307 research outputs found
A Context-aware Attention Network for Interactive Question Answering
Neural network based sequence-to-sequence models in an encoder-decoder
framework have been successfully applied to solve Question Answering (QA)
problems, predicting answers from statements and questions. However, almost all
previous models have failed to consider detailed context information and
unknown states under which systems do not have enough information to answer
given questions. These scenarios with incomplete or ambiguous information are
very common in the setting of Interactive Question Answering (IQA). To address
this challenge, we develop a novel model, employing context-dependent
word-level attention for more accurate statement representations and
question-guided sentence-level attention for better context modeling. We also
generate unique IQA datasets to test our model, which will be made publicly
available. Employing these attention mechanisms, our model accurately
understands when it can output an answer or when it requires generating a
supplementary question for additional input depending on different contexts.
When available, user's feedback is encoded and directly applied to update
sentence-level attention to infer an answer. Extensive experiments on QA and
IQA datasets quantitatively demonstrate the effectiveness of our model with
significant improvement over state-of-the-art conventional QA models.Comment: 9 page
An improved two-vector model predictive torque control based on RMS duty ratio optimization for pmsm
This paper proposes an improved two-vector model-predictive torque control (MPTC) strategy to reduce the average torque ripple and improve the flux tracking performance. When determining the duty ratio of vector combination, this method aims at restricting the root mean square (RMS) error of both torque and flux during the whole control period. Every vector combination and corresponding time duration are evaluated in the cost function, which leads to global restriction of torque ripple and flux ripple. In order to avoid increasing switching frequency and computational burden, a restriction is added on the second vector. The three candidates of the second vector are the two adjacent vectors of the first one and zero vector. Simulation results are provided to show the effectiveness of the proposed strategy
Lattice-Boltzmann simulation of laser interaction with weakly ionized helium plasmas
This paper presents a lattice Boltzmann method for laser interaction with weakly ionized plasmas considering electron impact ionization and three-body recombination. To simulate with physical properties of plasmas, the authors' previous work on the rescaling of variables is employed and the electromagnetic fields are calculated from the Maxwell equations by using the finite-difference time-domain method. To calculate temperature fields, energy equations are derived separately from the Boltzmann equations. In this way, we attempt to solve the full governing equations for plasma dynamics. With the developed model, the continuous-wave CO(2) laser interaction with helium is simulated successfully.open4
PACOL: Poisoning Attacks Against Continual Learners
Continual learning algorithms are typically exposed to untrusted sources that
contain training data inserted by adversaries and bad actors. An adversary can
insert a small number of poisoned samples, such as mislabeled samples from
previously learned tasks, or intentional adversarial perturbed samples, into
the training datasets, which can drastically reduce the model's performance. In
this work, we demonstrate that continual learning systems can be manipulated by
malicious misinformation and present a new category of data poisoning attacks
specific for continual learners, which we refer to as {\em Poisoning Attacks
Against Continual Learners} (PACOL). The effectiveness of labeling flipping
attacks inspires PACOL; however, PACOL produces attack samples that do not
change the sample's label and produce an attack that causes catastrophic
forgetting. A comprehensive set of experiments shows the vulnerability of
commonly used generative replay and regularization-based continual learning
approaches against attack methods. We evaluate the ability of label-flipping
and a new adversarial poison attack, namely PACOL proposed in this work, to
force the continual learning system to forget the knowledge of a learned
task(s). More specifically, we compared the performance degradation of
continual learning systems trained on benchmark data streams with and without
poisoning attacks. Moreover, we discuss the stealthiness of the attacks in
which we test the success rate of data sanitization defense and other outlier
detection-based defenses for filtering out adversarial samples
RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples
In some practical learning tasks, such as traffic video analysis, the number
of available training samples is restricted by different factors, such as
limited communication bandwidth and computation power; therefore, it is
imperative to select diverse data samples that contribute the most to the
quality of the learning system. One popular approach to selecting diverse
samples is Determinantal Point Process (DPP). However, it suffers from a few
known drawbacks, such as restriction of the number of samples to the rank of
the similarity matrix, and not being customizable for specific learning tasks
(e.g., multi-level classification tasks). In this paper, we propose a new way
of measuring task-oriented diversity based on the Rate-Distortion (RD) theory,
appropriate for multi-level classification. To this end, we establish a
fundamental relationship between DPP and RD theory, which led to designing
RD-DPP, an RD-based value function to evaluate the diversity gain of data
samples. We also observe that the upper bound of the diversity of data selected
by DPP has a universal trend of phase transition that quickly approaches its
maximum point, then slowly converges to its final limits, meaning that DPP is
beneficial only at the beginning of sample accumulation. We use this fact to
design a bi-modal approach for sequential data selection
Learning on Bandwidth Constrained Multi-Source Data with MIMO-inspired DPP MAP Inference
This paper proposes a distributed version of Determinant Point Processing
(DPP) inference to enhance multi-source data diversification under limited
communication bandwidth. DPP is a popular probabilistic approach that improves
data diversity by enforcing the repulsion of elements in the selected subsets.
The well-studied Maximum A Posteriori (MAP) inference in DPP aims to identify
the subset with the highest diversity quantified by DPP. However, this approach
is limited by the presumption that all data samples are available at one point,
which hinders its applicability to real-world applications such as traffic
datasets where data samples are distributed across sources and communication
between them is band-limited.
Inspired by the techniques used in Multiple-Input Multiple-Output (MIMO)
communication systems, we propose a strategy for performing MAP inference among
distributed sources. Specifically, we show that a lower bound of the
diversity-maximized distributed sample selection problem can be treated as a
power allocation problem in MIMO systems. A determinant-preserved sparse
representation of selected samples is used to perform sample precoding in local
sources to be processed by DPP. Our method does not require raw data exchange
among sources, but rather a band-limited feedback channel to send lightweight
diversity measures, analogous to the CSI message in MIMO systems, from the
center to data sources. The experiments show that our scalable approach can
outperform baseline methods, including random selection, uninformed individual
DPP with no feedback, and DPP with SVD-based feedback, in both i.i.d and
non-i.i.d setups. Specifically, it achieves 1 to 6 log-difference diversity
gain in the latent representation of CIFAR-10, CIFAR-100, StanfordCars, and
GTSRB datasets
Reference voltage vector based model predictive torque control with RMS solution for PMSM
To reduce the computational burden of a conventional model predictive torque controller (MPTC), a reference voltage vector based MPTC strategy is proposed. The reference voltage vector is obtained from the reference stator flux vector and the reference torque. According to the location of the reference voltage vector, a first optimal vector can be determined in a quite straightforward way, improving the system dynamic performance. Furthermore, in order to decrease the torque and flux ripple, a root mean square (RMS) based solution is employed to generate the reference voltage vector and calculate the duty ratio. This method aims at minimizing the RMS error of flux and torque during the whole control period. Then, the steady state performance is improved. Besides, since the new cost function contains only the reference voltage vector, the weighting factor in conventional MPTC is eliminated. In addition, to keep a balance between the steady state performance and switching frequency, the candidates for the second optimal vector are restricted to a certain scope. Simulations were carried out and the results verified the validation of the proposed MPTC strategy
- …