Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Contact-First Immunization Strategy on Complex Networks

Different from traditional immunization strategies, which suppose that the contact frequency of all nodes is same, in this paper, a new immunization strategy was established based on the principle of „High-Contact-First‟. The different link with different weight describes the relationship between different two nodes. The bigger weight means the higher contact. The higher contact means the higher risk which the two nodes be infected each other. Thus, to the purpose of immunization, the link with higher contact should be cut down first. The theory analysis and the simulations indicate that the proposed immunization strategy is effective, and it is also economic and feasible.
PatternRank: Jointly Ranking Patterns and Extractions for Relation Extraction Using Graph-Based Algorithms

In this paper we revisit the direction of using lexico-syntactic patterns for relation extraction instead of today’s ubiquitous neural classifiers. We propose a semi-supervised graph-based algorithm for pattern acquisition that scores patterns and the relations they extract jointly, using a variant of PageRank. We insert light supervision in the form of seed patterns or relations, and model it with several custom teleportation probabilities that bias random-walk scores of patterns/relations based on their proximity to correct information. We evaluate our approach on Few-Shot TACRED, and show that our method outperforms (or performs competitively with) more expensive and opaque deep neural networks. Lastly, we thoroughly compare our proposed approach with the seminal RlogF pattern acquisition algorithm of, showing that it outperforms it for all the hyper parameters tested, in all settings.
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling

Language models (LM) have played crucial roles in automatic speech recognition (ASR) to enhance end-to-end (E2E) ASR systems’ performance. There are two categories of approaches: finding better ways to integrate LMs into ASR systems and adapting on LMs to the task domain. This article will start with a reflection of interpolation-based integration methods of E2E ASR’s scores and LM’s scores. Then we will focus on LM augmentation approaches based on the noisy channel model, which is intrigued by insights obtained from the above reflection. The experiments show that we can enhance an ASR E2E model based on encoder-decoder architecture by pre-training the decoder with text data. This implies the decoder of an E2E model can be treated as an LM and reveals the possibility of enhancing the E2E model without an external LM. Based on those ideas, we proposed the implicit language model canceling method and then did more discussion about the decoder part of an E2E ASR model. The experimental results on the TED-LIUM2 dataset show that our approach achieves a 3.4% relative WER reduction compared with the baseline system, and more analytic experiments provide concrete experimental supports for our assumption.
Personalization and Recommender Systems

In this tutorial, we present an overview of the personalization field and review different types of personalization. We also discuss the general personalization process and position the field of recommender systems as an integral part of this process. We review the field of recommender systems by describing a number of “traditional” recommendation approaches and their extensions. Finally, we discuss several future research directions for personalization and recommender systems, including integrated personalization process, data acquisition for recommender systems, advanced modeling of user preferences, other model-based techniques for recommendation, evaluation of recommender systems, recommendation flexibility and scalability, and trust and privacy issues in recommender systems.
A double-labeling marker-based method for estimating inbreeding and parental genomic components in a population under conservation

Objective The objective of a conservation program is to maintain maximum genetic diversity and preserve the viability of a breed. However, the efficiency of a program is influenced by the ability to accurately measure and predict genetic diversity. Methods To examine this question, we conducted a simulation in which common measures (i.e. heterozygosity) and novel measures (identity-by-descent probabilities and parental genomic components) were used to estimate genetic diversity within a conserved population using double-labeled single nucleotide polymorphism markers. Results The results showed that the accuracy and sensitivity of identity-by-state probabilities and heterozygosity were close to identity by descent (IBD) probabilities, which reflect the true genetic diversity. Expected heterozygosity most closely aligned with IBD. All common measures suggested that practices used in the current Chinese pig conservation program result in a ~5% loss in genetic diversity every 10 generations. Parental genomic components were also analyzed to monitor real-time changes in genomic components for each male and female ancestor. The analysis showed that ~7.5% of male families and ~30% of female families were lost every 5 generations. After 50 generations of simulated conservation, 4 male families lost ~50% of their initial genomic components, and the genomic components for 24.8% of the female families were lost entirely. Conclusion In summary, compared with the true genetic diversity value obtained using double-labeled markers, expected heterozygosity appears to be the optimal indicator. Parental genomic components analysis provides a more detailed picture of genetic diversity and can be used to guide conservation management practices.
Individual Variation in Control Network Topography Supports Executive Function in Youth

