Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Genome Annotation of Rhizoctonia solani Using Proteogenomics

Background Rhizoctonia solani is a pathogenic fungus that causes serious diseases in many crops, including rice, wheat, and soybeans. In crop production, it is very important to understand the pathogenicity of this fungus, which is still elusive. It might be helpful to comprehensively understand its genomic information using different genome annotation strategies. Methods Aiming to improve the genome annotation of R. solani, we performed a proteogenomic study based on the existing data. Based on our study, a total of 1060 newly identified genes, 36 revised genes, 139 single amino acid variants (SAAVs), 8 alternative splicing genes, and diverse post-translational modifications (PTMs) events were identified in R. solani AG3. Further functional annotation on these 1060 newly identified genes was performed through homology analysis with its 5 closest relative fungi. Results Based on this, 2 novel candidate pathogenic genes, which might be associated with pathogen-host interaction, were discovered. In addition, in order to increase the reliability and novelty of the newly identified genes in R. solani AG3, 1060 newly identified genes were compared with the newly published available R. solani genome sequences of AG1, AG2, AG4, AG5, AG6, and AG8. There are 490 homologous sequences. We combined the proteogenomic results with the genome alignment results and finally identified 570 novel genes in R. solani. Conclusion These findings extended R. solani genome annotation and provided a wealth of resources for research on R. solani.
Energy use in Minnesota public buildings: data management and analysis

Data collected by the Minnesota Energy Agency from 1860 public buildings are examined. Considerable attention is devoted to questions of data quality. Validation tests are developed and applied to the data from these buildings. Roughly speaking, 15% of the data elements examined were missing and another 15% were almost surely incorrect. These errors seriously distort both the descriptive statistics and the regression results obtained from further analysis of this data. Examination of the clean data shows that total energy use in the 1977/78 fiscal year ranged from a low of 210 thousand Btu/ft/sup 2/ (2.39 billion J/m/sup 2/) for the typical garage to a high of 345 thousand Btu/ft/sup 2/ (3.92 billion J/m/sup 2/) for the typical hospital. More than half the buildings report natural gas and more than one-fourth report fuel oil as the primary heating fuel. Only 13% of the buildings are heated with electricity. Regression equations are developed to explain variations in energy use among buildings for fossil fuel, electricity, and total energy use per unit floorspace. Independent variables include fuel prices, heating degree days, annual operating hours for lights, fraction of the building air conditioned, and dummy variables for primary heating fuel and for hospitals. Thesemore » equations can be used to help decide which buildings should receive on-site energy audits. « less
Danny

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HIERARCHICAL METRICS FOR COMPUTING THE MINIMUM NUMBER OF TEST PATHS IN CONTROL-FLOW GRAPHS

The path-based testing of control-flow graphs is an important method for software testing. The minimum number of test paths with respect to different coverage criteria is used as a testability and quality measure of programs. Based on the prime decomposition of control-flow graphs, hierarchical metric formulas have been developed to compute the minimum number of test paths for a given control-flow graph. These recursive formulas have been developed for traditional primes, such as if-then and while statements, and with respect to visit-each-loop, simple path, statement, branch, and basis path (McCabe or structured) coverage. In this paper, we develop formulas for other primes that are available in most programming languages and have become very common in practice. Also, we illustrate an efficient algorithm that utilizes the hierarchical formulas to generate a set with the minimum number of test paths with respect to a given coverage criterion.
GPU Gems 3

