Experimental study of a membrane-based dehumidification cooling system

Membrane-based liquid desiccant dehumidification has attracted increasing interests with elimination of solution droplets carryover problem. A membrane-based hybrid liquid desiccant dehumidification cooling system is developed in this study, which has the ability to remove latent load by a liquid desiccant dehumidification unit and simultaneously to handle sensible load by an evaporative cooling unit. The hybrid system mainly consists of a dehumidifier, a regenerator and an evaporative cooler, calcium chloride is used as liquid desiccant in the system. This paper presents a performance evaluation study of the hybrid system based on experimental data. Series of tests have been conducted to clarify the influences of operating variables and conditions (i.e. desiccant solution concentration ratio, regeneration temperature, inlet air condition, etc.) on the system performance. The experimental results indicate that the system is viable for dehumidification cooling purpose, with which the supply air is provided at temperature of 20.4°C for the inlet air condition at temperature of 34°C and relative humidity of 73%. At desiccant solution concentration ratio of 36%, the thermal COPth of 0.70 and electrical COPel of 2.62 are achieved respectively under steady operating condition

Experimental study of a membrane-based dehumidification cooling system

Membrane-based liquid desiccant dehumidification has attracted increasing interests with elimination of solution droplets carryover problem. A membrane-based hybrid liquid desiccant dehumidification cooling system is developed in this study, which has the ability to remove latent load by a liquid desiccant dehumidification unit and simultaneously to handle sensible load by an evaporative cooling unit. The hybrid system mainly consists of a dehumidifier, a regenerator and an evaporative cooler, calcium chloride is used as liquid desiccant in the system. This paper presents a performance evaluation study of the hybrid system based on experimental data. Series of tests have been conducted to clarify the influences of operating variables and conditions (i.e. desiccant solution concentration ratio, regeneration temperature, inlet air condition, etc.) on the system performance. The experimental results indicate that the system is viable for dehumidification cooling purpose, with which the supply air is provided at temperature of 20.4°C for the inlet air condition at temperature of 34°C and relative humidity of 73%. At desiccant solution concentration ratio of 36%, the thermal COPth of 0.70 and electrical COPel of 2.62 are achieved respectively under steady operating condition

Structural and colored disruption as camouflage strategies in two sympatric Asian box turtle species (<i>Cuora</i> spp.)

Disruptive coloration is a common camouflage strategy that breaks body outlines and ostensibly blends into complex backgrounds. However, the contrasting false edge caused by the animal's structure can also break the outline, and there is no empirical evidence to support this strategy. Here, we examined the Gabor edge disruption ratio (GabRat) of two species with divergent carapaces, the keeled box turtle (Cuora mouhotii) and the Indochinese box turtle (C. galbinifrons), on preferred (e.g., deciduous leaves) and non-preferred (i.e., grass) substrates. We quantified edge disruption in different substrates to compare between-species differences in the GabRat of disruptive coloration among the turtles’ preferred and non-preferred (control) substrates. We found that both species exhibited higher GabRat on preferred substrates, but interestingly, the keeled box turtle, with a uniformly colored carapace containing flat scutes and two keels, had a higher GabRat than the Indochinese box turtle, characterized by two yellow stripes on its carapace. Our results indicated that the strong brightness gradients caused by the directional illumination of the flatted and keeled carapace creates disruptive coloration in the keeled box turtle, whereas a high chroma contrast creates disruptive coloration in the Indochinese box turtle. For these turtles, the structural modifications result in variations in brightness that lead to higher levels of disruption than the chromatic disruption of the Indochinese box turtle. Our study provides, to our knowledge, the first evidence of disruptive camouflage in turtles and the first comprehensive test of structural and colored disruption in vertebrates

Punica: Multi-Tenant LoRA Serving

Low-rank adaptation (LoRA) has become an important and popular method to adapt pre-trained models to specific domains. We present Punica, a system to serve multiple LoRA models in a shared GPU cluster. Punica contains a new CUDA kernel design that allows batching of GPU operations for different LoRA models. This allows a GPU to hold only a single copy of the underlying pre-trained model when serving multiple, different LoRA models, significantly enhancing GPU efficiency in terms of both memory and computation. Our scheduler consolidates multi-tenant LoRA serving workloads in a shared GPU cluster. With a fixed-sized GPU cluster, our evaluations show that Punica achieves 12x higher throughput in serving multiple LoRA models compared to state-of-the-art LLM serving systems while only adding 2ms latency per token. Punica is open source at https://github.com/punica-ai/punica

Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells

Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation. Despite the excellent performance, existing methods ignore the fact that a suitable depth geometry is also critical in MVS. In this paper, we demonstrate that different depth geometries have significant performance gaps, even using the same depth prediction error. Therefore, we introduce an ideal depth geometry composed of Saddle-Shaped Cells, whose predicted depth map oscillates upward and downward around the ground-truth surface, rather than maintaining a continuous and smooth depth plane. To achieve it, we develop a coarse-to-fine framework called Dual-MVSNet (DMVSNet), which can produce an oscillating depth plane. Technically, we predict two depth values for each pixel (Dual-Depth), and propose a novel loss function and a checkerboard-shaped selecting strategy to constrain the predicted depth geometry. Compared to existing methods,DMVSNet achieves a high rank on the DTU benchmark and obtains the top performance on challenging scenes of Tanks and Temples, demonstrating its strong performance and generalization ability. Our method also points to a new research direction for considering depth geometry in MVS.Comment: Accepted by ICCV 202

Towards Efficient Communications in Federated Learning: A Contemporary Survey

In the traditional distributed machine learning scenario, the user's private data is transmitted between nodes and a central server, which results in great potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a special distributed machine learning with a privacy protection mechanism, which can realize multi-party collaborative computing without revealing the original data. However, in practice, FL faces many challenging communication problems. This review aims to clarify the relationship between these communication problems, and focus on systematically analyzing the research progress of FL communication work from three perspectives: communication efficiency, communication environment, and communication resource allocation. Firstly, we sort out the current challenges existing in the communications of FL. Secondly, we have compiled articles related to FL communications, and then describe the development trend of the entire field guided by the logical relationship between them. Finally, we point out the future research directions for communications in FL

MathAttack: Attacking Large Language Models Towards Math Solving Ability

With the boom of Large Language Models (LLMs), the research of solving Math Word Problem (MWP) has recently made great progress. However, there are few studies to examine the security of LLMs in math solving ability. Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of security in solving math problems. Compared to traditional text adversarial attack, it is essential to preserve the mathematical logic of original MWPs during the attacking. To this end, we propose logical entity recognition to identify logical entries which are then frozen. Subsequently, the remaining text are attacked by adopting a word-level attacker. Furthermore, we propose a new dataset RobustMath to evaluate the robustness of LLMs in math solving ability. Extensive experiments on our RobustMath and two another math benchmark datasets GSM8K and MultiAirth show that MathAttack could effectively attack the math solving ability of LLMs. In the experiments, we observe that (1) Our adversarial samples from higher-accuracy LLMs are also effective for attacking LLMs with lower accuracy (e.g., transfer from larger to smaller-size LLMs, or from few-shot to zero-shot prompts); (2) Complex MWPs (such as more solving steps, longer text, more numbers) are more vulnerable to attack; (3) We can improve the robustness of LLMs by using our adversarial samples in few-shot prompts. Finally, we hope our practice and observation can serve as an important attempt towards enhancing the robustness of LLMs in math solving ability. We will release our code and dataset.Comment: 11 pages, 6 figure