Miracle-dz/UniFAR-FTUs huggingface dataset:
TwinVisor: Hardware-isolated Confidential Virtual Machines for ARM

Confidential VM, which offers an isolated execution environment for cloud tenants with limited trust in the cloud provider, has recently been deployed in major clouds such as AWS and Azure. However, while ARM has become increasingly popular in cloud data centers, existing confidential VM designs mainly leverage specialized x86 hardware extensions (e.g., AMD SEV and Intel TDX) to isolate VMs upon a shared hypervisor. This paper proposes TwinVisor, the first system that enables the hardware-enforced isolation of confidential VMs on ARM platforms. TwinVisor takes advantage of the mature ARM TrustZone to run two isolated hypervisors, one in the secure world (called S-visor in this paper) and the other in the normal world (called N-visor), to support normal VMs and confidential VMs respectively. Instead of building a new S-visor from scratch, our design decouples protection from resource management, and reuses most functionalities of a full-fledged N-visor to minimize the size of S-visor. We have built two prototypes of TwinVisor: one on an official ARM simulator with S-EL2 enabled to validate functional correctness and the other on an ARM development board to evaluate performance. The S-visor comprises 5.8K LoCs while the N-visor introduces 906 LoC changes to KVM. According to our evaluation, TwinVisor can run unmodified VM images as confidential VMs while incurring less than 5% performance overhead for various real-world workloads on SMP VMs.
RAS: Continuously Optimized Region-Wide Datacenter Resource Allocation

Capacity reservation is a common offering in public clouds and on-premise infrastructure. However, no prior work provides capacity reservation with SLO guarantees that takes into account random and correlated hardware failures, datacenter maintenance, and heterogeneous hardware. In this paper, we describe how Facebook's region-scale Resource Allowance System (RAS) addresses these issues and provides guaranteed capacity. RAS uses a capacity abstraction called reservation to represent a set of servers dynamically assigned to a logical cluster. We take a two-level approach to scale resource allocation to all datacenters in a region, where a mixed-integer-programming solver continuously optimizes server-to-reservation assignments off the critical path, and a traditional container allocator does real-time placement of containers on servers in a reservation. As a relatively new component of Facebook's 10-year old cluster manager Twine, RAS has been running in production for almost two years, continuously optimizing the allocation of millions of servers to thousands of reservations. We describe the design of RAS and share our experience of deploying it at scale.
PRISM: Rethinking the RDMA Interface for Distributed Systems

Remote Direct Memory Access (RDMA) has been used to accelerate a variety of distributed systems, by providing low-latency, CPU-bypassing access to a remote host's memory. However, most of the distributed protocols used in these systems cannot easily be expressed in terms of the simple memory READs and WRITEs provided by RDMA. As a result, designers face a choice between introducing additional protocol complexity (e.g., additional round trips) or forgoing the benefits of RDMA entirely. This paper argues that an extension to the RDMA interface can resolve this dilemma. We introduce the PRISM interface, which adds four new primitives: indirection, allocation, enhanced compare-and-swap, and operation chaining. These increase the expressivity of the RDMA interface, while still being implementable using the same underlying hardware features. We show their utility by designing three new applications using PRISM primitives, that require little to no server-side CPU involvement: (1) PRISM-KV, a key-value store; (2) PRISM-RS, a replicated block store; and (3) PRISM-TX, a distributed transaction protocol. Using a software-based implementation of the PRISM primitives, we show that these systems outperform prior RDMA-based equivalents.
Nuberu: reliable RAN virtualization in shared platforms

RAN virtualization will become a key technology for the last mile of next-generation mobile networks driven by initiatives such as the O-RAN alliance. However, due to the computing fluctuations inherent to wireless dynamics and resource contention in shared computing infrastructure, the price to migrate from dedicated to shared platforms may be too high. Indeed, we show in this paper that the baseline architecture of a base station's distributed unit (DU) collapses upon moments of deficit in computing capacity. Recent solutions to accelerate some signal processing tasks certainly help but do not tackle the core problem: a DU pipeline that requires predictable computing to provide carrier-grade reliability. We present Nuberu, a novel pipeline architecture for 4G/5G DUs specifically engineered for non-deterministic computing platforms. Our design has one key objective to attain reliability: to guarantee a minimum set of signals that preserve synchronization between the DU and its users during computing capacity shortages and, provided this, maximize network throughput. To this end, we use techniques such as tight deadline control, jitter-absorbing buffers, predictive HARQ, and congestion control. Using an experimental prototype, we show that Nuberu attains >95% of the theoretical spectrum efficiency in hostile environments, where state-of-art approaches lose connectivity, and at least 80% resource savings.
Long-range ambient LoRa backscatter with parallel decoding

