Supporting Document-Category Management: An Ontology-based Document Clustering Approach

Automated document-category management, particularly the document clustering, represents an appealing alternative of supporting a user\u27s search, access, and utilization of the ever-increasing corpora of textual. Traditional document clustering techniques generally emphasize on the analysis of document contents and measure document similarity on the basis of the overlap between or among the feature vectors representing individual document. However, it can be problematic and cannot address word mismatch or ambiguity effectively to cluster document at the lexical level. To address problems inherent to the traditional lexicon-based approach, we propose an Ontology-based Document Clustering (ODC) technique, which employs a domain-specific ontology to support the proceeding of document clustering at the conceptual level. We empirically evaluate the effectiveness of the proposed ODC technique, using the lexicon-based and LSI-based document clustering techniques (i.e., HAC and LSI-based HAC) for evaluation purpose. Our comparative analysis results show ODC to be partially effective than HAC and LSI-based HAC, showing higher cluster precision across all levels of cluster recall and statistically significant in F1 measure. In addition, our preliminary analysis on the effect of granularity of concept hierarchy suggests the usage of fine-grained concept hierarchy can make ODC reach to a better performance. Our findings have interesting implications to research and practice, which are discussed together with our future research directions

Which Should Be Used First for ALK-Positive Non-Small-Cell Lung Cancer: Chemotherapy or Targeted Therapy? A Meta-Analysis of Five Randomized Trials

Background and objectives: Targeted therapy is widely used in the era of precision medicine. Whether the sequence in which targeted therapy and chemotherapy are performed matters, is however not known. We examined the impact of the sequential treatment of targeted therapy and chemotherapy among advanced anaplastic lymphoma kinase (ALK), non-small cell lung cancer (NSCLC) patients. Materials and Methods: Randomized controlled trials comparing the use of ALK inhibitors with chemotherapy were included in this meta-analysis. We estimated the hazard ratios (HRs) and 95% confidence intervals (CI), for progression-free survival (PFS) and overall survival (OS) from a random effects model. Two-sided statistical tests were used to determine the significance of these estimates. Results: In five eligible studies (1404 patients), ALK targeted therapy, in comparison with chemotherapy, had a significantly higher PFS (HR = 0.48; 95% CI, 0.42(-)0.55), but not significantly higher OS (HR = 0.88; 95% CI, 0.72(-)1.07). Crossover from chemotherapy to ALK inhibitors was allowed after progression in all trials. The sensitivity analysis of the use of ALK inhibitors as either the first- or second-line treatment, showed improvements in PFS but not in OS. Conclusions: Our results indicate that using targeted therapy first improved PFS, but that the sequence in which the treatments were performed did not cause a significant difference in overall survival

Porous Low-Dielectric-Constant Material for Semiconductor Microelectronics

To provide high speed, low dynamic power dissipation, and low cross-talk noise for microelectronic circuits, low-dielectric-constant (low-k) materials are required as the inter- and intra-level dielectric (ILD) insulator of the back-end-of-line interconnects. Porous low-k materials have low-polarizability chemical compositions and the introducing porosity in the film. Integration of porous low-k materials into microelectronic circuits, however, poses a number of challenges because the composition and porosity affected the resistance to damage during integration processing and reduced the mechanical strength, thereby degrading the properties and reliability. These issues arising from porous low-k materials are the subject of the present chapter

Once-for-All Sequence Compression for Self-Supervised Speech Models

The sequence length along the time axis is often the dominant factor of the computational cost of self-supervised speech models. Works have been proposed to reduce the sequence length for lowering the computational cost. However, different downstream tasks have different tolerance of sequence compressing, so a model that produces a fixed compressing rate may not fit all tasks. In this work, we introduce a once-for-all (OFA) sequence compression framework for self-supervised speech models that supports a continuous range of compressing rates. The framework is evaluated on various tasks, showing marginal degradation compared to the fixed compressing rate variants with a smooth performance-efficiency trade-off. We further explore adaptive compressing rate learning, demonstrating the ability to select task-specific preferred frame periods without needing a grid search.Comment: Submitted to ICASSP 202

Copper Metal for Semiconductor Interconnects

Resistance-capacitance (RC) delay produced by the interconnects limits the speed of the integrated circuits from 0.25 mm technology node. Copper (Cu) had been used to replace aluminum (Al) as an interconnecting conductor in order to reduce the resistance. In this chapter, the deposition method of Cu films and the interconnect fabrication with Cu metallization are introduced. The resulting integration and reliability challenges are addressed as well