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This article is concerned with the rigorous connections between the inertial Qian--Sheng model and the Ericksen--Leslie model for the liquid crystal flow, under a more general condition of coefficients. More specifically, in the framework of Hilbert expansions, we show that: (i) when the elastic coefficients tend to zero (also called the uniaxial limit), the smooth solution to the inertial Qian--Sheng model converges to that to the full inertial Ericksen--Leslie model; (ii) when the elastic coefficients and the inertial coefficient tend to zero simultaneously, the smooth solution to the inertial Qian--Sheng model converges to that to the noninertial Ericksen--Leslie model.
Let $X$ be a smooth projective curve of genus $g$ over an algebraically closed field $k$ of characteristic $p>2$. We prove that any rank $3$ nilpotent semistable Higgs bundle $(E,θ)$ on $X$ is a strongly semistable Higgs bundle. This gives a partially affirmative answer to a conjecture of Lan-Sheng-Zuo \cite{LanShengZuo12ii}\footnotemark[1]. In addition, we prove a tensor product theorem for strongly semistable Higgs bundles with $p$ satisfying some bounds (Theorem \ref{TensorTheorem}). From this we reprove a tensor theorem for semistable Higgs bundles on the condition that the Lan-Sheng-Zuo conjecture holds (Corollary \ref{TensorStableBundle}).
In this paper, we rigorously justify the connection between Qian-Sheng's inertial $Q$-tensor model and the full Ericksen-Leslie model for the liquid crystal flow. By using the Hilbert expansion method, we prove that, when the elastic coefficients tend to zero(also called the uniaxial limit), the solution to the Qian-Sheng's inertial model will converge to the solution to the full inertial Ericksen-Leslie system.
Neutrino-electron scatterings are purely leptonic processes with robust Standard Model (SM) predictions. Their measurements can therefore provide constraints to physics beyond SM. The $ uebar$-e data taken at the Kuo-Sheng Reactor Neutrino Laboratory were used to probe two scenarios: Non-Standard Neutrino Interactions (NSI) and Unparticle Physics. New constraints were placed to the NSI parameters ($\el$,$\er$) and ($\etl$,$\etr$) for the Non-Universal and Flavor-Changing channels, respectively, as well as to the coupling constants for scalar ($λ_0$) and vector ($λ_1$) unparticles to the neutrinos and electrons.
For the inertial Qian-Sheng model of nematic liquid crystals in the $Q$-tensor framework, we illustrate the roles played by the entropy inequality and energy dissipation in the well-posedness of smooth solutions when we employ energy method. We first derive the coefficients requirements from the entropy inequality, and point out the entropy inequality is insufficient to guarantee energy dissipation. We then introduce a novel Condition (H) which ensures the energy dissipation. We prove that when both the entropy inequality and Condition (H) are obeyed, the local in time smooth solutions exist for large initial data. Otherwise, we can only obtain small data local solutions. Furthermore, to extend the solutions globally in time and obtain the decay of solutions, we require at least one of the two conditions: entropy inequality, or $\tildeμ_2= μ_2$, which significantly enlarge the range of the coefficients in previous works.
A laboratory has been set up at the Kuo-Sheng Nuclear Power Station at a distance of 28 m from the 2.9 GW reactor core to study low energy neutrino physics. A detector threshold of 5 keV and a background level of 1 counts/day-keV-kg at 12-60 keV was achieved with a high purity germanium detector of mass 1.06 kg surrounded by anti-Compton detectors with NaI(Tl) and CsI(Tl) crystal scintillators. Using 4712 and 1250 hours of Reactor ON and OFF data, respectively, limits of the neutrino magnetic moment of mu_nu < 1.3(1.0) X 10^{-10} mu_B at 90(68)% confidence level were derived. Indirect bounds of the nu_e_bar radiative lifetime of m_nu^3 * tau_nu > 2.8(4.8) X 10^{18} eV^3 s can be inferred.
The TEXONO collaboration has been built up among scientists from Taiwan and China to pursue an experimental program in neutrino and astro-particle physics. The flagship efforts have been the study of low energy neutrino physics at the Kuo-Sheng Power Reactor Plant in Taiwan. The Reactor Laboratory is equipped with flexibly-designed shieldings, cosmic veto systems, electronics and data acquisition systems which can function with different detector schemes. Data are taken during the Reactor Period June-01 till April-02 with a high purity germanium detector and 46 kg of CsI(Tl) crystal scintillator array operating in parallel. A threshold of 5 keV has been achieved for the germanium detector, and the background level comparable to those of Dark Matter experiments underground is achieved. Based on 62/46 days of analyzed Reactor ON/OFF data, a preliminary result of $\rm{(\munue / 10^{-10} \mub)^2 = - 1.1 \pm 2.5}$ can be derived for neutrino magnetic moment $\munue$. Sensitivity region on neutrino radiative decay lifetime is inferred. The complete data set would include 180/60 days of ON/OFF data.
