The complexity of financial data, characterized by its variability and low signal-to-noise ratio, necessitates advanced methods in quantitative investment that prioritize both performance and interpretability.Transitioning from early manual extraction to genetic programming, the most advanced approach in the alpha factor mining domain currently employs reinforcement learning to mine a set of combination factors with fixed weights. However, the performance of resultant alpha factors exhibits inconsistency, and the inflexibility of fixed factor weights proves insufficient in adapting to the dynamic nature of financial markets. To address this issue, this paper proposes a two-stage formulaic alpha generating framework AlphaForge, for alpha factor mining and factor combination. This framework employs a generative-predictive neural network to generate factors, leveraging the robust spatial exploration capabilities inherent in deep learning while concurrently preserving diversity. The combination model within the framework incorporates the temporal performance of factors for selection and dynamically adjusts the weights assigned to each component alpha factor. Experiments conducted on real-world datasets demonstrate that our proposed model outperforms contemporary benchmarks in formulaic alpha factor mining. Furthermore, our model exhibits a notable enhancement in portfolio returns within the realm of quantitative investment and real money investment.

The reliability of circuits is significantly affected by process variations in manufacturing and environmental variation during operation. Current yield optimization algorithms take process variations into consideration to improve circuit reliability. However, the influence of environmental variations (e.g., voltage and temperature variations) is often ignored in current methods because of the high computational cost. In this article, a novel and efficient approach named BNN-BYO is proposed to optimize the yield of analog circuits in multiple environmental corners. First, we use a Bayesian Neural Network (BNN) to simultaneously model the yields and performances of interest in multiple corners efficiently. Next, the multi-corner yield optimization can be performed by embedding BNN into a Bayesian optimization framework. Since the correlation among yields and performances of interest in different corners is implicitly encoded in the BNN model, it provides great modeling capabilities for yields and their uncertainties to improve the efficiency of yield optimization. Our experimental results demonstrate that the proposed method can save up to 45.3% of simulation cost compared to other baseline methods to achieve the same target yield. In addition, for the same simulation cost, our proposed method can find better design points with 3.2% yield improvement.

The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously improve accuracy. Self-supervised representation learning has achieved promising performances using centralized unlabeled data. However, the increasing awareness of privacy protection limits centralizing the distributed unlabeled image data on edge devices. While federated learning has been widely adopted to enable distributed machine learning with privacy preservation, without a data selection method to efficiently select streaming data, the traditional federated learning framework fails to handle these huge amounts of decentralized unlabeled data with limited storage resources on edge. To address these challenges, we propose a self-supervised on-device federated learning framework with coreset selection, which we call SOFed, to automatically select a coreset that consists of the most representative samples into the replay buffer on each device. It preserves data privacy as each client does not share raw data while learning good visual representations. Experiments demonstrate the effectiveness and significance of the proposed method in visual representation learning.

EMG based hand gesture recognition on convolutional neural networks (CNNs) has been widely learned, which gains high accuracy. However, CNN based systems are computationally complex and power consuming, thus hard to be deployed at edge. Biologically inspired, a new neuromorphic learning and computing approach for electromyogram (EMG) based hand gesture recognition tasks is proposed in this work. This approach designs an activate and inhibit joint processing spiking neural network (AIPS-SNN) which reaches an accuracy of 85.6% on Nina Pro dataset. Furthermore, the AIPS-SNN is deployed on the proposed memristor based computation in-memory (CIM) system, the power efficiency and area efficiency of which reach 10.146 TOPS/W and 35.399 GOPS/mm2, respectively. The experimental results indicate that the proposed neuromorphic CIM engine is promising for edge deployment.

Human-oriented image captioning with both high diversity and accuracy is a challenging task in vision+language modeling. The reinforcement learning (RL) based frameworks promote the accuracy of image captioning, yet seriously hurt the diversity. In contrast, other methods based on variational auto-encoder (VAE) or generative adversarial network (GAN) can produce diverse yet less accurate captions. In this work, we devote our attention to promote the diversity of RL-based image captioning. To be specific, we devise a partial off-policy learning scheme to balance accuracy and diversity. First, we keep the model exposed to varied candidate captions by sampling from the initial state before RL launched. Second, a novel criterion named max-CIDEr is proposed to serve as the reward for promoting diversity. We combine the above-mentioned offpolicy strategy with the on-policy one to moderate the exploration effect, further balancing the diversity and accuracy for human-like image captioning. Experiments show that our method locates the closest to human performance in the diversity-accuracy space, and achieves the highest Pearson correlation as 0.337 with human performance.

To efficiently estimate parametric yields over multiple process, voltage, temperature corners for binary output circuits, we propose a novel Bayesian Inference method based on Bernoulli distribution with conjugate prior in this paper. The key idea is to adopt a product of Beta distributions as the conjugate prior for the yields and encode circuit performance correlations among different corners into this prior. Next, the hyper-parameters are optimized by using multi-start Quasi-Newton method, and the yields over different corners are estimated via maximum-a-posteriori. Two circuit examples demonstrate that the proposed method achieves up to 3.0× cost reduction over the state-of-the-art methods without surrendering any accuracy.

Hardware Trojan is increasingly becoming a major threat in the filed of hardware security. To solve that security threat, we propose a novel strategy for hardware Trojan detection combining trustworthy design with thermal radiation analysis. We use ring oscillators to fill the vulnerable area of target IC, and their layouts can serve as the trustworthy reference. Ring oscillator's thermal radiation is related to its stage, so that the location and stage of ring oscillators can be extracted from thermal maps by k-means clustering. Removing, breaking or degrading ring oscillators to insert a hardware Trojan can be detected by thermal radiation analysis. Therefore, our countermeasure can efficiently and conveniently detect the insertion of hardware Trojan without fabricated golden-chip. Experimental results on FPGA show that our countermeasure can accurately identify the thermal radiation change of ring oscillators and be used for hardware Trojan detection.

Most recent researches in image captioning adopt attention mechanism based on encoder-decoder framework, where the attention module aligns input features for the decoder and boosts performance consequently. A common defect of traditional attention methods is that the inequality among different types of inputs is ignored, resulting in under-exploitation of certain informative features. In this paper, we propose a novel cascade attention module, which processes different types of input in a sequential manner. The cascade attention module enables inputs of higher priorities to affect the attention of other inputs so as to emphasize such inequality. We implement our model by introducing global feature of the image to the captioning process of R-CNN based frameworks, where such feature is rich of context information but takes few effects via traditional attention module. Experimental results demonstrate that our proposed method is able to exploit feature of different types, acquiring improvements on multiple automatic measurements.

In this paper, we propose a novel single image super-resolution (SR) method based on low-rank sparse representation with self-similarity learning. Sparse representation is known as a promising method for SR. However, the sparse codes for low resolution (LR) patches gained by conventional method are not faithful to those for the original high resolution (HR) ones. To overcome this defect, we explore the structures of sparse representation for nonlocal similar patches in natural images by low-rank strategy. It assumes that the sparse codes for nonlocal similar patches should be low-rank. By low-rank constraint, similar components of sparse codes are shared and coding noises are removed, which improves coding accuracy and SR performance. Furthermore, we utilize self-similarity learning framework to generate a self-examples dictionary compatible to the low-rank sparse representation based SR. Experimental results demonstrate that our proposed method can recover good SR results both quantitatively and perceptually.