Effects of degree distribution in mutual synchronization of neural networks

We study the effects of the degree distribution in mutual synchronization of two-layer neural networks. We carry out three coupling strategies: large-large coupling, random coupling, and small-small coupling. By computer simulations and analytical methods, we find that couplings between nodes with large degree play an important role in the synchronization. For large-large coupling, less couplings are needed for inducing synchronization for both random and scale-free networks. For random coupling, cutting couplings between nodes with large degree is very efficient for preventing neural systems from synchronization, especially when subnetworks are scale-free.Comment: 5 pages, 4 figure

Spectral function of the electron in a superconducting RVB state

We present a model calculation of the spectral function of an electron in a superconducting resonating valence bond (RVB) state. The RVB state, described by the phase-string mean field theory is characterized by three important features: (i) spin-charge separation, (ii) short range antiferromagnetic correlations, and (iii) holon condensation. The results of our calculation are in good agreement with data obtained from Angle Resolved Photoemission Spectroscopy (ARPES) in superconducting Bi 2212 at optimal doping concentration.Comment: 4 pages, 3 figure

Control of Anticoagulation Therapy in Patients with Atrial Fibrillation Treated with Warfarin:A Study from the Chinese Atrial Fibrillation Registry

Background: Several factors determine the efficacy of warfarin anticoagulation in patients with non-valvular atrial fibrillation (NVAF). This study aimed to use data from the Chinese Atrial Fibrillation Registry study to assess the control of anticoagulation therapy in Chinese patients with NVAF treated with warfarin. Material/Methods: From the Chinese Atrial Fibrillation Registry study the anticoagulant use and dosing, the time in therapeutic range (TTR) of the international normalized ratio (INR), and standard deviation of the observed INR values (SD INR), and their influencing factors were evaluated. Results: The median INR and SD INR were 2.04 (IQR 1.71–2.41) and 0.50 (IQR, 0.35–0.69), respectively. The median TTR was 51.7% (IQR, 30.6–70.1%) and only 25.1% had a TTR ≥70%. Age was ≥70 years (OR, 0.72; 95% CI, 0.55–0.94; P=0.015), bleeding history (OR 0.48; 95% CI, 0.23–0.89; P=0.029), the use of a single drug (OR, 0.62; 95% CI, 0.42–0.92; P=0.016), more than drug (OR, 0.60; 95% CI, 0.41–0.88; P=0.009), and lack of assessment of bleeding risk (OR, 0.72; 95% CI, 0.54–0.97; P=0.033) were associated with TTR &lt;70% (INR 2.0–3.0). Coronary heart disease (CHD) and peripheral artery disease (PAD) (OR, 0.69; 95% CI, 0.52–0.90; P=0.007) and diabetes mellitus (OR, 0.79; 95% CI, 0.62–0.99; P=0.044) were associated with increased variability in INR (SD INR ≥0.5). Conclusions: In Chinese patients with NVAF, warfarin anticoagulation was associated with lower TTR and less stable anticoagulation than in current guidelines, and risk factors for reduced safety and efficacy were identified. </p

Leveraging Large Language Models for Pre-trained Recommender Systems

Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectively integrating LLM's commonsense knowledge and reasoning abilities into recommendation systems remains a challenging problem. In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs. RecSysLLM retains LLM reasoning and knowledge while integrating recommendation domain knowledge through unique designs of data, training, and inference. This allows RecSysLLM to leverage LLMs' capabilities for recommendation tasks in an efficient, unified framework. We demonstrate the effectiveness of RecSysLLM on benchmarks and real-world scenarios. RecSysLLM provides a promising approach to developing unified recommendation systems by fully exploiting the power of pre-trained language models.Comment: 13 pages, 4 figure

Spectra of Baryons Containing Two Heavy Quarks in Potential Model

In this work, we employ the effective vertices for interaction between diquarks (scalar or axial-vector) and gluon where the form factors are derived in terms of the B-S equation, to obtain the potential for baryons including a light quark and a heavy diquark. The concerned phenomenological parameters are obtained by fitting data of $B^{(*)}-$mesons instead of the heavy quarkonia. The operator ordering problem in quantum mechanics is discussed. Our numerical results indicate that the mass splitting between $B_{3/2}(V), B_{1/2}(V)$ and $B_{1/2}(S)$ is very small and it is consistent with the heavy quark effective theory (HQET).Comment: 16 page

Enhancing Recommender Systems with Large Language Model Reasoning Graphs

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.Comment: 12 pages, 6 figure

Mesoscopic Effects in the Quantum Hall Regime

We report results of a study of (integer) quantum Hall transitions in a single or multiple Landau levels for non-interacting electrons in disordered two-dimensional systems, obtained by projecting a tight-binding Hamiltonian to corresponding magnetic subbands. In finite-size systems, we find that mesoscopic effects often dominate, leading to apparent non-universal scaling behaviour in higher Landau levels. This is because localization length, which grows exponentially with Landau level index, exceeds the system sizes amenable to numerical study at present. When band mixing between multiple Landau levels is present, mesoscopic effects cause a crossover from a sequence of quantum Hall transitions for weak disorder to classical behaviour for strong disorder. This behaviour may be of relevance to experimentally observed transitions between quantum Hall states and the insulating phase at low magnetic fields.Comment: 13 pages, 6 figures, Proceedings of the International Meeting on Mesoscopic and Disordered Systems, Bangalore December 2000, to appear in Pramana, February 200

Constitutional Flavonoids Derived from Epimedium Dose-Dependently Reduce Incidence of Steroid-Associated Osteonecrosis Not via Direct Action by Themselves on Potential Cellular Targets

Intravascular-thrombosis and extravascular-lipid-deposit are the two key pathogenic events considered to interrupt intraosseous blood supply during development of steroid-associated osteonecrosis (ON). However, there are no clinically employed agents capable of simultaneously targeting these two key pathogenic events. The present experimental study demonstrated that constitutional flavonoid glycosides derived from herb Epimedium (EF, composed of seven flavonoid compounds with common stem nuclear) exerted dose-dependent effect on inhibition of both thrombosis and lipid-deposition and accordingly reducing incidence of steroid-associated ON in rabbits, which was not via direct action by themselves rather by their common metabolite on potential cellular targets involved in the two pathogenic pathways. The underlying mechanism could be explained by counteracting endothelium injury and excessive adipogenesis. These findings encourage designing clinical trials to investigate potential of EF in prevention of steroid-associated ON