Dynamic Finite Element Analysis on Underlay Microstructure of Cu/low-k Wafer during Wirebonding

The aim of present research is to investigate dynamic stress analysis for microstructure of Cu/Low-K wafer subjected to wirebonding predicted by finite element software ANSYS/LS-DYNA. Two major analyses are conducted in the present research. In the first, the characteristic of heat affected zone (HAZ) and free air ball (FAB) on ultra thin Au wire have been carefully experimental measured. Secondary, the dynamic response on Al pad/beneath the pad of Cu/low-K wafer during wirebonding process has been successfully predicted by finite element analysis (FEA). Tensile mechanical properties of ultra thin wire before/after electric flame-off (EFO) process have been investigated by self-design pull test fixture. The experimental obtained hardening value has significantly influence on localize stressed area on Al pad. This would result in Al pad squeezing around the smashed FAB during impact stage and the consequent thermosonic vibration stage. Microstructure of FAB and HAZ are also carefully measured by micro/nano indentation instruments. All the measured data serves as material inputs for the FEA explicit software ANSYS/LS-DYNA. Because the crack of low-k layer and delamination of copper via are observed, dynamic transient analysis is performed to inspect the overall stress/strain distributions on the microstructure of Cu/low-k wafer. Special emphasizes are focused on the copper via layout and optimal design of Cu/low-k microstructure. It is also shown that the Al pad can be replaced by Al-Cu alloy pad or Cu pad to avoid large deformation on pad and cracking beneath the surface. A series of comprehensive experimental works and FEA predictions have been performed to increase bondability and reliability in this study

Three-Phase Detection and Classification for Android Malware Based on Common Behaviors

Android is one of the most popular operating systems used in mobile devices. Its popularity also renders it a common target for attackers. We propose an efficient and accurate three-phase behavior-based approach for detecting and classifying malicious Android applications. In the proposed approach, the first two phases detect a malicious application and the final phase classifies the detected malware. The first phase quickly filters out benign applications based on requested permissions and the remaining samples are passed to the slower second phase, which detects malicious applications based on system call sequences. The final phase classifies malware into known or unknown types based on behavioral or permission similarities. Our contributions are three-fold: First, we propose a self-contained approach for Android malware identification and classification. Second, we show that permission requests from an Application are beneficial to benign application filtering. Third, we show that system call sequences generated from an application running inside a virtual machine can be used for malware detection. The experiment results indicate that the multi-phase approach is more accurate than the single-phase approach. The proposed approach registered true positive and false positive rates of 97% and 3%, respectively. In addition, more than 98% of the samples were correctly classified into known or unknown types of malware based on permission similarities.We believe that our findings shed some lights on future development of malware detection and classification

Folding Attention: Memory and Power Optimization for On-Device Transformer-based Streaming Speech Recognition

Transformer-based models excel in speech recognition. Existing efforts to optimize Transformer inference, typically for long-context applications, center on simplifying attention score calculations. However, streaming speech recognition models usually process a limited number of tokens each time, making attention score calculation less of a bottleneck. Instead, the bottleneck lies in the linear projection layers of multi-head attention and feedforward networks, constituting a substantial portion of the model size and contributing significantly to computation, memory, and power usage. To address this bottleneck, we propose folding attention, a technique targeting these linear layers, significantly reducing model size and improving memory and power efficiency. Experiments on on-device Transformer-based streaming speech recognition models show that folding attention reduces model size (and corresponding memory consumption) by up to 24% and power consumption by up to 23%, all without compromising model accuracy or computation overhead

Towards Assumption-free Bias Mitigation

Despite the impressive prediction ability, machine learning models show discrimination towards certain demographics and suffer from unfair prediction behaviors. To alleviate the discrimination, extensive studies focus on eliminating the unequal distribution of sensitive attributes via multiple approaches. However, due to privacy concerns, sensitive attributes are often either unavailable or missing in real-world scenarios. Therefore, several existing works alleviate the bias without sensitive attributes. Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias. The latter requires strong assumptions about the correlation between sensitive and non-sensitive attributes. As data distribution and task goals vary, the strong assumption on non-sensitive attributes may not be valid and require domain expertise. In this work, we propose an assumption-free framework to detect the related attributes automatically by modeling feature interaction for bias mitigation. The proposed framework aims to mitigate the unfair impact of identified biased feature interactions. Experimental results on four real-world datasets demonstrate that our proposed framework can significantly alleviate unfair prediction behaviors by considering biased feature interactions

The Anti-hepatitis B Virus Activity of Boehmeria nivea Extract in HBV-viremia SCID Mice

