334 research outputs found
Emoticon-based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo
Recent decades have witnessed online social media being a big-data window for
quantificationally testifying conventional social theories and exploring much
detailed human behavioral patterns. In this paper, by tracing the emoticon use
in Weibo, a group of hidden "ambivalent users" are disclosed for frequently
posting ambivalent tweets containing both positive and negative emotions.
Further investigation reveals that this ambivalent expression could be a novel
indicator of many unusual social behaviors. For instance, ambivalent users with
the female as the majority like to make a sound in midnights or at weekends.
They mention their close friends frequently in ambivalent tweets, which attract
more replies and thus serve as a more private communication way. Ambivalent
users also respond differently to public affairs from others and demonstrate
more interests in entertainment and sports events. Moreover, the sentiment
shift of words adopted in ambivalent tweets is more evident than usual and
exhibits a clear "negative to positive" pattern. The above observations, though
being promiscuous seemingly, actually point to the self regulation of negative
mood in Weibo, which could find its base from the emotion management theories
in sociology but makes an interesting extension to the online environment.
Finally, as an interesting corollary, ambivalent users are found connected with
compulsive buyers and turn out to be perfect targets for online marketing.Comment: Data sets can be downloaded freely from www.datatang.com/data/47207
or http://pan.baidu.com/s/1mg67cbm. Any issues feel free to contact
[email protected]
Sneutrino DM in the NMSSM with inverse seesaw mechanism
In supersymmetric theories like the Next-to-Minimal Supersymmetric Standard
Model (NMSSM), the lightest neutralino with bino or singlino as its dominant
component is customarily taken as dark matter (DM) candidate. Since light
Higgsinos favored by naturalness can strength the couplings of the DM and thus
enhance the DM-nucleon scattering rate, the tension between naturalness and DM
direct detection results becomes more and more acute with the improved
experimental sensitivity. In this work, we extend the NMSSM by inverse seesaw
mechanism to generate neutrino mass, and show that in certain parameter space
the lightest sneutrino may act as a viable DM candidate, i.e. it can annihilate
by multi-channels to get correct relic density and meanwhile satisfy all
experimental constraints. The most striking feature of the extension is that
the DM-nucleon scattering rate can be naturally below its current experimental
bounds regardless of the higgsino mass, and hence it alleviates the tension
between naturalness and DM experiments. Other interesting features include that
the Higgs phenomenology becomes much richer than that of the original NMSSM due
to the relaxed constraints from DM physics and also due to the presence of
extra neutrinos, and that the signatures of sparticles at colliders are quite
different from those with neutralino as DM candidate.Comment: 33 page
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies.Comment: 20 page
Neutral top-pion and top-charm production in high energy collisions
We calculate the contributions of the neutral top-pion, predicted by
topcolor-assisted technicolor (TC2) theory, to top-charm production via the
processes and at the high energy linear
collider (LC) experiments. The cross section is of order in most of
the parameter space of TC2 theory, which may be detected at the LC experiments.
So the process can be used to detect the
signature of TC2 theory.Comment: Latex file, 8 pages with 4 eps figures. to be published Phys.Lett.
Miamia maimai n. sp., a new Pennsylvanian stem-orthopteran insect, and a case study on the application of cladotypic nomenclature
A new stem-orthopteran insect assignable to the – traditional – genus and the – cladotypic-defined – taxon Miamia Dana, 1864 is described based on abundant material collected from the Xiaheyan locality (Ningxia, China; Early Pennsylvanian). Intra-specific wing venation variability in Miamia maimai n. sp. is appreciated based on wing pairs of single individuals, and on a complete series of conditions. Rare variants are reported. Details of head and leg morphology are documented: the new species possesses a five-segmented tarsus provided with paired claws and arolium, and labial palps with at least four segments, probably five. The nomenclatural section is conducted under the cladotypic nomenclatural procedure, but in a way largely consistent with the traditional usage. This experiment demonstrates that a combination composed of a "genus level-taxon" name previously associated with a definition and type material (e.g. Miamia), a specific epithet (e.g. maimai), and authorship information (e.g. Béthoux et al. 2012), with further mention to "Miamia maimai" or "M. maimai", provides a suitable reference to the species under all nomenclatural procedures, including the traditional one.doi:10.1002/mmng.20120000
Singlino-dominated dark matter in general NMSSM
The general Next-to-Minimal Supersymmetric Standard Model (NMSSM) describes
the singlino-dominated dark-matter (DM) property by four independent
parameters: singlet-doublet Higgs coupling coefficient , Higgsino mass
, DM mass , and singlet Higgs self-coupling
coefficient . The first three parameters strongly influence the
DM-nucleon scattering rate, while usually affects the scattering only
slightly. This characteristic implies that singlet-dominated particles may form
a secluded DM sector. Under such a theoretical structure, the DM achieves the
correct abundance by annihilating into a pair of singlet-dominated Higgs bosons
by adjusting 's value. Its scattering with nucleons is suppressed when
is small. This speculation is verified by sophisticated
scanning of the theory's parameter space with various experiment constraints
considered. In addition, the Bayesian evidence of the general NMSSM and that of
-NMSSM is computed. It is found that, at the cost of introducing one
additional parameter, the former is approximately times the
latter. This result corresponds to Jeffrey's scale of 8.05 and implies that the
considered experiments strongly prefer the general NMSSM to the -NMSSM.Comment: 29 pages, 9 figure
A Language Agent for Autonomous Driving
Human-level driving is an ultimate goal of autonomous driving. Conventional
approaches formulate autonomous driving as a perception-prediction-planning
framework, yet their systems do not capitalize on the inherent reasoning
ability and experiential knowledge of humans. In this paper, we propose a
fundamental paradigm shift from current pipelines, exploiting Large Language
Models (LLMs) as a cognitive agent to integrate human-like intelligence into
autonomous driving systems. Our approach, termed Agent-Driver, transforms the
traditional autonomous driving pipeline by introducing a versatile tool library
accessible via function calls, a cognitive memory of common sense and
experiential knowledge for decision-making, and a reasoning engine capable of
chain-of-thought reasoning, task planning, motion planning, and
self-reflection. Powered by LLMs, our Agent-Driver is endowed with intuitive
common sense and robust reasoning capabilities, thus enabling a more nuanced,
human-like approach to autonomous driving. We evaluate our approach on the
large-scale nuScenes benchmark, and extensive experiments substantiate that our
Agent-Driver significantly outperforms the state-of-the-art driving methods by
a large margin. Our approach also demonstrates superior interpretability and
few-shot learning ability to these methods. Code will be released.Comment: Project Page: https://usc-gvl.github.io/Agent-Driver
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