6,055 research outputs found
Timeline Generation: Tracking individuals on Twitter
In this paper, we propose a unsupervised framework to reconstruct a person's
life history by creating a chronological list for {\it personal important
events} (PIE) of individuals based on the tweets they published. By analyzing
individual tweet collections, we find that what are suitable for inclusion in
the personal timeline should be tweets talking about personal (as opposed to
public) and time-specific (as opposed to time-general) topics. To further
extract these types of topics, we introduce a non-parametric multi-level
Dirichlet Process model to recognize four types of tweets: personal
time-specific (PersonTS), personal time-general (PersonTG), public
time-specific (PublicTS) and public time-general (PublicTG) topics, which, in
turn, are used for further personal event extraction and timeline generation.
To the best of our knowledge, this is the first work focused on the generation
of timeline for individuals from twitter data. For evaluation, we have built a
new golden standard Timelines based on Twitter and Wikipedia that contain PIE
related events from 20 {\it ordinary twitter users} and 20 {\it celebrities}.
Experiments on real Twitter data quantitatively demonstrate the effectiveness
of our approach
Energy-efficient algorithms for non-preemptive speed-scaling
We improve complexity bounds for energy-efficient speed scheduling problems
for both the single processor and multi-processor cases. Energy conservation
has become a major concern, so revisiting traditional scheduling problems to
take into account the energy consumption has been part of the agenda of the
scheduling community for the past few years.
We consider the energy minimizing speed scaling problem introduced by Yao et
al. where we wish to schedule a set of jobs, each with a release date, deadline
and work volume, on a set of identical processors. The processors may change
speed as a function of time and the energy they consume is the th power
of its speed. The objective is then to find a feasible schedule which minimizes
the total energy used.
We show that in the setting with an arbitrary number of processors where all
work volumes are equal, there is a approximation algorithm, where
is the generalized Bell number. This is the first constant
factor algorithm for this problem. This algorithm extends to general unequal
processor-dependent work volumes, up to losing a factor of
in the approximation, where is the maximum
ratio between two work volumes. We then show this latter problem is APX-hard,
even in the special case when all release dates and deadlines are equal and
is 4.
In the single processor case, we introduce a new linear programming
formulation of speed scaling and prove that its integrality gap is at most
. As a corollary, we obtain a
approximation algorithm where there is a single processor, improving on the
previous best bound of
when
Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry
Deep learning has achieved spectacular performance in image and speech
recognition and synthesis. It outperforms other machine learning algorithms in
problems where large amounts of data are available. In the area of measurement
technology, instruments based on the photonic time stretch have established
record real-time measurement throughput in spectroscopy, optical coherence
tomography, and imaging flow cytometry. These extreme-throughput instruments
generate approximately 1 Tbit/s of continuous measurement data and have led to
the discovery of rare phenomena in nonlinear and complex systems as well as new
types of biomedical instruments. Owing to the abundance of data they generate,
time-stretch instruments are a natural fit to deep learning classification.
Previously we had shown that high-throughput label-free cell classification
with high accuracy can be achieved through a combination of time-stretch
microscopy, image processing and feature extraction, followed by deep learning
for finding cancer cells in the blood. Such a technology holds promise for
early detection of primary cancer or metastasis. Here we describe a new deep
learning pipeline, which entirely avoids the slow and computationally costly
signal processing and feature extraction steps by a convolutional neural
network that directly operates on the measured signals. The improvement in
computational efficiency enables low-latency inference and makes this pipeline
suitable for cell sorting via deep learning. Our neural network takes less than
a few milliseconds to classify the cells, fast enough to provide a decision to
a cell sorter for real-time separation of individual target cells. We
demonstrate the applicability of our new method in the classification of OT-II
white blood cells and SW-480 epithelial cancer cells with more than 95%
accuracy in a label-free fashion
Modeling and simulation applications with potential impact in drug development and patient care
Indiana University-Purdue University Indianapolis (IUPUI)Model-based drug development has become an essential element to potentially make drug development more productive by assessing the data using mathematical and statistical approaches to construct and utilize models to increase the understanding of the drug and disease. The modeling and simulation approach not only quantifies the exposure-response relationship, and the level of variability, but also identifies the potential contributors to the variability. I hypothesized that the modeling and simulation approach can: 1) leverage our understanding of pharmacokinetic-pharmacodynamic (PK-PD) relationship from pre-clinical system to human; 2) quantitatively capture the drug impact on patients; 3) evaluate clinical trial designs; and 4) identify potential contributors to drug toxicity and efficacy. The major findings for these studies included: 1) a translational PK modeling approach that predicted clozapine and norclozapine central nervous system exposures in humans relating these exposures to receptor binding kinetics at multiple receptors; 2) a population pharmacokinetic analysis of a study of sertraline in depressed elderly patients with Alzheimer’s disease that identified site specific differences in drug exposure contributing to the overall variability in sertraline exposure; 3) the utility of a longitudinal tumor dynamic model developed by the Food and Drug Administration for predicting survival in non-small cell lung cancer patients, including an exploration of the limitations of this approach; 4) a Monte Carlo clinical trial simulation approach that was used to evaluate a pre-defined oncology trial with a sparse drug concentration sampling schedule with the aim to quantify how well individual drug exposures, random variability, and the food effects of abiraterone and nilotinib were determined under these conditions; 5) a time to event analysis that facilitated the identification of candidate genes including polymorphisms associated with vincristine-induced neuropathy from several association analyses in childhood acute lymphoblastic leukemia (ALL) patients; and 6) a LASSO penalized regression model that predicted vincristine-induced neuropathy and relapse in ALL patients and provided the basis for a risk assessment of the population. Overall, results from this dissertation provide an improved understanding of treatment effect in patients with an assessment of PK/PD combined and with a risk evaluation of drug toxicity and efficacy
Dynamics of grating formation in photovoltaic media
The Kukhtarev equations are solved taking into account the photovoltaic effect and different boundary conditions. In the case of open circuit, the voltage across the crystal is found to vary with a time scale similar to the photorefractive time constant. This effect explains the dynamic behavior observed experimentally
Experimental Study on the Effect of Nano-silica on Mud Density in Synthetic Based Mud
Drilling fluids play important roles in drilling operations to suspend cuttings, counter high formation pressure and to ensure wellbore stability. Amongst the different types
of drilling fluids, currently synthetic based muds are the choice drilling fluid due to its high performance in HPHT wells in terms of wellbore stability and high penetration rates. However, under HPHT conditions, the well will encounter thermal degradation of mud properties, which will affect the performance of the mud, such as fluid loss, unstable rheology and barite sag. Barite sag is an effect of high density and high solid content in muds, in which the heavy solids in the mud settle at the bottom of the wellbore causing pipe sticking and lost of circulation. The experiment was carried out at LPLT, starting of HPHT and extreme HPHT conditions with a varying nano-silica concentration of 0%(base case) to 40%. At different mud weights, the formulated drilling fluid will be tested for HPHT filtrate loss, stable rheology and static sag at a 45° tilt. Nano-silica has been proven in this project to be only effective for fluid loss and improve mud rheology due to the nature of nano-silica as a plugging agent. The nano-silica had no effect on barite sag as proven in this experiment. Nevertheless, the newly formulated mud is still effective for solving and preventing downhole problems
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