32 research outputs found
Outbreaks of coinfections: the critical role of cooperativity
Modeling epidemic dynamics plays an important role in studying how diseases
spread, predicting their future course, and designing strategies to control
them. In this letter, we introduce a model of SIR
(susceptible-infected-removed) type which explicitly incorporates the effect of
{\it cooperative coinfection}. More precisely, each individual can get infected
by two different diseases, and an individual already infected with one disease
has an increased probability to get infected by the other. Depending on the
amount of this increase, we observe different threshold scenarios. Apart from
the standard continuous phase transition for single disease outbreaks, we
observe continuous transitions where both diseases must coexist, but also
discontinuous transitions are observed, where a finite fraction of the
population is already affected by both diseases at the threshold. All our
results are obtained in a mean field model using rate equations, but we argue
that they should hold also in more general frameworks.Comment: 5 pages, including 5 figure
Decoding trust: A reinforcement learning perspective
Behavioral experiments on the trust game have shown that trust and
trustworthiness are universal among human beings, contradicting the prediction
by assuming \emph{Homo economicus} in orthodox Economics. This means some
mechanism must be at work that favors their emergence. Most previous
explanations however need to resort to some factors based upon imitative
learning, a simple version of social learning. Here, we turn to the paradigm of
reinforcement learning, where individuals update their strategies by evaluating
the long-term return through accumulated experience. Specifically, we
investigate the trust game with the Q-learning algorithm, where each
participant is associated with two evolving Q-tables that guide one's decision
making as trustor and trustee respectively. In the pairwise scenario, we reveal
that high levels of trust and trustworthiness emerge when individuals
appreciate both their historical experience and returns in the future.
Mechanistically, the evolution of the Q-tables shows a crossover that resembles
human's psychological changes. We also provide the phase diagram for the game
parameters, where the boundary analysis is conducted. These findings are robust
when the scenario is extended to a latticed population. Our results thus
provide a natural explanation for the emergence of trust and trustworthiness
without external factors involved. More importantly, the proposed paradigm
shows the potential in deciphering many puzzles in human behaviors.Comment: 12 pages, 11 figures. Comments are appreciate
APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection
Detecting out-of-domain (OOD) intents from user queries is essential for a
task-oriented dialogue system. Previous OOD detection studies generally work on
the assumption that plenty of labeled IND intents exist. In this paper, we
focus on a more practical few-shot OOD setting where there are only a few
labeled IND data and massive unlabeled mixed data that may belong to IND or
OOD. The new scenario carries two key challenges: learning discriminative
representations using limited IND data and leveraging unlabeled mixed data.
Therefore, we propose an adaptive prototypical pseudo-labeling (APP) method for
few-shot OOD detection, including a prototypical OOD detection framework
(ProtoOOD) to facilitate low-resource OOD detection using limited IND data, and
an adaptive pseudo-labeling method to produce high-quality pseudo OOD\&IND
labels. Extensive experiments and analysis demonstrate the effectiveness of our
method for few-shot OOD detection
Massive End-to-end Models for Short Search Queries
In this work, we investigate two popular end-to-end automatic speech
recognition (ASR) models, namely Connectionist Temporal Classification (CTC)
and RNN-Transducer (RNN-T), for offline recognition of voice search queries,
with up to 2B model parameters. The encoders of our models use the neural
architecture of Google's universal speech model (USM), with additional funnel
pooling layers to significantly reduce the frame rate and speed up training and
inference. We perform extensive studies on vocabulary size, time reduction
strategy, and its generalization performance on long-form test sets. Despite
the speculation that, as the model size increases, CTC can be as good as RNN-T
which builds label dependency into the prediction, we observe that a 900M RNN-T
clearly outperforms a 1.8B CTC and is more tolerant to severe time reduction,
although the WER gap can be largely removed by LM shallow fusion