The GPU Gems series features a collection of the most essential algorithms required by Next-Generation 3D Engines.Martin Mittring, Lead Graphics Programmer, CrytekThis third volume of the best-selling GPU Gems series provides a snapshot of todays latest Graphics Processing Unit (GPU) programming techniques. The programmability of modern GPUs allows developers to not only distinguish themselves from one another but also to use this awesome processing power for non-graphics applications, such as physics simulation, financial analysis, and even virus detectionparticularly with the CUDA architecture. Graphics remains the leading application for GPUs, and readers will find that the latest algorithms create ultra-realistic characters, better lighting, and post-rendering compositing effects. Major topics includeGeometryLight and ShadowsRenderingImage EffectsPhysics SimulationGPU ComputingContributors are from the following corporations and universities:3DfactoAdobe SystemsAppleBudapest University of Technology and EconomicsCGGVeritasThe Chinese University of Hong KongCornell UniversityCrytekCzech Technical University in PragueDartmouth CollegeDigital Illusions Creative EntertainmentEindhoven University of TechnologyElectronic ArtsHavokHelsinki University of TechnologyImperial College LondonInfinity WardJuniper NetworksLaBRIINRIA, University of Bordeauxmental imagesMicrosoft ResearchMove InteractiveNCsoft CorporationNVIDIA CorporationPerpetual EntertainmentPlaylogic Game FactoryPolytimeRainbow StudiosSEGA CorporationUFRGS (Brazil)Ulm UniversityUniversity of California, DavisUniversity of Central FloridaUniversity of CopenhagenUniversity of GironaUniversity of Illinois at Urbana-ChampaignUniversity of North Carolina Chapel HillUniversity of TokyoUniversity of WaterlooSection Editors include NVIDIA engineers: Cyril Zeller, Evan Hart, Ignacio Castao, Kevin Bjorke, Kevin Myers, and Nolan Goodnight.The accompanying DVD includes complementary examples and sample programs.
Clonos: Consistent Causal Recovery for Highly-Available Streaming Dataflows

Stream processing lies in the backbone of modern businesses, being employed for mission critical applications such as real-time fraud detection, car-trip fare calculations, traffic management, and stock trading. Large-scale applications are executed by scale-out stream processing systems on thousands of long-lived operators, which are subject to failures. Recovering from failures fast and consistently are both top priorities, yet they are only partly satisfied by existing fault tolerance methods due to the strong assumptions these make. In particular, prior solutions fail to address consistency in the presence of nondeterminism, such as calls to external services, asynchronous timers and processing-time windows. This paper describes Clonos, a fault tolerance approach that achieves fast, local operator recovery with exactly-once guarantees and high availability by instantly switching to passive standby operators. Clonos enforces causally consistent recovery, including output deduplication, by tracking nondeterminism within the system through causal logging. To implement Clonos we re-engineered many of the internal subsystems of a state of the art stream processor. We evaluate Clonos' overhead and recovery on the Nexmark benchmark against Apache Flink. Clonos achieves instant recovery with negligible overhead and, unlike previous work, does not make assumptions on the deterministic nature of operators.
Teachers’ Experiences of using Learning Technology in Pakistan

The advancements in learning technology such as computers, mobile devices and the Internet, correspondence with faculty members, linkage and connections with other students and administrative activities, have become extended to the students beyond t normal university office hours. Education technologists are exploiting the benefits of technology to transform teaching methods in universities and promote interaction and collaboration; aspects important to Networked Learning. Despite the extensive use of learning technology, little research has been conducted to understand how faculty members experience learning technology within their pedagogical practices; and much of the existing literature has been in the context of culturally western universities. This study aims to highlight the phenomenon of teachers’ experiences of using learning technology within their teaching practices at Hazara University (HU) located in Mansehra, Pakistan. Adopting an interpretivist paradigm, phenomenography was used in this study. Phenomenography aims to understand and describe the different ways people perceive or view a particular phenomenon. From the analysis, five qualitatively different conceptions of using learning technology were identified; ‘attention retainment’, ‘vocational skill development’, ‘information enrichment’, ‘connectivity’, ‘as omnipotential’. These categories of experience represent the referential aspect of the phenomenon. Their relationships to each other is elaborated with three dimensions of variations highlighted from the data which are ‘prior exposure to learning technology’, ‘academic literature-conscious teaching’ and ‘perceived scope of technological use’. These dimensions of variations represent the structural aspect of the phenomenon. The relationships between the referential and structural aspect of the phenomenon are summarized and presented in a tabular form. Findings point to how the ubiquitous nature of learning technology and also some of the affordances experienced by the teachers of using learning technology within pedagogical practices. This further highlights the importance and the reliance on learning technology of teachers to assist and contribute towards the different aspects of their pedagogical practices. As suggested research in a different setting and with a different cultural group could reveal ‘new’ conceptions. This study provides new ways of seeing learning technology within pedagogical practices which may prove insightful for further research in this field. On a broader scale, the findings could have implications on design of teacher’s education programmes, and course curriculums.
Earth Delivery of a Small NEO with an Ion Beam Shepherd

