Large Language Models (LLMs) show great capabilities in a wide range of applications, but serving them efficiently becomes increasingly challenging as requests (prompts) become more complex. Context caching improves serving performance by reusing Key-Value (KV) vectors, the intermediate representations of tokens that are repeated across requests. However, existing context caching requires exact prefix matches across requests, limiting reuse cases in settings such as few-shot learning and retrieval-augmented generation, where immutable content (e.g., documents) remains unchanged across requests but is preceded by varying prefixes. Position-Independent Caching (PIC) addresses this issue by enabling modular reuse of the KV vectors regardless of prefixes. We formalize PIC and advance prior work by introducing EPIC, a serving system incorporating our new LegoLink algorithm, which mitigates the inappropriate “attention sink” effect at every document beginning, to maintain accuracy with minimal computation. Experiments show that EPIC achieves up to 8$\times$ improvements in Time-To-First-Token (TTFT) and 7$\times$ throughput gains over existing systems, with negligible or no accuracy loss.

The scene classification of mining areas provides accurate foundational data to support geological environment monitoring and resource development planning. This study fuses multi-source data to construct a multi-modal mine land cover scene classification dataset. A significant challenge in mining area classification lies in the complex spatial layout and multi-scale characteristics of these regions. By extracting global and local features, it becomes possible to comprehensively reflect the spatial distribution and overall arrangement of different landforms, thereby enabling a more accurate capture of the holistic characteristics of mining scenes. We propose a dual-branch fusion model utilizing collaborative representation to decompose global features into a set of key semantic vectors. This model comprises three key components: (1) Multi-scale Global Transformer Branch: This branch leverages adjacent large-scale features to generate global channel attention features for small-scale features, effectively capturing the multi-scale feature relationships inherent in mining areas. (2) Local Enhancement Collaborative Representation Branch: This branch refines the attention weights by leveraging local features and reconstructed key semantic sets, ensuring that the local context and detailed characteristics of the mining area are effectively integrated. This enhances the model’s sensitivity to fine-grained spatial variations within the mining environment. (3) Dual-Branch Deep Feature Fusion Module: This module fuses the complementary features of the two branches to incorporate more scene information. This fusion strengthens the model’s ability to distinguish and classify complex mining landscapes. Finally, this study employs multi-loss computation to ensure a balanced integration of the modules. The overall accuracy of this model is 83.63%, which outperforms other comparative models. Additionally, it achieves the best performance across all other evaluation metrics. The experimental results demonstrate the effectiveness of the proposed dataset and model for classifying mining areas.

Generalized age feature extraction is crucial for age-related facial analysis tasks, such as age estimation and age-invariant face recognition (AIFR). Despite the recent successes of models in homogeneous-dataset experiments, their performance drops significantly in cross-dataset evaluations. Most of these models fail to extract generalized age features as they only attempt to map extracted features with training age labels directly without explicitly modeling the natural ordinal progression of aging. In this paper, we propose Order-Enhanced Contrastive Learning (OrdCon), a novel contrastive learning framework designed explicitly for ordinal attributes like age. Specifically, to extract generalized features, OrdCon aligns the direction vector of two features with either the natural aging direction or its reverse to model the ordinal process of aging. To further enhance generalizability, OrdCon leverages a novel soft proxy matching loss as a second contrastive objective, ensuring that features are positioned around the center of each age cluster with minimal intra-class variance and proportionally away from other clusters. By modeling the ageing process, the framework can enhance generalizability by improving the alignment of samples from the same class and reducing the divergence of direction vectors. We demonstrate that our proposed method achieves comparable results to state-of-the-art methods on various benchmark datasets in homogeneous-dataset evaluations for both age estimation and AIFR. In cross-dataset experiments, OrdCon outperforms other methods by reducing the mean absolute error by approximately 1.38 on average for the age estimation task and boosts the average accuracy for AIFR by 1.87%.

Cross-age facial images are typically challenging and expensive to collect, making noise-free age-oriented datasets relatively small compared to widely-used large-scale facial datasets. Additionally, in real scenarios, images of the same subject at different ages are usually hard or even impossible to obtain. Both of these factors lead to a lack of supervised data, which limits the versatility of supervised methods for age-invariant face recognition, a critical task in applications such as security and biometrics. To address this issue, we propose a novel semi-supervised learning approach named Cross-Age Contrastive Learning (CACon). Thanks to the identity-preserving power of recent face synthesis models, CACon introduces a new contrastive learning method that leverages an additional synthesized sample from the input image. We also propose a new loss function in association with CACon to perform contrastive learning on a triplet of samples. We demonstrate that our method not only achieves state-of-the-art performance in homogeneous-dataset experiments on several age-invariant face recognition benchmarks but also outperforms other methods by a large margin in cross-dataset experiments.

