Miracle-dz/UniFAR-FTUs huggingface dataset:
TBFT: Efficient Byzantine Fault Tolerance Using Trusted Execution Environment

With the rapid development of blockchain, Byzantine fault-tolerant protocols have attracted revived interest recently. To overcome the theoretical bounds of Byzantine fault tolerance, many protocols attempt to use Trusted Execution Environment (TEE) to prevent equivocation and improve fault tolerance from less than 1/3 to minority. However, due to the broken quorum intersection assumption caused by the reduction of replica number, most improvements introduce higher communication complexity or more protocol phases, which affects the performance and scalability of existing TEE-based protocols and prevents them to be applied to large-scale blockchain systems. In this paper, we propose TBFT, an efficient Byzantine fault-tolerant protocol in the partial synchrony setting, which has O(n) message complexity and only two protocol phases in normal-case. The key insight behind TBFT is introducing novel TEE-assisted primitives to limit malicious behaviors of replicas including not only equivocation but also message log forgery and message history forgery, therefore both the communication complexity and protocol phases can be reduced. We have implemented TBFT and evaluated it through systematic analysis and experiments, and the results show that TBFT has better performance and scalability compared to other protocols.
Multi-View Matrix Factorization for Sparse Mobile Crowdsensing

Mobile crowdsensing (MCS) has become a new paradigm for the environment sensing. However, the sparse sensory data prevent the practical and large-scale deployment of MCS systems. Recent studies have demonstrated that the matrix factorization is an effective technique which can estimate the missing sensory data entries based on a small set of observed data entries. However, there could be multiple sensory data sets with each regarded as a different view on the environment. Applying current matrix factorization individually to each data set, the recovery performance will be low as some data sets do not have enough observed data entries thus enough information. By partitioning the parameters involved in matrix factorization, we design some novel regularizations to encode the similarities among different data sets and specific knowledge in the single data set. Based on the regularizations, we propose one basic multiview matrix factorization (MVMF) model and one neural MVMF (NMVMF) model to combine multiple sensory data sets to mutually reinforce the estimation of each single data set. The extensive experimental results demonstrate that, with the help of other data sets, our models can estimate the missing entries in the data set with a very low sampling ratio accurately while the other five baseline algorithms cannot.
USP29 Deubiquitinates SETD8 and Regulates DNA Damage-Induced H4K20 Monomethylation and 53BP1 Focus Formation

SETD8 is a histone methyltransferase that plays pivotal roles in several cellular functions, including transcriptional regulation, cell cycle progression, and genome maintenance. SETD8 regulates the recruitment of 53BP1 to sites of DNA damage by controlling histone H4K20 methylation. Moreover, SETD8 levels are tightly regulated in a cell cycle-dependent manner by ubiquitin-dependent proteasomal degradation. Here, we identified ubiquitin-specific peptidase 29, USP29, as a novel regulator of SETD8. Depletion of USP29 leads to decreased SETD8 protein levels, an effect that is independent of the cell cycle. We demonstrate that SETD8 binds to USP29 in vivo, and this interaction is dependent on the catalytic activity of USP29. Wildtype USP29 can deubiquitinate SETD8 in vivo, indicating that USP29 directly regulates SETD8 protein levels. Importantly, USP29 knockdown inhibits the irradiation-induced increase in H4K20 monomethylation, thereby preventing focus formation of 53BP1 in response to DNA damage. Lastly, depletion of USP29 increases the cellular sensitivity to irradiation. These results demonstrate that USP29 is critical for the DNA damage response and cell survival, likely by controlling protein levels of SETD8.
Research Progress on Emerging Polysaccharide Materials Applied in Tissue Engineering

The development and application of polysaccharide materials are popular areas of research. Emerging polysaccharide materials have been widely used in tissue engineering fields such as in skin trauma, bone defects, cartilage repair and arthritis due to their stability, good biocompatibility and reproducibility. This paper reviewed the recent progress of the application of polysaccharide materials in tissue engineering. Firstly, we introduced polysaccharide materials and their derivatives and summarized the physicochemical properties of polysaccharide materials and their application in tissue engineering after modification. Secondly, we introduced the processing methods of polysaccharide materials, including the processing of polysaccharides into amorphous hydrogels, microspheres and membranes. Then, we summarized the application of polysaccharide materials in tissue engineering. Finally, some views on the research and application of polysaccharide materials are presented. The purpose of this review was to summarize the current research progress on polysaccharide materials with special attention paid to the application of polysaccharide materials in tissue engineering.
PS-Net: Point Shift Network for 3-D Point Cloud Completion

