Miracle-dz/UniFAR-FTUs huggingface dataset:
Heavy-Metal-Resistant Microorganisms in Deep-Sea Sediments Disturbed by Mining Activity: An Application Toward the Development of Experimental in vitro Systems

Future mining of polymetallic nodules in the Clarion Clipperton Fracture Zone (Northeastern Pacific) is expected to affect all benthic ecosystems. The diversity, distribution, and environmental functions of microorganisms inhabiting abyssal sediments are barely understood. To understand the ​consequences of deep-sea mining, experimental in vitro systems needs to be established to test hypotheses on the environmental impact of mining. For this, 40 bacterial strains, belonging to proteobacteria, actinobacteria and firmicutes were isolated from deep-sea sediments and nodules sampled at depths of  4000 m. Phenotypic characterization revealed a strong inter-species and moderate intra-species variability. Determination of minimum inhibitory concentrations indicated the presence of acute manganese-resistant bacteria such as Rhodococcus erythropolis [228.9 mM], Loktanella cinnabarina [57.2 mM], and Dietzia maris [14.3 mM] that might be suitable systems for testing the effects of release of microbes from nodules and their interactions with sediment particles in plumes generated during mining. Comparative genomic analysis indicated the presence of manganese efflux systems relevant for future transcriptomics or proteomics approaches with environmental samples and might serves in paving the way to develop model systems including representative organisms which are currently not cultivable. Monitoring deep-sea mining activity at abyssal depth is a challenge that has to be tackled. We proposed the use of API strips as a fast-on-board methodology for bacterial monitoring as an indicator for sediment plume dispersions within the water column.
Physiological Indicators of Attachment in Domestic Dogs (Canis familiaris) and Their Owners in the Strange Situation Test

Behaviorally, attachment is demonstrated when one individual maintains close proximity to another individual and shows distress upon separation. For 29 owner-dog dyads, we employed a modified Ainsworth’s Strange Situation Test (SST) to investigate whether both members would show a physiological reaction to separation. Dogs experienced a series of separation from and reuniting events with their owners and were introduced to a stranger. Before and after the SST, saliva samples were taken from each dyad to measure stress-related analytes: cortisol (CORT) and chromogranin A (CgA). Dogs exhibited attachment behaviors toward owners as evidenced by more time spent in close proximity, more contact initiated and less time spent near the door, compared to episodes with the stranger. Dogs that initiated more contact with their owners in re-uniting episodes had lower CgA than dogs that initiated less contact, but their owners had higher CgA levels. Also during re-uniting episodes, dogs and owners spent more time near each other when owner CgA levels were low, owner CORT levels were high, and the dog had owner-reported separation anxiety. During the episodes alone with the stranger, dogs with higher CORT spent more time with the stranger. Finally, dogs’ initial CgA levels were correlated with their owner’s initial CORT levels, and dog final CORT levels were correlated with their owners’ final CORT levels, suggesting some hormonal synchrony within the dyad. As all owner-dog dyads were assessed as securely attached, attachment style differences could not explain variation in hormonal or behavioral results. These results suggest that dogs may respond to owner hormonal state and/or behavior and demonstrate that individual differences in responses to a behavioral challenge reflect the stress physiology of both dogs and their owners.
Predicting Changes to Source Code

Predicting Changes to Source Code Justin Roll Organizations typically use issue tracking systems (ITS) such as Jira to plan software releases and assign requirements to developers. Organizations typically also use source control management (SCM) repositories such as Git to track historical changes to a code-base. These ITS and SCM repositories contain valuable data that remains largely untapped. As developers churn through an organization, it becomes expensive for developers to spend time determining which software artifact must be modified to implement a requirement. In this work we created, developed, tested and evaluated a tool called Class Change Predictor, otherwise known as CCP, for predicting which class will implement a requirement. Understanding which class will implement a requirement supports several software engineering tasks such as refactoring and assigning requirements to developers. CCP is a data-mining tool operating on top of ITS and SCM repositories which gathers a unique combination of metrics. CCP leverages requirement text to compare current requirements to past requirements and requirements to source code files. CCP performs static analysis on the code-base of each major release of the software artifact. We evaluated CCP on different open source datasets (and the Digital Democracy dataset) by using several machine learning classifiers and pre-processing procedures. Our results show that we can achieve high precision on three out of four datasets. We conclude that accurate class change prediction is feasible, and we propose numerous solutions to increase future accuracy.
Microbial Inoculum Composition and Pre-weaned Dairy Calf Age Alter the Developing Rumen Microbial Environment

