PIM-Enclave: Bringing Confidential Computation Inside Memory

Demand for data-intensive workloads and confidential computing are the prominent research directions shaping the future of cloud computing. Computer architectures are evolving to accommodate the computing of large data better. Protecting the computation of sensitive data is also an imperative yet challenging objective; processor-supported secure enclaves serve as the key element in confidential computing in the cloud. However, side-channel attacks are threatening their security boundaries. The current processor architectures consume a considerable portion of its cycles in moving data. Near data computation is a promising approach that minimizes redundant data movement by placing computation inside storage. In this paper, we present a novel design for Processing-In-Memory (PIM) as a data-intensive workload accelerator for confidential computing. Based on our observation that moving computation closer to memory can achieve efficiency of computation and confidentiality of the processed information simultaneously, we study the advantages of confidential computing \emph{inside} memory. We then explain our security model and programming model developed for PIM-based computation offloading. We construct our findings into a software-hardware co-design, which we call PIM-Enclave. Our design illustrates the advantages of PIM-based confidential computing acceleration. Our evaluation shows PIM-Enclave can provide a side-channel resistant secure computation offloading and run data-intensive applications with negligible performance overhead compared to baseline PIM model

Fragment-based Pretraining and Finetuning on Molecular Graphs

Property prediction on molecular graphs is an important application of Graph Neural Networks. Recently, unlabeled molecular data has become abundant, which facilitates the rapid development of self-supervised learning for GNNs in the chemical domain. In this work, we propose pretraining GNNs at the fragment level, a promising middle ground to overcome the limitations of node-level and graph-level pretraining. Borrowing techniques from recent work on principal subgraph mining, we obtain a compact vocabulary of prevalent fragments from a large pretraining dataset. From the extracted vocabulary, we introduce several fragment-based contrastive and predictive pretraining tasks. The contrastive learning task jointly pretrains two different GNNs: one on molecular graphs and the other on fragment graphs, which represents higher-order connectivity within molecules. By enforcing consistency between the fragment embedding and the aggregated embedding of the corresponding atoms from the molecular graphs, we ensure that the embeddings capture structural information at multiple resolutions. The structural information of fragment graphs is further exploited to extract auxiliary labels for graph-level predictive pretraining. We employ both the pretrained molecular-based and fragment-based GNNs for downstream prediction, thus utilizing the fragment information during finetuning. Our graph fragment-based pretraining (GraphFP) advances the performances on 5 out of 8 common molecular benchmarks and improves the performances on long-range biological benchmarks by at least 11.5%. Code is available at: https://github.com/lvkd84/GraphFP.Comment: 18 pages, 4 figures, published in NeurIPS 202

Restauration de défauts de S-VHS

Le VHS est encore le format de bande magnétique le plus populaire dans le domaine de l'enregistrement de vidéo pour les consommateurs. En raison de la limitation de la résolution horizontale, une version améliorée du format VHS a été introduite: le format S-VHS. Bien que le nouveau format permette de reproduire l'image avec une bonne qualité, il y a encore des défauts: (1) L'image semble brouillée, et les détails manquent de netteté. (2) Les couleurs sont dégradées et manquent d'éclat. Ce travail propose deux méthodes de restauration de défauts de S-VHS. La première combine les techniques de débrouillage avec les techniques de rehaussement. Dans cette méthode, la luminance et la chrominance sont traitées séparément. La deuxième approche proposée utilise une structure de réseaux de neurones en cascade et permet d'utiliser l'information dans la luminance traitée afin de mieux restaurer la chrominance. Les résultats d'expériences ont montré que les deux méthodes proposées sont capables de donner des images plus nettes et plus éclatantes. En plus, grâce à la simplicité et la non-itération, il est possible de les faire fonctionner en temps-reél

Indoor location prediction using multiple wireless received signal strengths

This paper presents a framework for indoor location prediction system using multiple wireless signals available freely in public or office spaces. We first propose an abstract architectural design for the system, outlining its key components and their functionalities. Different from existing works, such as robot indoor localization which requires as precise localization as possible, our work focuses on a higher grain: location prediction. Such a problem has a great implication in context-aware systems such as indoor navigation or smart self-managed mobile devices (e.g., battery management). Central to these systems is an effective method to perform location prediction under different constraints such as dealing with multiple wireless sources, effects of human body heats or mobility of the users. To this end, the second part of this pa- per presents a comparative and comprehensive study on different choices for modeling signals strengths and prediction methods under different condition settings. The results show that with simple, but effective modeling method, almost perfect prediction accuracy can be achieved in the static environment, and up to 85% in the presence of human movements. Finally, adopting the proposed framework we outline a fully developed system, named Marauder, that support user interface interaction and real-time voice-enabled location prediction.<br /

Capacity: Cryptographically-Enforced In-Process Capabilities for Modern ARM Architectures (Extended Version)

