irradiation, while other portions with lower H/C ratios and higher O/C ratios were more susceptible to photolysis, leading to the phototransformation of EPS to higher saturation and lower aromaticity. With the phototransformation of EPS, the RS derived from EPS could effectively promote the degradation of antibiotic tetracycline. The findings of this study provide new insights into the photoinduced self-evolution of EPS and the interrelated photochemical fate of contaminants in the aquatic environment.

Background: Few studies have focused on the prevalence of abdominal obesity in Chinese middle-aged and older adults with a normal body mass index (BMI). Furthermore, it is still unclear whether abdominal obesity is an independent risk factor for type 2 diabetes mellitus (T2DM). Participants with a normal BMI are usually neglected during assessments of abdominal obesity-associated T2DM risk since the current recommendations for medical interventions are mainly focused on overall body mass index rather than fat deposition patterns. Methods: In this study, 7942 normal-BMI participants aged over 45 years from the China Health and Retirement Longitudinal Study were included to assess the prevalence of abdominal obesity defined by waist circumference (WC) or waist-to-height ratio (WHtR). In addition, 4348 normal-BMI individuals with no diabetes at baseline were included to evaluate the association between abdominal obesity and the risk of T2DM with the Cox proportional hazards model. Results: The prevalence (95% confidence interval, CI) of increased WC and substantially increased WC among adults with a normal BMI was 22.0% (21.1%-22.9%) and 18.1% (17.3%-19.0%), respectively. The adjusted hazard ratios and 95% CIs for T2DM incidence were 1.39 (1.05– 1.85) and 1.89 (1.42– 2.53) for those with increased WC and substantially increased WC, respectively, compared to the individuals with a normal WC. Similar HRs were obtained for the association between WHtR and the risk of T2DM. In prediabetic patients, the HRs (95% CIs) for new-onset T2DM for those with increased WC and substantially increased WC were 1.85 (1.27– 2.69) and 2.46 (1.67– 3.61), respectively, when compared with individuals with normal WC. This positive association was observed in women but not in men or adults with normal glucose tolerance (NGT). Conclusion: Abdominal obesity is highly prevalent among middle-aged and older Chinese adults with a normal BMI, and maintaining a normal waist circumference may be benefi

Recent parameter-efficient language model tuning (PELT) methods manage to match the performance of fine-tuning with much fewer trainable parameters and perform especially well when training data is limited. However, different PELT methods may perform rather differently on the same task, making it nontrivial to select the most appropriate method for a specific task, especially considering the fast-growing number of new PELT methods and tasks. In light of model diversity and the difficulty of model selection, we propose a unified framework, UniPELT, which incorporates different PELT methods as submodules and learns to activate the ones that best suit the current data or task setup via gating mechanism. On the GLUE benchmark, UniPELT consistently achieves 1~4% gains compared to the best individual PELT method that it incorporates and even outperforms fine-tuning under different setups. Moreover, UniPELT generally surpasses the upper bound that takes the best performance of all its submodules used individually on each task, indicating that a mixture of multiple PELT methods may be inherently more effective than single methods., Comment: ACL 2022 (w. typo fixes)

Xuan Chen,1,&ast; Mingrui Duan,1,2,&ast; Rui Hou,1,2,&ast; Manqi Zheng,1,2 Haibin Li,3 Manjot Singh,4,5 Changwei Li,6 Kuo Liu,1,2 Feng Zhang,1,2 Xinghua Yang,1,2 Yanxia Luo,1,2 Yan He,1,2 Lijuan Wu,1,2 Deqiang Zheng1,2,7 1Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People’s Republic of China; 2Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People’s Republic of China; 3Heart Center & Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China; 4Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, University of Western Australia, Crawley, WA, Australia; 5School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia; 6Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA; 7Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden&ast;These authors contributed equally to this workCorrespondence: Deqiang ZhengDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, People’s Republic of ChinaTel/Fax +86 10 8391 1497Email dqzheng@ccmu.edu.cnLijuan WuDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, People’s Republic of ChinaTel +86 10 8391 1497Email xiaowu@ccmu.edu.cnBackground: Few studies have focused on the prevalence of abdominal obesity in Chinese middle-aged and older adults with a normal body mass index (BMI). Furthermore, it is still unclear whether abdominal obesity is an independent risk factor for type 2 diabetes mellitus (T2DM). Participants with a normal BMI are usua

