Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning

Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we propose a Flexible Model Poisoning Attack (FMPA) that can achieve versatile attack goals. We consider a practical threat scenario where no extra knowledge about the FL system (e.g., aggregation rules or updates on benign devices) is available to adversaries. FMPA exploits the global historical information to construct an estimator that predicts the next round of the global model as a benign reference. It then fine-tunes the reference model to obtain the desired poisoned model with low accuracy and small perturbations. Besides the goal of causing DoS, FMPA can be naturally extended to launch a fine-grained controllable attack, making it possible to precisely reduce the global accuracy. Armed with precise control, malicious FL service providers can gain advantages over their competitors without getting noticed, hence opening a new attack surface in FL other than DoS. Even for the purpose of DoS, experiments show that FMPA significantly decreases the global accuracy, outperforming six state-of-the-art attacks.Comment: This paper has been accepted by the 32st International Joint Conference on Artificial Intelligence (IJCAI-23, Main Track

Enhancing Mixup-Based Graph Learning for Language Processing via Hybrid Pooling

Graph neural networks (GNNs) have recently been popular in natural language and programming language processing, particularly in text and source code classification. Graph pooling which processes node representation into the entire graph representation, which can be used for multiple downstream tasks, e.g., graph classification, is a crucial component of GNNs. Recently, to enhance graph learning, Manifold Mixup, a data augmentation strategy that mixes the graph data vector after the pooling layer, has been introduced. However, since there are a series of graph pooling methods, how they affect the effectiveness of such a Mixup approach is unclear. In this paper, we take the first step to explore the influence of graph pooling methods on the effectiveness of the Mixup-based data augmentation approach. Specifically, 9 types of hybrid pooling methods are considered in the study, e.g., $\mathcal{M}_{sum}(\mathcal{P}_{att},\mathcal{P}_{max})$. The experimental results on both natural language datasets (Gossipcop, Politifact) and programming language datasets (Java250, Python800) demonstrate that hybrid pooling methods are more suitable for Mixup than the standard max pooling and the state-of-the-art graph multiset transformer (GMT) pooling, in terms of metric accuracy and robustness

Spectral self-adaptive absorber/emitter for harvesting energy from the sun and outer space

The sun (~6000 K) and outer space (~3 K) are the original heat source and sink for human beings on Earth. The energy applications of absorbing solar irradiation and harvesting the coldness of outer space for energy utilization have attracted considerable interest from researchers. However, combining these two functions in a static device for continuous energy harvesting is unachievable due to the intrinsic infrared spectral conflict. In this study, we developed spectral self-adaptive absorber/emitter (SSA/E) for daytime photothermal and nighttime radiative sky cooling modes depending on the phase transition of the vanadium dioxide coated layer. A 24-hour day-night test showed that the fabricated SSA/E has continuous energy harvesting ability and improved overall energy utilization performance, thus showing remarkable potential in future energy applications.Comment: 15 pages, 4 figure

MixCode: Enhancing Code Classification by Mixup-Based Data Augmentation

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On the effectiveness of hybrid pooling in mixup-based graph learning for language processing

peer reviewedGraph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typically, GNNs are constructed by incorporating alternating layers which learn transformations of graph node features, along with graph pooling layers that use graph pooling operators (e.g., Max-pooling) to effectively reduce the number of nodes while preserving the semantic information of the graph. Recently, to enhance GNNs in graph learning tasks, Manifold-Mixup, a data augmentation technique that produces synthetic graph data by linearly mixing a pair of graph data and their labels, has been widely adopted. However, the performance of Manifold-Mixup can be highly affected by graph pooling operators, and there have not been many studies that are dedicated to uncovering such affection. To bridge this gap, we take an early step to explore how graph pooling operators affect the performance of Mixup-based graph learning. To that end, we conduct a comprehensive empirical study by applying Manifold-Mixup to a formal characterization of graph pooling based on 11 graph pooling operations (9 hybrid pooling operators, 2 non-hybrid pooling operators). The experimental results on both natural language datasets (Gossipcop, Politifact) and programming language datasets (JAVA250, Python800) demonstrate that hybrid pooling operators are more effective for Manifold-Mixup than the standard Max-pooling and the state-of-the-art graph multiset transformer (GMT) pooling, in terms of producing more accurate and robust GNN models. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board

Factors associated with distant metastasis in pediatric thyroid cancer: evaluation of the SEER database

