Constraining interacting dark energy models with the halo concentration - mass relation

The interacting dark energy (IDE) model is a promising alternative cosmological model which has the potential to solve the fine-tuning and coincidence problems by considering the interaction between dark matter and dark energy. Previous studies have shown that the energy exchange between the dark sectors in this model can significantly affect the dark matter halo properties. In this study, utilising a large set of cosmological $N$-body simulations, we analyse the redshift evolution of the halo concentration - mass ($c$ - $M$) relation in the IDE model, and show that the $c$ - $M$ relation is a sensitive proxy of the interaction strength parameter $\xi_2$, especially at lower redshifts. Furthermore, we construct parametrized formulae to quantify the dependence of the $c$ - $M$ relation on $\xi_2$ at redshifts ranging from $z=0$ to $0.6$. Our parametrized formulae provide a useful tool in constraining $\xi_2$ with the observational $c$ - $M$ relation. As a first attempt, we use the data from X-ray, gravitational lensing, and galaxy rotational curve observations and obtain a tight constraint on $\xi_2$, i.e. $\xi_2 = 0.071 \pm 0.034$. Our work demonstrates that the halo $c$ - $M$ relation, which reflects the halo assembly history, is a powerful probe to constrain the IDE model.Comment: 9 pages, 5 figures, 5 table

Dark matter haloes in interacting dark energy models : formation history, density profile, spin, and shape

The interacting dark energy (IDE) model, which considers the interaction between dark energy and dark matter, provides a natural mechanism to alleviate the coincidence problem and can also relieve the observational tensions under the ?CDM model. Previous studies have put constraints on IDE models by observations of cosmic expansion history, cosmic microwave background, and large-scale structures. However, these data are not yet enough to distinguish IDE models from ?CDM effectively. Because the non-linear structure formation contains rich cosmological information, it can provide additional means to differentiate alternative models. In this paper, based on a set of N-body simulations for IDE models, we investigate the formation histories and properties of dark matter haloes and compare with their ?CDM counterparts. For the model with dark matter decaying into dark energy and the parameters being the best-fitting values from previous constraints, the structure formation is markedly slowed down, and the haloes have systematically lower mass, looser internal structure, higher spin, and anisotropy. This is inconsistent with the observed structure formation, and thus this model can be safely ruled out from the perspective of non-linear structure formation. Moreover, we find that the ratio of halo concentrations between IDE and ?CDM counterparts depends sensitively on the interaction parameter and is independent of halo mass. This can act as a powerful probe to constrain IDE models. Our results concretely demonstrate that the interaction of the two dark components can affect the halo formation considerably, and therefore the constraints from non-linear structures are indispensable.Peer reviewe

Language Models as Black-Box Optimizers for Vision-Language Models

Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities across a variety of vision and multimodal tasks. Currently, fine-tuning methods for VLMs mainly operate in a white-box setting, requiring access to model parameters for backpropagation. However, many VLMs rely on proprietary data and are not open-source, which restricts the use of white-box approaches for fine-tuning. Given that popular private large language models (LLMs) like ChatGPT still offer a language-based user interface, we aim to develop a novel fine-tuning approach for VLMs through natural language prompts, thereby avoiding the need to access model parameters, feature embeddings, or output logits. In this setup, we propose employing chat-based LLMs as black-box optimizers to search for the best text prompt on the illustrative task of few-shot image classification using CLIP. Specifically, we adopt an automatic "hill-climbing" procedure that converges on an effective prompt by evaluating the accuracy of current prompts and asking LLMs to refine them based on textual feedback, all within a conversational process without human-in-the-loop. In a challenging 1-shot learning setup, our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of 1.5% across 11 datasets including ImageNet. Our approach also outperforms OpenAI's manually crafted prompts. Additionally, we highlight the advantage of conversational feedback that incorporates both positive and negative prompts, suggesting that LLMs can utilize the implicit "gradient" direction in textual feedback for a more efficient search. Lastly, we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different CLIP architectures in a black-box manner

Periodicity of chaotic trajectories in realizations of finite computer precisions and its implication in chaos communications

Fundamental problems of periodicity and transient process to periodicity of chaotic trajectories in computer realization with finite computation precision is investigated by taking single and coupled Logistic maps as examples. Empirical power law relations of the period and transient iterations with the computation precisions and the sizes of coupled systems are obtained. For each computation we always find, by randomly choosing initial conditions, a single dominant periodic trajectory which is realized with major portion of probability. These understandings are useful for possible applications of chaos, e.g., chaotic cryptography in secure communication.Comment: 10 pages, 3 figures, 2 table