The spatial distribution of large-scale functional networks on the anatomic cortex differs between individuals, and is particularly variable in networks responsible for executive function. However, it remains unknown how this functional topography evolves in development and supports cognition. Capitalizing upon advances in machine learning and a large sample of youth (n=693, ages 8-23y) imaged with 27 minutes of high-quality fMRI data, we delineate how functional topography evolves during youth. We found that the functional topography of association networks is refined with age, allowing accurate prediction of an unseen individual’s brain maturity. Furthermore, the cortical representation of executive networks predicts individual differences in executive function. Finally, variability of functional topography is associated with fundamental properties of brain organization including evolutionary expansion, cortical myelination, and cerebral blood flow. Our results emphasize the importance of considering both the plasticity and diversity of functional neuroanatomy during development, and suggest advances in personalized therapeutics.
Zero-shot Disfluency Detection for Indian Languages

Disfluencies that appear in the transcriptions from automatic speech recognition systems tend to impair the performance of downstream NLP tasks. Disfluency correction models can help alleviate this problem. However, the unavailability of labeled data in low-resource languages impairs progress. We propose using a pretrained multilingual model, finetuned only on English disfluencies, for zero-shot disfluency detection in Indian languages. We present a detailed pipeline to synthetically generate disfluent text and create evaluation datasets for four Indian languages: Bengali, Hindi, Malayalam, and Marathi. Even in the zero-shot setting, we obtain F1 scores of 75 and higher on five disfluency types across all four languages. We also show the utility of synthetically generated disfluencies by evaluating on real disfluent text in Bengali, Hindi, and Marathi. Finetuning the multilingual model on additional synthetic Hindi disfluent text nearly doubles the number of exact matches and yields a 20-point boost in F1 scores when evaluated on real Hindi disfluent text, compared to training with only English disfluent text.
LTRC @MuP 2022: Multi-Perspective Scientific Document Summarization Using Pre-trained Generation Models

The MuP-2022 shared task focuses on multiperspective scientific document summarization. Given a scientific document, with multiple reference summaries, our goal was to develop a model that can produce a generic summary covering as many aspects of the document as covered by all of its reference summaries. This paper describes our best official model, a finetuned BART-large, along with a discussion on the challenges of this task and some of our unofficial models including SOTA generation models. Our submitted model out performedthe given, MuP 2022 shared task, baselines on ROUGE-2, ROUGE-L and average ROUGE F1-scores. Code of our submission can be ac- cessed here.
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training

Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples. They require the model to translate both the authentic source sentence and its adversarial counterpart into the identical target sentence within the same training stage, which may be a suboptimal choice to achieve robust NMT. In this paper, we first conduct a preliminary study to confirm this claim and further propose an Iterative Scheduled Data-switch Training Framework to mitigate this problem. Specifically, we introduce two training stages, iteratively switching between authentic and adversarial examples. Compared with previous studies, our model focuses more on just one type of examples at each single stage, which can better exploit authentic and adversarial examples, and thus obtaining a better robust NMT model. Moreover, we introduce an improved curriculum learning method with a sampling strategy to better schedule the process of noise injection. Experimental results show that our model significantly surpasses several competitive baselines on four translation benchmarks. Our source code is available at https://github.com/DeepLearnXMU/RobustNMT-ISDST.
FAMSPHERE : a computer program for parental allocation from known genotypic pools