A huge amount of water at supercritical conditions exists in Earth's interior, where its dielectric properties play a critical role in determining how it stores and transports materials. However, it is very challenging to obtain the static dielectric constant of water, T0, in a wide pressure-temperature (P-T) range as found in deep Earth either experimentally or by first-principles simulations. Here, we introduce a neural network dipole model, which, combined with molecular dynamics, can be used to compute P-T dependent dielectric properties of water as accurately as first-principles methods but much more efficiently. We found that T0 may vary by one order of magnitude in Earth's upper mantle, suggesting that the solvation properties of water change dramatically at different depths. Although T0 and the molecular dipole moment increase with an increase in pressure along an isotherm, the dipolar angular correlation has its maximum at 5 GPa-7 GPa, which may indicate that hydrogen bonds become weaker at high pressure. We also calculated the frequency-dependent dielectric constant of water in the microwave range, which, to the best of our knowledge, has not been calculated from first principles, and found that temperature affects the dielectric absorption more than pressure. Our results are of great use in many areas, e.g., modeling water-rock interactions in geochemistry. The computational approach introduced here can be readily applied to other molecular fluids. © 2020 Author(s).

A huge amount of water at supercritical conditions exists in Earth's interior, where its dielectric properties play a critical role in determining how it stores and transports materials. However, it is very challenging to obtain the static dielectric constant of water, T0, in a wide pressure-temperature (P-T) range as found in deep Earth either experimentally or by first-principles simulations. Here, we introduce a neural network dipole model, which, combined with molecular dynamics, can be used to compute P-T dependent dielectric properties of water as accurately as first-principles methods but much more efficiently. We found that T0 may vary by one order of magnitude in Earth's upper mantle, suggesting that the solvation properties of water change dramatically at different depths. Although T0 and the molecular dipole moment increase with an increase in pressure along an isotherm, the dipolar angular correlation has its maximum at 5 GPa-7 GPa, which may indicate that hydrogen bonds become weaker at high pressure. We also calculated the frequency-dependent dielectric constant of water in the microwave range, which, to the best of our knowledge, has not been calculated from first principles, and found that temperature affects the dielectric absorption more than pressure. Our results are of great use in many areas, e.g., modeling water-rock interactions in geochemistry. The computational approach introduced here can be readily applied to other molecular fluids. © 2020 Author(s).

A huge amount of water at supercritical conditions exists in Earth's interior, where its dielectric properties play a critical role in determining how it stores and transports materials. However, it is very challenging to obtain the static dielectric constant of water, T0, in a wide pressure-temperature (P-T) range as found in deep Earth either experimentally or by first-principles simulations. Here, we introduce a neural network dipole model, which, combined with molecular dynamics, can be used to compute P-T dependent dielectric properties of water as accurately as first-principles methods but much more efficiently. We found that T0 may vary by one order of magnitude in Earth's upper mantle, suggesting that the solvation properties of water change dramatically at different depths. Although T0 and the molecular dipole moment increase with an increase in pressure along an isotherm, the dipolar angular correlation has its maximum at 5 GPa-7 GPa, which may indicate that hydrogen bonds become weaker at high pressure. We also calculated the frequency-dependent dielectric constant of water in the microwave range, which, to the best of our knowledge, has not been calculated from first principles, and found that temperature affects the dielectric absorption more than pressure. Our results are of great use in many areas, e.g., modeling water-rock interactions in geochemistry. The computational approach introduced here can be readily applied to other molecular fluids. © 2020 Author(s).

Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports.Materials & Methods: This retrospective study included a total of 12,092 patients (mean age 57 +- 18; 6,172 women) for model development and testing (from 2012-2017). Rule-based algorithms were used to extract 19,225 disease labels from 13,667 body CT scans from 12,092 patients. Using a three-dimensional DenseVNet, three organ systems were segmented: lungs and pleura; liver and gallbladder; and kidneys and ureters. For each organ, a three-dimensional convolutional neural network classified no apparent disease versus four common diseases for a total of 15 different labels across all three models. Testing was performed on a subset of 2,158 CT volumes relative to 2,875 manually derived reference labels from 2133 patients (mean age 58 +- 18;1079 women). Performance was reported as receiver operating characteristic area under the curve (AUC) with 95% confidence intervals by the DeLong method. Results: Manual validation of the extracted labels confirmed 91% to 99% accuracy across the 15 different labels. AUCs for lungs and pleura labels were: atelectasis 0.77 (95% CI: 0.74, 0.81), nodule 0.65 (0.61, 0.69), emphysema 0.89 (0.86, 0.92), effusion 0.97 (0.96, 0.98), and no apparent disease 0.89 (0.87, 0.91). AUCs for liver and gallbladder were: hepatobiliary calcification 0.62 (95% CI: 0.56, 0.67), lesion 0.73 (0.69, 0.77), dilation 0.87 (0.84, 0.90), fatty 0.89 (0.86, 0.92), and no apparent disease 0.82 (0.78, 0.85). AUCs for kidneys and ureters were: stone 0.83 (95% CI: 0.79, 0.87), atrophy 0.92 (0.89, 0.94), lesion 0.68 (0.64, 0.72), cyst 0.70 (0.66, 0.73), and no apparent disease 0.79 (0.75, 0.83). Conclusion: Weakly-supervised deep learning models were able to classify diverse diseases in multiple organ systems., Comment: 22 pages, 6 figures, 2 tables; Accepted for publication at Radiology: Artificial Intelligence

Recently exposed vulnerabilities reveal the necessity to improve the security of branch predictors. Branch predictors record history about the execution of different programs, and such information from different processes are stored in the same structure and thus accessible to each other. This leaves the attackers with the opportunities for malicious training and malicious perception. Instead of flush-based or physical isolation of hardware resources, we want to achieve isolation of the content in these hardware tables with some lightweight processing using randomization as follows. (1) Content encoding. We propose to use hardware-based thread-private random numbers to encode the contents of the branch predictor tables (both direction and destination histories) which we call XOR-BP. Specifically, the data is encoded by XOR operation with the key before written in the table and decoded after read from the table. Such a mechanism obfuscates the information adding difficulties to cross-process or cross-privilege level analysis and perception. It achieves a similar effect of logical isolation but adds little in terms of space or time overheads. (2) Index encoding. We propose a randomized index mechanism of the branch predictor (Noisy-XOR-BP). Similar to the XOR-BP, another thread-private random number is used together with the branch instruction address as the input to compute the index of the branch predictor. This randomized indexing mechanism disrupts the correspondence between the branch instruction address and the branch predictor entry, thus increases the noise for malicious perception attacks. Our analyses using an FPGA-based RISC-V processor prototype and additional auxiliary simulations suggest that the proposed mechanisms incur a very small performance cost while providing strong protection., Comment: 13 pages, 10 figures, submitted to MICRO 2020

Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories., Comment: Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020)

Physical activities and social interactions are essential activities that ensure a healthy lifestyle. Public open spaces (POS), such as parks, plazas and greenways, are key environments that encourage those activities. To evaluate a POS, there is a need to study how humans use the facilities within it. However, traditional approaches to studying use of POS are manual and therefore time and labor intensive. They also may only provide qualitative insights. It is appealing to make use of surveillance cameras and to extract user-related information through computer vision. This paper proposes a proof-of-concept deep learning computer vision framework for measuring human activities quantitatively in POS and demonstrates a case study of the proposed framework using the Detroit Riverfront Conservancy (DRFC) surveillance camera network. A custom image dataset is presented to train the framework; the dataset includes 7826 fully annotated images collected from 18 cameras across the DRFC park space under various illumination conditions. Dataset analysis is also provided as well as a baseline model for one-step user localization and activity recognition. The mAP results are 77.5\% for {\it pedestrian} detection and 81.6\% for {\it cyclist} detection. Behavioral maps are autonomously generated by the framework to locate different POS users and the average error for behavioral localization is within 10 cm.

