Miracle-dz/UniFAR-FTUs huggingface dataset:
Offloading distributed applications onto smartNICs using iPipe

Emerging Multicore SoC SmartNICs, enclosing rich computing resources (e.g., a multicore processor, onboard DRAM, accelerators, programmable DMA engines), hold the potential to offload generic datacenter server tasks. However, it is unclear how to use a SmartNIC efficiently and maximize the offloading benefits, especially for distributed applications. Towards this end, we characterize four commodity SmartNICs and summarize the offloading performance implications from four perspectives: traffic control, computing capability, onboard memory, and host communication. Based on our characterization, we build iPipe, an actor-based framework for offloading distributed applications onto SmartNICs. At the core of iPipe is a hybrid scheduler, combining FCFS and DRR-based processor sharing, which can tolerate tasks with variable execution costs and maximize NIC compute utilization. Using iPipe, we build a real-time data analytics engine, a distributed transaction system, and a replicated key-value store, and evaluate them on commodity SmartNICs. Our evaluations show that when processing 10/25Gbps of application bandwidth, NIC-side offloading can save up to 3.1/2.2 beefy Intel cores and lower application latencies by 23.0/28.0 μs.
NV: An intermediate language for network verification

Introduction. Network devices often rely on distributed protocols, such as BGP, to make routing decisions. Network operators can enforce routing policies (that may express security, economic or other concerns) by configuring what routing protocols devices execute, and how they process routing messages. These configurations are expressed in low-level, vendor specific languages. Combined with the distributed nature of routing protocols, reasoning about the correctness of the configurations is a daunting task for operators. Network verification [1, 4] and simulation tools [3] have been proposed to aid operators. Additionally, as those techniques often face scaling problems, researchers have suggested ways [2] to simplify the complexity of networks. Regardless of the transformation or reasoning principles used, one needs to parse the original network configurations as provided by operators. To tackle the range of vendorspecific configurations, Batfish [3] uses a vendor-agnostic
Smile, you are in the spotlight!

In future smart factories, robots performing critical tasks will require ultra-reliable networks. Meanwhile, LED light is ubiquitous in smart factories. To make the emerging LED-enabled Visible Light Communication (VLC) a suitable candidate for networking robots in future smart factories, the VLC channels must be very robust and homogeneous. In this work, we present two techniques to achieve ultra-reliable VLC networks: (1) by increasing the transmitter density and (2) by orienting the transmitters to the receiver, i.e., creating a spotlight. We show that both of our techniques can improve the reliability performance of VLC networks greatly. A real-life demo video of creating such a spotlight is available on https://youtu.be/VNu48Gidz_c.
The first proven oxychilid land snail endemic to China (Eupulmonata, Gastrodontoidea)

Abstract A new and the first proven oxychilid species endemic to China is reported from Sichuan Province. Sinoxychilusgen. nov. is established based on this new species and has diagnostic traits of the sculptured protoconch, partial epiphallus wrapped by developed penis sheath, penial retractor muscle inserting on the top of penial caecum, spinelets on penial pilasters, absence of epiphallic papilla and perivaginal gland present on vagina and proximal bursa copulatrix duct. In light of shell morphology and through geometric morphometric analyses, Zonites scrobiculatus scrobiculatus Gredler and Z. scrobiculatus hupeina Gredler are proposed to be included in the new genus. A phylogenetic inference based on ITS2 gene indicates that the new genus is systematically close to Oxychilus Fitzinger, which is known from the Western Palearctic and the Southwestern Arabian Peninsula, regions that are geographically far from the distribution range of the new genus.
A millimeter wave network for billions of things

With the advent of the Internet of Things (IoT), billions of new connected devices will come online, placing a huge strain on today's WiFi and cellular spectrum. This problem will be further exacerbated by the fact that many of these IoT devices are low-power devices that use low-rate modulation schemes and therefore do not use the spectrum efficiently. Millimeter wave (mmWave) technology promises to revolutionize wireless networks and solve spectrum shortage problem through the usage of massive chunks of high-frequency spectrum. However, adapting this technology presents challenges. Past work has addressed challenges in using mmWave for emerging applications, such as 5G, virtual reality and data centers, which require multiple-gigabits-per-second links, while having substantial energy and computing power. In contrast, this paper focuses on designing a mmWave network for low-power, low-cost IoT devices. We address the key challenges that prevent existing mmWave technology from being used for such IoT devices. First, current mmWave radios are power hungry and expensive. Second, mmWave radios use directional antennas to search for the best beam alignment. Existing beam searching techniques are complex and require feedback from access points (AP), which makes them unsuitable for low-power, low-cost IoT devices. We present mmX, a novel mmWave network that addresses existing challenges in exploiting mmWave for IoT devices. We implemented mmX and evaluated it empirically.
Being more Effective and Interpretable: Bridging the Gap Between Heuristics and AI for ABR Algorithms

