Most recent paradigms of generative model-based recommendation still face challenges related to the cold-start problem. Existing models addressing cold item recommendations mainly focus on acquiring more knowledge to enrich embeddings or model inputs. However, many models do not assess the efficiency with which they utilize the available training knowledge, leading to the extraction of significant knowledge that is not fully used, thus limiting improvements in cold-start performance. To address this, we introduce the concept of epistemic uncertainty (which refers to uncertainty caused by a lack of knowledge of the best model) to indirectly define how efficiently a model uses the training knowledge. Since epistemic uncertainty represents the reducible part of the total uncertainty, we can optimize the recommendation model further based on epistemic uncertainty to improve its performance. To this end, we propose a Cold-Start Recommendation based on Epistemic Uncertainty (CREU) framework. Additionally, CREU is inspired by Pairwise-Distance Estimators (PaiDEs) to efficiently and accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The proposed method is evaluated through extensive offline experiments on public datasets, which further demonstrate the advantages and robustness of CREU. The source code is available at https://github.com/EsiksonX/CREU.

In bloodstain examination of collaborative medicine and forensics, the analysis and identification of time since deposition (TSD) plays a significant role. Traditional bloodstain analysis methods can only provide a rough estimate for the TSD of traces, and they are time-consuming. To address this issue, we propose a lightweight framework called Fourier Transform Infrared Network (FTIR-Net) that combines wavelet transform with deep learning. To be specific, we parallelly perform wavelet transform on infrared spectra and compute its second derivative to attain the sequential signal and spectral image. Then, the learning component employs two separate branches to extract features from the one-dimensional (1D) spectra signal and two-dimensional (2D) coefficient images provided by continuous wavelet transform (CWT). To effectively aggregate information from the spectral image, we design a Squeeze-and-Excitation Network (SENet) and combine it with 2D convolution. Finally, the extracted features are concatenated and flattened, followed by two fully connected (FC) layers for retention time analysis. Since the standard bloodstain dataset is lacking, we create a dataset that associates bloodstain with the attenuated total reflectance of Fourier transform infrared (ATR-FTIR). To demonstrate the effectiveness of our model in bloodstain analysis and exploit the properties of the proposed dataset, we present comprehensive experiments and ablation studies.