OBJECTIVE: We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years). MATERIALS AND METHODS: In our retrospective case study from the DiCAYA Network, SARS-CoV-2 was identified using labs and diagnoses from June 1, 2020 to December 31, 2021. Patients were followed through December 31, 2022 for new diabetes diagnoses. Sites examined incident diabetes by COVID-19 status using Cox proportional hazards models. Results were pooled in meta-analyses. A bias analysis examined potential impact of COVID-19 misclassification scenarios on results, guided by hypotheses that sensitivity would be <50% and would be higher among those who developed diabetes. RESULTS: Prevalence of documented COVID-19 was low overall and variable across sites (children: 4.4%-7.7%, young adults: 6.2%-22.7%). Individuals with documented COVID-19 were at higher risk of incident diabetes compared to those with no documented infection, but results were heterogeneous across sites. Findings were highly sensitive to COVID-19 misclassification assumptions. Observed results could be biased away from the null under several differential misclassification scenarios. DISCUSSION: Although EHR-based documentation of COVID-19 was associated with incident diabetes, COVID-19 phenotypes likely had low sensitivity, with considerable variation across sites. Misclassification assumptions strongly impacted interpretation of results. CONCLUSION: Given the potential for low phenotype sensitivity and misclassification, caution is warranted when interpreting analyses of COVID-19 and incident diabetes using clinical or administrative databases.

Deep neural networks (DNNs) are susceptible to adversarial examples (AEs), which are crafted by adding human-imperceptible perturbations to benign images. Although many existing adversarial attacks have achieved great surrogate, white-box model attack performance, they exhibit low transferability. In this work, we emphasize that existing input transformation-based attacks, which linearly mix the input image with images from other categories, induce significant semantic shifts and lack sufficient input diversity, leading to inaccurate update directions. To overcome the pitfall, we propose a new attack method for constructing multiple adversarial trajectories (MAT). Specifically, MAT achieves the intent of the mixing strategy by introducing targeted perturbations instead of relying on input transformation to obtain multiple data points that are closer to the decision boundary for gradient computation. Comprehensive experiments demonstrate our method’s effectiveness. We also show that MAT is highly flexible and can seamlessly integrate with existing transfer methods. Code is available at: github.com/britney-code/MAT-Attack.

Ribosome profiling (Ribo-seq) provides transcriptome-wide insights into protein synthesis dynamics, yet its analysis poses challenges, particularly for nonbioinformatics researchers. Large language model-based chatbots offer promising solutions by leveraging natural language processing. This review explores their convergence, highlighting opportunities for synergy. We discuss challenges in Ribo-seq analysis and how chatbots mitigate them, facilitating scientific discovery. Through case studies, we illustrate chatbots' potential contributions, including data analysis and result interpretation. Despite the absence of applied examples, existing software underscores the value of chatbots and the large language model. We anticipate their pivotal role in future Ribo-seq analysis, overcoming limitations. Challenges such as model bias and data privacy require attention, but emerging trends offer promise. The integration of large language models and Ribo-seq analysis holds immense potential for advancing translational regulation and gene expression understanding.

Scale-invariant method (SIM) is a state-of-the-art model augmentation method to improve the transferability of adversarial examples. However, we find that SIM is easily affected by the scaling operation with small scaling factors, and cannot stably enhance the transferability of the base attack. In this paper, we propose an enhanced transferable attack based on SIM. To alleviate the instability of SIM caused by the scaled copy which does not satisfy scale-invariance, we propose to ensemble logit-outputs of scale copies of the input image, rather than ensemble the gradients, to form an ensemble attack that generates transferable adversarial images from multiple models of the original CNN model. Compared with the existing ensemble methods, our method is fast yet effective and can be easily integrated into the gradient-based attacks. The experimental results show that the proposed integrated EL-NI-FGSM attack stably improves the transferability of NI-FGSM, and outperforms SI-NI-FGSM, achieving >8% higher of attack success rate for both white-box and black-box attacks on CIFAR-10.

Two entities, who only share a password and communicate over an insecure channel, authenticate each other and agree on a large session key for protecting their subsequent communication. This is called the password-authenticated key exchange (PAKE) protocol. PAKE protocol has been considered a suitable substitute for the prevailing hash-based authentication which is vulnerable to various attacks. However, vendors are discouraged by both its prohibitively computational overheads as well as integrating costs, leading to its limited use since being proposed.