Point cloud completion aims to infer the complete point clouds from incomplete ones, which is used in remote sensing applications such as reconstructing and autonomous driving. However, most existing methods cannot recover accurate structure details of the object. In this article, we propose point shift network (PS-Net). Our main contributions lie in the following three-folds. First, we propose a multiresolution encoder, which extracts and fuses multiresolution point cloud features hierarchically, thus avoiding information loss caused by a single global feature. Second, we design a multiresolution point cloud generation structure, which can be combined with the multiresolution encoder to generate gradually dense point clouds, avoiding the problem of nonuniformly density of the single-layer decoder. Third, we design the shift network (SN), which is used to generate shift vectors to shift the coordinates of each point cloud, so as to further fine-tune the coordinate positions of point clouds, achieving more accurate prediction. We conduct comprehensive experiments on the ShapeNet, KITTI, ScanObjectNN, and ModelNet40 datasets, which demonstrate that the proposed PS-Net achieves better performance than the existing methods and verify the robustness of the proposed method. This article contributes a new method to point cloud completion, realizes fine point cloud shape completion, and brings new possibilities to the research of autonomous driving, registration, and reconstruction.
Joint Active User Detection And Channel Estimation in Massive Access Systems

We consider the problems of active user detection (AUD) and channel estimation (CE) in the uplink of massive machine type communication (mMTC) network. We propose a coupled prior sparse Bayesian learning algorithm that exploits the sporadic user activity and variable-sized block channel sparsity. We first design a novel coupled hierarchical Gaussian prior model which captures the variable size block-sparsity. We then derive its sub-optimal precision hyperparameter updates using majorization-minimization technique. We extensively investigate the proposed algorithm performance and show that it, due to its generic prior, outperforms the existing ones.
ContMEC: An Architecture of Multi-access Edge Computing for Offloading Container-Based Mobile Applications

This paper proposes an architecture called Cont-MEC for enabling UEs (User Equipments) to offload a part of applications to MEC (Multi-access Edge Computing) servers. It is expected that a MEC infrastructure is composed of a small number of centralized data centers and geographically distributed edge stations, in each of which multiple edge servers are installed. It is also expected that applications on UEs are implemented as container clusters similar to cloud-native applications and they are deployed on computing clusters. ContMEC has the following three features: (i) constructing a computing cluster per edge station for scalability to the number of UEs, (ii) hierarchical resource management for scalability and efficient resource sharing among computing clusters, and (iii) overlapped computing clusters for efficient resource sharing. A PoC (Proof-of Concept) implementation of ContMEC employs Kubernetes as a container orchestration system without modifications although Kubernetes does not take account of the MEC infrastructure. The PoC implementation shows that advantage of offloading is larger than disadvantage of implementing applications as container clusters, control traffic is moderate against the number of UEs, and efficient resource sharing among computing clusters is achieved.
Spectral Development of Wind-Generated Water Waves in a Laboratory Facility,

Abstract : Research was conducted to investigate the growth of wind-generated waves on a laboratory scale. Water surface fluctuations were recorded for spectral and statistical analysis at eleven fetches and for three different wind speeds. The growth rates of individual components of the wave spectrum were examined to determine their possible functional relationship to fetch. Three distinct stages of spectral component development were observed. (Author)
Virtual Oscillator Control of Equivalent Voltage-Sourced and Current-Controlled Power Converters

The dynamics of a general class of weakly nonlinear oscillators can be used to control power converters to create a self-forming AC network of distributed generators. Many control stability results for these “virtual” oscillators consider the interaction of voltage-source converters, but most practical converters use a nested current loop. This paper develops a general method to extend voltage-source stability results to current-controlled converters using a virtual admittance. A fast current control loop allows a singular perturbations analysis to demonstrate the equivalence of the two. This virtual admittance can also manipulate load sharing between converters without changing the core nonlinear dynamics. In addition, Virtual Oscillator Control is experimentally demonstrated with three-phase voltage-sourced and current-controlled inverters. This validates the equivalence of the two formulations, and extends previous single phase testing into three phases. The extension to current-controlled converters enhances safety and increases the breadth of applications for existing control methods.
Direct photon production in hadron induced collisions

