Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Binary Precision Capabilities of Tensor Cores for Epistasis Detection

Genome-wide association studies are performed to correlate a number of diseases and other physical or even psychological conditions (phenotype) with substitutions of nucleotides at specific positions in the human genome, mainly single-nucleotide polymorphisms (SNPs). Some conditions, possibly because of the complexity of the mechanisms that give rise to them, have been identified to be more statistically correlated with genotype when multiple SNPs are jointly taken into account. However, the discovery of new associations between genotype and phenotype is exponentially slowed down by the increase of computational power required when epistasis, i.e., interactions between SNPs, is considered. This paper proposes a novel graphics processing unit (GPU)-based approach for epistasis detection that combines the use of modern tensor cores with native support for processing binarized inputs with algorithmic and target-focused optimizations. Using only a single mid-range Turing-based GPU, the proposed approach is able to evaluate 64.8×1012 and 25.4×1012 sets of SNPs per second, normalized to the number of patients, when considering 2-way and 3-way epistasis detection, respectively. This proposal is able to surpass the state-of-the-art approach by 6× and 8.2× in terms of the number of pairs and triplets of SNP allelic patient data evaluated per unit of time per GPU.
Linear Quadratic Regulator Control Scheme on Hybrid Energy Storage System

This research aims to design and control the Hybrid Energy Storage System (HESS). The HESS had been designed by combined the battery and supercapacitor. A lead-acid battery, supercapacitor module and DC-DC converter were used to build the parallel topology of HESS. The battery connected directly to the dc bus, while, the supercapacitor module was connect to the to the dc bus via bidirectional DC-DC converter. The Linear Quadratic Regulator (LQR) had been used to drive the DC-DC converter. The HESS and the proposed LQR controller simulate in MATLAB/Simulink environment. The response of the proposed controller had been analysed for different coefficients of LQR. The simulation results of LQR show a stable response of the suggested HESS over a wide load range. This hybridization will improve the reliability of HESS and increase the battery life-cycle.
Extending Network Lifetime by Exploiting Wireless Charging in WSN

In recent years, wireless energy transfer technology has emerged as a promising solution to supply energy for sensors and extend the network lifetime. In the literature, although significant efforts have been devoted to optimizing the charging schedule, it still remains a challenging problem. In this paper, we study how to prolong the network lifetime by optimizing the charging time. Specifically, we aim at proposing a charging schedule that maximizes the minimum residual energy of sensors. We first mathematically formulate the problem under mixed integer and linear programming. Then, we propose a lightweight heuristic algorithm. The experiment results show that our proposed algorithms extend network lifetime more than 10% compared to the state-of-the-art.
Packet-in Request Redirection for Minimizing Control Plane Response Time

A distributed control plane is more scalable and robust in software defined networking. This paper focuses on controller load balancing using packet-in request redirection, that is, given the instantaneous state of the system, determining whether to redirect packet-in requests for each switch, such that the overall control plane response time (CPRT) is minimized. To address the above problem, we propose a framework based on Lyapunov optimization. First, we use the drift-plus-penalty algorithm to combine CPRT minimization problem with controller capacity constraints, and further derive a non-linear program, whose optimal solution is obtained with brute force using standard linearization techniques. Second, we present a greedy strategy to efficiently obtain a solution with a bounded approximation ratio. Third, we reformulate the program as a problem of maximizing a non-monotone submodular function subject to matroid constraints. We implement a controller proto-type for packet-in request redirection, and conduct trace-driven simulations to validate our theoretical results. The results show that our algorithms can reduce the average CPRT by 81.6% compared to static controller-switch assignment, and achieve a 3× improvement in maximum controller capacity violation ratio.
On the Solvability of Linear Matrix Inequalities under Quadratic Constraints