In-process compartmentalization and access control have been actively explored to provide in-place and efficient isolation of in-process security domains. Many works have proposed compartmentalization schemes that leverage hardware features, most notably using the new page-based memory isolation feature called Protection Keys for Userspace (PKU) on x86. Unfortunately, the modern ARM architecture does not have an equivalent feature. Instead, newer ARM architectures introduced Pointer Authentication (PA) and Memory Tagging Extension (MTE), adapting the reference validation model for memory safety and runtime exploit mitigation. We argue that those features have been underexplored in the context of compartmentalization and that they can be retrofitted to implement a capability-based in-process access control scheme. This paper presents Capacity, a novel hardware-assisted intra-process access control design that embraces capability-based security principles. Capacity coherently incorporates the new hardware security features on ARM that already exhibit inherent characteristics of capability. It supports the life-cycle protection of the domain's sensitive objects -- starting from their import from the file system to their place in memory. With intra-process domains authenticated with unique PA keys, Capacity transforms file descriptors and memory pointers into cryptographically-authenticated references and completely mediates reference usage with its program instrumentation framework and an efficient system call monitor. We evaluate our Capacity-enabled NGINX web server prototype and other common applications in which sensitive resources are isolated into different domains. Our evaluation shows that Capacity incurs a low-performance overhead of approximately 17% for the single-threaded and 13.54% for the multi-threaded webserver.Comment: Accepted at ACM CCS 202

High accuracy context recovery using clustering mechanisms

This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point id and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing state-of-the-art probabilistic clustering technique, the Latent Dirichiet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.<br /

Facile Synthesis of Carbon Quantum Dots by Plasma-liquid Interaction Method

Carbon quantum dots (CQDs) are a novel type of fluorescent nano-materials with various unique properties. They are recently attracting enormous interest due to their superiority in water solubility, chemical inertness, low toxicity, ease of functionalization as well as resistance to photo-bleaching and potential applications in biomedical indication, photo-catalysis, energy conversion, optoelectronics, and sensing. In this work, we present a facile and environmentally friendly synthesis of CQDs based on plasma - liquid interaction method. This is a single-step method and does not use toxic chemicals. The size distribution of obtained CQDs is rather uniform at approximately 3 nm. The emission peak of CQDs shifts from 427 nm to 523 nm as the excitation wavelength is varied from 340 nm to 460 nm. The non-equilibrium reactive chemistry of plasma liquid interaction is responsible for acceleration of the CQDs formation process

Management of Patients with Refractory Cardiogenic Shock and Cardiointestinal Syndrome with Impella 5.5 as Bridge to Decision: Case Series

Patients with advanced heart failure require multi-system management as a majority succumb to end-organ dysfunction, including gastrointestinal sequelae. Temporizing measures, such as early mechanical circulatory support, can assist in the recovery of patients with acute cardiogenic shock. The temporary support can improve patient characteristics to enable future definitive heart failure therapies such as durable left ventricular assist devices and orthotopic heart transplantation. We present two cases of cardiogenic shock that were successfully bridged with an Impella 5.5 (Abiomed). The management enabled the patients to recover from reversible cardiointestinal syndrome and undergo successful definitive therapies

CSF from Parkinson disease Patients Differentially Affects Cultured Microglia and Astrocytes

<p>Abstract</p> <p>Background</p> <p>Excessive and abnormal accumulation of alpha-synuclein (α-synuclein) is a factor contributing to pathogenic cell death in Parkinson's disease. The purpose of this study, based on earlier observations of Parkinson's disease cerebrospinal fluid (PD-CSF) initiated cell death, was to determine the effects of CSF from PD patients on the functionally different microglia and astrocyte glial cell lines. Microglia cells from human glioblastoma and astrocytes from fetal brain tissue were cultured, grown to confluence, treated with fixed concentrations of PD-CSF, non-PD disease control CSF, or control no-CSF medium, then photographed and fluorescently probed for α-synuclein content by deconvolution fluorescence microscopy. Outcome measures included manually counted cell growth patterns from day 1-8; α-synuclein density and distribution by antibody tagged 3D model stacked deconvoluted fluorescent imaging.</p> <p>Results</p> <p>After PD-CSF treatment, microglia growth was reduced extensively, and a non-confluent pattern with morphological changes developed, that was not evident in disease control CSF and no-CSF treated cultures. Astrocyte growth rates were similarly reduced by exposure to PD-CSF, but morphological changes were not consistently noted. PD-CSF treated microglia showed a significant increase in α-synuclein content by day 4 compared to other treatments (p ≤ 0.02). In microglia only, α-synuclein aggregated and redistributed to peri-nuclear locations.</p> <p>Conclusions</p> <p>Cultured microglia and astrocytes are differentially affected by PD-CSF exposure compared to non-PD-CSF controls. PD-CSF dramatically impacts microglia cell growth, morphology, and α-synuclein deposition compared to astrocytes, supporting the hypothesis of cell specific susceptibility to PD-CSF toxicity.</p