The possibility of capturing a small Near Earth Asteroid (NEA) and deliver it to the vicinity of the Earth has been recently explored by different authors. The key advantage would be to allow a cheap and quick access to the NEA for science, resource utilization and other purposes. Among the different challenges related to this operation stands the difficulty of robotically capturing the object, whose composition and dynamical state could be problematic. In order to simplify the capture operation we propose the use of a collimated ion beam ejected from a hovering spacecraft in order to maneuver the object without direct physical contact. The feasibility of a possible asteroid retrieval mission is evaluated.
A Unified Approach to Discourse Relation Classification in nine Languages

This paper presents efforts to solve the shared task on discourse relation classification (disrpt task 3). The intricate prediction task aims to predict a large number of classes from the Rhetorical Structure Theory (RST) framework for nine target languages. Labels include discourse relations such as background, condition, contrast and elaboration. We present an approach using euclidean distance between sentence embeddings that were extracted using multlingual sentence BERT (sBERT) and directionality as features. The data was combined into five classes which were used for initial prediction. The second classification step predicts the target classes. We observe a substantial difference in results depending on the number of occurrences of the target label in the training data. We achieve the best results on Chinese, where our system achieves 70 % accuracy on 20 labels.
On the Cross-lingual Transferability of Contextualized Sense Embeddings

In this paper we analyze the extent to which contextualized sense embeddings, i.e., sense embeddings that are computed based on contextualized word embeddings, are transferable across languages.To this end, we compiled a unified cross-lingual benchmark for Word Sense Disambiguation. We then propose two simple strategies to transfer sense-specific knowledge across languages and test them on the benchmark.Experimental results show that this contextualized knowledge can be effectively transferred to similar languages through pre-trained multilingual language models, to the extent that they can out-perform monolingual representations learnednfrom existing language-specific data.
Developing Conversational Data and Detection of Conversational Humor in Telugu

In the field of humor research, there has been a recent surge of interest in the sub-domain of Conversational Humor (CH). This study has two main objectives. (a) develop a conversational (humorous and non-humorous) dataset in Telugu. (b) detect CH in the compiled dataset. In this paper, the challenges faced while collecting the data and experiments carried out are elucidated. Transfer learning and non-transfer learning techniques are implemented by utilizing pre-trained models such as FastText word embeddings, BERT language models and Text GCN, which learns the word and document embeddings simultaneously of the corpus given. State-of-the-art results are observed with a 99.3% accuracy and a 98.5% f1 score achieved by BERT.
Testing Cross-Database Semantic Parsers With Canonical Utterances

The benchmark performance of cross-database semantic parsing has climbed steadily in recent years, catalyzed by the wide adoption of pre-trained language models. Yet existing work have shown that state-of-the-art cross-database semantic parsers struggle to generalize to novel user utterances, databases and query structures. To obtain transparent details on the strengths and limitation of these models, we propose a diagnostic testing approach based on controlled synthesis of canonical natural language and SQL pairs. Inspired by the C heck L ist (Ribeiro et al., 2020), we characterize a set of essential capabilities for cross-database semantic parsing models, and detailed the method for synthesizing the corresponding test data. We evaluated a variety of high performing models using the proposed approach, and identiﬁed several non-obvious weaknesses across models (e.g. unable to correctly select many columns). Our dataset and code are released as a test suite at http://github.com/hclent/ BehaviorCheckingSemPar .
The CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

In this paper, we provide an overview of the CODI-CRAC 2021 Shared-Task: Anaphora Resolution in Dialogue. The shared task focuses on detecting anaphoric relations in different genres of conversations. Using five conversational datasets, four of which have been newly annotated with a wide range of anaphoric relations: identity, bridging references and discourse deixis, we defined multiple subtasks focusing individually on these key relations. We discuss the evaluation scripts used to assess the system performance on these subtasks, and provide a brief summary of the participating systems and the results obtained across ? runs from 5 teams, with most submissions achieving significantly better results than our baseline methods.
Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems

The lack of labeled training data for new features is a common problem in rapidly changing real-world dialog systems. As a solution, we propose a multilingual paraphrase generation model that can be used to generate novel utterances for a target feature and target language. The generated utterances can be used to augment existing training data to improve intent classification and slot labeling models. We evaluate the quality of generated utterances using intrinsic evaluation metrics and by conducting downstream evaluation experiments with English as the source language and nine different target languages. Our method shows promise across languages, even in a zero-shot setting where no seed data is available.
A Transformer Based Approach towards Identification of Discourse Unit Segments and Connectives