The use of semidefinite relaxation in the problem of the sign-definiteness of a quadratic form under quadratic constraints allows one to obtain new sufficient conditions for the solvability of a linear matrix inequality (LMI) under quadratic constraints in the case when the S-procedure does not give an answer. Results are illustrated by an example.
Inorganic nitrogen assimilation and regeneration in the coastal upwelling region of the Iberian Peninsula

N‐regeneration and N‐assimilation rates were investigated in surface waters of the Iberian Peninsula, northeast Atlantic, during a period of seasonal upwelling. Stations were selected on the basis of near–real‐time remotely sensed data to provide a contrast between upwelling and oligotrophic conditions. Inorganic nitrogen (NO−3 and NH+4) was simultaneously regenerated and assimilated in the short term (i.e., hours), highlighting the importance of N‐regeneration in sustaining productivity. The regeneration of both NH+4 and NO−3 would introduce error in N‐assimilation rate estimations using 15N techniques, if not accounted for. The rate of ammonium assimilation (ρNH+4) frequently exceeded the rate of NO−3 assimilation (ρNO−3). Although the assimilation of NO−3 and NH+4 continued at night, dark NO−3 assimilation was lower than during the day. The separate oxidation steps of the nitrification process occurred at similar rates at most stations, and NO−2 oxidation made a significant, but variable, contribution to hourly phytoplankton NO−3 uptake. NO−2 accumulated in near‐surface waters of stations experiencing upwelling, suggesting an imbalance between production and consumption. At these stations, NO−2 release by phytoplankton may have stimulated NO−2 oxidation, uncoupling it from NH+4 oxidation. 15N‐based f‐ratio as an estimate of exportable production must be corrected for isotope dilution, and this requires information on NH+4 regeneration and nitrification rate; in addition, the assumed close coupling between the separate oxidation steps of nitrification must be verified. These corrections may be especially pertinent to upwelling regions, which represent an important route for new‐N input to the surface ocean.
A Novel Hybrid PSO-K-Means Clustering Algorithm Using Gaussian Estimation of Distribution Method and Lévy Flight

Clustering is an important data analysis technique, which has been applied to many practical scenarios. However, many partitioning based clustering algorithms are sensitive to the initial state of cluster centroids, may get trapped in a local optimum, and have poor robustness. In recent years, particle swarm optimization (PSO) has been regarded as an effective solution to the problem. However, it has the possibility of converging to a local optimum, especially when solving complex problems. In this paper, we propose a hybrid PSO-K-means algorithm, which uses the Gaussian estimation of distribution method (GEDM) to assist PSO in updating the population information and adopts Lévy flight to escape from the local optimum. The proposed algorithm is named a GEDM and Lévy flight based PSO-K-means (GLPSOK) clustering algorithm. Firstly, during initialization, a few particles are initialized using the cluster centroids generated by K-means, while other particles are randomly initialized in the search space. Secondly, GEDM and PSO are selected with different probability to update the population information at different optimization stages. Thirdly, Lévy flight is adopted to help the search escape from the local optimum. Finally, the greedy strategy is carried out to select the promising particles from the parents and the newly generated candidates. Experimental results on both synthetic data sets and real-world data sets show that the proposed algorithm can produce better clustering results and is more robust than existing classic or state-of-the-art clustering algorithms.
Ecological Factors Affecting Response of Dark-Eyed Juncos to Prescribed Burning

Abstract We compared abundance, daily survival rate, nest site characteristics, food availability, nest activity, and nestling size of Dark-eyed Juncos (Junco hymenalis) between burned and unburned mechanically-thinned ponderosa pine (Pinus ponderosa) forest units. Dark-eyed Junco territory density, number of detections in point counts, and daily nest survival were similar between treatments. Average bare ground was 4.8 times higher and litter cover was 2.6 times lower at nest sites in burned units compared to unburned nest sites. However, there was 28% less burned area around nests compared to random points in burned units, indicating that juncos placed nests in unburned portions of burned units. They also selected non-traditional nesting sites in burned units such as root holes and in trees. Arthropod abundance was higher in burned units 1-year post burn although numbers were similar in the second-year post burn. Nest attentiveness and feeding rates were three times higher in burned units, possibly in response to increased food availability. The potentially negative effect of prescribed burning through reduction of litter and increase in bare ground was offset by novel nesting strategies and increased food availability.
An Edge-based Distributed Ledger Architecture for Supporting Decentralized Incentives in Mobile Crowdsensing