In this poster, we propose several novel ABR approaches, namely BBA+ and MPC+, which are the fusion of heuristics and AI-based schemes. Results indicate that the proposed methods perform better than recent heuristic ABR methods. Meanwhile, such methods have also become more interpretable compared with AI-based schemes.
Using Restriction to Optimize Unification Parsing

Since Shieber (1985), restriction has been recognized as an important operation in unification parsing. 1 As Shieber points out, the most straightforward adap­ tation of Earley’s algorithm 2 for use with unification grammars fails because the infinite number of categories in these grammars can cause the predictor step in the algorithm to go into an infinite loop, creating ever more and more new predictions (i.e. the problem is that new predictions are not subsumed by pre­ vious predictions). The basic idea of restriction is to avoid making predictions on the basis of all of the information in a DAG, but rather to take some subset of that information (i.e. a restricted DAG-henceforth RD) and use just that information to make new predictions. Since there are only a finite number of possible RDs the predictor step will no longer go into the infinite loop described above. The price you pay for this move is that some spurious predictions will be made, but as Shieber points out, the algorithm is still correct since any spunous predictions will be weeded out by the completer step.
Nitrosketch: robust and general sketch-based monitoring in software switches

Software switches are emerging as a vital measurement vantage point in many networked systems. Sketching algorithms or sketches, provide high-fidelity approximate measurements, and appear as a promising alternative to traditional approaches such as packet sampling. However, sketches incur significant computation overhead in software switches. Existing efforts in implementing sketches in virtual switches make sacrifices on one or more of the following dimensions: performance (handling 40 Gbps line-rate packet throughput with low CPU footprint), robustness (accuracy guarantees across diverse workloads), and generality (supporting various measurement tasks). In this work, we present the design and implementation of NitroSketch, a sketching framework that systematically addresses the performance bottlenecks of sketches without sacrificing robustness and generality. Our key contribution is the careful synthesis of rigorous, yet practical solutions to reduce the number of per-packet CPU and memory operations. We implement NitroSketch on three popular software platforms (Open vSwitch-DPDK, FD.io-VPP, and BESS) and evaluate the performance. We show that accuracy is comparable to unmodified sketches while attaining up to two orders of magnitude speedup, and up to 45% reduction in CPU usage.
Efficient Dynamic Isolation of Congestion in Lossless DataCenter Networks

The architecture of modern DataCenters (DCs) has evolved to meet the stringent communication latency requirements of applications. RDMA technologies such as RoCEv2 have become mainstream to reduce latency, but their performance is impaired in systems with lossy networks due to the overload introduced by packet retransmissions. Thus, lossless networks are increasingly used in DCs to avoid retransmissions delays. However, lossless networks favor the occurrence of congestion, degrading network and system performance. Traditional congestion solutions, such as backpressure or injection throttling, may be ineffective when congestion arises from traffic generated by DC applications. Hence, new efficient congestion management strategies suited to the lossless networks of modern DCs are required. In this paper, we analyze congestion and its negative effects in these scenarios. In addition, we propose and evaluate a congestion management strategy that effectively eliminates the main negative effects of congestion, based on the dynamic isolation of congested flows in special queues. Unlike previous proposals based on this approach, a single special queue is shared by all the congested flows reaching a port. We also propose enhancements to this basic strategy to optimize its efficiency.
Learning Tree: Neural Network-based Index for NDN Forwarding Plane

Named Data Networking (NDN) is a recent promising future Internet architecture, which forwards packets based on the named data. To realize this paradigm, a quick enough index with high capacity for forwarding processes has to be utilized in NDN forwarding plane. Unfortunately, all indexes proposed are based on the traditional data structures, which have to make a trade-off between the memory consumption and the lookup speed. In view of this, a neural network-based index, called Learning Tree, is proposed in NDN forwarding plane. Preliminary evaluations indicate that Learning Tree can minimize the memory consumption to 20% compared with traditional hash table and has a practicable high name lookup speed.
Deep Learning-based Short Video Recommendation and Prefetching for Mobile Commuting Users

Mobile short video application is growing rapidly and it is quickly occupying people's life. In this paper, we consider an emerging yet common scenario of short video application usage: mobile users watching short videos on their daily commuting trip on high speed public transport, where the network condition is unsatisfactory. To reduce users waiting time and improve the QoE, we propose a deep learning-based data recommendation and prefetching scheme which obtains user interests and pushes the preferred short video content to the most likely base station that users will be connected to. We use Principal Component Analysis (PCA) plus dropout to reduce the feature dimensions of Inception structure to improve the short video recommendation speed without degrading the accuracy. Through experimental evaluations, we show that the proposed scheme can effectively recommend short video and predict user trajectory, with a recall rate of 100%.
Corner-3D: a RF Simulator for UAV Mobility in Smart Cities

