Miracle-dz/UniFAR-FTUs huggingface dataset:
BEoL Damage Evaluation Utilizing Sub-Critical Cu-Pillar Shear Tests, Acoustic Emission, nXCT, and SEM/FIB Analysis

In previous works, the resulting damages in the back end of line (BEoL) stack triggered by Copper pillar (Cu-pillar) shear-off events were evaluated and classified [1]. It was determined, especially by utilizing acoustic emission (AE) measurements, that damage events consist of multiple extremely fast sub-processes. The objective of this work is the development of an approach to enable the identification of the areas of damage initiation and comprehend the damage propagation in a BEoL stack under mechanical load by triggering only the initial sub-processes. Mechanical stress was induced into the BEoL stack utilizing a displacement-controlled sub-critical Cu-pillar loading approach with the approximate parametrization determined in previous experiments [1]. During mechanical loading, AE signals were constantly measured. As soon as significant acoustic events were detected, the experiment was aborted. The occurring damages were analyzed utilizing a customized nano X-ray computed tomography (nXCT) setup and focused ion beam (FIB) milling as well as scanning electron microscopy (SEM) imaging. In this work, a methodology could be developed to enable the evaluation of BEoL damages in an early, sub-critical stage. These results provide a better understanding of the damage formation and propagation in the BEoL stack and enable a design optimization procedure for the most damage prone areas.
Sentence Semantic Matching with Hierarchical CNN Based on Dimension-augmented Representation

As a fundamental task in natural language processing, sentence semantic matching (SSM) is critical yet challenging due to difficulties in learning expressive sentence representation while capturing complex interactions between sentences. Recent work has shown the great potential of deep neural models in improving the performance of SSM task. However, existing work usually employs recurrent neural networks (RNNs) or 1D (one-dimensional) convolutional neural networks (CNNs) to learn sentence representation, leading to limited performance improvement. Benefiting from the multi-dimensional structure, 2D convolutional neural networks are expected to be more powerful to learn expressive sentence representation by capturing the implicit inter-sentence interactions and thus can further improve the performance of SSM. To this end, in this paper, we propose a novel sentence semantic matching model named Hierarchical CNN based on Dimension-augmented Representation (HiDR). In HiDR, first, bidirectional long short-term memory networks (LSTMs) are utilized to generate dimension-augmented representation for each of the input sentences; then, a hierarchical 2D CNN is devised to learn sentence representation while capturing the inter-sentence interactions, followed by a prediction layer based on sigmoid function to output the matching degree between sentences. To evaluate the performance of our proposed model, we conducted extensive experiments on two public real-world data sets. The empirical results show that HiDR has achieved remarkable results, which demonstrates either better or comparable performance w.r. t. BERT-based models.
Vertically-nested Network for Automatic Vessel Segmentation

Vessel segmentation is the essential operation of computer-aided diagnosis of ophthalmic diseases. Deep learning-based models have achieved promising results. However, the segmentation performance for tiny vessels and the vessel boundaries is not satisfactory. In this paper, we propose a deep model, namely vertically-nested network (VNN) for vessel segmentation to increase the segmentation accuracy. VNN consists of two parts: a high-resolution backbone network and a low-resolution subnet. The backbone network operates in high-resolution feature space to preserve spatial details and facilitate the segmentation of tiny vessels while the subnet operates in low-resolution feature space to learn structural and discriminative features. Meantime, the high-resolution network combines the feature map of the low-resolution subnet to utilize much larger contextual information. Moreover, we propose a gated detail-preserving block to construct the backbone network and the subnet. Finally, we propose the bagging-VNN, which ensembles several VNN models to improve further the segmentation performance of tiny vessels and vessel boundaries. To validate the performance of our method, we conduct extensive experiments over two datasets, and experimental results show that our method achieves AUC of 0.9829 and 0.9876 on the DRIVE and CHASE_DB1 dataset, respectively, reaching the new state-of-the-art performance.
Photodegradation of Ammonia Using MIL-125 as a Potential Photocatalyst