Lipid Extraction From Spirulina sp. and Schizochytrium sp. Using Supercritical CO2 With Methanol

Microalgae are one of the most promising feedstocks for biodiesel production due to their high lipid content and easy farming. However, the extraction of lipids from microalgae is energy intensive and costly and involves the use of toxic organic solvents. Compared with organic solvent extraction, supercritical CO2 (SCCO2) has demonstrated advantages through lower toxicity and no solvent-liquid separation. Due to the nonpolar nature of SCCO2, polar organic solvents such as methanol may need to be added as a modifier in order to increase the extraction ability of SCCO2. In this paper, pilot scale lipid extraction using SCCO2 was studied on two microalgae species: Spirulina sp. and Schizochytrium sp. For each species, SCCO2 extraction was conducted on 200 g of biomass for 6 h. Methanol was added as a cosolvent in the extraction process based on a volume ratio of 4%. The results showed that adding methanol in SCCO2 increased the lipid extraction yield significantly for both species. Under an operating pressure of 4000 psi, the lipid extraction yields for Spirulina sp. and Schizochytrium sp. were increased by 80% and 72%, respectively. It was also found that a stepwise addition of methanol was more effective than a one-time addition. In comparison with Soxhlet extraction using methylene chloride/methanol (2:1, v/v), the methanol-SCCO2 extraction demonstrated its high effectiveness for lipid extraction. In addition, the methanol-SCCO2 system showed a high lipid extraction yield after increasing biomass loading fivefold, indicating good potential for scaling up this method. Finally, a kinetic study of the SCCO2 extraction process was conducted, and the results showed that methanol concentration in SCCO2 has the strongest influence on the lipid extraction yield

Investigation of Electrolytic Flocculation for Microalga Scenedesmus sp Using Aluminum and Graphite Electrodes

Electrolytic flocculation using non-sacrificial electrodes with flocculants added was studied on harvesting Scenedesmus sp. In order to optimize the operating conditions of the electrolytic flocculation process and to quantify the amount of flocculants added, aluminum electrodes were first used in the process. It was found that under optimal conditions, the microalgae removal efficiency using aluminum electrodes could reach 98.5%, while 34.2 mg L-1 of aluminum ions were released during the process. Different metal electrodes were also studied, but high microalgae removal efficiency was witnessed only using aluminum electrodes, indicating the influence of the aluminum ion in flocculation. When non-sacrificial graphite electrodes were used in the electrolytic flocculation process, the corresponding amount of aluminum sulfate was added so that the aluminum ion concentration in water was also equal to 34.2 mg L-1. The result showed that the microalgae removal efficiency of graphite electrodes could reach above 90% after aluminum sulfate was added. In contrast, using graphite electrodes alone and using the metal salt alone only yielded 22.9% and 7.1% of microalgae removal efficiency, respectively. These results indicated that the presence of metal ions is necessary in the electrolytic flocculation process. The energy consumption of the process was found to be 0.3 kW h m-3 or 0.88 kW h kg-1, which is considered to be low energy consumption. The total cost of the process, including energy and chemicals, was found to be $ 0.21 m-3, proving a cost competitive method in microalgae harvesting

Interparental Conflict Relative to Suicidal Ideation in Chinese Adolescents: The Roles of Coping Strategies and Meaning in Life

The aim of this study was to explore the paths between interparental conflict and Chinese adolescents’ suicidal ideation. Altogether 931 adolescents (Mage = 17.84, SD = 0.77, females = 531) completed the Dyadic Consensus Scale, Self-Report Coping Scale, Meaning in Life Questionnaire, and Positive and Negative Suicide Ideation questionnaires. Mediation analyses were conducted, focusing on the relations between interparental conflict and suicidal ideation along with coping styles and a sense of meaning in life. The results showed that interparental conflict indirectly predicted adolescents’ suicidal ideation via three mediators: coping-approach strategies, presence of meaning, and the joint serial effects of coping-approach strategies and presence of meaning in Chinese adolescents. In addition, boys were more likely to be at risk for suicidal ideation than girls, so were 10th graders compared to 11th graders. These findings supported a combined distress-to-meaninglessness line of thinking along with the use of coping-approach strategies to depress self-harm ideation. Generally, interparental conflict should be kept out of youngsters’ immediate vicinity as a preventive measure of suicidal ideation