FAMSPHERE is a computer program that implements a distance-based method to allocate families based on microsatellite marker information. It formalizes the exclusion method considering genotyping errors or mutations. Contrary to the standard exclusion method, FAMSPHERE provides a way to resolve conflicts in the case of multiple paternities. The information linked to the defined distances is similar to that of the transmission probabilities used in the likelihood approaches. However, in FAMSPHERE the information of loci is used as a whole, contrary to the likelihood approaches. FAMSPHERE is free software and can be downloaded from www.uvigo. es/webs/c03/webc03/XENETICA/XB2/antonio/famsphere/ famsphere.htm
Drought and freezing vulnerability of the isolated hybrid aspen Populus x smithii relative to its parental species, P. tremuloides and P. grandidentata

Abstract Aim We assessed the vulnerability of an isolated, relictual Pleistocene hybrid aspen population of conservation interest (Populus x. smithii) and the nearest populations of its parent species (Populus grandidentata and Populus tremuloides) to springtime post‐bud break freezing and growing season drought stress. Response to these stressors in the three taxa was compared in terms of avoidance and tolerance. Location North American Midwest; USA. Methods Unique genets from the hybrid Niobrara River population and from the two parental populations were propagated in a common garden from rhizome cuttings. We tracked their phenology before and after bud break and measured their vulnerability to freezing (stem electrolyte leakage and leaf chlorophyll fluorescence) and to drought (stem hydraulic conductance, leaf osmotic potential, stomatal pore index, and gas exchange). Results Populus grandidentata was slower to leaf out, showed lower vulnerability to stem freezing and drought‐induced cavitation, but exhibited a lower capacity to tolerate drought stress through leaf resistance traits compared to P. tremuloides. Hybrids were similar to P. grandidentata in their overwintering strategy, exhibiting later bud break, and in their higher resistance to stem freezing damage, but they were more similar to P. tremuloides in their higher vulnerability to drought‐induced cavitation. The hybrids shared various leaf‐level gas exchange traits with both parents. All aspens showed limited loss of leaf photosynthetic function following moderate freezing. Main Conclusions The Niobrara River hybrid population is vulnerable to drought due to its combination of inherited drought avoidance and tolerance traits. As climate changes, P. x smithii will likely suffer from increased drought stress, while being unaffected by frost during warmer springs. The two parental species contrast in their survival mechanisms in response to climatic stress, with P. tremuloides tending toward freezing tolerance but drought avoidance and P. grandidentata tending toward freezing avoidance and drought tolerance.
Predicting spring wheat yields based on water use-yield production function in a semi-arid climate

Grain yield of spring wheat (Triticum aestivum L.) fluctuates greatly in Western Loess Plateau of China due to limited and highly variable precipitation. Farmers in this area need a simple tool to predict spring wheat grain yield and assess yield loss risk efficiently. The objectives of this study were to establish relations between water use and grain yield of spring wheat for predicting actual yield and attainable yield (water limited yield) under conventional management practice and mulching practices. Reference data during 1993-2013 and field experiment conducted from 1987 to 2011 were used to determine water use-yield production function and boundary function for spring wheat. Probability of achieving a given spring wheat grain yield threshold is determined based on available soil water content at sowing plus expected precipitation during growing season. Single linear equation was obtained with slope of 14.6 kg ha-1 mm-1 and x intercept at 126.3 mm for spring wheat water use-yield production function with different wheat varieties under varying climatic patterns. The slopes of the boundary function were 16.2 kg ha-1 mm-1 and 19.1 kg ha-1 mm-1 under conventional management practice and mulching practices, respectively. With increase of available soil water content at sowing, the probability of achieving at least 2000 and 4000 kg ha-1 of spring wheat for actual and attainable yield increased under different agricultural management practices.
Visualization of movie recommendation system using the sentimental vocabulary distribution map

