901 research outputs found
Linking bacterial population dynamics and nutrient removal in the granular sludge biofilm ecosystem engineered for wastewater treatment
Intensive nutrient removal from wastewater in anaerobic-aerobic systems using granular sludge should rely on optimal balances at biofilm and microbial ecology levels. This study targets the impacts of reactor characteristics and fluctuations in operation conditions on nutrient removal and bacterial community structures by means of microbial and numerical ecology methods. The dynamics of both predominant and accompanying populations were investigated with high resolution on temporal and phylogenetic scales in two reactors operated during 5 months with synthetic wastewater. Multivariate analyses highlighted significant correlations from process to microbial scales in the first reactor, whereas nitrification and phosphorus removal might have been affected by oxygen mass transfer limitations with no impact at population level in the second system. The bacterial community continuum of the first reactor was composed of two major antagonistic Accumulibacter-Nitrosomonas-Nitrospira and Competibacter-Cytophaga-Intrasporangiaceae clusters that prevailed under conditions leading to efficient P- (> 95%) and N-removal (> 65%) and altered P- (< 90%) and N-removal (< 60%), respectively. A third cluster independent of performances was dominated by Xanthomonadaceae affiliates that were on average more abundant at 25 °C (31 ± 5%) than at 20 °C (22 ± 4%). Starting from the physiological traits of the numerous phylotypes identified, a conceptual model is proposed as a base for functional analysis in the granular sludge microbiome and for future investigations with complex real wastewate
Improving Search through A3C Reinforcement Learning based Conversational Agent
We develop a reinforcement learning based search assistant which can assist
users through a set of actions and sequence of interactions to enable them
realize their intent. Our approach caters to subjective search where the user
is seeking digital assets such as images which is fundamentally different from
the tasks which have objective and limited search modalities. Labeled
conversational data is generally not available in such search tasks and
training the agent through human interactions can be time consuming. We propose
a stochastic virtual user which impersonates a real user and can be used to
sample user behavior efficiently to train the agent which accelerates the
bootstrapping of the agent. We develop A3C algorithm based context preserving
architecture which enables the agent to provide contextual assistance to the
user. We compare the A3C agent with Q-learning and evaluate its performance on
average rewards and state values it obtains with the virtual user in validation
episodes. Our experiments show that the agent learns to achieve higher rewards
and better states.Comment: 17 pages, 7 figure
Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)
Massive open online courses (MOOC) describe platforms where users with
completely different backgrounds subscribe to various courses on offer. MOOC
forums and discussion boards offer learners a medium to communicate with each
other and maximize their learning outcomes. However, oftentimes learners are
hesitant to approach each other for different reasons (being shy, don't know
the right match, etc.). In this paper, we propose a reciprocal recommender
system which matches learners who are mutually interested in, and likely to
communicate with each other based on their profile attributes like age,
location, gender, qualification, interests, etc. We test our algorithm on data
sampled using the publicly available MITx-Harvardx dataset and demonstrate that
both attribute importance and reciprocity play an important role in forming the
final recommendation list of learners. Our approach provides promising results
for such a system to be implemented within an actual MOOC.Comment: 10 pages, accepted as full paper @ ICWL 201
Neural Attentive Session-based Recommendation
Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short
sessions. Previous work only considers the user's sequential behavior in the
current session, whereas the user's main purpose in the current session is not
emphasized. In this paper, we propose a novel neural networks framework, i.e.,
Neural Attentive Recommendation Machine (NARM), to tackle this problem.
Specifically, we explore a hybrid encoder with an attention mechanism to model
the user's sequential behavior and capture the user's main purpose in the
current session, which are combined as a unified session representation later.
