1,425 research outputs found
Revisiting Visual Question Answering Baselines
Visual question answering (VQA) is an interesting learning setting for
evaluating the abilities and shortcomings of current systems for image
understanding. Many of the recently proposed VQA systems include attention or
memory mechanisms designed to support "reasoning". For multiple-choice VQA,
nearly all of these systems train a multi-class classifier on image and
question features to predict an answer. This paper questions the value of these
common practices and develops a simple alternative model based on binary
classification. Instead of treating answers as competing choices, our model
receives the answer as input and predicts whether or not an
image-question-answer triplet is correct. We evaluate our model on the Visual7W
Telling and the VQA Real Multiple Choice tasks, and find that even simple
versions of our model perform competitively. Our best model achieves
state-of-the-art performance on the Visual7W Telling task and compares
surprisingly well with the most complex systems proposed for the VQA Real
Multiple Choice task. We explore variants of the model and study its
transferability between both datasets. We also present an error analysis of our
model that suggests a key problem of current VQA systems lies in the lack of
visual grounding of concepts that occur in the questions and answers. Overall,
our results suggest that the performance of current VQA systems is not
significantly better than that of systems designed to exploit dataset biases.Comment: European Conference on Computer Visio
Moment instabilities in multidimensional systems with noise
We present a systematic study of moment evolution in multidimensional
stochastic difference systems, focusing on characterizing systems whose
low-order moments diverge in the neighborhood of a stable fixed point. We
consider systems with a simple, dominant eigenvalue and stationary, white
noise. When the noise is small, we obtain general expressions for the
approximate asymptotic distribution and moment Lyapunov exponents. In the case
of larger noise, the second moment is calculated using a different approach,
which gives an exact result for some types of noise. We analyze the dependence
of the moments on the system's dimension, relevant system properties, the form
of the noise, and the magnitude of the noise. We determine a critical value for
noise strength, as a function of the unperturbed system's convergence rate,
above which the second moment diverges and large fluctuations are likely.
Analytical results are validated by numerical simulations. We show that our
results cannot be extended to the continuous time limit except in certain
special cases.Comment: 21 pages, 15 figure
A Solution to the Galactic Foreground Problem for LISA
Low frequency gravitational wave detectors, such as the Laser Interferometer
Space Antenna (LISA), will have to contend with large foregrounds produced by
millions of compact galactic binaries in our galaxy. While these galactic
signals are interesting in their own right, the unresolved component can
obscure other sources. The science yield for the LISA mission can be improved
if the brighter and more isolated foreground sources can be identified and
regressed from the data. Since the signals overlap with one another we are
faced with a ``cocktail party'' problem of picking out individual conversations
in a crowded room. Here we present and implement an end-to-end solution to the
galactic foreground problem that is able to resolve tens of thousands of
sources from across the LISA band. Our algorithm employs a variant of the
Markov Chain Monte Carlo (MCMC) method, which we call the Blocked Annealed
Metropolis-Hastings (BAM) algorithm. Following a description of the algorithm
and its implementation, we give several examples ranging from searches for a
single source to searches for hundreds of overlapping sources. Our examples
include data sets from the first round of Mock LISA Data Challenges.Comment: 19 pages, 27 figure
Multi-State Image Restoration by Transmission of Bit-Decomposed Data
We report on the restoration of gray-scale image when it is decomposed into a
binary form before transmission. We assume that a gray-scale image expressed by
a set of Q-Ising spins is first decomposed into an expression using Ising
(binary) spins by means of the threshold division, namely, we produce (Q-1)
binary Ising spins from a Q-Ising spin by the function F(\sigma_i - m) = 1 if
the input data \sigma_i \in {0,.....,Q-1} is \sigma_i \geq m and 0 otherwise,
where m \in {1,....,Q-1} is the threshold value. The effects of noise are
different from the case where the raw Q-Ising values are sent. We investigate
which is more effective to use the binary data for transmission or to send the
raw Q-Ising values. By using the mean-field model, we first analyze the
performance of our method quantitatively. Then we obtain the static and
dynamical properties of restoration using the bit-decomposed data. In order to
investigate what kind of original picture is efficiently restored by our
method, the standard image in two dimensions is simulated by the mean-field
annealing, and we compare the performance of our method with that using the
Q-Ising form. We show that our method is more efficient than the one using the
Q-Ising form when the original picture has large parts in which the nearest
neighboring pixels take close values.Comment: latex 24 pages using REVTEX, 10 figures, 4 table
Geodesics of Random Riemannian Metrics
We analyze the disordered Riemannian geometry resulting from random
perturbations of the Euclidean metric. We focus on geodesics, the paths traced
out by a particle traveling in this quenched random environment. By taking the
point of the view of the particle, we show that the law of its observed
environment is absolutely continuous with respect to the law of the random
metric, and we provide an explicit form for its Radon-Nikodym derivative. We
use this result to prove a "local Markov property" along an unbounded geodesic,
demonstrating that it eventually encounters any type of geometric phenomenon.
We also develop in this paper some general results on conditional Gaussian
measures. Our Main Theorem states that a geodesic chosen with random initial
conditions (chosen independently of the metric) is almost surely not
minimizing. To demonstrate this, we show that a minimizing geodesic is
guaranteed to eventually pass over a certain "bump surface," which locally has
constant positive curvature. By using Jacobi fields, we show that this is
sufficient to destabilize the minimizing property.Comment: 55 pages. Supplementary material at arXiv:1206.494
Smart sensing systems for in-home health status and emotional well-being monitoring during COVID-19
The COVID-19 pandemic has restricted the mobility of the population. The experts propose several solutions in order to decrease the number of patients infected with this new virus by treating and monitoring them within the comfort of their own home. A new direction for the research has been identified including healthcare smart sensing systems which can provide medical diagnoses, surveillance, and treatment partially or totally remotely. The field of wearable, smart sensing solutions is becoming nowadays a widely accepted solution characterized also by the increased level of acceptance with regard to home health status monitoring. Pervasive computing and wearable solutions are frequently a topic included in current projects and are expected in new future developments, particularly in the pandemic context which forces people to remain mostly at home. As part of wearable devices the design of textiles, computer science, and smart materials are the three major development directions. The latest developments associated with the monitoring of health status and emotional well-being are presented and discussed in this chapter.info:eu-repo/semantics/submittedVersio
A Quantum Approach to Classical Statistical Mechanics
We present a new approach to study the thermodynamic properties of
-dimensional classical systems by reducing the problem to the computation of
ground state properties of a -dimensional quantum model. This
classical-to-quantum mapping allows us to deal with standard optimization
methods, such as simulated and quantum annealing, on an equal basis.
Consequently, we extend the quantum annealing method to simulate classical
systems at finite temperatures. Using the adiabatic theorem of quantum
mechanics, we derive the rates to assure convergence to the optimal
thermodynamic state. For simulated and quantum annealing, we obtain the
asymptotic rates of and , for the temperature and magnetic field, respectively. Other
annealing strategies, as well as their potential speed-up, are also discussed.Comment: 4 pages, no figure
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