This thesis presents an analysis of direct photon production by 515 Ge V / c 7rand 530 and 800 GeV /c proton beams incident on beryllium target. The data on which this analysis is based was collected during the 1990 and 1991 fixed-target run of Fermilab experiment E706. A description of the experimental apparatus as well as reconstruction and analysis techniques is given. Inclusive direct photon and 7ro cross sections are presented as a function of PT over the kinematic range 3 < PT < 12 Ge V / c averaged over 1.5 units of rapidity. Compa-risons of the measured cross sections to NLL QCD calculations are also made. Table of
Distributed mmWave Massive MIMO NOMA – A Graph-Theoretic Perspective

We propose a graph-theoretic analytical framework to solve the sum rate maximization problem of a non-orthogonal-multiple-access (NOMA)-aided distributed millimeter wave massive multiple-input multiple-output (MIMO) system. The optimal solution for this system-wide sum rate maximization problem is neither mathematically tractable nor computationally-efficient when a traditional communication-theoretic analytical approach is solely invoked. Thus, the original problem is decoupled into two sub-problems, namely, a user access point (AP) association/clustering and a pilot resource allocation. In the first subproblem, APs optimally select a set of users having the highest average channel power gains, while the second sub-problem optimally assigns a set of limited orthogonal pilots among concurrently served users such that the pilot contamination is minimized. We propose a graph-theoretic analytical framework to find practically-viable and computationally-efficient solutions to both these sub-problems by virtue of modeling them via bipartite graph matching and vertex coloring problems. Thereby, we propose an algorithm to compute the minimum number of orthogonal pilots required for a given user-AP association/clustering. By exploiting the minimum pilot length and leveraging the benefits of our graph-theoretic approach, we propose a pragmatic solution of the coexistence of NOMA and orthogonal multiple-access schemes to further boost the achievable rate performance of our proposed system set-up.
A Novel Homozygous Variant in DYSF Gene Is Associated with Autosomal Recessive Limb Girdle Muscular Dystrophy R2/2B

Mutations in the DYSF gene, encoding dysferlin, are responsible for Limb Girdle Muscular Dystrophy type R2/2B (LGMDR2/2B), Miyoshi myopathy (MM), and Distal Myopathy with Anterior Tibialis onset (MDAT). The size of the gene and the reported inter and intra familial phenotypic variability make early diagnosis difficult. Genetic analysis was conducted using Next Gene Sequencing (NGS), with a panel of 40 Muscular Dystrophies associated genes we designed. In the present study, we report a new missense variant c.5033G>A, p.Cys1678Tyr (NM_003494) in the exon 45 of DYSF gene related to Limb Girdle Muscular Dystrophy type R2/2B in a 57-year-old patient affected with LGMD from a consanguineous family of south Italy. Both healthy parents carried this variant in heterozygosity. Genetic analysis extended to two moderately affected sisters of the proband, showed the presence of the variant c.5033G>A in both in homozygosity. These data indicate a probable pathological role of the variant c.5033G>A never reported before in the onset of LGMDR2/2B, pointing at the NGS as powerful tool for identifying LGMD subtypes. Moreover, the collection and the networking of genetic data will increase power of genetic-molecular investigation, the management of at-risk individuals, the development of new therapeutic targets and a personalized medicine.
Robot gluing localization method based on monocular vision

A localization method based on monocular vision is proposed to solve the problem of poor flexibility, high cost and unstable accuracy of glue dispensing robot. The method includes the workpiece image feature extraction method based on distribution model and the optimized PNP algorithm based on depth calibration, which can locate the threedimensional coordinates of the workpiece and further generate the gluing track. Firstly, the layout and local coordinates of feature points are determined according to the workpiece model and gluing process, and the feature distribution model and template set are established. Then the image coordinates of feature points are extracted step by step by using workpiece contour features and image gray features, combining multi template and multi angle matching with shape detection, and using acceleration strategies such as image pyramid and angle layer by layer subdivision. Finally, the PNP algorithm is optimized in the Z direction through the depth calibration method to realize the high-precision positioning of the workpiece. The localization experiments of various types of reducer shells under different imaging environments were carried out. The experimental results show that the method has better feature extraction effect for workpieces with complex structure in chaotic environment, and the maximum localization error in one direction is within ± 0.5 mm, which meets the application needs of robot glue positioning. The method can detect the offset of 6 degrees of freedom of the target workpiece at the same time, which has a wider application than the general 2D visual localization method. It can also be used for the localization of parts in other scenes.
Spatial-Temporal Air Quality Inference based on Matrix Factorization

