Miracle-dz/UniFAR-FTUs huggingface dataset:
Resistance to Spot Blotch in Two Mapping Populations of Common Wheat Is Controlled by Multiple QTL of Minor Effects

Spot blotch (SB) is an important fungal disease of wheat in South Asia and South America. Host resistance is regarded as an economical and environmentally friendly approach of controlling SB, and the inheritance of resistance is mostly quantitative. In order to gain a better understanding on the SB resistance mechanism in CIMMYT germplasm, two bi-parental mapping populations were generated, both comprising 232 F2:7 progenies. Elite CIMMYT breeding lines, BARTAI and WUYA, were used as resistant parents, whereas CIANO T79 was used as susceptible parent in both populations. The two populations were evaluated for field SB resistance at CIMMYT’s Agua Fria station for three consecutive years, from the 2012–2013 to 2014–2015 cropping seasons. Phenological traits like plant height (PH) and days to heading (DH) were also determined. Genotyping was performed using the DArTSeq genotyping-by-sequencing (GBS) platform, and a few D-genome specific SNPs and those for phenological traits were integrated for analysis. The most prominent quantitative trait locus (QTL) in both populations was found on chromosome 5AL at the Vrn-A1 locus, explaining phenotypic variations of 7–27%. Minor QTL were found on chromosomes 1B, 3A, 3B, 4B, 4D, 5B and 6D in BARTAI and on chromosomes 1B, 2A, 2D and 4B in WUYA, whereas minor QTL contributed by CIANO T79 were identified on chromosome 1B, 1D, 3A, 4B and 7A. In summary, resistance to SB in the two mapping populations was controlled by multiple minor QTL, with strong influence from Vrn-A1.
AligneR: A Process-in-Memory Architecture for Short Read Alignment in ReRAMs

Genomics is the key to enable the personal customization of medical care. How to fast and energy-efficiently analyze the huge amounts of genomic sequence data generated by next generation sequencing technologies has become one of the most significant challenges facing genomics today. Existing hardware platforms achieve low genome sequencing throughput with significant hardware and power overhead. In this paper, we propose AligneR, a ReRAM-based process-in-memory architecture, to accelerate the bottleneck of genome sequencing, i.e., short read alignment. Compared to state-of-the-art accelerators, AligneR improves the short read alignment throughput per Watt per <inline-formula><tex-math notation="LaTeX">$mm^2$</tex-math><alternatives><inline-graphic xlink:href="jiang-ieq1-2854700.gif"/></alternatives></inline-formula> by <inline-formula><tex-math notation="LaTeX">$13\times$</tex-math><alternatives><inline-graphic xlink:href="jiang-ieq2-2854700.gif"/></alternatives></inline-formula>.
Low Temperature Co-Fired Ceramic Half-Mode Substrate Integrated Waveguide Leaky-Wave Antenna

A novel leaky-wave antenna implemented in the low temperature co-fired ceramic (LTCC) is presented. Introducing cavities in the structure and removing the conductor planes between tape layers improve radiation efficiency and gain. Moreover, tapering the aperture of the antenna leads to about 10dB reduction of side-lobe level. The antenna has an operating band from 28.3 to 29.1 GHz that is among the proposed 5G frequency bands. The realized peak gain and radiation efficiency of the proposed antenna over the operating band are about 8.5 dB and 80%, respectively. The length, width, and height of the antenna are 40 mm, 16 mm, and 0.5 mm, respectively. The compactness of the antenna and the characteristics of LTCC structure makes it a suitable nominee for modern circuit integration.
Passengers' Intention to Use LBS in Metro Emergencies