We then compute the recommendation scores for each candidate item with a
bi-linear matching scheme based on this unified session representation. We
train NARM by jointly learning the item and session representations as well as
their matchings. We carried out extensive experiments on two benchmark
datasets. Our experimental results show that NARM outperforms state-of-the-art
baselines on both datasets. Furthermore, we also find that NARM achieves a
significant improvement on long sessions, which demonstrates its advantages in
modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and
Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939,
arXiv:1606.08117 by other author
Verification and Control of Partially Observable Probabilistic Real-Time Systems
We propose automated techniques for the verification and control of
probabilistic real-time systems that are only partially observable. To formally
model such systems, we define an extension of probabilistic timed automata in
which local states are partially visible to an observer or controller. We give
a probabilistic temporal logic that can express a range of quantitative
properties of these models, relating to the probability of an event's
occurrence or the expected value of a reward measure. We then propose
techniques to either verify that such a property holds or to synthesise a
controller for the model which makes it true. Our approach is based on an
integer discretisation of the model's dense-time behaviour and a grid-based
abstraction of the uncountable belief space induced by partial observability.
The latter is necessarily approximate since the underlying problem is
undecidable, however we show how both lower and upper bounds on numerical
results can be generated. We illustrate the effectiveness of the approach by
implementing it in the PRISM model checker and applying it to several case
studies, from the domains of computer security and task scheduling
Hidden Markov Models and their Application for Predicting Failure Events
We show how Markov mixed membership models (MMMM) can be used to predict the
degradation of assets. We model the degradation path of individual assets, to
predict overall failure rates. Instead of a separate distribution for each
hidden state, we use hierarchical mixtures of distributions in the exponential
family. In our approach the observation distribution of the states is a finite
mixture distribution of a small set of (simpler) distributions shared across
all states. Using tied-mixture observation distributions offers several
advantages. The mixtures act as a regularization for typically very sparse
problems, and they reduce the computational effort for the learning algorithm
since there are fewer distributions to be found. Using shared mixtures enables
sharing of statistical strength between the Markov states and thus transfer
learning. We determine for individual assets the trade-off between the risk of
failure and extended operating hours by combining a MMMM with a partially
observable Markov decision process (POMDP) to dynamically optimize the policy
for when and how to maintain the asset.Comment: Will be published in the proceedings of ICCS 2020;
@Booklet{EasyChair:3183, author = {Paul Hofmann and Zaid Tashman}, title =
{Hidden Markov Models and their Application for Predicting Failure Events},
howpublished = {EasyChair Preprint no. 3183}, year = {EasyChair, 2020}
A personalized and context-aware news offer for mobile devices
For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer
Short Note: Nocturnal calling by a Pied Flycatcher on arrival in breeding habitat during spring migration
Optimal client recommendation for market makers in illiquid financial products
The process of liquidity provision in financial markets can result in
prolonged exposure to illiquid instruments for market makers. In this case,
where a proprietary position is not desired, pro-actively targeting the right
client who is likely to be interested can be an effective means to offset this
position, rather than relying on commensurate interest arising through natural
demand. In this paper, we consider the inference of a client profile for the
purpose of corporate bond recommendation, based on typical recorded information
available to the market maker. Given a historical record of corporate bond
transactions and bond meta-data, we use a topic-modelling analogy to develop a
probabilistic technique for compiling a curated list of client recommendations
for a particular bond that needs to be traded, ranked by probability of
interest. We show that a model based on Latent Dirichlet Allocation offers
promising performance to deliver relevant recommendations for sales traders.Comment: 12 pages, 3 figures, 1 tabl
Reducing Violence and Building Trust: Data to Guide Enforcement of Gun Laws in Baltimore
This report is the product of the Reducing Violence, Building Trust: Data to Guide Gun Law Enforcement in Baltimore project. Researchers from the Johns Hopkins Center for Gun Policy and Research (JHCGPR) collected and analyzed data relevant to the enforcement of laws restricting the possession of firearms by prohibited individuals and unlawful carrying of concealed firearms to provide data-driven recommendations for more fair and effective practices. The project was designed to help inform the response to the dual crises in Baltimore—extraordinarily high rates of gun violence, and gun law enforcement practices that, in some cases, have violated the law and more generally weakened community members' trust in the police
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