Used ubiquitously in a huge amount of security protocols or applications, elliptic curve cryptography (ECC) is one of the most important cryptographic primitives, featuring efficiency and short key size compared with other public-key cryptosystems such as DSA and RSA. However, as a computation-intensive public-key cryptographic primitive, ECC arithmetic is still the bottleneck that restrains the overall performance of the end applications. In this paper, instead of the conventional and straightforward integer-based methods, we present a general framework to accelerate ECC schemes over prime field, called DPF-ECC, that deeply exploits double precision floating-point (DPF) computing power. The DPF-ECC framework finely manages each bit of the DPF numbers and minimizes the overhead brought by additional data format conversion, by making use of the DPF representation, the rounding operations, and fused multiply-add instruction supported by the IEEE 754 floating point standard. We also conduct two comprehensive case studies on Crandall primes and Solinas primes to demonstrate how the DPF-ECC framework is applied to the prevailing ECC schemes. To evaluate the proposed DPF-ECC framework in the real world, leveraging the floating-point computing power of GPUs, we implement Curve25519/448 and Edwards25519/448, the popular ECC schemes widely used in TLS 1.3, SSH, etc. The experimental result in Tesla P100 achieves a record-setting performance that outperforms the existing fastest integer work with 2x to 3x throughput. With dependency only on the very commonly supported IEEE 754 floating point standard, DPF-ECC framework can be a very competent and promising candidate for ECC implementation in most of general-purpose platforms.

Deep learning has proved to be a powerful tool for medical image analysis in recent years. Data imbalance is a common problem in medical images. Dice Loss is widely used in medical image segmentation tasks to address the data imbalance problem. However, it only addresses the imbalance problem between foreground and background yet overlooks another imbalance between easy and hard examples that also severely affects the training process of a learning model. Empirically speaking, an easy example generally contributes less to the overall loss than a hard example. However, in practice, compared with hard examples, a large number of easy examples will be generated from a medical image and will dominate the training model, resulting in sub-optimal training or worse. To tackle this problem, we propose a novel Focal Dice Loss to alleviate the imbalance between hard examples and easy examples. Focal Dice Loss is able to reduce the contribution from easy examples and make the model focus on hard examples through our proposed novel balanced sampling strategy during the training process. Furthermore, to evaluate the effectiveness of our proposed loss functions, we conduct extensive experiments on two real-world medical image datasets with 2D and 3D convolutional neural networks. The experimental results show that our proposed Focal Dice Loss brings a significant improvement in segmentation performance compared to Dice Loss. Moreover, we find that our proposed Focal Dice Loss can effectively alleviate the over-fitting problem.

Summary In recent years, multilabel learning has been applied to a lot of application areas and is yet a challenging task. In multilabel learning, an instance often belongs to multiple class labels simultaneously. The labels usually have correlations with others, and mining label correlations is helpful to enhance the multilabel classification performance. Aiming at increasing the accuracy of prediction, Label embedding (LE) is an important technique, and conducive to extracting label information for multilabel learning. In this paper, we present a novel multilabel learning approach via exploiting label correlations, which can be naturally extended to tackle feature selection problem. First, to obtain the discriminative features shared by all labels, the proposed algorithm learns a latent space by employing deep canonical correlation analysis. Then we exploit label correlations by enforcing predictions on similar labels to be similar, thereby improving the prediction performance. Results on several multiple datasets illustrate that the proposed algorithm has the advantages on multilabel classification and feature selection.

The redundant via (RV) insertion is a widely used technique to enhance chip reliability. However, inserting an RV adjacent to a single via (SV) may create extra critical area (CA) between nets and worsen the circuit's yield. In this paper, we present a short CA (SCA)-constrained RV insertion method for yield optimization with a consideration of the SCA. First, we find the candidate ranges of the SVs, judge whether they can be inserted by RVs, and set the values of their corresponding direction marks and weight. Then, we update the direction marks and the weight of the candidate ranges. Finally, according to the direction marks and the weight of the candidate ranges, we determine the optimization sequence of the candidate ranges and complete the insertion of the RVs. The simulation results show that our method can get a high insertion rate and a good control of the incremental SCA between nets for yield.