The objective of this experiment was to determine if dosing pre-weaned calves with enriched ruminal microbiota alters the rumen microbial environment and growth performance. Twenty Holstein bull calves were removed from their dam at birth, fed 3.8 L colostrum within 4 h after birth, and housed individually. Calves were fed pasteurized milk 3×/d from 0 to 7 weeks of age and offered a texturized calf starter ad libitum at 6 days of age. A randomized complete block design with repeated measures and a 2 × 2 factorial arrangement of treatments was used to evaluate responses. Treatments were administered by stomach intubation once per week from 3 to 6 weeks of age and included: 50 mL autoclaved rumen fluid (RF), 50 mL bacterial-enriched RF (BE), 50 mL protozoal-enriched RF (PE); or 50 mL of each BE and PE inoculum. A rumen content composite was collected from 4 rumen fistulated, lactating cows and used to create the inocula. BE inocula were microscopically confirmed to be free of ciliate protozoa before inoculation, while PE contained 2.9 ± 2.2 × 105 protozoa/mL. RF was collected from the calves once per week before 50 mL of the inoculum was administered. Animal performance (e.g., weight gain and dry matter intake) was not altered by inocula type. All calves were microscopically free of rumen ciliates before inoculum administration and calves that did not receive PE remained ciliate-free. Ciliate protozoa were observed in RF from 6, 8, and 6 PE treated calves (n = 10) at weeks 4, 5, and 6, respectively. Ruminal NH3 was lower in PE treated calves (3.3 vs. 6.8 ± 1.0 mM), while ruminal butyrate molar percent was greater in BE treated calves (10.8 vs. 8.3 ± 0.8). Rumen bacterial diversity measures did not differ by treatment at 3–6 weeks. Individual calf bacterial communities from treated calves became temporarily similar to the inocula at 4 weeks of age, but these communities diverged from the inocula at 5 weeks. This study provides new information about two types of rumen-derived inocula and insight into the challenges of directing the rumen microbial environment in the pre-weaned calf.
The Roles of GmERF135 in Improving Salt Tolerance and Decreasing ABA Sensitivity in Soybean

Abscisic acid (ABA) mediates various abiotic stress responses, and ethylene responsive factors (ERFs) play vital role in resisting stresses, but the interaction of these molecular mechanisms remains elusive. In this study, we identified an ABA-induced soybean ERF gene GmERF135 that was highly up-regulated by ethylene (ET), drought, salt, and low temperature treatments. Subcellular localization assay showed that the GmERF135 protein was targeted to the nucleus. Promoter cis-acting elements analysis suggested that numerous potential stress responsive cis-elements were distributed in the promoter region of GmERF135, including ABA-, light-, ET-, gibberellin (GA)-, and methyl jasmonate (MeJA)-responsive elements. Overexpression of GmERF135 in Arabidopsis enhanced tolerance to drought and salt conditions. In addition, GmERF135 promoted the growth of transgenic hairy roots under salt and exogenous ABA conditions. These results suggest that soybean GmERF135 may participate in both ABA and ET signaling pathways to regulate the responses to multiple stresses.
Black And White Styles In Conflict

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Reproducible IR needs an (IR) (Graph) Query Language

IR research should concentrate on the retrieval model, and not be hindered by its implementation in terms of low-level data structures; and we should embrace graph databases to realize that vision! Even results from retrieval systems based on the classic Okapi BM25 retrieval model (an approach that seems straightforward to implement) have been remarkably difficult to actually reproduce in practice. Pin-pointing the cause of not obtaining identical results on an identical collection is surprisingly hard, especially since retrieval systems usually mix the retrieval model itself with the code necessary to make IR efficient. Unlike the database community, our field has never moved away from implementing query processing directly on the file system; historically best attributed to a need for speed on large data sets of heterogeneous documents, never a good match for our database colleagues’ solutions. Nevertheless, this position paper calls for a shift toward a declarative approach to specify retrieval. Not only has the state of the art in database query processing reached a level where doing IR as database queries is not unimaginable, advancing retrieval effectiveness has become dependent on systems that handle complex data models in multiple layers of processing, usually involving machine learning and thereby including the data itself into the retrieval model. If the results of ranking with a straightforward combination of term frequency and document frequency can hardly be reproduced, how will we advance our field when researchers face the implementation of ranking formulas that integrate the data itself, knowledge bases and advanced machine learning methods? Reproducibility in IR will be much easier to achieve if the retrieval model can be expressed concisely (and precisely! ) in terms of operations over the document-term graph, or, probable today, a document-metadata-entity-term-graph. We propose to adopt G-core, a language proposed by the database community to become a standard graph query language, and where necessary extend it for IR purposes. IR researchers and practitioners can represent documents and annotations naturally as graphs. We discuss the advantages of a graph model over previous work using relational databases, and illustrate how the key properties of simple and extended IR models can be expressed naturally. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). OSIRRC 2019 co-located with SIGIR 2019, 25 July 2019, Paris, France. CCS CONCEPTS • Information systems → Query languages; Document representation; Evaluation of retrieval results; Search engine indexing.
Learning to Diversify for E-commerce Search with Multi-Armed Bandit

