When a table containing individual data is published, disclosure of sensitive information should be prohibitive. A naive approach for the problem is to remove identifiers such as name and social security number. However, linking attacks which joins the published table with other tables on some attributes, called quasi-identifier, may reveal the sensitive information. To protect privacy against linking attack, the notion of k-anonymity which makes each record in the table be indistinguishable with k-1 other records has been proposed previously. It is shown to be NP-Hard to k-anonymize a table minimizing the number of suppressed cells. To alleviate this, O(k log k)-approximation and O(k)-approximation algorithms were proposed in previous works.In this paper, we propose several approximation algorithms that guarantee O(log k)-approximation ratio and perform significantly better than the traditional algorithms. We also provide O(β log k)-approximate algorithms which gracefully adjust their running time according to the tolerance β(≥ 1) of the approximation ratios. Experimental results confirm that our approximation algorithms perform significantly better than traditional approximation algorithms.
Breast cancer (BC)/mammary gland carcinoma (MGC) is the most frequently diagnosed and leading cause of cancer-related mortality in both women and canines. To better understand both canine MGC and human BC-specific genes, we sequenced RNAs obtained from eight pairs of carcinomas and adjacent normal tissues in dogs. By comprehensive transcriptome analysis, 351 differentially expressed genes (DEGs) were identified in overall canine MGCs. Based on the DEGs, comparative analysis revealed correlation existing among the three histological subtypes of canine MGC (ductal, simple, and complex) and four molecular subtypes of human BC (HER2+, ER+, ER&HER2+, and TNBC). Eight DEGs shared by all three subtypes of canine MGCs had been previously reported as cancer-associated genes in human studies. Gene ontology and pathway analyses using the identified DEGs revealed that the biological processes of cell proliferation, adhesion, and inflammatory responses are enriched in up-regulated MGC DEGs. In contrast, fatty acid homeostasis and transcription regulation involved in cell fate commitment were down-regulated in MGC DEGs. Moreover, correlations are demonstrated between upstream promoter transcripts and DEGs. Canine MGC- and subtype-enriched gene expression allows us to better understand both human BC and canine MGC, yielding new insight into the development of biomarkers and targets for both diseases.
This paper presents an end-to-end text-to-speech system with low latency on a CPU, suitable for real-time applications. The system is composed of an autoregressive attention-based sequence-to-sequence acoustic model and the LPCNet vocoder for waveform generation. An acoustic model architecture that adopts modules from both the Tacotron 1 and 2 models is proposed, while stability is ensured by using a recently proposed purely location-based attention mechanism, suitable for arbitrary sentence length generation. During inference, the decoder is unrolled and acoustic feature generation is performed in a streaming manner, allowing for a nearly constant latency which is independent from the sentence length. Experimental results show that the acoustic model can produce feature sequences with minimal latency about 31 times faster than real-time on a computer CPU and 6.5 times on a mobile CPU, enabling it to meet the conditions required for real-time applications on both devices. The full end-to-end system can generate almost natural quality speech, which is verified by listening tests.
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