CSD: Discriminance with Conic Section for Improving Reverse k Nearest Neighbors Queries

The reverse $k$ nearest neighbor (R$k$NN) query finds all points that have the query point as one of their $k$ nearest neighbors ($k$NN), where the $k$NN query finds the $k$ closest points to its query point. Based on the characteristics of conic section, we propose a discriminance, named CSD (Conic Section Discriminance), to determine points whether belong to the R$k$NN set without issuing any queries with non-constant computational complexity. By using CSD, we also implement an efficient R$k$NN algorithm CSD-R$k$NN with a computational complexity at $O(k^{1.5}\cdot log\,k)$. The comparative experiments are conducted between CSD-R$k$NN and other two state-of-the-art RkNN algorithms, SLICE and VR-R$k$NN. The experimental results indicate that the efficiency of CSD-R$k$NN is significantly higher than its competitors

Transport of titanium dioxide nanoparticles in saturated porous media under various solution chemistry conditions

Because of its wide applications, nanosized titanium dioxide may become a potential environmental risk to soil and groundwater system. It is therefore important to improve current understanding of the environmental fate and transport of titanium oxides nanoparticles (TONPs). In this work, the effect of solution chemistry (i.e., pH, ionic strength, and natural organic matter (NOM) concentration) on the deposition and transport of TONPs in saturated porous media was examined in detail. Laboratory columns packed with acid-cleaned quartz sand were used in the experiment as porous media. Transport experiments were conducted with various chemistry combinations, including four ionic strengths, three pH levels, and two NOM concentrations. The results showed that TONP mobility increased with increasing solution pH, but decreased with increasing solution ionic strength. It is also found that the presence of NOM in the system enhanced the mobility of TONPs in the saturated porous media. The Derjaguin–Landau–Verwey–Overbeek (DLVO) theory was used to justify the mobility trends observed in the experimental data. Predictions from the theory agreed excellently with the experimental data

ηQ\eta_{Q} meson photoproduction in ultrarelativistic heavy ion collisions

The transverse momentum distributions for inclusive $\eta_{c,b}$ meson described by gluon-gluon interactions from photoproduction processes in relativistic heavy ion collisions are calculated. We considered the color singlet (CS) and color octet (CO) components with the framework of non-relativistic Quantum Chromodynamics (NRQCD) into the production of heavy quarkonium. The phenomenological values of the matrix elements for the color-singlet and color-octet components give the main contribution to the production of heavy quarkonium from the gluon-gluon interaction caused by the emission of additional gluon in the initial state. The numerical results indicate that the contribution of photoproduction processes cannot be negligible for mid-rapidity in p-p and Pb-Pb collisions at the Large Hadron Collider (LHC) energies.Comment: 11 pages, 2 figure

Measuring Value Understanding in Language Models through Discriminator-Critique Gap

Recent advancements in Large Language Models (LLMs) have heightened concerns about their potential misalignment with human values. However, evaluating their grasp of these values is complex due to their intricate and adaptable nature. We argue that truly understanding values in LLMs requires considering both "know what" and "know why". To this end, we present the Value Understanding Measurement (VUM) framework that quantitatively assesses both "know what" and "know why" by measuring the discriminator-critique gap related to human values. Using the Schwartz Value Survey, we specify our evaluation values and develop a thousand-level dialogue dataset with GPT-4. Our assessment looks at both the value alignment of LLM's outputs compared to baseline answers and how LLM responses align with reasons for value recognition versus GPT-4's annotations. We evaluate five representative LLMs and provide strong evidence that the scaling law significantly impacts "know what" but not much on "know why", which has consistently maintained a high level. This may further suggest that LLMs might craft plausible explanations based on the provided context without truly understanding their inherent value, indicating potential risks