This paper addresses the critical challenge of reliable object detection in autonomous vehicles operating in dynamic urban environments, particularly when facing adversarial attacks on perception systems. It presents a novel dualvalidation methodology leveraging the synergy of image based and point cloud based object detection systems. The approach comprises sensor calibration to align data from both sources, an attack detection algorithm utilizing cross-validation technique to identify inconsistencies, and a self-restoration strategy to ensure correct detection despite malicious manipulation. The methodology has been tested in a complex urban environment with adversarial scenarios including patch and random removal attacks. Experimental results demonstrate the robustness and accuracy of the proposed method in maintaining reliable object detection under adversarial conditions.

In this paper, we present an AoI-based content prediction model utilizing a temporal graph attention network model to enhance cache hit rates and ensure content freshness. By capturing and aggregating time-based embedding features of both users and content, our model accurately predicts the popularity of requested content. Specifically, the concept of (Age of Information) AoI is incorporated to eliminate stale caching. Moreover, we also devise an optimal window for updating popular content, thereby markedly reducing user transmission latency and enhancing overall user experience quality. Experimental results demonstrate that our proposed AoI-based cache update strategy, combined with the temporal graph attention mechanism, significantly enhances cache hit rates and ensures the timeliness of cached content.

Protein solubility plays a crucial role in various biotechnological, industrial, and biomedical applications. With the reduction in sequencing and gene synthesis costs, the adoption of high-throughput experimental screening coupled with tailored bioinformatic prediction has witnessed a rapidly growing trend for the development of novel functional enzymes of interest (EOI). High protein solubility rates are essential in this process and accurate prediction of solubility is a challenging task. As deep learning technology continues to evolve, attention-based protein language models (PLMs) can extract intrinsic information from protein sequences to a greater extent. Leveraging these models along with the increasing availability of protein solubility data inferred from structural database like the Protein Data Bank holds great potential to enhance the prediction of protein solubility. In this study, we curated an Updated Escherichia coli protein Solubility DataSet (UESolDS) and employed a combination of multiple PLMs and classification layers to predict protein solubility. The resulting best-performing model, named Protein Language Model-based protein Solubility prediction model (PLM_Sol), demonstrated significant improvements over previous reported models, achieving a notable 6.4% increase in accuracy, 9.0% increase in F1_score, and 11.1% increase in Matthews correlation coefficient score on the independent test set. Moreover, additional evaluation utilizing our in-house synthesized protein resource as test data, encompassing diverse types of enzymes, also showcased the good performance of PLM_Sol. Overall, PLM_Sol exhibited consistent and promising performance across both independent test set and experimental set, thereby making it well suited for facilitating large-scale EOI studies. PLM_Sol is available as a standalone program and as an easy-to-use model at https://zenodo.org/doi/10.5281/zenodo.10675340.

Directed test generation is an effective method to detect potential hardware Trojan (HT) in RTL. While the existing works are able to activate hard-to-cover Trojans by covering security targets, the effectiveness and efficiency of identifying the targets to cover are ignored. We propose a static probability analysis method for identifying the hard-to-active data channel targets and generating the corresponding assertions for the HT test generation. Our method could generate test vectors to trigger Trojans from Trusthub, DeTrust, and OpenCores in 1 minute and get 104.33X time improvement on average compared with the existing method.

With the increasing popularity of convolutional neural networks (CNNs), recent works on face-based age estimation employ these networks as the backbone. However, state-of-the-art CNN-based methods treat each facial region equally, thus entirely ignoring the importance of some facial patches that may contain rich age-specific information. In this paper, we propose a face-based age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF). In ADPF, two separate CNNs are implemented, namely the AttentionNet and the FusionNet. The AttentionNet dynamically locates and ranks age-specific patches by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA) mechanism. The FusionNet uses the discovered patches along with the facial image to predict the age of the subject. Since the proposed RMHHA mechanism ranks the discovered patches based on their importance, the length of the learning path of each patch in the FusionNet is proportional to the amount of information it carries (the longer, the more important). ADPF also introduces a novel diversity loss to guide the training of the AttentionNet and reduce the overlap among patches so that the diverse and important patches are discovered. Through extensive experiments, we show that our proposed framework outperforms state-of-the-art methods on several age estimation benchmark datasets.