Cache side channel attacks obtain victim cache line access footprint to infer security-critical information. Among them, cross-core attacks exploiting the shared last level cache are more threatening as their simplicity to set up and high capacity. Stateful approaches of detection-based mitigation observe precise cache behaviors and protect specific cache lines that are suspected of being attacked. However, their recording structures incur large storage overhead and are vulnerable to reverse engineering attacks. Exploring the intrinsic non-determinate layout of a traditional Cuckoo filter, this paper proposes a space efficient Auto-Cuckoo filter to record access footprints, which succeed to decrease storage overhead and resist reverse engineering attacks at the same time. With Auto-Cuckoo filter, we propose PiPoMonitor to detect \textit{Ping-Pong patterns} and prefetch specific cache line to interfere with adversaries' cache probes. Security analysis shows the PiPoMonitor can effectively mitigate cross-core attacks and the Auto-Cuckoo filter is immune to reverse engineering attacks. Evaluation results indicate PiPoMonitor has negligible impact on performance and the storage overhead is only 0.37$\%$, an order of magnitude lower than previous stateful approaches., Comment: This paper is going to appear in 2021 Design, Automation and Test in Europe Conference (DATE)

Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of negative slices along multiple planes. Furthermore, although deep learning segmentation and classification models extract distinctly unique combinations of anatomical features from the same target class(es), they are typically seen as two independent processes in a computer-aided diagnosis (CAD) pipeline, with little to no feature reuse. In this research, we propose a medical classifier that leverages the semantic structural concepts learned via multi-resolution segmentation feature maps, to guide weakly supervised 3D classification of chest CT volumes. Additionally, a comparative analysis is drawn across two different types of feature aggregation to explore the vast possibilities surrounding feature fusion. Using a dataset of 1593 scans labeled on a case-level basis via rule-based model, we train a dual-stage convolutional neural network (CNN) to perform organ segmentation and binary classification of four representative diseases (emphysema, pneumonia/atelectasis, mass and nodules) in lungs. The baseline model, with separate stages for segmentation and classification, results in AUC of 0.791. Using identical hyperparameters, the connected architecture using static and dynamic feature aggregation improves performance to AUC of 0.832 and 0.851, respectively. This study advances the field in two key ways. First, case-level report data is used to weakly supervise a 3D CT classifier of multiple, simultaneous diseases for an organ. Second, segmentation and classification models are connected with two different feature aggregation strategies to enhance the classification performance., Comment: Accepted to 2020 SPIE Medical Imaging: Computer-Aided Diagnosis [oral presentation]

Luo Jiang,1 Rui Hou2 1Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, People’s Republic of China; 2Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, People’s Republic of ChinaCorrespondence: Rui HouDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang 110004, People’s Republic of ChinaEmail hourui6789@126.comBackground: Paclitaxel (PTX) resistance is a great obstacle for the treatment of ovarian cancer. A previous study indicated that tetrandrine (TET) could induce the apoptosis of ovarian cancer cells. This study aimed to explore the effect of TET in combination with PTX on PTX resistance in ovarian cancer cells.Materials and Methods: CCK-8 assay, flow cytometry and wound healing assays were used to detect the proliferation, apoptosis and migration of PTX-resistant SKOV3 cells (SKOV3/PTX). The expressions of Bax, Bcl-2, cleaved caspase 3, β-catenin, c-Myc, cyclin D1 and p21 in SKOV3/PTX cells were detected with Western blot. In vivo animal study was performed finally.Results: In this study, the inhibitory effects of PTX on the proliferation and migration of SKOV3/PTX cells were markedly enhanced by TET. In addition, PTX-induced apoptosis in SKOV3/PTX cells was significantly enhanced by the treatment of TET via upregulating the levels of Bax and cleaved caspase 3, and downregulating the expression of Bcl-2. Moreover, combination of TET and PTX obviously induced cell cycle arrest in SKOV3/PTX cells via increasing the level of p21 and decreasing the levels of c-Myc and Cyclin D1. Meanwhile, combination of TET with PTX significantly decreased the expression of β-catenin in SKOV3/PTX cells. In vivo experiments further confirmed that TET enhanced the anti-tumor effect of PTX i