Gr\"uneisen parameters for Lieb-Liniger and Yang-Gaudin models

Using the Bethe ansatz solution, we analytically study expansionary, magnetic and interacting Gr\"uneisen parameters (GPs) for one-dimensional (1D) Lieb-Liniger and Yang-Gaudin models. These different GPs elegantly quantify the dependences of characteristic energy scales of these quantum gases on the volume, the magnetic field and the interaction strength, revealing the caloric effects resulted from the variations of these potentials. The obtained GPs further confirm an identity which is incurred by the symmetry of the thermal potential. We also present universal scaling behavior of these GPs in the vicinities of the quantum critical points driven by different potentials. The divergence of the GPs not only provides an experimental identification of non-Fermi liquid nature at quantum criticality but also elegantly determine low temperature phases of the quantum gases. Moreover, the pairing and depairing features in the 1D attractive Fermi gases can be captured by the magnetic and interacting GPs, facilitating experimental observation of quantum phase transitions. Our results open to further study the interaction- and magnetic-field-driven quantum refrigeration and quantum heat engine in quantum gases of ultracold atoms.Comment: 6 figures, 13 page

Genetic learning particle swarm optimization

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO

Deriving Weeklong Activity-Travel Dairy from Google Location History: Survey Tool Development and A Field Test in Toronto

This paper introduces an innovative travel survey methodology that utilizes Google Location History (GLH) data to generate travel diaries for transportation demand analysis. By leveraging the accuracy and omnipresence among smartphone users of GLH, the proposed methodology avoids the need for proprietary GPS tracking applications to collect smartphone-based GPS data. This research enhanced an existing travel survey designer, Travel Activity Internet Survey Interface (TRAISI), to make it capable of deriving travel diaries from the respondents' GLH. The feasibility of this data collection approach is showcased through the Google Timeline Travel Survey (GTTS) conducted in the Greater Toronto Area, Canada. The resultant dataset from the GTTS is demographically representative and offers detailed and accurate travel behavioural insights.Comment: The manuscript has been accepted for presentation at the 103rd Transportation Research Board (TRB) Annual Meeting, January 7-11, 202

Novel Ir-X thermal protection coatings designed for extreme aerodynamic heating environment

Due to the rapid evaporation of SiO2 protective layer, most Si-containing oxidation resistant coatings could not withstand a temperature above 1800℃, which is not enough for hypersonic voyage in upper atmosphere. With a higher melting point (2440℃) and lower oxygen permeability（10-20g·m-1·s-1）, iridium is supposed to be a promising coating material for ultra-high temperature applications. However, Iridium has a low emissivity ε(0.017 for 2.5-25μm) and high recombination coefficient γ(0.64 at 1200℃) of atomic oxygen, resulting in a much higher thermal response compared with the ceramic materials under the same aerodynamic environment. To solve this problem, elements such as Al, Cr, Zr etc. were selected to modify pure Ir to form Ir-X (X=Al, Cr or Zr) coating. The modification element X in Ir-X coating forms high emissivity and low recombination coeffcient oxide on Ir, which meanwhile prevents the Ir from atomic oxygen. It was found that Ir-Al, Ir-Cr, Ir-Ti, Ir-Zr, Ir-Ta and Ir-Hf diffusion coating could be prepared via pack cementation. The recombination coefficient and emissivity of as-oxidized Ir-Al were changed to 0.0089 and 0.723, respectively. Please click Additional Files below to see the full abstract

Can I Trust Your Answer? Visually Grounded Video Question Answering

We study visually grounded VideoQA in response to the emerging trends of utilizing pretraining techniques for video-language understanding. Specifically, by forcing vision-language models (VLMs) to answer questions and simultaneously provide visual evidence, we seek to ascertain the extent to which the predictions of such techniques are genuinely anchored in relevant video content, versus spurious correlations from language or irrelevant visual context. Towards this, we construct NExT-GQA -- an extension of NExT-QA with 10.5$K$ temporal grounding (or location) labels tied to the original QA pairs. With NExT-GQA, we scrutinize a variety of state-of-the-art VLMs. Through post-hoc attention analysis, we find that these models are weak in substantiating the answers despite their strong QA performance. This exposes a severe limitation of these models in making reliable predictions. As a remedy, we further explore and suggest a video grounding mechanism via Gaussian mask optimization and cross-modal learning. Experiments with different backbones demonstrate that this grounding mechanism improves both video grounding and QA. Our dataset and code are released. With these efforts, we aim to push towards the reliability of deploying VLMs in VQA systems.Comment: Preprint. Data and code: https://github.com/doc-doc/NExT-GQ