With the rapid growth of mobile networks, scholars around the world have tried to cover this gap by using location-based information services. LBS is able to transfer emergency information between metro passengers and emergency management department through mobile networks in direct and efficient way. However, passengers' intention to use LBS in metro emergency will have a great impact on the actual operation. As a result, this research attempt to find out the factors, which affect the using of LBS in metro emergency situation. A model based on TAM is constructed by literature review, with external factors such as information quality, system quality and risk perception. Questionnaire is also established to measure the adaptability of the model in the specified problem by structural equation modeling. The research results show that information quality of LBS has a great impact passengers' perceived usefulness, and indirectly affect intention to use. System quality of LBS has a great impact on passengers' perceived ease of use, which will directly and indirectly affect intention to use. Passengers' risk perception of metro emergency has the strongest affection on the intention to use LBS, and it will also affect the intention to use indirectly through affecting perceived usefulness. If management department wish to use LBS to manage emergency information, they should try to promote the information quality and system quality of LBS systems. And they also should try to increase passengers' risk perception of metro emergencies.
An Information-Theoretical Approach for Calcium Signaling Specificity

Calcium is a key signaling agent in animals and plants. Its involvement in the regulation of a wide range of processes has led to the question of how calcium signals can activate stimulus-specific responses. We introduce a computational framework for studying intracellular calcium signaling using elements of information theory. We use mutual information to quantify the differential activation of proteins in response to different calcium signals to provide an operational definition of specificity. Using optimization procedures this framework allows us to explore the biochemical determinants of calcium decoding. We explore simple toy models and general binding kinetics approaches to demonstrate the utility and limitations of the proposed framework. Unravelling signaling specificity is key for understanding information processing within cells and for the future design of synthetic nanodevices for molecular communications.
SDN Programming for Heterogeneous Switches with Flow Table Pipelining

High-level programming is one of the critical building blocks of the effective use of software-defined networking (SDN). Existing solutions, however, either (1) cannot utilize the state-of-the-art switches with flow table pipelining, a key technique to prevent flow rule set explosion or (2) force programmers to manually organize and manage hardware flow table pipelines, which is time-consuming and error-prone. This paper presents a high-level SDN programming framework to address these issues. The framework can automatically (1) generate rule sets for heterogeneous switches with different flow table pipelining designs and (2) update installed rules when the network state changes. As a result, the framework can not only generate efficient rule sets for switches but also provide programmers a centralized, intuitive, and hence easy-to-use programming API. Experiments show that the framework can generate compact rule sets that are 29–116 times smaller than those generated by other open-source SDN controllers. Besides, the framework is 5 times faster to recover from network link failures in comparison to other controllers.
Comparison of Ultra Low Power Full Adder Cells in 22 nm FDSOI Technology

Five ultra low voltage and low power full adders have been designed and analyzed with CMOS logic structure. To compare these adders, different metrics including worst case delay, average power, PDP, and PDP*Leakage have been investigated in the supply voltage varying from 140-160 mV. All the full adders have been designed and verified with Cadence Virtuoso design in a commercially available 22 nm FDSOI technology. An extended body bias voltages introduced in a 22 nm FDSOI technology have been used to balance Pull Up/Pull Down Networks and have a high functional yield. The test bench has been used to verify the functionality of full adders automatically in different conditions of temperature and supply voltage. The simulation results show that an Xor based adder is the best of all having the lowest delay, power, PDP, and PDP*Leakage in different conditions.
Exploiting Global Semantic Similarity Biterms for Short-Text Topic Discovery

The demand for mining massive short-text data from the Internet has promoted researches on topic models. There exist many schemes trying to solve the sparsity problems brought by short texts, mainly based on data aggregation or model improvement. Among them, Biterm Topic Model changes the way of modeling topics, which is on document-level biterms and has shown creativity and effectiveness. However, this may ignore those semantically similar and rarely co-occurrent word pairs, which are denoted as global biterms in this paper. Inspired by the successful application of word embeddings in GPU-DMM, we exploit word embeddings to extract semantically similar word pairs from the whole corpus to help discover better topics. We call this model as GloSS, which takes advantages of both the approach to model topics and word embeddings. Experimental results on two open-source and real datasets are superior to state-of-the-art topic models for short texts.
Rotor Flux MRAS based Speed Sensorless Indirect Field Oriented Control of Induction Motor Drive for Electric and Hybrid Electric Vehicles