LoRa backscatter is a promising technology to achieve low-power and long-distance communication for connecting millions of devices in the Internet of Things. We present P2LoRa, the first ambient LoRa backscatter system with parallel decoding and long-range communication. The high level idea of P2LoRa is to modulate data by shifting ambient LoRa packets with a small frequency. To achieve long distance communication, we enhance the SNR of the backscatter signal by concentrating leaked energy in both the frequency domain and time domain. We propose a method to accurately reconstruct and cancel the in-band excitation signal, which is orders of magnitude higher than the backscatter signal. For parallel decoding, we propose a method to cancel inter-tag interference with very low overhead and address the signal misalignment problem due to different time of flight. We prototype the P2LoRa tag with customized low-cost hardware and implement the P2LoRa gateway on USRP. Through extensive evaluations, we show that P2LoRa achieves a long communication distance of 2.2 km with ambient LoRa, and supports 101 parallel tag transmissions.
Cuckoo Trie: Exploiting Memory-Level Parallelism for Efficient DRAM Indexing

We present the Cuckoo Trie, a fast, memory-efficient ordered index structure. The Cuckoo Trie is designed to have memory-level parallelism---which a modern out-of-order processor can exploit to execute DRAM accesses in parallel--- without sacrificing memory efficiency. The Cuckoo Trie thus breaks a fundamental performance barrier faced by current indexes, whose bottleneck is a series of dependent pointer-chasing DRAM accesses---e.g., traversing a search tree path--- which the processor cannot parallelize. Our evaluation shows that the Cuckoo Trie outperforms state-of-the-art-indexes by up to 20%-360% on a variety of datasets and workloads, typically with a smaller or comparable memory footprint.
MoVi-Fi: motion-robust vital signs waveform recovery via deep interpreted RF sensing

Vital signs are crucial indicators for human health, and researchers are studying contact-free alternatives to existing wearable vital signs sensors. Unfortunately, most of these designs demand a subject human body to be relatively static, rendering them very inconvenient to adopt in practice where body movements occur frequently. In particular, radio-frequency (RF) based contact-free sensing can be severely affected by body movements that overwhelm vital signs. To this end, we introduce MoVi-Fi as a motion-robust vital signs monitoring system, capable of recovering fine-grained vital signs waveform in a contact-free manner. Being a pure software system, MoVi-Fi can be built on top of virtually any commercial-grade radars. What inspires our design is that RF reflections caused by vital signs, albeit weak, do not totally disappear but are composited with other motion-incurred reflections in a nonlinear manner. As nonlinear blind source separation is inherently hard, MoVi-Fi innovatively employs deep contrastive learning to tackle the problem; this self-supervised method requires no ground truth in training, and it exploits contrastive signal features to distinguish vital signs from body movements. Our experiments with 12 subjects and 80hour data demonstrate that MoVi-Fi accurately recovers vital signs waveform under severe body movements.
Crash Consistent Non-Volatile Memory Express

This paper presents crash consistent Non-Volatile Memory Express (ccNVMe), a novel extension of the NVMe that defines how host software communicates with the non-volatile memory (e.g., solid-state drive) across a PCI Express bus with both crash consistency and performance efficiency. Existing storage systems pay a huge tax on crash consistency, and thus can not fully exploit the multi-queue parallelism and low latency of the NVMe interface. ccNVMe alleviates this major bottleneck by coupling the crash consistency to the data dissemination. This new idea allows the storage system to achieve crash consistency by taking the free rides of the data dissemination mechanism of NVMe, using only two lightweight memory-mapped I/Os (MMIO), unlike traditional systems that use complex update protocol and heavyweight block I/Os. ccNVMe introduces transaction-aware MMIO and doorbell to reduce the PCIe traffic as well as to provide atomicity. We present how to build a high-performance and crash-consistent file system namely MQFS atop ccNVMe. We experimentally show that MQFS increases the IOPS of RocksDB by 36% and 28% compared to a state-of-the-art file system and Ext4 without journaling, respectively.
Preface: Mass spectrometry in Alzheimer disease

