Mixture of Soft Prompts for Controllable Data Generation

Large language models (LLMs) effectively generate fluent text when the target output follows natural language patterns. However, structured prediction tasks confine the output format to a limited ontology, causing even very large models to struggle since they were never trained with such restrictions in mind. The difficulty of using LLMs for direct prediction is exacerbated in few-shot learning scenarios, which commonly arise due to domain shift and resource limitations. We flip the problem on its head by leveraging the LLM as a tool for data augmentation rather than direct prediction. Our proposed Mixture of Soft Prompts (MSP) serves as a parameter-efficient procedure for generating data in a controlled manner. Denoising mechanisms are further applied to improve the quality of synthesized data. Automatic metrics show our method is capable of producing diverse and natural text, while preserving label semantics. Moreover, MSP achieves state-of-the-art results on three benchmarks when compared against strong baselines. Our method offers an alternate data-centric approach for applying LLMs to complex prediction tasks.Comment: 19 pages, 13 Tables, 2 Figures. Accepted at EMNLP 202

Perspectives on Privacy in the Post-Roe Era: A Mixed-Methods of Machine Learning and Qualitative Analyses of Tweets

Abortion is a controversial topic that has long been debated in the US. With the recent Supreme Court decision to overturn Roe v. Wade, access to safe and legal reproductive care is once again in the national spotlight. A key issue central to this debate is patient privacy, as in the post-HITECH Act era it has become easier for medical records to be electronically accessed and shared. This study analyzed a large Twitter dataset from May to December 2022 to examine the public's reactions to Roe v. Wade's overruling and its implications for privacy. Using a mixed-methods approach consisting of computational and qualitative content analysis, we found a wide range of concerns voiced from the confidentiality of patient-physician information exchange to medical records being shared without patient consent. These findings may inform policy making and healthcare industry practices concerning medical privacy related to reproductive rights and women's health.Comment: Paper accepted for the proceedings of the 2023 American Medical Informatics Association Annual Symposium (AMIA

Optimal Channel Selection for Simultaneous RF Energy Harvesting and Data Transmission in Cognitive Radio Networks

In this paper, an RF-powered cognitive radio network is considered, in which the secondary users are powered by an RF energy harvester (Rectenna). Unlike most existing works, we consider a realistic Rectenna characteristic function, and derive the actual amount of harvested energy and thus, the resulting actual energy level of the secondary users. We consider a system architecture at which simultaneous energy harvesting and data transmission for each secondary user is possible. We introduce a strategy to manage the challenge of network throughput decreasing due to lack of the secondary users’ energy, via selecting the best possible channels for energy harvesting and simultaneously by allocating the best channels for data transmission. Therefore, we implement cognition in spectrum utilization and in energy harvesting. We show that the amount of harvested energy affects the available energy of the secondary user and consequently the throughput, therefore, the channels selection to maximize energy harvesting affects the network throughput. To maximize the network throughput, the Hungarian algorithm is employed, and then, an algorithm with lower complexity based on the matching theory is proposed. Finally, we compare our proposed approach with some existing benchmarks and show its high performance in energy harvesting and system throughput

Positive response to trastuzumab deruxtecan in a patient with HER2-mutant NSCLC after multiple lines therapy, including T-DM1: a case report

Human epidermal growth factor 2 (HER2) mutations are uncommon in non-small cell lung cancer (NSCLC), and the lack of established, effective, targeted drugs has resulted in a persistently poor prognosis. Herein, we report the case of a non-smoking, 58-year-old man diagnosed with lung adenocarcinoma (cT3N0M1c, stage IVB) harboring a HER2 mutation (Y772_A775dupYVMA) and PD-L1 (-). The patient’s Eastern Cooperative Oncology Group performance status (PS) score was assessed as 1. He commenced first-line treatment with chemotherapy, followed by immuno-chemotherapy, and with disease progression, he received HER2-targeted therapy and chemotherapy with an anti-angiogenic agent. However, HER2-targeted therapy, including pan-HER tyrosine kinase inhibitors (afatinib, pyrotinib, and pozitinib) and antibody–drug conjugate (T-DM1), produced only stable disease (SD) as the best response. After the previously described treatment, primary tumor recurrence and multiple brain metastases were observed. Despite the patient’s compromised overall physical condition with a PS score of 3-4, he was administered T-DXd in addition to whole-brain radiotherapy (WBRT). Remarkably, both intracranial metastases and primary lesions were significantly reduced, he achieved a partial response (PR), and his PS score increased from 3-4 to 1. He was then treated with T-DXd for almost 9 months until the disease again progressed, and he did not discontinue the drug despite the occurrence of myelosuppression during this period. This is a critical case as it exerted an effective response to T-DXd despite multiple lines therapy, including T-DM1. Simultaneously, despite the occurrence of myelosuppression in the patient during T-DXd, it was controlled after aggressive treatment

A Splice Isoform of DNedd4, DNedd4-Long, Negatively Regulates Neuromuscular Synaptogenesis and Viability in Drosophila