This paper presents the operational performance of speed observer applied on indirect rotor flux based field oriented control of induction motor (IM) traction drive for the application of alternative energy vehicles such as electric vehicles (EVs) and hybrid electric vehicles (HEVs). Dynamic model of both IM and EV are discussed first so as to get the better understanding about the operation of electric motor both in steady state and in transient mode of operation and net tractive force effort from the EV respectively. Machine model based rotor speed estimation technique rotor flux model reference adaptive system (MRAS) is designed and applied in indirect rotor flux field oriented IM traction drive control. The operational performance of the proposed estimator is investigated and compare with the motor actual speed through different driving speed profiles of EVs / HEVs such as vehicle accelerating condition, constant speed, un-smooth road surface condition and decelerating mode etc. MATLAB/Simulink software is used for the simulation purpose and obtained results present the satisfactory speed regulation performance of the proposed speed estimator using different speed driving profiles. Results show the effective and reliable application of rotor flux error based MRAS observer in speed sensorless traction drive control for EVs / HEVs.
Gene Sequencing: Where Time Goes

Next Generation Sequencing (NGS) processes raw DNA data samples to extract differences between a genome under investigation and a reference genome [1]. This study profiles the execution time of a state-of-the-art NGS software pipeline. This analysis can inform software-or hardware-based NGS acceleration efforts. We find that two steps dominate execution time, Read Alignment (RA) and Variant Calling (VC). We further analyzed the runtime behavior of these two steps at the microarchitecture level. We find that I/O is not a major bottleneck and that parallelization can effectively accelerate only RA. Further analysis shows which functions within each step dominate execution time. Follow up acceleration work may thus focus on further analyzing these steps.
Structure-based switch of regioselectivity in the flavin-dependent tryptophan 6-halogenase Thal

Flavin-dependent halogenases increasingly attract attention as biocatalysts in organic synthesis, facilitating environmentally friendly halogenation strategies that require only FADH2, oxygen, and halide salts. Different flavin-dependent tryptophan halogenases regioselectively chlorinate or brominate trypto-phan's indole moiety at C5, C6, or C7. Here, we present the first substrate-bound structure of a tryptophan 6-halogenase, namely Thal, also known as ThdH, from the bacterium Streptomyces albogriseolus at 2.55 Å resolution. The structure revealed that the C6 of tryptophan is positioned next to the ϵ-amino group of a conserved lysine, confirming the hypothesis that proximity to the catalytic residue determines the site of electrophilic aromatic substitution. Although Thal is more similar in sequence and structure to the tryptophan 7-halogenase RebH than to the tryptophan 5-halogenase PyrH, the indole binding pose in the Thal active site more closely resembled that of PyrH than that of RebH. The difference in indole orientation between Thal and RebH appeared to be largely governed by residues positioning the Trp backbone atoms. The sequences of Thal and RebH lining the substrate binding site differ in only few residues. Therefore, we exchanged five amino acids in the Thal active site with the corresponding counterparts in RebH, generating the quintuple variant Thal-RebH5. Overall conversion of l-Trp by the Thal-RebH5 variant resembled that of WT Thal, but its regioselectivity of chlorination and bromination was almost completely switched from C6 to C7 as in RebH. We conclude that structure-based protein engineering with targeted substitution of a few residues is an efficient approach to tailoring flavin-dependent halogenases.
Analysis and Design of a Doherty-Like RF-Input Load Modulated Balanced Amplifier

