2,251 research outputs found
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers
Online Multi-Object Tracking (MOT) from videos is a challenging computer
vision task which has been extensively studied for decades. Most of the
existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm
combined with popular machine learning approaches which largely reduce the
human effort to tune algorithm parameters. However, the commonly used
supervised learning approaches require the labeled data (e.g., bounding boxes),
which is expensive for videos. Also, the TBD framework is usually suboptimal
since it is not end-to-end, i.e., it considers the task as detection and
tracking, but not jointly. To achieve both label-free and end-to-end learning
of MOT, we propose a Tracking-by-Animation framework, where a differentiable
neural model first tracks objects from input frames and then animates these
objects into reconstructed frames. Learning is then driven by the
reconstruction error through backpropagation. We further propose a
Reprioritized Attentive Tracking to improve the robustness of data association.
Experiments conducted on both synthetic and real video datasets show the
potential of the proposed model. Our project page is publicly available at:
https://github.com/zhen-he/tracking-by-animationComment: CVPR 201
Phase structure of lattice QCD with two flavors of Wilson quarks at finite temperature and chemical potential
We present results for phase structure of lattice QCD with two degenerate
flavors () of Wilson quarks at finite temperature and small baryon
chemical potential . Using the imaginary chemical potential for which
the fermion determinant is positive, we perform simulations at points where the
ratios of pseudo-scalar meson mass to the vector meson mass are
between and as well as in the quenched limit. By analytic
continuation to real quark chemical potential , we obtain the transition
temperature as a function of small . We attempt to determine the nature
of transition at imaginary chemical potential by histogram, MC history, and
finite size scaling. In the infinite heavy quark limit, the transition is of
first order. At intermediate values of quark mass corresponding to the
ratio of in the range from to at
, the MC simulations show absence of phase transition.Comment: 10 pages, 17 figures;16 figures;9 pages,10 figures;10 pages,11
figure
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network can be increased by widening and adding layers. However, usually the former introduces additional parameters, while the latter increases the runtime. As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution. By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence. Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model
Series Expansions for Excited States of Quantum Lattice Models
We show that by means of connected-graph expansions one can effectively
generate exact high-order series expansions which are informative of low-lying
excited states for quantum many-body systems defined on a lattice. In
particular, the Fourier series coefficients of elementary excitation spectra
are directly obtained. The numerical calculations involved are straightforward
extensions of those which have already been used to calculate series expansions
for ground-state correlations and susceptibilities in a wide variety of
models. As a test, we have reproduced the known elementary excitation spectrum
of the transverse-field Ising chain in its disordered phase.Comment: 9 pages, no figures, Revtex 3.0 The revised version corrects the
incorrect (and unnecessary) statement in the original that H and H^eff are
related by a unitary transformation; in fact they are related by via a
similarity transformation. This has no implications for the calculations of
spectra, but is important for matrix element
Chronic Kidney Disease in Cats and the Risk of Total Hypercalcemia
BACKGROUND: Chronic kidney disease (CKD) is a common comorbidity in cats with hypercalcemia, but whether CKD is a risk factor for hypercalcemia is unclear. Hypercalcemia often is diagnosed based on total calcium concentration (tCa), which tends to underestimate the ionized calcium concentration (iCa) in cats. OBJECTIVES: Assessment of the performance of tCa for the diagnosis of ionized hypercalcemia, and exploration of factors influencing the relationship between iCa and tCa. Determination of risk factors for incident total hypercalcemia (ie, the development of hypercalcemia based on tCa during followâup). ANIMALS: Records of a crossâsection (n = 477) and observational cohort (n = 367) of clientâowned cats with and without azotemic CKD from first opinion practice. METHODS: Retrospective crossâsectional and retrospective cohort study. The diagnostic accuracy of tCa as an index test for ionized hypercalcemia was evaluated, and risk factors for underestimation were explored by binary logistic and linear regression in a crossâsection of cats with and without azotemic CKD. Chronic kidney disease and clinicopathological variables were assessed as predictors of incident total hypercalcemia by both timeâinvariant and timeâdependent Cox regression in a cohort of cats. RESULTS: Specificity of tCa for identification of ionized hypercalcemia was high (100%), but sensitivity was low. Underestimation was associated with lower venous bicarbonate concentrations. Cats with CKD had increased risk for incident total hypercalcemia (hazard ratio, 4.29; 95% confidence interval, 1.96â9.37; P < .001). Higher tCa predicted incident total hypercalcemia in both azotemic and nonazotemic cats (P < .001). CONCLUSIONS AND CLINICAL IMPORTANCE: Chronic kidney disease is a risk factor for incident total hypercalcemia, and most cats with increased tCa had concurrent ionized hypercalcemia. Higher baseline tCa predicts incident total hypercalcemia. Prospective studies assessing changes in iCa are warranted
Longitudinal diffusion tensor imaging in dementia with Lewy bodies and Alzheimer's disease.
OBJECTIVE: Changes in the white matter of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) have been reported using diffusion weighted MRI, though few longitudinal studies have been done. METHODS: We performed diffusion weighted MRI twice, a year apart on 23 AD, 14 DLB, and 32 healthy control subjects. Mean diffusivity (MD) and fractional anisotropy (FA) were calculated. RESULTS: In AD, there were widespread regions where the longitudinal MD increase was greater than in controls, and small areas in the parietal and temporal lobes where it was greater in AD than DLB. In AD, decrease in brain volume correlated with increased MD. There were no significant differences in progression between DLB and controls. CONCLUSIONS: In AD the white matter continues to degenerate during the disease process, whereas in DLB, changes in the white matter structure are a relatively early feature. Different mechanisms are likely to underpin changes in diffusivity.The study was supported by the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, and the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. Elijah Mak was in receipt of a Gates Cambridge PhD studentship.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.parkreldis.2016.01.00
The DSF family of quorum sensing signals: diversity, biosynthesis, and turnover
The diffusible signaling factor (DSF)-based quorum sensing (QS) system has emerged as a widely conserved cellâcell communication mechanism in Gram-negative bacteria. Typically, signals from the DSF family are cis-2-unsaturated fatty acids which regulate diverse biological functions. Recently, substantial progress has been made on the characterization of new members of this family of signals. There have also been new developments in the understanding of the biosynthesis of these molecules where dual enzymatic activities of the DSF synthase and the use of various substrates have been described. The recent discovery of a naturally occurring DSF turnover mechanism and its regulation provides a new dimension in our understanding of how DSF-dependent microorganisms modulate virulence gene expression in response to changes in the surrounding environment
Series Expansions for the Massive Schwinger Model in Hamiltonian lattice theory
It is shown that detailed and accurate information about the mass spectrum of
the massive Schwinger model can be obtained using the technique of
strong-coupling series expansions. Extended strong-coupling series for the
energy eigenvalues are calculated, and extrapolated to the continuum limit by
means of integrated differential approximants, which are matched onto a
weak-coupling expansion. The numerical estimates are compared with exact
results, and with finite-lattice results calculated for an equivalent lattice
spin model with long-range interactions. Both the heavy fermion and the light
fermion limits of the model are explored in some detail.Comment: RevTeX, 10 figures, add one more referenc
Development and Optimization of Chromosomally-Integrated Fluorescent <i>Mycobacterium tuberculosis</i> Reporter Constructs
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