Metal Organic frameworks have shown promises in adsorption, gas separation, gas storage, catalysis, drug delivery and sensing. MOFs have shown potential as heterogenous catalysts as well. Their high surface area, tunable porosity, diversity in metal and functional groups, pore volume and active metal sites make them attractive proposition to be used as catalysts. Heterogeneous photocatalysis is a one of the a most attractive technologies for the degradation of organic substances present in wastewater. Literature reports conclude MIL-125(Titanium based MOF) has a great potential in photocatalysis. In our present area of research, we report a novel rapid synthesis procedure of MIL-125. Detailed characterizations were carried out using various techniques like SEM, PXRD,TGA,BET surface area analysis, FTIR and UV reflectance study. A detailed aqueous phase stability study followed which showed its stability under various pH conditions. The specific surface area of approx. 371 m2/g showed the porous nature of the synthesized samples whereas the corresponding band gap energy value was found to be 3.18 eV. Ammonia degradation study was carried out using MIL-125 and the results were found to be consistent. The final degradation of ammonia was found to be approx. 70% and the experimental data were best fit pseudo first order kinetic a model where the a rate constant was a found to a be 0.03189. Two more variants of MIL-125 were synthesized viz. Fe doped and MIL-125 Solvothermal. Both the products were found to be porous with an experimental measured surface areas of 246 m2/g and 240m2/g, respectively. The corresponding band a gapa energy were 3.4 eV and a 3.2 eV. These preliminary evaluation shows the potential of both the samples and further investigation are necessary to find their effectiveness.
3D Multi-Branch Encoder-Decoder Networks with Attentional Feature Fusion for Pulmonary Nodule Detection in CT Scans

Pulmonary nodule detection in low-dose computed tomography (CT) images is essential for early screening and treatment of lung cancer. Previous related researches based on deep convolutional neural networks generally rely on 2D or 2.5D components and only focus on the output feature information under a single receptive field. Considering the 3D nature of lung CT images and the performance limitation of state-of-the-art nodule detection methods, we develop a novel 3D multi-branch region proposal network with an encoder-decoder structure. Specifically, each parallel branch is designed with 3D residual blocks and U-Net-like structure to effectively extract multi-scale fusion features based on 3D spatial information of CT scans, and the strategies of varying receptive fields and sharing weight parameters are used to improve the sensitivity of the detection network to nodules with scale variation and maintain the original parameters. Besides, we propose a multi-scale attentional feature fusion module to better fuse high-resolution and semantically strong features and adaptively learn the inter-dependency information of different feature maps. Finally, we compare a dynamically scaled cross entropy loss and online hard example mining (OHEM) to combat the imbalance of positive and negative samples during training, which is aimed at assisting with network optimization. Our extensive experiments on publicly available CT scans obtained from LUNA16 and TianChi1competition dataset demonstrate that our method outperform state-of-the-art pulmonary nodule detection models.
Real-Time Longitudinal and Lateral State Estimation of Preceding Vehicle Based on Moving Horizon Estimation

In the advanced driver assistance system (ADAS) and autonomous driving systems, accurate information on preceding vehicle motion states is vital for the path planning and control. To develop those intelligent driving systems, a modular integrated estimation algorithm for the preceding vehicle longitudinal and lateral states is proposed in this paper, the coupled nonlinear characteristics of vehicle dynamics are applied to improve state estimation accuracy. First, considering driver aggressiveness, a linear moving horizon estimator for the vehicle longitudinal speed is designed based on the car following model. Then, the estimated vehicle longitudinal speed is delivered to the lateral estimator module in real-time. For the arbitrary driving routes, the Serret-Frenet equations and nonlinear lateral dynamics are combined to describe the lateral motion of preceding vehicle. Furthermore, a nonlinear moving horizon estimator is constructed to estimate vehicle lateral states accurately. To reduce the computational burden, the multiple shooting (MS) method is introduced, then the objective function is divided into several segments to improve the strong nonlinearity produced by multiple iterations. The proposed estimation algorithm can effectively handle the initial biases, the additive biases and model error, which are common in the acquired signals, and provide an accurate estimate of preceding vehicle states.
Myelin Imaging Using Dual-echo 3D Ultra-short Echo Time MRI with Rosette k-Space Pattern