This paper presents an analysis and design technique for an RF-input load modulated balanced amplifier (LMBA) with Doherty-like efficiency enhancement characteristics. The LMBA is a recently introduced power amplifier (PA) architecture in which a control signal is injected into the isolation port of the output coupler of a balanced amplifier. This control signal modulates the apparent load impedance of the two main devices. In the RF-input LMBA presented in this paper, the control signal is generated from the single-modulated RF input using a class-C control amplifier, producing an overall Doherty-like response. A complete theoretical analysis of the RF-input LMBA is presented, by assuming generalized networks for the output combiner and input splitter which are then solved in terms of transistor parameters and boundary conditions for high efficiency. Based on this analysis, a generalized design technique is presented. The LMBA is, thereby, shown to have advantages over the Doherty PA, including the device periphery scaling between the main and control devices for a given back-off range. The design technique is demonstrated at 2.4 GHz using packaged GaN devices. The prototype has a peak CW output power of 45.6 dBm, with a power-added efficiency of 60% at peak power and 50% at 6-dB output back-off, including the power dissipated by the control PA. A long-term evolution signal at 2.4 GHz with a 7.5-dB peak-to-average power ratio is demonstrated with an unlinearized ACLR of −27 dBc and an average drain efficiency of 47% for a 38-dBm average output power.
Research on Semantic Judgment of Key Words in Ontology - Based Dynamic Requirements Traceability

The accuracy of trace links can be improved by using ontology-based dynamic requirements traceability method, but it is a complicated and tedious process to establish a reasonable and effective ontology. Because the size of the ontology affects the experimental results directly, this paper shows a method to combine the modifiers with the general ontology. Firstly, the method makes semantic choices for the keywords through the collocation rules of the modifier and the keyword. Then the similarity of the keywords are calculated by the semantic distance in the WordNet and adjusted through the modifier ontology. The semantic choices of the keywords are reflected by the similarity scores. Finally, the similarity between the source artifacts and target artifacts are calculated by the similarity of the keywords. The number of modifiers is small in the requirements documents, design documents and etc. so this can reduce time and labor cost brought by the construction of domain ontology. In order to verify the effectiveness of this method, the method in this paper will be compared with VSM based methods and domain based ontology methods
Duplicon Cache: Mitigating Off-Chip Memory Bank and Bank Group Conflicts Via Data Duplication

Bank and bank group conflicts are major performance bottlenecks for memory intensive workloads. Idealized experiments show removing bank and bank group conflicts collectively can improve performance by up to 37.5% and by 22.5% on average for our mix of multi-programmed memory intensive workloads. We propose the Duplicon Cache to mitigate bank and bank group conflict penalties by duplicating select lines of data to an alternative bank group, giving the memory controller the freedom to source the data from the bank group which avoids conflicts. The Duplicon Cache is entirely implemented in the memory controller and does not require changes to commodity memory. We identify and address the main challenges associated with duplication: 1) tracking duplicated data efficiently, 2) identifying which data to duplicate, and 3) replacing stale duplicated data while protecting useful ones. Our evaluations show the Duplicon Cache configured with 128MB of storage (out of 16GB of main memory) improves performance by 8.3% while reducing energy by 5.6%.
A Comparative Study on Evolutionary Multi-objective Optimization Algorithms Estimating Surface Duct

The problem of atmospheric duct inversion is usually solved as a single objective optimization problem. Based on ground-based Global Positioning System (GPS) phase delay and propagation loss, this paper develops a multi-objective method including the effect of source frequency and receiving antenna height. The diversity and convergence of solution sets are evaluated for seven multi-objective evolutionary algorithms with three performance metrics: Hypervolume (HV), Inverted Generational Distance (IGD), and the averaged Hausdorff distance (Δ2). The inversion results are compared with the simulation results, and the experimental comparison is conducted on three groups of test situations. The results demonstrate that the ranking of algorithm performance varies because of the different methods used to calculate performance metrics. Moreover, when the algorithms show overwhelming performance using performance metrics, the inversion result is not more close to the real value. In the comparison of computational experiments, it was found that, as the retrieved parameter dimension increases, the inversion result becomes more unstable. When the observed data are sufficient, the inversion result seems to be improved.
A Hardware-Efficient Hybrid Beamforming Solution for mmWave MIMO Systems