Discourse parsing, which involves understanding the structure, information flow, and modeling the coherence of a given text, is an important task in natural language processing. It forms the basis of several natural language processing tasks such as question-answering, text summarization, and sentiment analysis. Discourse unit segmentation is one of the fundamental tasks in discourse parsing and refers to identifying the elementary units of text that combine to form a coherent text. In this paper, we present a transformer based approach towards the automated identification of discourse unit segments and connectives. Early approaches towards segmentation relied on rule-based systems using POS tags and other syntactic information to identify discourse segments. Recently, transformer based neural systems have shown promising results in this domain. Our system, SegFormers, employs this transformer based approach to perform multilingual discourse segmentation and connective identification across 16 datasets encompassing 11 languages and 3 different annotation frameworks. We evaluate the system based on F1 scores for both tasks, with the best system reporting the highest F1 score of 97.02% for the treebanked English RST-DT dataset.
End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?

In this work, we design an end-to-end model for poetry generation based on conditioned recurrent neural network (RNN) language models whose goal is to learn stylistic features (poem length, sentiment, alliteration, and rhyming) from examples alone. We show this model successfully learns the ‘meaning’ of length and sentiment, as we can control it to generate longer or shorter as well as more positive or more negative poems. However, the model does not grasp sound phenomena like alliteration and rhyming, but instead exploits low-level statistical cues. Possible reasons include the size of the training data, the relatively low frequency and difficulty of these sublexical phenomena as well as model biases. We show that more recent GPT-2 models also have problems learning sublexical phenomena such as rhyming from examples alone.
Architecting of Learning Organizations: The IS Practitioners' Challenge in Systems Thinking

This paper investigates the architecting of learning organizations in the study of information systems (IS) design. Specifically, we emphasize the role of a suitable organizational context in the development of various information systems supporting the specific requirements of today’s organizational dynamics. The paper describes our initiatives in systems thinking to substantiate IS education in terms of expositing the practitioners’ challenge to tailor the design of appropriate IS’s according to the evolving contexts of human activity systems. To realize the various IS services in a learning organization, whose requirements are increasingly innovated over different organizational scenarios, we stress the importance of identifying the locus of transformation so as to ensure the fulfillment of organizational goals. This is done by presenting our 4-R framework to direct the IS efforts in pursuit of the learning organization ideal, and by discussing the collaboration required of the IS teams to facilitate the incremental transformation into a learning organization through a concerted effort in organizational modeling.
Data Augmentation of Incorporating Real Error Patterns and Linguistic Knowledge for Grammatical Error Correction

Data augmentation aims at expanding training data with clean text using noising schemes to improve the performance of grammatical error correction (GEC). In practice, there are a great number of real error patterns in the manually annotated training data. We argue that these real error patterns can be introduced into clean text to effectively generate more real and high quality synthetic data, which is not fully explored by previous studies. Moreover, we also find that linguistic knowledge can be incorporated into data augmentation for generating more representative and more diverse synthetic data. In this paper, we propose a novel data augmentation method that fully considers the real error patterns and the linguistic knowledge for the GEC task. We conduct extensive experiments on public data sets and the experimental results show that our method outperforms several strong baselines with far less external unlabeled clean text data, highlighting its extraordinary effectiveness in the GEC task that lacks large-scale labeled training data.
Study of Manifestation of Civil Unrest on Twitter

Twitter is commonly used for civil unrest detection and forecasting tasks, but there is a lack of work in evaluating how civil unrest manifests on Twitter across countries and events. We present two in-depth case studies for two specific large-scale events, one in a country with high (English) Twitter usage (Johannesburg riots in South Africa) and one in a country with low Twitter usage (Burayu massacre protests in Ethiopia). We show that while there is event signal during the events, there is little signal leading up to the events. In addition to the case studies, we train Ngram-based models on a larger set of Twitter civil unrest data across time, events, and countries and use machine learning explainability tools (SHAP) to identify important features. The models were able to find words indicative of civil unrest that generalized across countries. The 42 countries span Africa, Middle East, and Southeast Asia and the events range occur between 2014 and 2019.
Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits

In the PDTB-3, several thousand implicit discourse relations were newly annotated within individual sentences, adding to the over 15,000 implicit relations annotated across adjacent sentences in the PDTB-2. Given that the position of the arguments to these intra-sentential implicits is no longer as well-defined as with inter-sentential implicits, a discourse parser must identify both their location and their sense. That is the focus of the current work. The paper provides a comprehensive analysis of our results, showcasing model performance under different scenarios, pointing out limitations and noting future directions.