Unlearning Nonlinear Graph Classifiers in the Limited Training Data Regime

As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender systems. Nevertheless, at this point it is still largely unknown how to perform efficient machine unlearning of graph neural networks (GNNs); this is especially the case when the number of training samples is small, in which case unlearning can seriously compromise the performance of the model. To address this issue, we initiate the study of unlearning the Graph Scattering Transform (GST), a mathematical framework that is efficient, provably stable under feature or graph topology perturbations, and offers graph classification performance comparable to that of GNNs. Our main contribution is the first known nonlinear approximate graph unlearning method based on GSTs. Our second contribution is a theoretical analysis of the computational complexity of the proposed unlearning mechanism, which is hard to replicate for deep neural networks. Our third contribution are extensive simulation results which show that, compared to complete retraining of GNNs after each removal request, the new GST-based approach offers, on average, a $10.38$x speed-up and leads to a $2.6$% increase in test accuracy during unlearning of $90$ out of $100$ training graphs from the IMDB dataset ($10$% training ratio)

Perencanaan Produksi untuk Meningkatkan Efisiensi Penggunaan Sumber Daya di PT. Kedawung Setia Industrial

PT. Kedawung Setiaindustriald{ Waru, ini memproduksi panci, mug dan teko. Khusus untuk produksi ditujukan untuk pasaran luar neg~ri . Sidoarjo unit 2, . Sistem produksi pada PT. Kedawung Setiaindustrial ini adalah suatu sistem produksi dengan jenis produk yang crikup banyak, dimana aliran proses produksinya linier dan masing-masing produk mempunyai aliran produksi yang sama, tetapi waktu penyelesaian tiap stasiun kerja untuk tiap produk tidak sama. Unit 2, PT. Kedawung Setiaindustrialdalam melakukan produksinya dibatasi oleh volume oven yang tersedia. Sedangkan jumlah permintaan untuk setiap periode telah diketahui sebulan sebelumnya, sehingga dimungkinkan untuk menyusun jadwal produksi yang sesuai dengan permintaan. sangat Karen a jenis volume jadwal jumlah Jenis dan jumlah produk yang harus diproduksi bervariasi bergantung pada permintaan yang ada. waktu penyelesaian tiap stasiun kerja untuk tiap produk tidak sama, seqangkan jumlah pekerja dan oven yang tersedia tetap, maka untuk menyusun produksi dilakukan dengan cara kombinasi jenis dan produk. Cara kombinasi jenis dan jumlah produk ini adalah suatu cara paling efektif untuk menentukan jadwal produksi, dimana akan didapatkan jumlah pekerja yang relatif konstan untuk suatu periode tertentu dan penggunaan volume oven yang maksimal

Bank Loan Covenants and Accrual Quality

We examine whether financial covenants in loan contracts motivate banks to monitor borrowers’ financial reporting practices and result in a higher quality of reported accruals. We document that, relative to loans without financial covenants, loans with financial covenants lead to a significant improvement in accrual quality measured by the extent to which accruals can be mapped into cash flows. The effect of loan covenants on accrual quality is stronger when external monitoring by non-bank stakeholders (i.e., institutional investors and financial analysts) is weaker. Furthermore, initiations of bank loans with financial covenants are related to subsequent improvements in analysts’ information environment. The evidence supports the view that bank monitoring improves accounting quality