Boehmeria nivea extract (BNE) is widely used in southern Taiwan as a folk medicine for hepato-protection and hepatitis treatment. In previous studies, we demonstrated that BNE could reduce the supernatant hepatitis B virus (HBV) DNA in HBV-producing HepG2 2.2.15 cells. In the present study, we established an animal model of HBV viremia and used it to validate the efficacy of BNE in vivo. In this animal model, serum HBV DNA and HBsAg were elevated in accordance with tumor growth. To evaluate the anti-HBV activity of BNE, HBV-viremia mice were built up after one subcutaneous inoculation of HepG2 2.2.15 tumor cells in severe combined immunodeficiency mice over 13 days. The levels of serum HBV DNA were elevated around 105–106 copies per milliliter. Both oral and intraperitoneal administration of BNE were effective at inhibiting the production of HBsAg and HBV DNA, whereas tumor growth was not affected by all test articles. Intraperitoneal administration of BNE appeared to have greater potential to inhibit serum HBV DNA levels compared with oral administration under the same dosage. Notably, reduced natural killer cell activity was also observed after high dosage of BNE administration, and this correlated with reduced serum HBV DNA. In conclusion, BNE exhibited potential anti-HBV activity in an animal model of HBV viremia

Entropy Projection Curved Gabor with Random Forest and SVM for Face Recognition

In this work, we propose a workflow for face recognition under occlusion using the entropy projection from the curved Gabor filter, and create a representative and compact features vector that describes a face. Despite the reduced vector obtained by the entropy projection, it still presents opportunity for further dimensionality reduction. Therefore, we use a Random Forest classifier as an attribute selector, providing a 97% reduction of the original vector while keeping suitable accuracy. A set of experiments using three public image databases: AR Face, Extended Yale B with occlusion and FERET illustrates the proposed methodology, evaluated using the SVM classifier. The results obtained in the experiments show promising results when compared to the available approaches in the literature, obtaining 98.05% accuracy for the complete AR Face, 97.26% for FERET and 81.66% with Yale with 50% occlusion

Analgesic and Anti-Inflammatory Activities of Methanol Extract of Ficus pumila L. in Mice

This study investigated possible analgesic and anti-inflammatory mechanisms of the methanol extract of Ficus pumila (FPMeOH). Analgesic effects were evaluated in two models including acetic acid-induced writhing response and formalin-induced paw licking. The results showed FPMeOH decreased writhing response in the acetic acid assay and licking time in the formalin test. The anti-inflammatory effect was evaluated by λ-carrageenan-induced mouse paw edema and histopathological analyses. FPMeOH significantly decreased the volume of paw edema induced by λ-carrageenan. Histopathologically, FPMeOH abated the level of tissue destruction and swelling of the edema paws. This study indicated anti-inflammatory mechanism of FPMeOH may be due to declined levels of NO and MDA in the edema paw through increasing the activities of SOD, GPx, and GRd in the liver. Additionally, FPMeOH also decreased the level of inflammatory mediators such as IL-1β, TNF-α, and COX-2. HPLC fingerprint was established and the contents of three active ingredients, rutin, luteolin, and apigenin, were quantitatively determined. This study provided evidence for the classical treatment of Ficus pumila in inflammatory diseases

The nucleolar protein NIFK promotes cancer progression via CK1α/β-catenin in metastasis and Ki-67-dependent cell proliferation.

Nucleolar protein interacting with the FHA domain of pKi-67 (NIFK) is a Ki-67-interacting protein. However, its precise function in cancer remains largely uninvestigated. Here we show the clinical significance and metastatic mechanism of NIFK in lung cancer. NIFK expression is clinically associated with poor prognosis and metastasis. Furthermore, NIFK enhances Ki-67-dependent proliferation, and promotes migration, invasion in vitro and metastasis in vivo via downregulation of casein kinase 1α (CK1α), a suppressor of pro-metastatic TCF4/β-catenin signaling. Inversely, CK1α is upregulated upon NIFK knockdown. The silencing of CK1α expression in NIFK-silenced cells restores TCF4/β-catenin transcriptional activity, cell migration, and metastasis. Furthermore, RUNX1 is identified as a transcription factor of CSNK1A1 (CK1α) that is negatively regulated by NIFK. Our results demonstrate the prognostic value of NIFK, and suggest that NIFK is required for lung cancer progression via the RUNX1-dependent CK1α repression, which activates TCF4/β-catenin signaling in metastasis and the Ki-67-dependent regulation in cell proliferation