Nowadays, the exploitation of distributed ledger technology (DLT) is increasing among different domains and use cases. Not only within the context of cryptocurrencies, DLT could help the cooperation among untrusted parties in a wide variety of application scenarios. In particular, crowdsensing platforms can benefit from DLT because they need to federate systems belonging to different organizations to share end-user profiles, finally free to move within different domains, maintaining their identity. In this paper, we propose an edge-based distributed ledger architecture for supporting decentralised incentives in a specific mobile crowdsensing paltform called ParticipAct. To motivate the choice we describe two different deployments of ParticipAct, one based on a classical client-server architecture and the other one based on an edge-based model, and we highlight their pro and cons. In particular, our more notable findings rely on an approach based on edge computing and highlight how the three-tier solution improves the scalability, the performance, the security and the fault tolerance of the infrastructure responsible for the management of the federation among untrusted crowdsensing platforms.
Sunstone: Navigating the Way Through the Fog

Fog Computing extends Cloud Computing by placing resources in the core network between cloud resources and (mobile) users. While a lot of frameworks to implement Fog Computing have been proposed, most do not consider loose coupling between federated fog resources. In such environments, the scalable discovery and orchestration of fog resources across multiple administrative domains and autonomous systems remains a challenge.In this paper, we present Sunstone1, a discovery mechanism for Fog Computing that works on Internet-scale by combining on-path and off-path discovery mechanisms. Leveraging protocols present in the global routing infrastructure, Sunstone operates across multiple fog-domains while requiring no modifications to existing network middleboxes in the transit network. Furthermore, Sunstone includes a customizable orchestrator, which— given the results of the discovery process and application-specific policies—allows for a QoS-aware placement of applications in the fog.
Performance Evaluation of Security-Aware List Scheduling Algorithms in IaaS Cloud

Efficient workflow scheduling algorithms are crucial for attaining high performance in large-scale heterogeneous distributed infrastructures, such as cloud computing. List scheduling algorithms are one of the most efficient heuristic methods for assigning task graphs to fully connected heterogeneous systems. However, most existing list-based scheduling algorithms do not consider the applications’ security requirements and the security services offered by cloud providers. In this paper, we extend four list scheduling algorithms for security-aware workflow scheduling in the IaaS cloud. The idea of the extension is to consider the security overheads in both tasks prioritizing phase and virtual machine selection phase of the four original algorithms. Based on real-world applications, we evaluate the performance of the proposed algorithms in terms of scheduling length, speedup and efficiency.
Advantages and Disadvantages of Eportfolio Implementation in Primary Education

: The research presented in this article attempts to capture the views of teachers of elementary education about the advantages, disadvantages, difficulties and obstacles in the application of the portal as a rating and self-evaluation tool by the students. The survey, which constitutes the second part of a major research within the context of master thesis, was conducted in the second semester of 2016-2017 school year using anonymous written and electronic questionnaires, filled in by 215 elementary education teachers of all specialties from the first educational area of Athens. Most respondents are cautious about the benefits of using eportfolio, drawing attention to caution, lack of culture as barriers to its implementation, as well as basic problems such as lack of appropriate classroom infrastructure and the absense of eportfolio-related training.
From Ideation to Realization: Essential Steps and Activities for Realizing Data-Driven Business Models

