204 research outputs found
An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss
Affect conveys important implicit information in human communication. Having
the capability to correctly express affect during human-machine conversations
is one of the major milestones in artificial intelligence. In recent years,
extensive research on open-domain neural conversational models has been
conducted. However, embedding affect into such models is still under explored.
In this paper, we propose an end-to-end affect-rich open-domain neural
conversational model that produces responses not only appropriate in syntax and
semantics, but also with rich affect. Our model extends the Seq2Seq model and
adopts VAD (Valence, Arousal and Dominance) affective notations to embed each
word with affects. In addition, our model considers the effect of negators and
intensifiers via a novel affective attention mechanism, which biases attention
towards affect-rich words in input sentences. Lastly, we train our model with
an affect-incorporated objective function to encourage the generation of
affect-rich words in the output responses. Evaluations based on both perplexity
and human evaluations show that our model outperforms the state-of-the-art
baseline model of comparable size in producing natural and affect-rich
responses.Comment: AAAI-1
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Electroencephalography (EEG) measures the neuronal activities in different
brain regions via electrodes. Many existing studies on EEG-based emotion
recognition do not fully exploit the topology of EEG channels. In this paper,
we propose a regularized graph neural network (RGNN) for EEG-based emotion
recognition. RGNN considers the biological topology among different brain
regions to capture both local and global relations among different EEG
channels. Specifically, we model the inter-channel relations in EEG signals via
an adjacency matrix in a graph neural network where the connection and
sparseness of the adjacency matrix are inspired by neuroscience theories of
human brain organization. In addition, we propose two regularizers, namely
node-wise domain adversarial training (NodeDAT) and emotion-aware distribution
learning (EmotionDL), to better handle cross-subject EEG variations and noisy
labels, respectively. Extensive experiments on two public datasets, SEED and
SEED-IV, demonstrate the superior performance of our model than
state-of-the-art models in most experimental settings. Moreover, ablation
studies show that the proposed adjacency matrix and two regularizers contribute
consistent and significant gain to the performance of our RGNN model. Finally,
investigations on the neuronal activities reveal important brain regions and
inter-channel relations for EEG-based emotion recognition
Existence of -attractor and estimate of their attractive velocity for infinite-dimensional dynamical systems
This paper is devoted to the quantitative study of the attractive velocity of
generalized attractors for infinite-dimensional dynamical systems. We introduce
the notion of~-attractor whose attractive speed is characterized by a
general non-negative decay function~, and prove that~-decay
with respect to noncompactness measure is a sufficient condition for a
dissipitive system to have a~-attractor. Furthermore, several criteria
for~-decay with respect to noncompactness measure are provided.
Finally, as an application, we establish the existence of a generalized
exponential attractor and the specific estimate of its attractive velocity for
a semilinear wave equation with a critical nonlinearity.Comment: arXiv admin note: substantial text overlap with arXiv:2108.0741
An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks
The new detection of blue straggler stars in 50 open clusters using Gaia DR3
The particularly abundant presence of blue straggler stars (BSS) in Galactic
open clusters offers favorable conditions for detailed studies on the
statistical properties and the origin of the blue straggler population. With
the help of Gaia DR3, the number of identified open clusters continuously
increases, and the determination of star cluster members is more reliable. We
performed a more thorough search for BSS in newly found open clusters based on
Gaia data. We implemented a uniform membership determination for over one
thousand newly identified open clusters with larger sky coverage based on the
astrometric and photometric data from Gaia DR3. The membership probabilities of
stars were assigned by the pyUPMASK algorithm. Then we estimated the physical
parameters of these clusters by isochrone fitting on their CMDs and picked out
BSS in the specific region of these CMDs. We identified 138 BSS that had not
been reported before in 50 open clusters. Compared with recent catalogs that
present more than 1500 BSS in 339 open clusters, our new catalog increased the
number of BSS in Galactic open clusters by about 10%, and the number of open
clusters with BSS by nearly 17%. In the future, more accurate abundance
measurements are anticipated to better probe the origin of BSS in open
clusters.Comment: 9 pages, 10 figures, 2 tables. Published in A&
Keyword-Guided Neural Conversational Model
We study the problem of imposing conversational goals/keywords on open-domain
conversational agents, where the agent is required to lead the conversation to
a target keyword smoothly and fast. Solving this problem enables the
application of conversational agents in many real-world scenarios, e.g.,
recommendation and psychotherapy. The dominant paradigm for tackling this
problem is to 1) train a next-turn keyword classifier, and 2) train a
keyword-augmented response retrieval model. However, existing approaches in