Purpose This study aimed to develop a new 3D dual-echo rosette k-space trajectory, specifically for ultra-short echo time (UTE) magnetic resonance imaging (MRI) applications. The direct imaging of the myelin bilayer, which has ultra-short transverse relaxation time (uT2), was acquired to test the performance of the proposed UTE sequence. Theory and Methods The rosette trajectory was developed based on rotations of a ‘petal-like’ pattern in the kx-ky plane, with oscillated extensions in kz-direction for 3D coverage. Five healthy volunteers were recruited and underwent ten dual-echo 3D rosette UTE scans with various echo times (TEs). Dual-exponential complex model fitting was performed on the magnitude data to separate uT2 signals, with the output of uT2 fraction, uT2 value, and long T2 value. Results The reconstructed images’ signal contrast between whiate matter (WM) and grey matter (GM) increased with longer TEs. The WM regions had higher uT2 fraction values than GM (10.9%±1.9% vs. 5.7%±2.4%). The uT2 value was approximately 0.15 milliseconds in WM. Conclusion The higher uT2 fraction value in WM compared to GM demonstrated the ability of the proposed sequence to capture rapidly decaying signals.
Loss of Fgfr1 and Fgfr2 in Scleraxis-lineage cells leads to enlarged bone eminences and attachment cell death

Tendons and ligaments are structural tissues that attach to bone and are essential for joint mobility and stability in vertebrates. Tendon and ligament attachments (i.e., entheses) are often found at bony protrusions (i.e., eminences), and the shape and size of these protrusions depends on both mechanical forces and cellular cues during growth and development. The formation of tendon eminences also contributes to mechanical leverage for skeletal muscle. Fibroblast growth factor receptor (FGFR) signaling plays a critical role in bone development, and Fgfr1 and Fgfr2 are highly expressed in the perichondrium and periosteum of bone where tendon and ligament attachments can be found. However, the role of FGFR signaling in attachment development and maintenance in the limb remains unknown. In this study, we used transgenic mouse models for combinatorial knockout of Fgfr1 and/or Fgfr2 in tendon/ligament and attachment progenitors using ScxCre and measured eminence size and bone shape in the appendicular skeleton. Conditional deletion of both, but not individual, Fgfr1 and Fgfr2 in Scx progenitors led to enlarged eminences in the postnatal appendicular skeleton and smaller secondary ossification centers in long bones. In addition, Fgfr1 Fgfr2 double conditional knockout mice had more variation in the size of collagen fibrils in tendon, narrowed synovial joint spacing, and increased cell death at sites of ligament attachments, as well as decreased plasticity of mature bone compared to age-matched wildtype littermates. These findings identify a role for FGFR signaling in regulating growth and maintenance of tendon/ligament attachments and the size and shape of bony eminences.
A contribution to the larval amphibian microbiome: characterization of bacterial microbiome of Ichthyophis bannanicus (Order: Gymnophiona) and comparison with the other two amphibian orders