Data have become a key resource for competition in several industries. As a response to this challenge, companies seek to create and realize data-driven business models (DDBMs). Although the ideation of DDBMs has been the subject of research, the realization of DDBMs remains an under-researched area. In this paper, we present a four-step process for guiding the realization of DDBMs. This process is grounded in a two-step literature review of research related to DDBMs and business model realization (BMR). By drawing on the four steps of BMR and six dimensions of DDBMs, the process locates 54 activities for realizing DDBMs. Furthermore, we cluster the activities to develop a consolidated model and demonstrate the application of the process by applying it to three case studies from the literature. The process is a starting point for further research on the realization of DDBMs and helps companies structure their activities for realizing a DDBM.
ClusterSR: Cluster-Aware Scattered Repair in Erasure-Coded Storage

Erasure coding is a storage-efficient means to guarantee data reliability in today’s commodity storage systems, yet its repair performance is seriously hindered by the substantial repair traffic. Repair in clustered storage systems is even complicated because of the scarcity of the cross-cluster bandwidth. We present ClusterSR, a cluster-aware scattered repair approach. ClusterSR minimizes the cross-cluster repair traffic by carefully choosing the clusters for reading and repairing chunks. It further balances the cross-cluster repair traffic by scheduling the repair of multiple chunks. Large-scale simulation and Alibaba Cloud ECS experiments show that ClusterSR can reduce 6.7-52.7% of the cross-cluster repair traffic and improve 14.1-68.8% of the repair throughput.
Securing Nuclear Power ICS with Mimic Defense Object Storage System

With the development of Industrial Internet, the industrial control systems continue to evolve. Nuclear power industrial control system, as the core information system of nuclear power plants, has extremely high requirements for security. But at present, all industrial security control products still have a large amount of vulnerabilities. Meanwhile, traditional protection methods can only resist known attacks, and have no way to defend against unknown attacks. Based on the mimic defense theory, this paper proposes a mimic object storage system based on multiple erasure codes. In addition to the dynamics, heterogeneity, and redundancy attribute of mimic defense, the use of erasure codes can reduce storage costs and improve data reliability. This paper introduces the system architecture and the laboratory simulation of the system. This system can resist multiple types of attacks and can be used to improve the security level of nuclear industry control systems.
Gram-Schmidt-Based Sparsification for Kernel Dictionary

Kernel methods have been used to solve nonlinear problems in signal processing and machine learning. To avoid a too large growth of the dictionary, different sparsification techniques have emerged in the literature. In this letter, we propose a Gram-Schmidt-based sparsification technique for kernel dictionaries. It projects the mapped vectors by the kernel into a finite-dimensional subspace spanned by an orthonormal basis and can be used with any Mercer kernel. In particular, we apply it to a least-mean-square-type (LMS) algorithm. Simulation results show that the proposed algorithm presents a lower computational cost when compared to kernel LMS implemented with other sparsification techniques, while maintaining the performance.
Cloning and In Vitro-Transcription of Chymosin Gene in E. coli

Chymosin, commonly known as rennin, is the main milk-coagulating enzyme available in rennet. RNA was extracted from the abomasum of a suckling calf water buffalo and was subjected to RT-PCR using degenerate primers to amplify 850bp of the chymosin gene. The sequence was aligned with 19 different mammals' chymosin genes. The sequence revealed that there is a similarity to them ranging from 64% to 98%. The purified recombinant proteins were obtained from the transformed E. coli and yeast. The clotting activity of both of the resulting proteins was examined compared to the commercial peers. It was noticed that the concentration of the purified protein ranged from 15,000 to 40,000 MCU. Therefore, the activity of the obtained proteins was the same and it was 105% when compared to the commercial peer. Having examined the cytotoxicity of the purified proteins, the results revealed no toxicity. We can conclude that the obtained recombinant protein is more active and safe even when expressed in bacteria rather than yeast.
Resource Allocation and Node Placement in Multi-Hop Heterogeneous Integrated-Access-and-Backhaul Networks

