582 research outputs found
A Descriptive Model of Robot Team and the Dynamic Evolution of Robot Team Cooperation
At present, the research on robot team cooperation is still in qualitative
analysis phase and lacks the description model that can quantitatively describe
the dynamical evolution of team cooperative relationships with constantly
changeable task demand in Multi-robot field. First this paper whole and static
describes organization model HWROM of robot team, then uses Markov course and
Bayesian theorem for reference, dynamical describes the team cooperative
relationships building. Finally from cooperative entity layer, ability layer
and relative layer we research team formation and cooperative mechanism, and
discuss how to optimize relative action sets during the evolution. The dynamic
evolution model of robot team and cooperative relationships between robot teams
proposed and described in this paper can not only generalize the robot team as
a whole, but also depict the dynamic evolving process quantitatively. Users can
also make the prediction of the cooperative relationship and the action of the
robot team encountering new demands based on this model. Journal web page & a
lot of robotic related papers www.ars-journal.co
Mediation pathway selection with unmeasured mediator-outcome confounding
Causal mediation analysis aims to investigate how an intermediary factor,
called a mediator, regulates the causal effect of a treatment on an outcome.
With the increasing availability of measurements on a large number of potential
mediators, methods for selecting important mediators have been proposed.
However, these methods often assume the absence of unmeasured mediator-outcome
confounding. We allow for such confounding in a linear structural equation
model for the outcome and further propose an approach to tackle the mediator
selection issue. To achieve this, we firstly identify causal parameters by
constructing a pseudo proxy variable for unmeasured confounding. Leveraging
this proxy variable, we propose a partially penalized method to identify
mediators affecting the outcome. The resultant estimates are consistent, and
the estimates of nonzero parameters are asymptotically normal. Motivated by
these results, we introduce a two-step procedure to consistently select active
mediation pathways, eliminating the need to test composite null hypotheses for
each mediator that are commonly required by traditional methods. Simulation
studies demonstrate the superior performance of our approach compared to
existing methods. Finally, we apply our approach to genomic data, identifying
gene expressions that potentially mediate the impact of a genetic variant on
mouse obesity.Comment: 35 page
Latent Jailbreak: A Test Suite for Evaluating Both Text Safety and Output Robustness of Large Language Models
Considerable research efforts have been devoted to ensuring that large
language models (LLMs) align with human values and generate safe text. However,
an excessive focus on sensitivity to certain topics can compromise the model's
robustness in following instructions, thereby impacting its overall performance
in completing tasks. Previous benchmarks for jailbreaking LLMs have primarily
focused on evaluating the safety of the models without considering their
robustness. In this paper, we propose a benchmark that assesses both the safety
and robustness of LLMs, emphasizing the need for a balanced approach. To
comprehensively study text safety and output robustness, we introduce a latent
jailbreak prompt dataset, each involving malicious instruction embedding.
Specifically, we instruct the model to complete a regular task, such as
translation, with the text to be translated containing malicious instructions.
To further analyze safety and robustness, we design a hierarchical annotation
framework. We present a systematic analysis of the safety and robustness of
LLMs regarding the position of explicit normal instructions, word replacements
(verbs in explicit normal instructions, target groups in malicious
instructions, cue words for explicit normal instructions), and instruction
replacements (different explicit normal instructions). Our results demonstrate
that current LLMs not only prioritize certain instruction verbs but also
exhibit varying jailbreak rates for different instruction verbs in explicit
normal instructions. Code and data are available at
https://github.com/qiuhuachuan/latent-jailbreak.Comment: Code and data are available at
https://github.com/qiuhuachuan/latent-jailbrea
Artaserse
Theoretical linguists claim that the notorious reflexive ziji 'self' in Mandarin Chinese, if occurring more than once in a single sentence, can take distinct antecedents. This study tackles possibly the most interesting puzzle in the linguistic literature, investigating how two occurrences of ziji in a single sentence are interpreted and whether or not there are mixed readings, i.e., these zijis are interpretively bound by distinct antecedents. Using 15 Chinese sentences each having two zijis, we conducted two sentence reading experiments based on a modified self-paced reading paradigm. The general interpretation patterns observed showed that the majority of participants associated both zijis with the same local antecedent, which was consistent with Principle A of the Standard Binding Theory and previous experimental findings involving a single ziji. In addition, mixed readings also occurred, but did not pattern as claimed in the theoretical linguistic literature (i.e., one ziji is bound by a long-distance antecedent and the other by a local antecedent). Based on these results, we argue that: (i) mixed readings were due to manifold, interlocking and conflicting perspectives taken by the participants; and (ii) cases of multiple occurrences of ziji taking distinct antecedents are illicit in Chinese syntax, since the speaker, when expressing a sentence, can select only one P(erspective)-Center that referentially denotes the psychological perspective in which the sentence is situated
Understanding Client Reactions in Online Mental Health Counseling
Communication success relies heavily on reading participants' reactions. Such
feedback is especially important for mental health counselors, who must
carefully consider the client's progress and adjust their approach accordingly.
However, previous NLP research on counseling has mainly focused on studying
counselors' intervention strategies rather than their clients' reactions to the
intervention. This work aims to fill this gap by developing a theoretically
grounded annotation framework that encompasses counselors' strategies and
client reaction behaviors. The framework has been tested against a large-scale,
high-quality text-based counseling dataset we collected over the past two years
from an online welfare counseling platform. Our study shows how clients react
to counselors' strategies, how such reactions affect the final counseling
outcomes, and how counselors can adjust their strategies in response to these
reactions. We also demonstrate that this study can help counselors
automatically predict their clients' states.Comment: Accept to ACL 2023, oral. For code and data, see
https://github.com/dll-wu/Client-Reac
Hyperphalangy in a new sinemydid turtle from the Early Cretaceous Jehol Biota
Hyperphalangy is a rare condition in extant aquatic turtles, and mainly limited to soft-shelled turtles. Here we report a new freshwater turtle, Jeholochelys lingyuanensis gen. et sp. nov. from the Early Cretaceous Jehol Biota of western Liaoning, China. This new turtle is characterized by a hyperphalangy condition with one additional phalanx in pedal digit V, rather than the primitive condition (phalangeal formula: 2-3-3-3-3) of crown turtles. J. lingyuanensis is recovered with other coexisting turtles in the family Sinemydidae in the phylogenetic analysis. This discovery further confirms that hyperphalangy occurred multiple times in the early evolutionary history of the crown turtles. Hyperphalangy is possibly a homoplasy in Jeholochelys and the soft-shelled turtles to adapt to the aquatic environments
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