Search is central to e-commerce platforms. Diversification of search results is essential to cater to the diverse preferences of the customers. One of the primary metrics of e-commerce businesses is revenue. On the other hand, the prices of the products shown influence customer preferences. Hence, diversifying e-commerce search results requires learning the diverse price preferences of the customers and simultaneously maximizing the revenue without hurting the relevance of the results. In this paper, we introduce the learning to diversify problem for e-commerce search. We also show that diversification improves the median customer lifetime value (CLV), which is a critical long-term business metric for an e-commerce business. We design three algorithms for the task. The first two algorithms are modifications of algorithms that are in the past developed in the context of the diversification problem in web search. The third algorithm is a novel approximate knapsack based semi-bandit algorithm. We derive the regret and pay-off bounds of all these algorithms and conduct experiments with synthetic data and simulation to validate and compare the algorithms. We compute revenue, median CLV, and purchase based mean reciprocal rank (PMRR) under various scenarios such as with changing user preferences with time in our simulation to compare the performances of these algorithms. We show that our proposed third algorithm is more practical and efficient compared to the first two algorithms and can produce higher revenue, maintain a better median CLV and PMRR. ACM Reference Format: Anjan Goswami, Chengxiang Zhai, and Prasant Mohapatra. 2019. Learning to Diversify for E-commerce Search with Multi-Armed Bandit. In Proceedings of the SIGIR 2019 Workshop on eCommerce (SIGIR 2019 eCom), 11 pages.
Modeling and exploiting QoS prediction in cloud and service computing

of thesis entitled: Modeling and Exploiting QoS Prediction in Cloud and Service Computing Submitted by ZHANG, Yilei for the degree of Doctor of Philosophy at The Chinese University of Hong Kong in September 2013 Cloud computing is a new type of Internet-based computing, whereby shared resources, software, and information are provided as services to computers and other devices on demand. The architecture of the software systems involved in the delivery of cloud computing, typically involves multiple cloud components communicating with each other over application programming interfaces (API), usually implemented as Web services. Cloud computing has become a scalable service consumption and delivery platform. Web services are software systems designed to support interoperable machine-to-machine interaction over a network. The technical foundations of cloud computing include Service-Oriented Architecture (SOA), which is becoming a popular and major framework for building Web applications in the era of Web 2.0, whereby Web services offered by different providers are discovered and integrated over the Internet. Quality-of-Service (QoS) is usually employed to describe the nonfunctional properties of services in cloud and service computing. It becomes important to evaluate the QoS performance of services to differentiate the qualities of service candidates. However, QoS evaluation is time and resource consuming. Conducting real-world evaluation is difficult in practice. Moreover, in some scenarios, QoS evaluation becomes impossible (e.g., the cloud
E-commerce Query Classification Using Product Taxonomy Mapping: A Transfer Learning Approach

In web search, query classification (QC) is used to map a query to a user’s search intent. In the e-commerce domain, user’s product search queries can be broadly categorised into product specific queries and category specific queries [9]. In these instances, accurate classification of queries will help with identifying the right product categories from which relevant products can be retrieved. Thus, mapping a query to a pre-defined product taxonomy is an important step in e-commerce query understanding pipeline. A typical e-commerce website has thousands of categories, and curating a labeled data set for query classification is expensive, time consuming, and labor intensive. In addition, product search queries are short, and the vocabulary changes over time as the catalogue evolves. Reducing this effort of generating query-category labels would save time and resources. In this work we show how an existing product-taxonomy mapping can improve query classification, and reduce the need for labeled data, using transfer learning. Our results demonstrate that such an approach can match, and often exceed, the performance of direct training with a smaller computational budget. We further explore how performance varies as the amount of available training data varies, and show that transfer learning is most useful when the target data set size is small. In addition, we make available a large query data set of 535, 506 unique e-commerce labeled queries, mapped over 58 categories. The results and transfer learning approaches presented in this work can act as strong baselines for this collection and task.
BM25 Pseudo Relevance Feedback Using Anserini at Waseda University

