Recursive integral method for transmission eigenvalues

Recently, a new eigenvalue problem, called the transmission eigenvalue problem, has attracted many researchers. The problem arose in inverse scattering theory for inhomogeneous media and has important applications in a variety of inverse problems for target identification and nondestructive testing. The problem is numerically challenging because it is non-selfadjoint and nonlinear. In this paper, we propose a recursive integral method for computing transmission eigenvalues from a finite element discretization of the continuous problem. The method, which overcomes some difficulties of existing methods, is based on eigenprojectors of compact operators. It is self-correcting, can separate nearby eigenvalues, and does not require an initial approximation based on some a priori spectral information. These features make the method well suited for the transmission eigenvalue problem whose spectrum is complicated. Numerical examples show that the method is effective and robust.Comment: 18 pages, 8 figure

Evaluation of Pedestrian Level of Service at Signalised Intersections from the Elderly Perspective

The crossing decisions and behaviour of elderly pedestrians are affected by the pedestrian level of service (PLOS). In this paper, an evaluation model was established to analyse the relationship between the traffic environment and the perceived evaluation of elderly pedestrians. Firstly, the characteristic parameters of the selected intersections and the perceived evaluation data of elderly pedestrians at the synchronisation scenery were extracted using manual recording and questionnaire-based truncation methods. The correlation between the perceived evaluation data of elderly pedestrians and the traffic parameters were tested with respect to the dimensions of safety, convenience and efficiency. Then, the significant parameters affecting PLOS were recognised. Based on the traffic characteristic parameters, the PLOS evaluation model from the elderly perspective was established using the fuzzy linear regression method. PLOS classification thresholds were obtained using the fuzzy C-means clustering algorithm. The data from two intersections were used to validate the model. The results show that the difference between the actual and the predicted PLOS values of the two crosswalks were 0.2 and 0.1, respectively. Thus, the proposed PLOS evaluation model in this paper can be used to accurately predict the PLOS from the elderly perspective using the traffic data of signalised intersections

A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization

Indoor localization is getting increasing demands for various cutting-edged technologies, like Virtual/Augmented reality and smart home. Traditional model-based localization suffers from significant computational overhead, so fingerprint localization is getting increasing attention, which needs lower computation cost after the fingerprint database is built. However, the accuracy of indoor localization is limited by the complicated indoor environment which brings the multipath signal refraction. In this paper, we provided a scheme to improve the accuracy of indoor fingerprint localization from the frequency domain by predicting the channel state information (CSI) values from another transmitting channel and spliced the multi-band information together to get more precise localization results. We tested our proposed scheme on COST 2100 simulation data and real time orthogonal frequency division multiplexing (OFDM) WiFi data collected from an office scenario

Evaluation of Pedestrian Level of Service at Signalised Intersections from the Elderly Perspective

The crossing decisions and behaviour of elderly pedestrians are affected by the pedestrian level of service (PLOS). In this paper, an evaluation model was established to analyse the relationship between the traffic environment and the perceived evaluation of elderly pedestrians. Firstly, the characteristic parameters of the selected intersections and the perceived evaluation data of elderly pedestrians at the synchronisation scenery were extracted using manual recording and questionnaire-based truncation methods. The correlation between the perceived evaluation data of elderly pedestrians and the traffic parameters were tested with respect to the dimensions of safety, convenience and efficiency. Then, the significant parameters affecting PLOS were recognised. Based on the traffic characteristic parameters, the PLOS evaluation model from the elderly perspective was established using the fuzzy linear regression method. PLOS classification thresholds were obtained using the fuzzy C-means clustering algorithm. The data from two intersections were used to validate the model. The results show that the difference between the actual and the predicted PLOS values of the two crosswalks were 0.2 and 0.1, respectively. Thus, the proposed PLOS evaluation model in this paper can be used to accurately predict the PLOS from the elderly perspective using the traffic data of signalised intersections

Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Quantization of transformer language models faces significant challenges due to the existence of detrimental outliers in activations. We observe that these outliers are asymmetric and concentrated in specific channels. To address this issue, we propose the Outlier Suppression+ framework. First, we introduce channel-wise shifting and scaling operations to eliminate asymmetric presentation and scale down problematic channels. We demonstrate that these operations can be seamlessly migrated into subsequent modules while maintaining equivalence. Second, we quantitatively analyze the optimal values for shifting and scaling, taking into account both the asymmetric property and quantization errors of weights in the next layer. Our lightweight framework can incur minimal performance degradation under static and standard post-training quantization settings. Comprehensive results across various tasks and models reveal that our approach achieves near-floating-point performance on both small models, such as BERT, and large language models (LLMs) including OPTs, BLOOM, and BLOOMZ at 8-bit and 6-bit settings. Furthermore, we establish a new state of the art for 4-bit BERT

Distribution-sensitive Information Retention for Accurate Binary Neural Network

Model binarization is an effective method of compressing neural networks and accelerating their inference process. However, a significant performance gap still exists between the 1-bit model and the 32-bit one. The empirical study shows that binarization causes a great loss of information in the forward and backward propagation. We present a novel Distribution-sensitive Information Retention Network (DIR-Net) that retains the information in the forward and backward propagation by improving internal propagation and introducing external representations. The DIR-Net mainly relies on three technical contributions: (1) Information Maximized Binarization (IMB): minimizing the information loss and the binarization error of weights/activations simultaneously by weight balance and standardization; (2) Distribution-sensitive Two-stage Estimator (DTE): retaining the information of gradients by distribution-sensitive soft approximation by jointly considering the updating capability and accurate gradient; (3) Representation-align Binarization-aware Distillation (RBD): retaining the representation information by distilling the representations between full-precision and binarized networks. The DIR-Net investigates both forward and backward processes of BNNs from the unified information perspective, thereby providing new insight into the mechanism of network binarization. The three techniques in our DIR-Net are versatile and effective and can be applied in various structures to improve BNNs. Comprehensive experiments on the image classification and objective detection tasks show that our DIR-Net consistently outperforms the state-of-the-art binarization approaches under mainstream and compact architectures, such as ResNet, VGG, EfficientNet, DARTS, and MobileNet. Additionally, we conduct our DIR-Net on real-world resource-limited devices which achieves 11.1x storage saving and 5.4x speedup

Exploring the Potential of Flexible 8-bit Format: Design and Algorithm

Neural network quantization is widely used to reduce model inference complexity in real-world deployments. However, traditional integer quantization suffers from accuracy degradation when adapting to various dynamic ranges. Recent research has focused on a new 8-bit format, FP8, with hardware support for both training and inference of neural networks but lacks guidance for hardware design. In this paper, we analyze the benefits of using FP8 quantization and provide a comprehensive comparison of FP8 with INT quantization. Then we propose a flexible mixed-precision quantization framework that supports various number systems, enabling optimal selection of the most appropriate quantization format for different neural network architectures. Experimental results demonstrate that our proposed framework achieves competitive performance compared to full precision on various tasks, including image classification, object detection, segmentation, and natural language understanding. Our work furnishes critical insights into the tangible benefits and feasibility of employing FP8 quantization, paving the way for heightened neural network efficiency in tangible scenarios. Our code is available in the supplementary material