Nonlinear Energy Harvesting Under White Noise

While purposeful introduction of stiffness nonlinearities into the dynamics of energy harvesters is aimed at enhancing performance under non-stationary and random excitations, most of the conclusions reported in the current literature are based on the steady-state response which assumes a harmonic fixed-frequency excitation. As a result, we still do not have a clear understanding of how the nature of the excitation influences the output power, or what role stiffness nonlinearities play in the transduction of energy harvesters under random excitations. To fill this gap in the current knowledge, this thesis investigates the response of nonlinear mono- and bi-stable energy harvesters to environmental excitations that can be approximated via a white noise process. For the mono-stable case, statistical linearization is utilized to analytically approximate the statistical averages of the response. The influence of the nonlinearity and the symmetry of the restoring force on the mean power is investigated under optimal electric loading conditions. It is shown that the nonlinearity has no influence on the output power unless the ratio between the time constant of the harvesting circuit and the period of the mechanical oscillator is small. In such case, a mono-stable harvester with a symmetric nonlinear restoring force can never produce higher mean power levels than an equivalent linear harvester regardless of the magnitude or nature of the nonlinearity. On the other hand, asymmetries in the restoring force are shown to provide performance improvements over an equivalent linear harvester. For energy harvesters with a bi-stable potential function, statistical linearization, direct numerical integration of the stochastic differential equations, and finite element solution of the Fokker-Plank-Kolmogorov equation governing the response probability density function are utilized to understand how the shape and symmetry of the potential energy function influence the mean output power of the harvester. It is observed that, both of the finite element solution and the direct numerical integration provide close predictions for the mean power regardless of the shape of the potential energy function. Statistical linearization, on the other hand, yields non-unique and erroneous predictions unless the potential energy function has shallow potential wells. It is shown that the mean power exhibits a maximum value at an optimal potential shape. This optimal shape is not directly related to the shape that maximizes the mean square displacement even when the time constant ratio, i.e., ratio between the time constants of the mechanical and electrical systems is small. Maximizing the mean square displacement yields a potential shape with a global maximum (unstable potential) for any value of the time constant ratio and any noise intensity, whereas maximizing the average power yields a bi-stable potential which possesses deeper potential wells for larger noise intensities and vise versa. Away from the optimal shape, the mean power drops significantly highlighting the importance of characterizing the noise intensity of the vibration source prior to designing a bi-stable harvester for the purpose of harnessing energy from white noise excitations. Furthermore, it is demonstrated that, the optimal time constant ratio is not necessarily small which challenges previous conceptions that a bi-stable harvester provides better output power when the time constant ratio is small. While maximum variation of the mean power with the nonlinearity occurs for smaller values of the time constant ratio, these values do not necessarily correspond to the optimal performance of the harvester. Finally, it is shown that asymmetries in the potential shape of bi-stable harvesters do not improve the mean power unless the symmetric potential function is designed away from its optimal parameters

Operation Analysis on Refrigeration System Combined with Heat Pipe

In data center, telco equipment station and some central control rooms, there are some cases with cooling requirement all the year, their temperatures are usually controlled strictly whenever in summer or in winter. Moreover, due to clearness and safety requirement, the chiller should provide cooling water all the time even the outside environment is very cold. There had been some considerations about provide cooling capacity in cold climate with free environmental air by various heat exchangers directly. Without operating chillers, annual energy consumption on temperature control could be decreased. This paper proposed a system combined a chiller with an air-cooled heat exchanger, shown as Fig 1., there is one evaporator to provide cooled water, two condensers for the chiller and the separate type heat pipe alternatively. In warm climate, the chiller operates normally with the compressor and its condenser, the air cooling heat pipe is blocked by switching valves. In cold climate, the system is switched to the no compressor mode, the air-cooler heat exchanger could connect with the evaporator directly, the work pattern follows the separated type heat pipe, the working medium is the refrigerant still, the air cooling heat exchanger serves as the condenser of the separated type heat pipe, therefore, the evaporator is the evaporating part of the separated type heat pipe as well, the cooled water could be delivered through normal pipelines. In application of the combined refrigeration, they operation strategy for refrigeration and the heat pipe should be discussed in different climate zones. Therefore, in this paper we discussed energy consumption for cooling requirement in data center at several typical regions based on the combined cooling system. In different regions, their climates condition are different, the switching between the normal refrigeration and the heat pipe results to various annual energy consumptions

Attack Prompt Generation for Red Teaming and Defending Large Language Models

Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or automatic methods, which have their own limitations on construction cost and quality. To address these issues, we propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts. Specifically, considering the impressive capabilities of newly emerged LLMs, we propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning. Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework to enhance their safety against red teaming attacks. Extensive experiments on different LLMs validate the effectiveness of our proposed attack and defense frameworks. Additionally, we release a series of attack prompts datasets named SAP with varying sizes, facilitating the safety evaluation and enhancement of more LLMs. Our code and dataset is available on https://github.com/Aatrox103/SAP .Comment: Accepted to EMNLP 2023 (Findings

CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation

Leveraging Large Language Models as Recommenders (LLMRec) has gained significant attention and introduced fresh perspectives in user preference modeling. Existing LLMRec approaches prioritize text semantics, usually neglecting the valuable collaborative information from user-item interactions in recommendations. While these text-emphasizing approaches excel in cold-start scenarios, they may yield sub-optimal performance in warm-start situations. In pursuit of superior recommendations for both cold and warm start scenarios, we introduce CoLLM, an innovative LLMRec methodology that seamlessly incorporates collaborative information into LLMs for recommendation. CoLLM captures collaborative information through an external traditional model and maps it to the input token embedding space of LLM, forming collaborative embeddings for LLM usage. Through this external integration of collaborative information, CoLLM ensures effective modeling of collaborative information without modifying the LLM itself, providing the flexibility to employ various collaborative information modeling techniques. Extensive experiments validate that CoLLM adeptly integrates collaborative information into LLMs, resulting in enhanced recommendation performance. We release the code and data at https://github.com/zyang1580/CoLLM

Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation

Historical interactions are the default choice for recommender model training, which typically exhibit high sparsity, i.e., most user-item pairs are unobserved missing data. A standard choice is treating the missing data as negative training samples and estimating interaction likelihood between user-item pairs along with the observed interactions. In this way, some potential interactions are inevitably mislabeled during training, which will hurt the model fidelity, hindering the model to recall the mislabeled items, especially the long-tail ones. In this work, we investigate the mislabeling issue from a new perspective of aleatoric uncertainty, which describes the inherent randomness of missing data. The randomness pushes us to go beyond merely the interaction likelihood and embrace aleatoric uncertainty modeling. Towards this end, we propose a new Aleatoric Uncertainty-aware Recommendation (AUR) framework that consists of a new uncertainty estimator along with a normal recommender model. According to the theory of aleatoric uncertainty, we derive a new recommendation objective to learn the estimator. As the chance of mislabeling reflects the potential of a pair, AUR makes recommendations according to the uncertainty, which is demonstrated to improve the recommendation performance of less popular items without sacrificing the overall performance. We instantiate AUR on three representative recommender models: Matrix Factorization (MF), LightGCN, and VAE from mainstream model architectures. Extensive results on two real-world datasets validate the effectiveness of AUR w.r.t. better recommendation results, especially on long-tail items

Kick Bad Guys Out! Zero-Knowledge-Proof-Based Anomaly Detection in Federated Learning

Federated learning (FL) systems are vulnerable to malicious clients that submit poisoned local models to achieve their adversarial goals, such as preventing the convergence of the global model or inducing the global model to misclassify some data. Many existing defense mechanisms are impractical in real-world FL systems, as they require prior knowledge of the number of malicious clients or rely on re-weighting or modifying submissions. This is because adversaries typically do not announce their intentions before attacking, and re-weighting might change aggregation results even in the absence of attacks. To address these challenges in real FL systems, this paper introduces a cutting-edge anomaly detection approach with the following features: i) Detecting the occurrence of attacks and performing defense operations only when attacks happen; ii) Upon the occurrence of an attack, further detecting the malicious client models and eliminating them without harming the benign ones; iii) Ensuring honest execution of defense mechanisms at the server by leveraging a zero-knowledge proof mechanism. We validate the superior performance of the proposed approach with extensive experiments

FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs

This paper introduces FedMLSecurity, a benchmark that simulates adversarial attacks and corresponding defense mechanisms in Federated Learning (FL). As an integral module of the open-sourced library FedML that facilitates FL algorithm development and performance comparison, FedMLSecurity enhances the security assessment capacity of FedML. FedMLSecurity comprises two principal components: FedMLAttacker, which simulates attacks injected into FL training, and FedMLDefender, which emulates defensive strategies designed to mitigate the impacts of the attacks. FedMLSecurity is open-sourced 1 and is customizable to a wide range of machine learning models (e.g., Logistic Regression, ResNet, GAN, etc.) and federated optimizers (e.g., FedAVG, FedOPT, FedNOVA, etc.). Experimental evaluations in this paper also demonstrate the ease of application of FedMLSecurity to Large Language Models (LLMs), further reinforcing its versatility and practical utility in various scenarios

Prioritizing human cancer microRNAs based on genes’ functional consistency between microRNA and cancer

The identification of human cancer-related microRNAs (miRNAs) is important for cancer biology research. Although several identification methods have achieved remarkable success, they have overlooked the functional information associated with miRNAs. We present a computational framework that can be used to prioritize human cancer miRNAs by measuring the association between cancer and miRNAs based on the functional consistency score (FCS) of the miRNA target genes and the cancer-related genes. This approach proved successful in identifying the validated cancer miRNAs for 11 common human cancers with area under ROC curve (AUC) ranging from 71.15% to 96.36%. The FCS method had a significant advantage over miRNA differential expression analysis when identifying cancer-related miRNAs with a fine regulatory mechanism, such as miR-27a in colorectal cancer. Furthermore, a case study examining thyroid cancer showed that the FCS method can uncover novel cancer-related miRNAs such as miR-27a/b, which were showed significantly upregulated in thyroid cancer samples by qRT-PCR analysis. Our method can be used on a web-based server, CMP (cancer miRNA prioritization) and is freely accessible at http://bioinfo.hrbmu.edu.cn/CMP. This time- and cost-effective computational framework can be a valuable complement to experimental studies and can assist with future studies of miRNA involvement in the pathogenesis of cancers