This preface introduces the content of the special issue on ‘Mass Spectrometry in Alzheimer Disease’. Here, an overview is provided on how mass spectrometry is contributing to a broader understanding of AD pathobiology. Mass spectrometry has become a major technology in biomedical analysis and research. This includes biochemical and clinical studies that aim to detail our understanding of Alzheimer disease pathogenesis and pathobiology (AD). In this special issue, key experts in the field present exciting developments and applications of MS in the context of studying AD pathology. These studies span from basic biochemical and neuropathological studies, over advanced metabolomics‐ and proteomics, towards comprehensive biomarker studies, as well as more recently, in situ mass spectrometry‐based imaging (MSI). Together, these studies highlight the key relevance of current and emerging MS technologies to detect, delineate and understand principle pathogenic mechanisms underlying AD.
Wild cereal grain consumption among Early Holocene foragers of the Balkans predates the arrival of agriculture

Forager focus on wild cereal plants has been documented in the core zone of domestication in southwestern Asia, while evidence for forager use of wild grass grains remains sporadic elsewhere. In this paper, we present starch grain and phytolith analyses of dental calculus from 61 Mesolithic and Early Neolithic individuals from five sites in the Danube Gorges of the central Balkans. This zone was inhabited by likely complex Holocene foragers for several millennia before the appearance of the first farmers ∼6200 cal BC. We also analyzed forager ground stone tools for evidence of plant processing. Our results based on the study of dental calculus show that certain species of Poaceae (species of the genus Aegilops) were used since the Early Mesolithic, while ground stone tools exhibit traces of a developed grass grain processing technology. The adoption of domesticated plants in this region after ∼6500 cal BC might have been eased by the existing familiarity with wild cereals.
Simulation atomic force microscopy to predict correlated conformational dynamics in proteins from topographic imaging

Atomic force microscopy (AFM) of proteins can detect only changes within the scanned molecular surface, missing all motions in other regions and thus information about functionally relevant conformational couplings. We show that simulation AFM can overcome this drawback by reconstruction of 3D molecular structures from topographic AFM images. A proof of principle demonstration is provided for an in-silico AFM experiment visualizing the conformational dynamics of a membrane transporter. The application shows that the alternating access mechanism underlying its operation can be retrieved from only AFM imaging of one membrane side. Simulation AFM is implemented in the freely available BioAFMviewer software platform, providing the convenient applicability to better understand experimental AFM observations.
Assessing species coverage and assembly quality of rapidly accumulating sequenced genomes

Ambitious initiatives to coordinate genome sequencing of Earth’s biodiversity mean that the accumulation of genomic data is growing rapidly. In addition to cataloguing biodiversity, these data provide the basis for understanding biological function and evolution. Accurate and complete genome assemblies offer a comprehensive and reliable foundation upon which to advance our understanding of organismal biology at genetic, species, and ecosystem levels. However, ever-changing sequencing technologies and analysis methods mean that available data are often heterogeneous in quality. In order to guide forthcoming genome generation efforts and promote efficient prioritisation of resources, it is thus essential to define and monitor taxonomic coverage and quality of the data. Here we present an automated analysis workflow that surveys genome assemblies from the United States National Center for Biotechnology Information (NCBI), assesses their completeness using the relevant Benchmarking Universal Single-Copy Orthologue (BUSCO) datasets, and collates the results into an interactively browsable resource. We apply our workflow to produce a community resource of available assemblies from the phylum Arthropoda, the Arthropoda Assembly Assessment Catalogue. Using this resource, we survey current taxonomic coverage and assembly quality at the NCBI, we examine how key assembly metrics relate to gene content completeness, and we compare results from using different BUSCO lineage datasets. These results demonstrate how the workflow can be used to build a community resource that enables large-scale assessments to survey species coverage and data quality of available genome assemblies, and to guide prioritisations for ongoing and future sampling, sequencing, and genome generation initiatives.
Whole Organism Profiling of the Timp Gene Family

Tissue inhibitor of metalloproteinases (TIMPs/Timps) are an endogenous family of widely expressed matrisome-associated proteins that were initially identified as inhibitors of matrix metalloproteinase activity (Metzincin family proteases). Consequently, TIMPs are often considered simply as protease inhibitors by many investigators. However, an evolving list of new metalloproteinase-independent functions for TIMP family members suggests that this concept is outdated. These novel TIMP functions include direct agonism/antagonism of multiple transmembrane receptors, as well as functional interactions with matrisome targets. While the family was fully identified over two decades ago, there has yet to be an in-depth study describing the expression of TIMPs in normal tissues of adult mammals. An understanding of the tissues and cell-types that express TIMPs 1 through 4, in both normal and disease states are important to contextualize the growing functional capabilities of TIMP proteins, which are often dismissed as non-canonical. Using publicly available single cell RNA sequencing data from the Tabula Muris Consortium, we analyzed approximately 100,000 murine cells across eighteen tissues from non-diseased organs, representing seventy-three annotated cell types, to define the diversity in Timp gene expression across healthy tissues. We describe the unique expression profiles across tissues and organ-specific cell types that all four Timp genes display. Within annotated cell-types, we identify clear and discrete cluster-specific patterns of Timp expression, particularly in cells of stromal and endothelial origins. Differential expression and gene set pathway analysis provide evidence of the biological significance of Timp expression in these identified cell sub-types, which are consistent with novel roles in normal tissue homeostasis and changing roles in disease progression. This understanding of the tissues, specific cell types and microenvironment conditions in which Timp genes are expressed adds important physiological context to the growing array of novel functions for TIMP proteins.
iHypoxia: an integrative database of the expression dynamics of proteins in response to hypoxia in animals