Communication between human-made devices keeps changing. The evolution of the society is tightly linked to the technological and communication development. Today's IoT momentum is fuelling an unstoppable advancement in the communication research field. The emerging communication paradigms outcome of that, represent one of the key enablers of UAVs diffusion in both industrial and leisure applications. Thanks to using cases as infrastructure inspection, surveillance, and rescue, UAVs are quickly becoming one of the pillars of future smart cities. Despite their huge potential, a lot of research and testing is still to be done to fully exploit them. In particular, accurately modeling UAV communications is among the hardest challenges the research community has not properly faced, yet. In this paper, we present CORNER-3D, a lightweight simulator to model the path loss suffered by UAV-to-UAV communications in an urban scenario.
Socksdirect: datacenter sockets can be fast and compatible

Communication intensive applications in hosts with multi-core CPU and high speed networking hardware often put considerable stress on the native socket system in an OS. Existing socket replacements often leave significant performance on the table, as well have limitations on compatibility and isolation. In this paper, we describe SocksDirect, a user-space high performance socket system. SocksDirect is fully compatible with Linux socket and can be used as a drop-in replacement with no modification to existing applications. To achieve high performance, SocksDirect leverages RDMA and shared memory (SHM) for inter-host and intra-host communication, respectively. To bridge the semantics gap between socket and RDMA / SHM, we optimize for the common cases while maintaining compatibility in general. SocksDirect achieves isolation by employing a trusted monitor daemon to handle control plane operations such as connection establishment and access control. The data plane is peer-to-peer between processes, in which we remove multi-thread synchronization, buffer management, large payload copy and process wakeup overheads in common cases. Experiments show that SocksDirect achieves 7~20x better message throughput and 17~35x better latency than Linux socket, and reduces Nginx HTTP latency to 1/5.5.
Neuropathic pain may arise as a consequence of a lesion or disease affecting the somatosensory system

A quantitative, peripherally accessible biomarker for neuropathic pain has great potential to improve clinical outcomes. Based on the premise that peripheral and central immunity contribute to neuropathic pain mechanisms, we hypothesized that biomarkers could be identified from the whole blood of adult male rats, by integrating graded chronic constriction injury (CCI), ipsilateral lumbar dorsal quadrant (iLDQ) and whole blood transcriptomes, and pathway analysis with pain behavior. Correlational bioinformatics identified a range of putative biomarker genes for allodynia intensity,manyencoding forproteinswitha recognized role in immune/nociceptive mechanisms. A selection of these genes was validated in a separate replication study. Pathway analysis of the iLDQ transcriptome identified Fcc and Fce signaling pathways, among others. This study is the first to employ the whole blood transcriptome to identify pain biomarker panels. The novel correlational bioinformatics, developed here, selected such putative biomarkers based on a correlation with pain behavior and formation of signaling pathways with iLDQ genes. Future studies may demonstrate the predictive ability of these biomarker genes across other models and additional variables.
Imbalanced Corticospinal and Reticulospinal Contributions to Spasticity in Humans with Spinal Cord Injury

Damage to the corticospinal and reticulospinal tract has been associated with spasticity in humans with upper motor neuron lesions. We hypothesized that these descending motor pathways distinctly contribute to the control of a spastic muscle in humans with incomplete spinal cord injury (SCI). To test this hypothesis, we examined motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation over the leg representation of the primary motor cortex, maximal voluntary contractions (MVCs), and the StartReact response (shortening in reaction time evoked by a startling stimulus) in the quadriceps femoris muscle in male and females with and without incomplete SCI. A total of 66.7% of the SCI participants showed symptoms of spasticity, whereas the other 33.3% showed no or low levels of spasticity. We found that participants with spasticity had smaller MEPs and MVCs and larger StartReact compared with participants with no or low spasticity and control subjects. These results were consistently present in spastic subjects but not in the other populations. Clinical scores of spasticity were negatively correlated with MEP-max and MVC values and positively correlated with shortening in reaction time. These findings provide evidence for lesser corticospinal and larger reticulospinal influences to spastic muscles in humans with SCI and suggest that these imbalanced contributions are important for motor recovery. SIGNIFICANCE STATEMENT Although spasticity is one of the most common symptoms manifested in humans with spinal cord injury (SCI) to date, its mechanisms of action remain poorly understood. We provide evidence, for the first time, of imbalanced contributions of the corticospinal and reticulospinal tract to control a spastic muscle in humans with chronic incomplete SCI. We found that participants with SCI with spasticity showed small corticospinal responses and maximal voluntary contractions and larger reticulospinal gain compared with participants with no or low spasticity and control subjects. These results were consistently present in spastic subjects but not in the other populations. We showed that imbalanced corticospinal and reticulospinal tract contributions are more pronounced in participants with chronic incomplete SCI with lesser recovery.
Comparing Changes and Transitions of Home Care Clients in Retirement Homes and Private Homes

