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 e+e−e^{+}e^{-} collisions

We calculate the contributions of the neutral top-pion, predicted by topcolor-assisted technicolor (TC2) theory, to top-charm production via the processes $\gamma\gamma \longrightarrow\bar{t}c$ and $e^{+}e^{-}\longrightarrow \gamma\gamma\longrightarrow \bar{t}c$ at the high energy linear $e^{+}e^{-}$ collider (LC) experiments. The cross section is of order $10^{-2}pb$ in most of the parameter space of TC2 theory, which may be detected at the LC experiments. So the process $e^{+}e^{-}\longrightarrow \bar{t}c$ 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 $\lambda$, Higgsino mass $\mu_{tot}$, DM mass $m_{\tilde{\chi}_1^0}$, and singlet Higgs self-coupling coefficient $\kappa$. The first three parameters strongly influence the DM-nucleon scattering rate, while $\kappa$ 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 $\kappa$'s value. Its scattering with nucleons is suppressed when $\lambda v/\mu_{tot}$ 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 $Z_3$-NMSSM is computed. It is found that, at the cost of introducing one additional parameter, the former is approximately $3.3 \times 10^3$ 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 $Z_3$-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