We consider a heterogeneous MIMO-OFDMA based dense small cell (SC) system in which each macro cell base station (MBS) serves its coverage area with the help of small cell base stations (SBSs) through multi-hop wireless connections. The SBSs act as integrated access and backhaul (IAB) nodes that handle both access and backhaul traffics with wireless links. We first develop an optimal (sum-rate maximization) resource allocation (RA) algorithm which considers subcarriers/spatial subchannels assignment and the associated power allocations. We also present two low-complexity suboptimal RA schemes which, as verified by simulations, incur only minor performance loss in the high SNR region. Our RA algorithms can be applied to other multi-hop networks with general UE association rule and node location distributions. We study the channel aging effect caused by the time lag between the time channel state information (CSI) is measured and that when data transmission occurs. We show the benefit of channel prediction and the limit of a centralized RA approach. The advantages of frequency (channel) reuse and the multi-hop architecture are demonstrated as well. A related but perhaps more important system design issue for an IAB cellular network is the IAB node placement problem. With the given UE association rule and UE location distribution, we present systematic approaches to find the optimal node locations. For two special propagation models, we derive closed-form expressions for the node locations that maximizes a spectral efficiency lower bound. Numerical results validate the accuracy of our estimates based on either numerical evaluations or closed-form solutions.
New Approach of Energy-Efficient Hierarchical Clustering Based on Neighbor Rotation for RWSN

Energy Efficient Hierarchical Clustering (EEHC) method is a distributed random clustering algorithm for residential wireless sensor networks (RWSNs) with the goal of maximizing network lifetime. However, during selection of the cluster head stage, a node declares itself as a cluster head to neighboring nodes with probability p, which does not consider well the topological characteristics of the network. The new energy-efficient hierarchical clustering approach based on neighbor rotation (EEHCN) for RWSN has been proposed in this paper, the new idea of which determines whether a node of RWSN can become a cluster head node by comparing the comprehensive weighting of the degree of the sensor node and the distance between the sensor node and the center of the subarea. When the cluster head is replaced, a member node in the set of neighbor nodes of the current cluster head becomes the new cluster head for a random time slice, and the merged information is transmitted to the nearest sink node in a multi-hop manner according to the routing information. The experimental results demonstrate that the proposed method is more effective in saving network energy, improving data transmission efficiency, and maximizing network lifetime of RWSN. The innovative research work is very useful for the relative application of the RWSN, such as smart home or smart community.
One Design of Pseudo Pilot to Suppress the Nonlinear Interference in Optical OFDM/OQAM System

Orthogonal frequency-division multiplexing offsetquadrature amplitude modulation (OFDM/OQAM) system relaxes the orthogonal condition of the sub-carriers from the complexed domain to the real field. With the improve of transmission efficiency, it also greatly increases the interference of nonlinear effect. In this paper, we design the Pseudo Pilot according to the nonlinear transmission model established before, which can immensely promote the tolerance of optical OFDM/OQAM system to nonlinear interference.
The Link Attenuation Model Based on Monte Carlo Simulation for Laser Transmission in Fog Channel

Free space optical (FSO) systems were usually designed according to the link budget analysis. However, the previous link attenuation estimations for fog channel were not precise. The link budget would be distorted, and resulting in the robustness degeneration of FSO system. Therefore, it is necessary to establish an accurate link attenuation model of fog channel. The previous models were mainly adopted by the Beer-Lambert law, or by classical Monte Carlo simulation combined with the fog drop size distribution. Unfortunately, the estimation deviation was eventually not fall within the acceptable range for FSO link budget analysis. We established a link attenuation model based on Monte Carlo simulation and Kim formula. Our model took the influence of multiple scattering and receiving parameters into consideration, and without the measurement of the fog drop size distribution and scattering cross-sections. The fog experiment was set up to verify the analysis results. And the experiment results revealed that the estimation deviation RMSE (root mean square error) $<\! 1$, which validated the accuracy of link attenuation model. The link attenuation model can be used to strengthen the link budget analysis of FSO system and guide the parameter design of future FSO communication system under fog channel.