The vanilla Generative Adversarial Networks (GANs) are commonly used to generate realistic images depicting aged and rejuvenated faces. However, the performance of such vanilla GANs in the age-oriented face synthesis task is often compromised by the mode collapse issue, which may produce poorly synthesized faces with indistinguishable visual variations. In addition, recent age-oriented face synthesis methods use the L1 or L2 constraint to preserve the identity information in synthesized faces, which implicitly limits the identity permanence capabilities when these constraints are associated with a trivial weighting factor. In this paper, we propose a method for the age-oriented face synthesis task that achieves high synthesis accuracy with strong identity permanence capabilities. Specifically, to achieve high synthesis accuracy, our method tackles the mode collapse issue with a novel Conditional Discriminator Pool, which consists of multiple discriminators, each targeting one particular age category. To achieve strong identity permanence capabilities, our method uses a novel Adversarial Triplet loss. This loss, which is based on the Triplet loss, adds a ranking operation to further pull the positive embedding towards the anchor embedding to significantly reduce intra-class variances in the feature space. Through extensive experiments, we show that our proposed method outperforms state-of-the-art methods in terms of synthesis accuracy and identity permanence capabilities, both qualitatively and quantitatively.

In this paper, we note that the hardware Trojans that leak information through the unspecified output pins are difficult to detect by functional testing or side-channel signal analysis. Especially, the Trojans that leak the information through the side channel has proven stealthy to be detected. To solve this problem, we propose a feature matching method based on information flow tracking at high abstraction level. In this paper, the Trojans features are summarized with the format of high-level information flow tracking, which can be used to detect the Trojans. Experimental results show that our method can successfully identify the above-mentioned Trojans from Trust-hub, DeTrust, and OpenCores in less than 20 ms, showing significantly lower time complexity compared with the existing works.

Hardware Trojans (HT) has been one of the major concerns of IC designers, and formal methods have been applied to the HT detection. In general, the assertions for detecting HT are manually defined, which is time-consuming and error-prone even for an expert engineer. However, there is a lack of studies on the automatic definition for security assertions. To fill in this gap, we propose an automatic security assertion extraction (ASAX) tool. ASAX labels the candidate signals and infers the proposed register transfer level (RTL) invariants from simulation traces. Next, the security assertions are mined from the inferred RTL invariants. By adopting a two-step invariants inferring technique, ASAX can extract high-coverage assertions with a low runtime. We validate the effectiveness and efficiency of ASAX through experiments on the benchmarks from Trust-hub, DeTrust and OpenCores. The results show that the HT can be 100% detected by model checking with the extracted security assertions.

Electromagnetic (EM) analysis is to reveal the secret information by analyzing the EM emission from a cryptographic device. EM analysis (EMA) attack is emerging as a serious threat to hardware security. It has been noted that the on-chip power grid (PG) has a security implication on EMA attack by affecting the fluctuations of supply current. However, there is little study on exploiting this intrinsic property as an active countermeasure against EMA. In this paper, we investigate the effect of PG on EM emission and propose an active countermeasure against EMA, i.e. EM Equalizer (EME). By adjusting the PG impedance, the current waveform can be flattened, equalizing the EM profile. Therefore, the correlation between secret data and EM emission is significantly reduced. As a first attempt to the co-optimization for power and EM security, we extend the EME method by fixing the vulnerability of power analysis. To verify the EME method, several cryptographic designs are implemented. The measurement to disclose (MTD) is improved by 1138x with area and power overheads of 0.62% and 1.36%, respectively.

Convolutional Neural Networks (CNN) have been applied to age-related research as the core framework. Although faces are composed of numerous facial attributes, most works with CNNs still consider a face as a typical object and do not pay enough attention to facial regions that carry age-specific feature for this particular task. In this paper, we propose a novel CNN architecture called Fusion Network (Fusion-Net) to tackle the age estimation problem. Apart from the whole face image, the FusionNet successively takes several age-specific facial patches as part of the input to emphasize the age-specific features. Through experiments, we show that the FusionNet significantly outperforms other state-of-the-art models on the MORPH II benchmark.