Decreased oxygen concentrations (hypoxia) outside of the physiological range may severely subvert cell, tissue, and organism survival. Mammals have evolved mechanisms to sense hypoxia and induce a series of hypoxic responses. In recent years, high-throughput techniques have greatly promoted global perturbation studies of protein expression during hypoxia, and these studies have contributed to the understanding of the complex regulatory networks of hypoxia. In this study, we developed an integrated resource for the expression dynamics of proteins in response to hypoxia (iHypoxia), which contains 1,629 expression events of 1,215 proteins identified by low-throughput experiments (LTEs) and 154,953 quantitative expression events of 36,194 proteins identified by high-throughput experiments (HTEs) from five mammals that exhibit a response to hypoxia. Various experimental details such as the hypoxic experimental conditions, expression patterns, and samples were carefully collected and integrated. In addition, we conducted an orthologous search and identified 581,763 proteins that may respond to hypoxia among 50 animals. An enrichment analysis of human proteins identified from LTEs showed that these proteins were enriched in certain drug targets and cancer genes. The annotation of known posttranslational modification (PTM) sites to proteins identified by LTEs revealed that these proteins underwent extensive PTMs, particularly phosphorylation, ubiquitination and acetylation. Based on the results, iHypoxia provides a convenient and user-friendly method for users to obtain hypoxia-related information of interest. We anticipate that iHypoxia, which is freely accessible at http://ihypoxia.omicsbio. info, will advance the understanding of hypoxia and serve as a valuable data resource.
Synaptotagmin-7 enhances phasic dopamine release

Dopamine released from substantia nigra pars compacta (SNc) neurons modulates movement, motivation, and reward. In addition to their tonic firing pattern, dopamine neurons also fire high-frequency bursts that cause superlinear increases in dopamine release. To study this poorly understood form of short-term plasticity, we used the fluorescent dopamine sensor dLight1.3b to examine the role of the calcium-binding protein synaptotagmin-7 (SYT7). We report that SYT7 mediates a hidden component of facilitation, which was unmasked by lowering initial release probability, or by low-frequency stimulation of nerve terminals. In Syt7 KO neurons, there was profound synaptic depression that significantly reduced release during stimulations that mimic in vivo firing patterns of SNc neurons. D2-mediated inhibitory postsynaptic currents in the SNc revealed a similar role for SYT7 in somatodendritic release. Our results indicate that SYT7 drives short-term facilitation of release from dopamine neurons, which likely underlies frequency-dependence of dopamine signaling in vivo.
Spatial signatures of anesthesia-induced burst-suppression differ between primates and rodents

During deep anesthesia, the electroencephalographic (EEG) signal of the brain alternates between bursts of activity and periods of relative silence (suppressions). The origin of burst-suppression and its distribution across the brain remain matters of debate. In this work, we used functional magnetic resonance imaging (fMRI) to map the brain areas involved in anesthesia-induced burst-suppression across four mammalian species: humans, long-tailed macaques, common marmosets, and rats. At first, we determined the fMRI signatures of burst-suppression in human EEG-fMRI data. Applying this method to animal fMRI datasets, we found distinct burst-suppression signatures in all species. The burst-suppression maps revealed a marked inter-species difference: in rats the entire neocortex engaged in burst-suppression, while in primates most sensory areas were excluded—predominantly the primary visual cortex. We anticipate that the identified species-specific fMRI signatures and whole-brain maps will guide future targeted studies investigating the cellular and molecular mechanisms of burst-suppression in unconscious states.
Backpropagating through Markov Logic Networks

We integrate Markov Logic networks with deep learning architectures operating on high-dimensional and noisy feature inputs. Instead of relaxing the discrete components into smooth functions, we propose an approach that allows us to backpropagate through standard statistical relational learning components using perturbation-based differentiation. The resulting hybrid models are shown to outperform models solely relying on deep learning based function fitting. We find that using noise perturbations is required to allow the proposed hybrid models to robustly learn from the training data.