Neuromuscular (NM) synaptogenesis is a tightly regulated process. We previously showed that in flies, Drosophila Nedd4 (dNedd4/dNedd4S) is required for proper NM synaptogenesis by promoting endocytosis of commissureless from the muscle surface, a pre-requisite step for muscle innervation. DNedd4 is an E3 ubiquitin ligase comprised of a C2-WW(x3)-Hect domain architecture, which includes several splice isoforms, the most prominent ones are dNedd4-short (dNedd4S) and dNedd4-long (dNedd4Lo).We show here that while dNedd4S is essential for NM synaptogenesis, the dNedd4Lo isoform inhibits this process and causes lethality. Our results reveal that unlike dNedd4S, dNedd4Lo cannot rescue the lethality of dNedd4 null (DNedd4(T121FS)) flies. Moreover, overexpression of UAS-dNedd4Lo specifically in wildtype muscles leads to NM synaptogenesis defects, impaired locomotion and larval lethality. These negative effects of dNedd4Lo are ameliorated by deletion of two regions (N-terminus and Middle region) unique to this isoform, and by inactivating the catalytic activity of dNedd4Lo, suggesting that these unique regions, as well as catalytic activity, are responsible for the inhibitory effects of dNedd4Lo on synaptogenesis. In accord with these findings, we demonstrate by sqRT-PCR an increase in dNedd4S expression relative to the expression of dNedd4Lo during embryonic stages when synaptogenesis takes place.Our studies demonstrate that splice isoforms of the same dNedd4 gene can lead to opposite effects on NM synaptogenesis

Tor Anonymous Traffic Identification Based on Parallelizing Dilated Convolutional Network

The widespread use of the onion browser (Tor) has provided a breeding ground for the proliferation of cybercriminal activities and the Tor anonymous traffic identification method has been used to fingerprint anonymous web traffic and identify the websites visited by illegals. Despite the considerable progress in existing methods, problems still exist, such as high training resources required for the identification model, bias in fingerprint features due to the fast iteration of anonymous traffic and singularity in the definition of traffic direction features. On this basis, a Tor anonymous traffic identification model based on parallelizing dilated convolutions multi-feature analysis has been proposed in this paper in order to address these problems and perform better in website fingerprinting. A single-sample augmentation of the traffic data and a model combining multi-layer RBMs and parallelizing dilated convolutions are performed, and binary classification and multi-classification of websites are conducted for different scenarios. Our experiment shows that the proposed Tor anonymous traffic recognition method achieves 94.37% accuracy and gains a significant drop in training time in both closed-world and open-world scenarios. At the same time, the enhanced traffic data enhance the robustness and generalization of our model. With our techniques, our training efficiency has been improved and we are able to achieve the advantage of bi-directional deployability on the communication link

Generative Label Enhancement with Gaussian Mixture and Partial Ranking

Label distribution learning (LDL) is an effective learning paradigm for dealing with label ambiguity. When applying LDL, the datasets annotated with label distributions (i.e., the real-valued vectors like the probability distribution) are typically required. Unfortunately, most existing datasets only contain the logical labels, and manual annotating with label distributions is costly. To address this problem, we treat the label distribution as a latent vector and infer its posterior by variational Bayes. Specifically, we propose a generative label enhancement model to encode the process of generating feature vectors and logical label vectors from label distributions in a principled way. In terms of features, we assume that the feature vector is generated by a Gaussian mixture dominated by the label distribution, which captures the one-to-many relationship from the label distribution to the feature vector and thus reduces the feature generation error. In terms of logical labels, we design a probability distribution to generate the logical label vector from a label distribution, which captures partial label ranking in the logical label vector and thus provides a more accurate guidance for inferring the label distribution. Besides, to approximate the posterior of the label distribution, we design a inference model, and derive the variational learning objective. Finally, extensive experiments on real-world datasets validate our proposal

Self–Supporting Mn–RuO₂ Nanoarrays for Stable Oxygen Evolution Reaction in Acid

Currently, the process of an acidic oxygen evolution reaction (OER) necessitates the use of Iridium dioxygen (IrO2), which is both expensive and incredibly scarce on Earth. Ruthenium dioxygen (RuO2) offers high activity for acidic OERs and presents a potential substitution for IrO2. Nevertheless, its practical application is hindered by its relatively poor stability. In this study, we have developed Mn–doped RuO2 (Mn–RuO2) nanoarrays that are anchored on a titanium (Ti) mesh utilizing a two–step methodology involving the preparation of MnO2 nanoarrays followed by a subsequent Ru exchange and annealing process. By precisely optimizing the annealing temperature, we have managed to attain a remarkably low overpotential of 217 mV at 10 mA cm−2 in a 0.5 M H2SO4 solution. The enhanced catalytic activity of our Mn–RuO2 nanoarrays can be attributed to the electronic modification brought about by the high exposure of active sites, Mn dopant, efficient mass transfer, as well as the efficient transfer of electrons between the Ti mesh and the catalyst arrays. Furthermore, these self–supported Mn–RuO2 nanoarrays demonstrated excellent long–term stability throughout a chronoamperometry test lasting for 100 h, with no discernible changes observed in the Ru chemical states

Multi-Scale Attention 3D Convolutional Network for Multimodal Gesture Recognition

Gesture recognition is an important direction in computer vision research. Information from the hands is crucial in this task. However, current methods consistently achieve attention on hand regions based on estimated keypoints, which will significantly increase both time and complexity, and may lose position information of the hand due to wrong keypoint estimations. Moreover, for dynamic gesture recognition, it is not enough to consider only the attention in the spatial dimension. This paper proposes a multi-scale attention 3D convolutional network for gesture recognition, with a fusion of multimodal data. The proposed network achieves attention mechanisms both locally and globally. The local attention leverages the hand information extracted by the hand detector to focus on the hand region, and reduces the interference of gesture-irrelevant factors. Global attention is achieved in both the human-posture context and the channel context through a dual spatiotemporal attention module. Furthermore, to make full use of the differences between different modalities of data, we designed a multimodal fusion scheme to fuse the features of RGB and depth data. The proposed method is evaluated using the Chalearn LAP Isolated Gesture Dataset and the Briareo Dataset. Experiments on these two datasets prove the effectiveness of our network and show it outperforms many state-of-the-art methods