ABSTRACT Long-stay home care clients mostly reside in private homes or retirement homes, and the type of residence may influence risk factors for long-term care placement. This multi-state analytic study uses RAI-Home Care and administrative data from the Hamilton Niagara Haldimand Brant Local Health Integration Network to model conceptualized states of risk at baseline through a 13-month follow-up period. Modifiable risk factors in these states were client loneliness or depressive symptoms, and caregiver distress. A higher adjusted likelihood of being discharged deceased was found for the lowest-risk clients in retirement homes. Adjusting for client, service, and caregiver characteristics, retirement home residency was associated with higher likelihood of placement in a long-term care home; reduced caregiver distress; and increased client loneliness/depression. As an alternative to private home settings as the location for aging in place among these long-stay home care clients, retirement home residency represents some trade-offs between client and informal caregiver.
Infection with genotoxin‐producing Salmonella enterica synergises with loss of the tumour suppressor APC in promoting genomic instability via the PI3K pathway in colonic epithelial cells

Several commensal and pathogenic Gram‐negative bacteria produce DNA‐damaging toxins that are considered bona fide carcinogenic agents. The microbiota of colorectal cancer (CRC) patients is enriched in genotoxin‐producing bacteria, but their role in the pathogenesis of CRC is poorly understood. The adenomatous polyposis coli (APC) gene is mutated in familial adenomatous polyposis and in the majority of sporadic CRCs. We investigated whether the loss of APC alters the response of colonic epithelial cells to infection by Salmonella enterica, the only genotoxin‐producing bacterium associated with cancer in humans. Using 2D and organotypic 3D cultures, we found that APC deficiency was associated with sustained activation of the DNA damage response, reduced capacity to repair different types of damage, including DNA breaks and oxidative damage, and failure to induce cell cycle arrest. The reduced DNA repair capacity and inability to activate adequate checkpoint responses was associated with increased genomic instability in APC‐deficient cells exposed to the genotoxic bacterium. Inhibition of the checkpoint response was dependent on activation of the phosphatidylinositol 3‐kinase pathway. These findings highlight the synergistic effect of the loss of APC and infection with genotoxin‐producing bacteria in promoting a microenvironment conducive to malignant transformation.
Evaluation of Physiological Effects Induced by Manuka Honey Upon Staphylococcus aureus and Escherichia coli

Several studies have explored the antimicrobial properties of manuka honey (MkH). However, the data available regarding antibacterial action mechanisms are scarcer. The aim of this study was to scrutinize and characterize primary effects of manuka honey (MkH) upon the physiological status of Staphylococcus aureus and Escherichia coli (as Gram-positive and Gram-negative bacteria models, respectively), using flow cytometry (FC) to reveal its antibacterial action mechanisms. Effects of MkH on membrane potential, membrane integrity and metabolic activity were assessed using different fluorochromes in a 180 min time course assay. Time-kill experiments were carried out under the same conditions. Additionally, MkH effect on efflux pumps was also studied in an E. coli strain with an over-expression of several efflux pumps. Exposure of bacteria to MkH resulted in physiological changes related to membrane potential and membrane integrity; these effects displayed slight differences among bacteria. MkH induced a remarkable metabolic disruption as primary physiological effect upon S. aureus and was able to block efflux pump activity in a dose-dependent fashion in the E. coli strain.
MicroRNA expression in infertile men: its alterations and effects

Summary Infertility is an important reproductive health problem, and male infertility is especially important in more than half of infertility cases. Due to the importance of genetic factors in this condition, analysis of semen alone is not enough to recognize men with idiopathic infertility. A molecular non-invasive investigation is necessary to gain valuable information. Currently, microRNAs (miRNAs) are being used as non-invasive diagnostic biomarkers. miRNAs, single-stranded non-coding RNA molecules, act as post-transcriptional gene silencing regulators either by inhibition or repression of translation. Changes in the regulation of miRNAs have been investigated in several different types of male infertility, therefore the biological role of miRNA and gene targets has been defined. The purpose of this study was to review recent research on the altered expression of miRNA in semen, sperm, and testicular biopsy samples in infertile males with different types of unexplained infertility. Changes in miRNA regulation were investigated using microarray and the miRNA levels were confirmed by real-time qRT-PCR. This review explains why creating a non-invasive diagnostic method for male infertility is necessary and how changes in miRNA expression can be used as new diagnostic biomarkers in patients with differing spermatogenic and histopathologic injury.