Providing a fine-grained air quality map is important to support people’s outside planning and protecting them from adverse health impacts. However, the sparseness of air quality stations, the incompleteness of related feature data and the nonlinear variation of air quality with locations and time pose substantial challenges for accurate air quality inference. To solve these challenges, in this paper, a matrix factorization-based approach is proposed to infer air quality by fusing the knowledge from low-rank structure, air quality measurements and various kinds of features such as meteorology, traffic, road network, land use and point of interests. Different from existing works that solve feature recovery, feature extraction and air quality inference separately, this paper unifies the three problems into one model. In the model, spatial and temporal feature matrices are factorized into smaller factors to implement feature recovery and extraction. By sharing common factors with air quality matrix, the knowledge of spatial and temporal features can be transferred into air quality domain for improving the inference performance. We evaluate the proposed approach with real data sources obtained in Beijing city. Experiment results show that our approach outperforms the benchmark approaches up to one order of magnitude in terms of accuracy.
Intelligent UAV Navigation: A DRL-QiER Solution

In cellular-connected unmanned aerial vehicle (UAV) network, a minimization problem on the weighted sum of time cost and expected outage duration is considered. Taking advantage of UAV’s adjustable mobility, an intelligent UAV navigation approach is formulated to achieve the aforementioned optimization goal. Specifically, after mapping the navigation task into a Markov decision process (MDP), a deep reinforcement learning (DRL) solution with novel quantum-inspired experience replay (QiER) framework is proposed to help the UAV find the optimal flying direction within each time slot. Via relating experienced transition’s importance to its associated quantum bit (qubit) and applying Grover-iteration-based amplitude amplification technique, the proposed DRL-QiER solution commits a better trade-off between sampling priority and diversity. Compared to several representative baselines, the effectiveness and supremacy of the proposed DRL-QiER solution are demonstrated and validated in numerical results.
Neighbourhood rental market integration and private rent trajectories – evidence from the city of Vienna

ABSTRACT The urban housing regime in Vienna is often regarded as one of the last integrated rental markets once described by Kemeny. Thus, according to the theory, private landlords should be forced to charge lower rents due to direct competition from the social sector. This paper formally tests this hypothesis on a very local level by linking private rent trajectories across Viennese subdistricts to their housing market structure while controlling for possible effects of location, initial prices and socioeconomic variation. Indeed significant evidence for a price dampening effect is found, as higher shares of limited-profit housing within the local rental market substantially increase the probability of a subdistrict joining a lower rent development path. However, the magnitude of the effect increases considerably for more peripheral locations associated to less demand pressure. A similar relationship is found for municipal housing, but with much lower impact, most likely due to comparatively stronger entry barriers. Thus this paper concludes a price dampening neighbourhood effect which varies with the openness of the social housing supply and the centrality of the location.
Fine-Grained Service Offloading in B5G/6G Collaborative Edge Computing Based on Graph Neural Networks

Fine-grained service offloading in collaborative edge computing can make full use of the limited resource of edge nodes to achieve efficient parallel computing. It is imperative to select appropriate edge nodes for the subtask offloading in order to ensure the network’s load balance. However, there is a lack of research on computing offloading of end-to-end fine-grained services, and existing node selection algorithms can only be used in small-scale scenarios or networks with a fixed number of nodes. In this paper, we construct an end-to-end fine-grained computing offloading model, with load balancing as the optimization goal. Especially, a deep graph matching method, based on graph neural networks, is used for offloading node selection. It can be applied to dynamic and large-scale scenarios with strong generalization capability and fast execution speed. Compared with baseline algorithms, it greatly reduces the network load imbalance degree while ensuring a high acceptance ratio of services and meeting delay, location and resource constraints.
The normative role of experts in policy development: a comparison of two Scandinavian cultural policy reports

ABSTRACT The article summarises a study of two Scandinavian cultural policy expert reports, namely Sweden’s SOU 2009:16 and Norway’s NOU 2013:4, comparing how strongly these reports focus on advice relating to overall policy objectives. The two reports were produced roughly during the same period and in culturally interconnected countries, so they might be expected to have many similarities. The study finds that they are similar in some ways. Both the reports show that the normative power of experts in Scandinavian cultural policy is significant, although this is shown in different ways. In the case of SOU 2009:16, the normative power of the experts is primarily demonstrated in the mandate of the report, which explicitly requests the experts to suggest changes in overall policy objectives. On the other hand, the discussions of overall policy objectives are significantly more elaborate in NOU 2013:4 than in SOU 2009:16. Also, in NOU 2013:4, the normative power of experts is confirmed by their suggestions of controversial overall policy objectives. Thus, although both the reports confirm the normative power of experts in cultural policy, the reports also differ in important ways. The article explores possible explanations for these differences.