This paper suggests a method to refine a massive collective intelligence data, and visualize with multilevel sentiment network, in order to understand information in an intuitive and semantic way. For this study, we first calculated a frequency of sentiment words from each movie review. Second, we designed a Heatmap visualization to effectively discover the main emotions on each online movie review. Third, we formed a Sentiment-Movie Network combining the MDS Map and Social Network in order to fix the movie network topology, while creating a network graph to enable the clustering of similar nodes. Finally, we evaluated our progress to verify if it is actually helpful to improve user cognition for multilevel analysis experience compared to the existing network system, thus concluded that our method provides improved user experience in terms of cognition, being appropriate as an alternative method for semantic understanding.                       소셜 네트워크 분석(Social Network Analysis)은 네트워크가 가지는 고유의 구조 및 관계를 분석하여 사회적 기능 문제를 파악하고 해결하는 데 큰 역할을 한다. 따라서 데이터의 유사도를 기반으로 형성되는 네트워크 분석 및 사회 과학적 현상의 네트워크 분석, 그래프 이론, 추천시스템 등 광범위한 분야에서 활용되고 있다. 특히 네트워크 그래프를 그리는 대표적인 레이아웃 알고리즘인 Force-directed layout은 관련 있는 노드 간의 클러스터를 형성하게 함으로써 네트워크 분석을 위한 그래프를 그리는 데 유용하다[1]. 그러나 Force-directed layout을 통해 그려지는 그래프는 노드 위치의 초깃값이 무작위로 설정되고, 노드 사이의 상대적인 관계에 따라 최종위치를 결정하기 때문에 데이터가 추가되거나 그래프를 새로 그릴 때마다 노드의 위치가 달라지는 문제점이 있다. 따라서 네트워크를 관찰하려는 사용자는 시스템에 대한 학습을 반복해야하는 불편함이 생긴다. (그림 1 참고) 이러한 문제점은 네트워크를 이루고 있는 데이터의 양이 많아질 때 네트워크 해석에 큰 장애요소가 될 수 있다. 또한, Force–directed layout을 그대로 적용하여 데이터를 시각화 한다면, 노드들의 위치가 달라진다는 문제로 인해서 집단 지성 정보의 의미 전달력을 상실할 가능성이 있다.
Analysis of LAPAN-IPB image lossless compression using differential pulse code modulation and huffman coding

LAPAN-A3/IPB satellite is the latest Indonesian experimental microsatellite with remote sensing and earth surveillance missions. The satellite has three optical payloads, which are multispectral push-broom imager, digital matrix camera and video camera. To increase data transmission efficiency, the multispectral imager data can be compressed using either lossy or lossless compression method. This paper aims to analyze Differential Pulse Code Modulation (DPCM) method and Huffman coding that are used in LAPAN-IPB satellite image lossless compression. Based on several simulation and analysis that have been done, current LAPAN-IPB lossless compression algorithm has moderate performance. There are several aspects that can be improved from current configuration, which are the type of DPCM code used, the type of Huffman entropy-coding scheme, and the use of sub-image compression method. The key result of this research shows that at least two neighboring pixels should be used for DPCM calculation to increase compression performance. Meanwhile, varying Huffman tables with sub-image approach could also increase the performance if on-board computer can support for more complicated algorithm. These results can be used as references in designing Payload Data Handling System (PDHS) for an upcoming LAPAN-A4 satellite.
NUMERICAL ANALYSIS OF CO2 TRANSIENT BEHAVIOR IN THE GAS COOLER DURING VARIABLE START-UP CONDITIONS OF A TRANSCRITICAL REFRIGERATION SYSTEM

Transient behavior of CO2 in th gas cooler during variable start-up conditions of a transcritical refrigeration system is numerically investigated. The analysis is based on three cases including cold and warm start-up with zero initial refrigerant mass flow rate, and warm start-up with nominal CO2 mass flow rate. The numerical analysis is performed using a 3D model and simulations are carried out using CFD code FLUENT. Effects of the transient change in the gas cooler inlet boundary conditions on the CO2 temperature, mass flow rate, thermophysical properties, heat transfer parameters and thermal effectiveness are analyzed. Results show that, when the refrigeration system starts at high temperature and nominal refrigerant mass flow rate, the maximum thermal effectiveness of the gas cooler is 85%. When it starts at high temperature, with zero initial mass flow rate, the thermal effectiveness decreases up to 60% and the transient period lengthens about 50 s.
Agronomic biofortification as a means of enriching plant foodstuffs with iodine

