Combinatorial Identities on Multinomial Coefficients and Graph Theory

We study combinatorial identities on multinomial coefficients. In particular, we present several new ways to count the connected labeled graphs using multinomial coefficients

Feature Investigation for Stock Returns Prediction Using XGBoost and Deep Learning Sentiment Classification

This paper attempts to quantify predictive power of social media sentiment and financial data in stock prediction by utilizing a comprehensive set of stock-related fundamental and technical variables and social media sentiments. For conducting sentiment analysis, this study employs a pretrained finBERT model that provides three different sentiment classifications and respective softmax scores. Hence, the significance of these variables is evaluated with XGBoost regression and Shapley Additive exPlanations (SHAP) frameworks. Through investigating feature importance, this study finds that statistical properties of sentiment variables provide a stronger predictive power than a weighted sentiment score and that it is possible to quantify the impact features make on so-called “black box” models

Critical flux-based membrane fouling control of forward osmosis: Behavior, sustainability, and reversibility

Membrane fouling is closely related to the concept of critical flux. Therefore, a fouling control strategy for forward osmosis (FO) membranes that is based on the critical flux is necessary. This study systematically investigated the critical flux behavior of FO membranes (CTA and PA-TFC) in the short-term using a stepping method (draw solution (DS) concentration stepping). In addition, to test the reliability of this method, long-term experiments were conducted to evaluate the influences of operational critical flux on the fouling behavior (sustainable operation and fouling reversibility/irreversibility), thereby determining the critical flux for reversibility. Our results showed that the DS concentration stepping could be applied for critical flux determination in FO. Both membranes exhibited higher critical flux values for alginate fouling compared to other single foulants such as colloidal silica or gypsum. The values were 15.9 LMH for a cellulose triacetate membrane (CTA) and 20.5 LMH for the polyamide thin-film composite (PA-TFC). Whilst these values should be adequate in FO applications they were determined for single foulants. The presence of multispecies of foulants caused a significant decline in the critical flux values. This study found 5.4 LMH for the CTA membrane and 8.3 LMH for the PA-TFC membrane for the combined foulants of alginate + gypsum. This indicates that the critical flux behavior in FO was dependent on the foulant type and membrane type. Importantly, the high restoration of water flux was achieved with the PA-TFC membrane at an operation either close to critical flux (92–98%) or below critical flux (98–100%) (i.e., with negligible irreversible fouling). The critical fluxes for reversibility obtained in this study will aid the efficient operation of practical FO processes

Electricity Demand Profile of Australian Low Energy Houses

AbstractThe paper demonstrates the profiles of electricity consumption in the low energy housing sector using various time frames, and provides a solid basis for energy estimation by analysing actual 12 month electricity data from 60 comprehensively monitored low energy houses in Australia's leading sustainable green village (Lochiel Park), located in South Australia. The results highlight that although considerable electricity reduction is achieved in low energy houses, the outdoor ambient air temperature is still a highly influential factor that determines the total and peak demand in these houses. It also suggests that energy estimation should focus on residents’ basic life style and appliance usage behaviour. The results presented here can be used to refine end-use electricity demand modelling for low energy houses in South Australia, and can hence assist the design of electrical infrastructure requirements in new low energy housing developments

Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels

This study demonstrates a cost-effective approach to semantic segmentation using self-supervised vision transformers (SSVT). By freezing the SSVT backbone and training a lightweight segmentation head, our approach effectively utilizes imperfect labels, thereby improving robustness to label imperfections. Empirical experiments show significant performance improvements over existing methods for various annotation types, including scribble, point-level, and image-level labels. The research highlights the effectiveness of self-supervised vision transformers in dealing with imperfect labels, providing a practical and efficient solution for semantic segmentation while reducing annotation costs. Through extensive experiments, we confirm that our method outperforms baseline models for all types of imperfect labels. Especially under the zero-shot vision-language-model-based label, our model exhibits 11.5\%p performance gain compared to the baseline.Comment: AAAI2024 Edge Intelligence Workshop (EIW) accepte

CMB Spectral μ\mu-Distortion of Multiple Inflation Scenario

In multiple inflation scenario having two inflations with an intermediate matter-dominated phase, the power spectrum is estimated to be enhanced on scales smaller than the horizon size at the beginning of the second inflation, $k > k_{\rm b}$. We require $k_{\rm b} > 10 {\rm Mpc}^{-1}$ to make sure that the enhanced power spectrum is consistent with large scale observation of cosmic microwave background (CMB). We consider the CMB spectral distortions generated by the dissipation of acoustic waves to constrain the power spectrum. The $\mu$-distortion value can be $10$ times larger than the expectation of the standard $\Lambda$CDM model ($\mu_{\Lambda\mathrm{CDM}} \simeq 2 \times 10^{-8}$) for $k_{\rm b} \lesssim 10^3 {\rm Mpc}^{-1}$, while the $y$-distortion is hardly affected by the enhancement of the power spectrum.Comment: 16 pages, 5 figure