We report in situ neutron background measurements at the Kuo-Sheng Reactor Neutrino Laboratory (KSNL) by a hybrid neutron detector (HND) with a data size of 33.8 days under identical shielding configurations as during the neutrino physics data taking. The HND consists of BC-501A liquid and BC-702 phosphor powder scintillation neutron detectors, which is sensitive to both fast and thermal neutrons, respectively. Neutron-induced events for the two channels are identified and differentiated by pulse shape analysis, such that background of both are simultaneously measured. The fast neutron fluxes are derived by an iterative unfolding algorithm. Neutron induced background in the germanium detector under the same fluxes, both due to cosmic-rays and ambient radioactivity, are derived and compared with the measurements. The results are valuable to background understanding of the neutrino data at the KSNL. In particular, neutron-induced background events due to ambient radioactivity as well as from reactor operation are negligible compared to intrinsic cosmogenic activity and ambient $γ$-activity. The detector concept and analysis procedures are applicable to neutron background characteriza
Studies on electron antineutrino-electron elastic scattering were performed using a 200-kg CsI(Tl) scintillating crystal detector array at the Kuo-Sheng Nuclear Power Plant in Taiwan. The measured cross section of R(exp) = [1.00 +- 0.32(stat)]xR(SM) is consistent with the Standard Model expectation and the corresponding weak mixing angle derived is sin2T = 0.24 +- 0.05 (stat). The results are consistent with a destructive interference effect between neutral and charged-currents in this process. Limits on neutrino magnetic moment of mu(nu_(e)) < 2.0 x 10^(-10) mu_(B) at 90% confidence level and on electron antineutrino charge radius of r^(2) < (0.12 +- 2.07)x10^(-32) cm^2 were also derived.
We consider the inertial Qian-Sheng model of liquid crystals which couples a hyperbolic-type equation involving a second-order material derivative with a forced incompressible Navier-Stokes system. We study the energy law and prove a global well-posedness result. We further provide an example of twist-wave solutions, that is solutions of the coupled system for which the flow vanishes for all times.
Relativistic millicharged particles ($χ_q$) have been proposed in various extensions to the Standard Model of particle physics. We consider the scenarios where they are produced at nuclear reactor core and via interactions of cosmic-rays with the earth's atmosphere. Millicharged particles could also be candidates for dark matter, and become relativistic through acceleration by supernova explosion shock waves. The atomic ionization cross section of $χ_q$ with matter are derived with the equivalent photon approximation. Smoking-gun signatures with significant enhancement in the differential cross section are identified. New limits on the mass and charge of $χ_q$ are derived, using data taken with a point-contact germanium detector with 500g mass functioning at an energy threshold of 300~eV at the Kuo-Sheng Reactor Neutrino Laboratory.
In this paper we complete the study of the Lan-Sheng-Zuo conjecture proposed in arXiv:1210.8280 for the curve case. Precisely, we prove that every semistable Higgs bundle is strongly semistable for curves of genus $g\leq 1$, and over any curves of genus $g\ge2$ construct explicit examples of semistable Higgs bundles of arbitrary big rank (the first example is $p=2,r=3$) which are not strongly semistable. These results are complementary to the strongly semistability theorem of Lan-Sheng-Yang-Zuo and Langer for semistable Higgs bundles of small rank.
Three-dimensional (3D) topological ferroelectric (FE) insulators, in which topological and FE orders naturally coexist, enable field-controlled spintronic devices. In this work, we predict a new structure of bismuth monohalides Bi4Br4 and Bi4I4, denoted $γ$ phase, and demonstrate that it is an ideal 3D topological FE insulator. Systematic first-principles calculations confirm the stability and synthesizability of $γ$-Bi4X4 (X=Br, I). Although the noncentrosymmetric $γ$ phase crystallizes in the space group $Cmc2_1$ with no symmetry-based classifications/indicators, the nontrivial topology can be characterized by the spin Chern number (SCN). Spin-resolved Wilson loops show the $s_z$ SCN $C_{s_z}=2$, indicating the spin-resolved topology of a 3D quantum spin Hall insulator state. The $z$-direction polarization can be switched by interlayer sliding, requiring only crossing a small energy barrier. Finally, we design an electrically controlled spin-filter device on bilayer films that can generate a switchable spin-polarized current. Combining a single-phase crystal, a sizable band gap, and robust band topology against FE switching, these bismuth monohalides serve as a prototype of intri
Task failures in prior fine-grained robotic manipulation methods often stem from suboptimal initial grasping, which is critical for subsequent manipulation and reducing the requirement for complex pose adjustments. To address this, we propose Grasp-Pretraining Augmentation (GPA), a general multi-modal learning framework that enhances grasp perception without additional grasp pose data collection and labeling. GPA achieves evident enhancement on RLBench multi-task benchmark (from 79.3% to 84.2%) and ALOHA bimanual manipulation tasks (from 86%/16% to 98%/38%). Although GPA enhances fine-grained grasping performance by leveraging increased model capacity, it incurs computational latency and hinders real-time deployment. To mitigate this limitation, we propose Robotic Attention Mamba (RAM). This architecture synergizes attention mechanisms with state space models (SSMs), effectively capturing complex spatial features while maintaining superior inference efficiency. Our unified GPA-RAM framework balances model capacity with efficiency and applies to both discrete and continuous action generation. GPA-RAM demonstrates superior performance across four robotic systems with diverse camera c