Iodine is indispensable in the diet of humans and other mammals and iodine deficiencies cause serious illnesses. The content of iodine in food (with the exception of marine foodstuffs) does not meet the nutritional needs of humans, and for this reason prophylactic iodination of salt is currently carried out in many countries. Biofortification of plants with iodine can become a widespread, alternative means of supplying iodine-rich foods. In the present study, we discuss the main issues related to the cultivation of plants enriched with iodine. We describe the effect of various forms of iodine fertilizer on crops, such as natural iodine sources, organic iodine, iodate and iodide salts, as well as ways of biofortifying crops: fertigation, foliar and soil application, and by hydroponics. Effective biofortification of plants with iodine increases its concentration to levels corresponding to human nutritional requirements whilst preserving the desirable eating qualities of the plants. Because each species reacts in a specific manner to a particular chemical form of iodine application, fertilization and cultivation methods, and other conditions, the development of proper cultivation technologies is essential to bring about widespread biofortification with iodine.
Response of Faba Bean to Starter Nitrogen Dose Application and Rhizobial Inoculation in the Major Growing Areas of Arsi Zone

Application of starter Nitrogenous fertilizer with rhizobial strains have been hypothesized to increase yield and yield parameters. Hence, to exploit productivity potential of Faba bean(Vicia faba L) a field study was conducted in Arsi zone 2015/16 and 2016/17.The test crop was uninoculated, inoculated with strain FB-1017,and supplied with six rates of Nitrogen (T1 = -Ve control ( No input),T2 = 9 N kg/ha,T3 = 18 N (kg /ha),T4 = 27 - N (kg /ha),T5 = 36 - N (kg /ha),T6 = 54 - N (kg /ha)). The treatments were laid out in randomized complete block design with split plot arrangements with three replications. Application of inoculants and starter nitrogen at different levels indicated that plant height (at three locations), grain yield (at one location) and number of seeds per pod were improved significantly (p<0.05) otherwise did not improve none of yield and yield parameters in both years. In that case, though inconsistent a grain yield of more than five quintals per hectare than the negative control (uninoculated, unfertilized) was found by the highest fertilized treatment (54 kg ha-1). The results suggest that application of starter nitrogen to small holder farmers is not economical and inoculation with trust worthy and viable strains would suffice in compromise to environmental and profit margins and more studies in different locations ought to be executed for a few locations as such might not give required result. Keywords: Arsi, Biological Nitrogen Fixation, Faba Bean, FB-1017, N starter DOI : 10.7176/JBAH/9-5-05 Publication date :March 31 st 2019
A Time Windowing Technique for the Incremental Maintenance of Association Rules

Knowledge discovery in databases is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in large data set. Many techniques have been developed for knowledge discovery, however, comparatively little investigation has been made on maintenance and update of the discovered knowledge as the underlying data changes over time. We propose a time windowing technique for the incremental maintenance of association rules, which can focus on finding strong association rules within current data and avoid the use of outdated data. To avoid re-finding upon existing data and reduce data storage overhead, we store near strong association rules.
Investigation of the Common Mode Voltage for a Neutral-Point-Clamped Multilevel Inverter Drive and its Innovative Elimination through SVPWM Switching-State Redundancy

The purpose of this paper is to provide a comprehensive Investigations and its control on the common mode Voltage (CMV) of the three-phase three-level neutral-point diode-clamped (NPC) multilevel inverter (MLI). A widespread space-vector pulse width modulation (SVPWM) technique to mitigate the perpetual problem of the NPC-MLI, the CMV, proposed. The proposed scheme is an effectual blend of nearest three vector (NTV) and selected three vector (STV) techniques. This scheme is capable to reduce the CMV without compromise the inverter output voltage and Total harmonics distraction (THD). CMV reduction achieved less than +Vdc/6 using the proposed vector selection procedure. The theoretical Investigations, the MATLAB software based computer simulation and Field Programmable Gate Array (FPGA) supported hardware corroboration have shown the superiority of the proposed technique over the conventional SVPWM schemes.