In mmWave communication systems, existing hybrid beamforming solutions generally require a large number of high-resolution phase shifters (PSs) to realize analog beamformers, which still suffer from high hardware complexity and power consumption. Targeting at this problem, this article introduces a novel hardware-efficient hybrid precoding/combining architecture, which only employs a limited number of simple phase over-samplers (POSs) and a switch (SW) network to achieve maximum hardware efficiency while maintaining satisfactory spectral efficiency performance. The POS can be realized by a simple circuit and simultaneously outputs several parallel signals with different phases. With the aid of a simple switch network, the analog precoder/ combiner is implemented by feeding the signals with appropriate phases to antenna arrays or RF chains. We analyze the design challenges of this POS-SW-based hybrid beamforming architecture and present potential solutions to the fundamental issues, especially the precoder/combiner design and the channel estimation strategy. Simulation results demonstrate that this hardware-efficient structure can achieve comparable spectral efficiency but much higher energy efficiency than that of traditional structures.
The recovery of North Atlantic right whales, Eubalaena glacialis, has been constrained by human-caused mortality

North Atlantic right whales (NARW), Eubalaena glacialis, were nearly exterminated by historical whaling. Their abundance slowly increased up until 2010, to a maximum of fewer than 500 whales, and since then they have been in decline. We assessed the extent to which the relatively slow increase demonstrated by NARW was intrinsic, and how much could be due to anthropogenic impacts. In order to do so, we first compared calf counts of three populations of Southern right whales (SRW), E. australis, with that of NARW, over the period 1992–2016. By this index, the annual rate of increase of NARW was approximately one-third of that of SRW. Next we constructed a population projection model for female NARW, using the highest annual survival estimates available from recent mark–resight analysis, and assuming a four-year calving interval. The model results indicated an intrinsic rate of increase of 4% per year, approximately twice that observed, and that adult female mortality is the main factor influencing this rate. Necropsy records demonstrate that anthropogenic mortality is the primary cause of known mortality of NARW. Anthropogenic mortality and morbidity has limited the recovery of NARW, and baseline conditions prior to their recent decline were already jeopardizing NARW recovery.
An Improved Deep Belief Network Model for Road Safety Analyses

Crash prediction is a critical component of road safety analyses. A widely adopted approach to crash prediction is application of regression based techniques. The underlying calibration process is often time-consuming, requiring significant domain knowledge and expertise and cannot be easily automated. This paper introduces a new machine learning (ML) based approach as an alternative to the traditional techniques. The proposed ML model is called regularized deep belief network, which is a deep neural network with two training steps: it is first trained using an unsupervised learning algorithm and then fine-tuned by initializing a Bayesian neural network with the trained weights from the first step. The resulting model is expected to have improved prediction power and reduced need for the time-consuming human intervention. In this paper, we attempt to demonstrate the potential of this new model for crash prediction through two case studies including a collision data set from 800 km stretch of Highway 401 and other highways in Ontario, Canada. Our intention is to show the performance of this ML approach in comparison to various traditional models including negative binomial (NB) model, kernel regression (KR), and Bayesian neural network (Bayesian NN). We also attempt to address other related issues such as effect of training data size and training parameters.
Compressive Traffic Monitoring in Hybrid SDN

Hybrid software-defined network (SDN) is a network where legacy routers and SDN routers coexist during the incremental deployment of SDNs. The existing SDN router placement methods mainly focus on maximizing the traffic engineering performance under a limited budget. Traffic engineering requires real-time link-load information. However, the latency for collecting the global link-load information can be prohibitively long in a wide area network due to the long interior gateway protocol convergence time. Inspired by the sparsity of the link load, we propose a novel compressive traffic monitoring method for collecting real-time load information of all links. In this method, the controller only needs to collect the load of a small subset of important links and then estimates the link load of the rest. The minimal number of SDN routers is placed to cover these important links. We use real-world topologies and traffic matrices to evaluate our method. Experiment results show that our method can quickly estimate the global link load at an error rate of 5% within sub-seconds. Compared with the state-of-the-art methods, our method has better adaptability to the dynamic traffic changes and can reduce the maximal link usage by 39%.