Bose-Einstein condensation of spin-polarized triplet excitons can give rise to an intriguing spin supercurrent, enabling experimental detection of exciton condensation. In this work, we predict that Ta3X8 (X=I, Br) ferromagnetic monolayers are spin-polarized triplet excitonic insulators (EIs), based on the systematic first-principles GW calculations coupled with the Bethe-Salpeter equation (GW+BSE). The single-particle calculations of spin-polarized band structures reveal that these monolayers are bipolar magnetic semiconductors, where the highest valence band and the lowest conduction band possess opposite spin polarization. The two low-energy bands, primarily originating from Ta $d_{z^2}$ orbitals, are almost flat. The same-orbital parity and opposite-spin natures of the band-edge states effectively suppress dielectric screening, promoting the emergence of the EI state. The GW+BSE calculations reveal that the binding energy of the lowest-energy exciton is 1.499 eV for Ta3I8 monolayer and 1.986 eV for Ta3Br8 monolayer. Since both values exceed the respective GW band gaps, these results indicate a strong excitonic instability in these monolayers. A wavefunction analysis confirms th
Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. U
The gravitational interaction between the Milky Way (MW) and the Large Magellanic Cloud (LMC) perturbs the MW halo's density and kinematics, encoding information about both galaxies' masses and structures. We present a suite of 2,848 high-resolution ($10^7$ particles) N-body simulations that systematically vary the mass and shape of both galaxies' haloes. We model how the mean velocities and velocity dispersions of halo stars (30--120 kpc) depend on system parameters, and forecast constraints achievable with current and future observations. Assuming Gaia DR3-level astrometry, 20 km/s radial velocity precision, 10% distance precision, and a sample of $\sim$4,000 RR Lyrae stars, we achieve 1$σ$ uncertainties of $0.11 \times 10^{12} M_\odot$ in MW mass, $2.33 \times 10^{10} M_\odot$ in LMC mass, 2.38 in halo concentration ($c$), and 0.06 in halo flattening ($q$). These correspond to fractional uncertainties of 11%, 16%, 25%, and 6% respectively, relative to fiducial values. Improved Gaia proper motions (DR5) yield modest gains (up to 14%), while adding radial velocities improves constraints by up to 60% relative to using Gaia astrometry alone. Doubling the sample size to $\sim$8,000 s
Despite advances in improving large language model (LLM) to refuse to answer malicious instructions, widely used LLMs remain vulnerable to jailbreak attacks where attackers generate instructions with distributions differing from safety alignment corpora. New attacks expose LLMs' inability to recognize unseen malicious instructions, highlighting a critical distributional mismatch between training data and real-world attacks that forces developers into reactive patching cycles. To tackle this challenge, we propose IMAGINE, a synthesis framework that leverages embedding space distribution analysis to generate jailbreak-like instructions. This approach effectively fills the distributional gap between authentic jailbreak patterns and safety alignment corpora. IMAGINE follows an iterative optimization process that dynamically evolves text generation distributions across iterations, thereby augmenting the coverage of safety alignment data distributions through synthesized data examples. Based on the safety-aligned corpus enhanced through IMAGINE, our framework demonstrates significant decreases in attack success rate on Qwen2.5, Llama3.1, and Llama3.2 without compromising their utility.
Federated Learning (FL) is a distributed machine learning technique that preserves data privacy by sharing only the trained parameters instead of the client data. This makes FL ideal for highly dynamic, heterogeneous, and time-critical applications, in particular, the Internet of Vehicles (IoV) networks. However, FL encounters considerable challenges in such networks owing to the high data and device heterogeneity. To address these challenges, we propose FedCLF, i.e., FL with Calibrated Loss and Feedback control, which introduces calibrated loss as a utility in the participant selection process and a feedback control mechanism to dynamically adjust the sampling frequency of the clients. The envisaged approach (a) enhances the overall model accuracy in case of highly heterogeneous data and (b) optimizes the resource utilization for resource constrained IoV networks, thereby leading to increased efficiency in the FL process. We evaluated FedCLF vis-à-vis baseline models, i.e., FedAvg, Newt, and Oort, using CIFAR-10 dataset with varying data heterogeneity. Our results depict that FedCLF significantly outperforms the baseline models by up to a 16% improvement in high data heterogeneity
This paper reviews the Stochastic Recurrent Neural Network (SRNN) as applied to the light curves of Active Galactic Nuclei by Sheng et al. (2022). Astronomical data have inherent limitations arising from telescope capabilities, cadence strategies, inevitable observing weather conditions, and current understanding of celestial objects. When applying machine learning methods, it is vital to understand the effects of data limitations on our analysis and ability to make inferences. We take Sheng et al. (2022) as a case study, and illustrate the problems and limitations encountered in implementing the SRNN for simulating AGN variability as seen by the Rubin Observatory.