We built a Docker image for BM25PRF (BM25 with Pseudo Relevance Feedback) retrieval model with Anserini. Also, grid search is provided in the Docker image for parameter tuning. Experimental results suggest that BM25PRF with default parameters outperforms vanilla BM25 on robust04, but tuning parameters on 49 topics of robust04 did not further improve its effectiveness. Image Source: github.com/osirrc/anserini-bm25prf-docker Docker Hub: hub.docker. com/r/osirrc2019/anserini-bm25prf
Improving Semantic Matching via Multi-Task Learning in E-Commerce

Semantic matching plays a critical role in an e-commerce search engine, while one of the biggest challenges is the language gap between queries and products. Traditionally, some auxiliary functions, such as the category navigation, are designed to help buyers to clarify their intent. Recently, the advances in deep learning provide new opportunities to bridge the gap, however, these techniques suffer from the data sparseness problem. To address this issue, in addition to the click-through data from buyers, we exploit other types of semantic knowledge from the product category taxonomy and sellers’ behavior. We investigate the correlation between query intent classification and semantic textual similarity, and propose a multi-task framework to boost their performance simultaneously. Moreover, we design a Progressively Hierarchical Classification (PHC) network architecture with the taxonomy to solve the category imbalance problem . We conduct extensive offline and online A/B experiments on a real-world e-commerce platform, and the results show that the proposed method in this paper significantly outperforms the baseline and achieves higher commercial value.
A Docker-Based Replicability Study of a Neural Information Retrieval Model

In this work, we propose a Docker image architecture for the replicability of Neural IR (NeuIR) models. We also share two self-contained Docker images to run the Neural Vector Space Model (NVSM) [22], an unsupervised NeuIR model. The first image we share (nvsm_cpu) can run on most machines and relies only on CPU to perform the required computations. The second image we share (nvsm_gpu) relies instead on the Graphics Processing Unit (GPU) of the host machine, when available, to perform computationally intensive tasks, such as the training of the NVSM model. Furthermore, we discuss some insights on the engineering challenges we encountered to obtain deterministic and consistent results from NeuIR models, relying on TensorFlow within Docker. We also provide an in-depth evaluation of the differences between the runs obtained with the shared images. The differences are due to the usage within Docker of TensorFlow and CUDA libraries – whose inherent randomness alter, under certain circumstances, the relative order of documents in rankings.
Energy-scalable speech recognition circuits

As people become more comfortable with speaking to machines, the applications of speech interfaces will diversify and include a wider range of devices, such as wearables, appliances, and robots. Automatic speech recognition (ASR) is a key component of these interfaces that is computationally intensive. This thesis shows how we designed special-purpose integrated circuits to bring local ASR capabilities to electronic devices with a small size and power footprint. This thesis adopts a holistic, system-driven approach to ASR hardware design. We identify external memory bandwidth as the main driver in system power consumption and select algorithms and architectures to minimize it. We evaluate three acoustic modeling approaches-Gaussian mixture models (GMMs), subspace GMMs (SGMMs), and deep neural networks (DNNs)-and identify tradeoffs between memory bandwidth and recognition accuracy. DNNs offer the best tradeoffs for our application; we describe a SIMD DNN architecture using parameter quantization and sparse weight matrices to save bandwidth. We also present a hidden Markov model (HMM) search architecture using a weighted finite-state transducer (WFST) representation. Enhancements to the search architecture, including WFST compression and caching, predictive beam width control, and a word lattice, reduce memory bandwidth to 10 MB/s or less, despite having just 414 kB of on-chip SRAM. The resulting system runs in real-time with accuracy comparable to a software recognizer using the same models. We provide infrastructure for deploying recognizers trained with open-source tools (Kaldi) on the hardware platform. We investigate voice activity detection (VAD) as a wake-up mechanism and conclude that an accurate and robust algorithm is necessary to minimize system power, even if it results in larger area and power for the VAD itself. We design fixed-point digital implementations of three VAD algorithms and explore their performance on two synthetic tasks with SNRs from -5 to 30 dB. The best algorithm uses modulation frequency features with an NN classifier, requiring just 8.9 kB of parameters. Throughout this work we emphasize energy scalability, or the ability to save energy when high accuracy or complex models are not required. Our architecture exploits scalability from many sources: model hyperparameters, runtime parameters such as beam width, and voltage/frequency scaling. We demonstrate these concepts with results from five ASR tasks, with vocabularies ranging from 11 words to 145,000 words. Thesis Supervisor: Anantha Chandrakasan Title: Vannevar Bush Professor of Electrical Engineering and Computer Science Thesis Supervisor: James Glass Title: Senior Research Scientist