this paradigm have two limitations: 1) the training and evaluation datasets for
next-turn keyword classification are directly extracted from conversations
without human annotations, thus, they are noisy and have low correlation with
human judgements, and 2) during keyword transition, the agents solely rely on
the similarities between word embeddings to move closer to the target keyword,
which may not reflect how humans converse. In this paper, we assume that human
conversations are grounded on commonsense and propose a keyword-guided neural
conversational model that can leverage external commonsense knowledge graphs
(CKG) for both keyword transition and response retrieval. Automatic evaluations
suggest that commonsense improves the performance of both next-turn keyword
prediction and keyword-augmented response retrieval. In addition, both
self-play and human evaluations show that our model produces responses with
smoother keyword transition and reaches the target keyword faster than
competitive baselines.Comment: AAAI-202
DHX33 transcriptionally controls genes involved in the cell cycle
The RNA helicase DHX33 has been shown to be a critical regulator of cell proliferation and growth. However, the underlying mechanisms behind DHX33 function remain incompletely understood. We present original evidence in multiple cell lines that DHX33 transcriptionally controls the expression of genes involved in the cell cycle, notably cyclin, E2F1, cell division cycle (CDC), and minichromosome maintenance (MCM) genes. DHX33 physically associates with the promoters of these genes and controls the loading of active RNA polymerase II onto these promoters. DHX33 deficiency abrogates cell cycle progression and DNA replication and leads to cell apoptosis. In zebrafish, CRISPR-mediated knockout of DHX33 results in downregulation of cyclin A2, cyclin B2, cyclin D1, cyclin E2, cdc6, cdc20, E2F1, and MCM complexes in DHX33 knockout embryos. Additionally, we found the overexpression of DHX33 in a subset of non-small-cell lung cancers and in Ras-mutated human lung cancer cell lines. Forced reduction of DHX33 in these cancer cells abolished tumor formation in vivo. Our study demonstrates for the first time that DHX33 acts as a direct transcriptional regulator to promote cell cycle progression and plays an important role in driving cell proliferation during both embryo development and tumorigenesis
Extended Perron complements of M-matrices
This paper aims to consider the extended Perron complements for the collection of M-matrices. We first exhibit the connection between the extended Perron complements of M-matrices and nonnegative matrices. Moreover, we present some common inequalities involving extended Perron complements, Schur complements, and principal submatrices of irreducible M-matrices by utilizing the properties of M-matrices. We also discuss the monotonicity of the extended Perron complements and minimum eigenvalue. For the collection of M-matrices, we demonstrate that all (extended) Perron complements are M-matrices. Especially, we deduce that M-matrices and their Perron complements share the same minimum eigenvalue. Finally, a simple example is presented to illustrate our findings
Towards Persona-Based Empathetic Conversational Models
Empathetic conversational models have been shown to improve user satisfaction
and task outcomes in numerous domains. In Psychology, persona has been shown to
be highly correlated to personality, which in turn influences empathy. In
addition, our empirical analysis also suggests that persona plays an important
role in empathetic conversations. To this end, we propose a new task towards
persona-based empathetic conversations and present the first empirical study on
the impact of persona on empathetic responding. Specifically, we first present
a novel large-scale multi-domain dataset for persona-based empathetic
conversations. We then propose CoBERT, an efficient BERT-based response
selection model that obtains the state-of-the-art performance on our dataset.
Finally, we conduct extensive experiments to investigate the impact of persona
on empathetic responding. Notably, our results show that persona improves
empathetic responding more when CoBERT is trained on empathetic conversations
than non-empathetic ones, establishing an empirical link between persona and
empathy in human conversations.Comment: Accepted to EMNLP 2020 (A new dataset is proposed:
https://github.com/zhongpeixiang/PEC
CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts
Rationality and emotion are two fundamental elements of humans. Endowing
agents with rationality and emotion has been one of the major milestones in AI.
However, in the field of conversational AI, most existing models only
specialize in one aspect and neglect the other, which often leads to dull or
unrelated responses. In this paper, we hypothesize that combining rationality
and emotion into conversational agents can improve response quality. To test
the hypothesis, we focus on one fundamental aspect of rationality, i.e.,
commonsense, and propose CARE, a novel model for commonsense-aware emotional
response generation. Specifically, we first propose a framework to learn and
construct commonsense-aware emotional latent concepts of the response given an
input message and a desired emotion. We then propose three methods to
collaboratively incorporate the latent concepts into response generation.
Experimental results on two large-scale datasets support our hypothesis and
show that our model can produce more accurate and commonsense-aware emotional
responses and achieve better human ratings than state-of-the-art models that
only specialize in one aspect.Comment: AAAI-202
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