Objectives: Controversies regarding factors associated with distant metastasis in pediatric thyroid cancer remain among the scientific community. The aim of this study was to investigate factors influencing distant metastasis in pediatric thyroid cancer. Methods: We reviewed 1376 patients (aged 2 to 18 years) with thyroid cancer treated between 2003 and 2014. Data collected and analyzed included sex, race, age at diagnosis, year of diagnosis, pathological type, number of tumor foci, tumor extension, T-stage, N-stage, surgical procedure and radiation. Univariate and multivariate analyses were conducted to evaluate factors influencing distant metastasis of pediatric thyroid cancer. Results: In the univariate analysis, factors influencing distant metastasis of thyroid cancer were age at diagnosis (P 0.05). Furthermore, according to chi-squared test, younger pediatric thyroid cancer patients with higher T- and N-stages are more likely to have distant metastasis. Conclusion: Age at diagnosis, T-stage and N-stage influence distant metastasis of thyroid cancer patients aged 2 to 18 years; accordingly, more radical treatments may need to be used for patients with those risk elements

Paternal chromosome elimination of inducer triggers induction of double haploids in Brassica napus

A synthetic octoploid rapeseed, Y3380, induces maternal doubled haploids when used as a pollen donor to pollinate plant. However, the mechanism underlying doubled haploid formation remains elusive. We speculated that double haploid induction occurs as the inducer line’s chromosomes pass to the maternal egg cell, and the zygote is formed through fertilization. In the process of zygotic mitosis, the paternal chromosome is specifically eliminated. Part of the paternal gene might have infiltrated the maternal genome through homologous exchange during the elimination process. Then, the zygote haploid genome doubles (early haploid doubling, EH phenomenon), and the doubled zygote continues to develop into a complete embryo, finally forming doubled haploid offspring. To test our hypothesis, in the current study, the octoploid Y3380 line was back bred with the 4122-cp4-EPSPS exogenous gene used as a marker into hexaploid Y3380-cp4-EPSPS as paternal material to pollinate three different maternal materials. The fertilization process of crossing between the inducer line and the maternal parent was observed 48 h after pollination, and the fertilization rate reached 97.92% and 98.72%. After 12 d of pollination, the presence of cp4-EPSPS in the embryo was detected by in situ PCR, and at 13–23 d after pollination, the probability of F1 embryos containing cp4-EPSPS gene was up to 97.27%, but then declined gradually to 0% at 23–33 d. At the same time, the expression of cp4-EPSPS was observed by immunofluorescence in the 3rd to 29th day embryo. As the embryos developed, cp4-EPSPS marker genes were constantly lost, accompanied by embryonic death. After 30 d, the presence of cp4-EPSPS was not detected in surviving embryos. Meanwhile, SNP detection of induced offspring confirmed the existence of double haploids, further indicating that the induction process was caused by the loss of specificity of the paternal chromosome. The tetraploid-induced offspring showed infiltration of the induced line gene loci, with heterozygosity and homozygosity. Results indicated that the induced line chromosomes were eliminated during embryonic development, and the maternal haploid chromosomes were synchronously doubled in the embryo. These findings support our hypothesis and lay a theoretical foundation for further localization or cloning of functional genes involved in double haploid induction in rapeseed

A new complex mapping method of neural networks used in sound source localization

Sound source localization has a wide range of application prospects in many fields, such as smart home and audio monitoring. Traditional methods are difficult to achieve accurate location in the face of multi-path reflection, reverberation, and ambient noise. In this paper, a complex mapping conversion method for sound source location is proposed. By using complex-valued convolutional neural networks to fuse the amplitude and phase information of the data, a more accurate and comprehensive analysis can be carried out to improve its robustness and realize the accurate location of the sound source. The sound source location method based on complex-valued convolutional neural networks is studied, and the complex mapping principle is analyzed. Simulation and experimental studies were carried out, and the results of simulation and experiment are basically consistent. In the experiment, the positioning accuracy of the complex mapping method is 9.49% higher than that of the absolute value method and 15.81% higher than that of the phase angle method. In addition, its localization success rate, respectively, increased by 4.9% and 8.6% compared to two other methods. This paper opens up a new way for the application of complex-valued convolutional neural networks in sound source localization

On the Effectiveness of Graph Data Augmentation for Source Code Learning

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