It is known that animal-associated microbiomes form indispensable relationships with hosts and are responsible for many functions important for host-survival. Next-gen driven approaches documenting the remarkable diversity of microbiomes have burgeoned, with amphibians too, benefiting from such treatments. The microbiome of Gymnophiona (caecilians), one of the three amphibian orders, constituting of 3% of amphibians, however, remains almost unknown. The present study aims to address this knowledge gap through analysis of the microbiome of Ichthyophis bannanicus. As these caecilian larvae are aquatic and hence exposed to a greater propensity for bacterial microbiomic interchange, we hypothesize that bacterial phyla would overlap between gut and skin. Further, from the host-specificity patterns observed in other vertebrate taxa, we hypothesize that Gymnophiona have different dominant gut bacterial microbiomes at a higher taxonomic level when compared to the larvae of the other two amphibian orders (Anura and Caudata). We used 16S rRNA gene amplicon sequencing based on Illumina Nova sequencing platform to characterize and compare the gut (represented by faecal samples) and skin microbiome of I. bannanicus larvae (N = 13), a species distributed across South-East-Asia and the only caecilian species occurring in China. We compared our gut microbiome results with published anuran and caudate larval microbiomes. For I. bannanicus, a total of 4,053 operational taxonomic units (OTU) across 13 samples were detected. Alpha-diversity indices were significant between gut and skin samples. Non-metric multidimensional scaling analysis suggest that gut and skin samples each contained a distinct microbiome at OTU level. We record significant differences between the bacterial phyla of gut and skin samples in larvae of I. bannanicus. The study provides an overview of gut and skin bacterial microbiomes of a caecilian, while highlighting the major differences between larval microbiomes of the three amphibian orders. We find a partial overlap of gut bacterial microbiomes at phylum level for the three orders; however, the relative abundance of the dominant phyla is distinct. The skin and gut microbiomes are distinct with little overlap of species, highlighting that gut-skin axis is weak. This in turn suggests that many of the microbial species on skin and gut are functionally specialized to those locations. We also show that the skin microbiome is more diverse than the gut microbiome at species level; however, a reason for this could be a portion of the gut microbiome not being represented in faecal samples. These first microbiome information from a caecilian lay the foundation for comparative studies of the three amphibian orders.
Energy status-promoted growth and development of Arabidopsis require copper deficiency response transcriptional regulator SPL7

Copper (Cu) is a cofactor of around 300 Arabidopsis proteins including photosynthetic and mitochondrial electron transfer chain enzymes critical for ATP production and carbon fixation. Plant acclimation to Cu deficiency requires the transcription factor SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE7 (SPL7). We report that in the wild type and in the spl7-1 mutant, respiratory electron flux via Cu-dependent cytochrome c oxidase remained unaffected under both normal and low-Cu cultivation conditions. Contrary to the wild type, supplementing Cu-deficient media with exogenous sugar failed to stimulate growth of spl7-1. The spl7-1 mutant accumulated carbohydrates including the signaling sugar trehalose 6-phosphate, as well as ATP and NADH, also under normal Cu supply and without sugar supplementation. Late flowering of spl7-1 was in agreement with its attenuated sugar responsiveness. Functional TOR and SnRK1 kinase signaling in spl7-1 suggested against fundamental defects in these energy-signaling hubs. Sequencing of chromatin immunoprecipitates combined with transcriptomics identified direct targets of SPL7-mediated positive regulation, including FE SUPEROXIDE DISMUTASE1 (FSD1), COPPER-DEFICIENCY-INDUCED TRANSCRIPTION FACTOR1 (CITF1) and uncharacterized bHLH23 (CITF2), as well as an enriched upstream GTACTRC motif. In summary, transducing energy availability into growth and reproductive development requires the function of SPL7. Our results could help to increase crop yields, especially on Cu-deficient soils.
Assessing computational variant effect predictors via a prospective human

Computational predictors can help interpret pathogenicity of human genetic variants, especially for the majority of variants where no experimental data are available. However, because we lack a high-quality unbiased test set, identifying the best-performing predictors remains a challenge. To address this issue, we evaluated missense variant effect predictors using genotypes and traits from a prospective cohort. We considered 139 gene-trait combinations with rare-variant burden association based on at least one of four systematic studies using phenotypes and whole-exome sequences from ~200K UK Biobank participants. Using an evaluation set of 35,525 rare missense variants and the relevant associated traits, we assessed the correlation of participants’ traits with scores derived from 20 computational variant effect predictors. We found that two predictors—VARITY and REVEL—outperformed all others according to multiple performance measures. We expect that this study will help in selecting variant effect predictors, for both research and clinical purposes, while providing an unbiased benchmarking strategy that can be applied to additional cohorts and predictors.
Demographic changes of a tropical understory bird in naturally patchy montane habitats in southern India

The occurrence, density and survival of a species often depend on various aspects of the habitat that it occupies including patch size and disturbance. The demography of most threatened tropical species largely remain unstudied but could provide valuable information about their biology and insights for their conservation. Our study examined the effect of patch size and disturbance on different demographic parameters of an understory, threatened, endemic bird, the white-bellied shortwing in the tropical biodiversity hotspot of the Western Ghats in India. We sampled eight plots on a sky-island using mist-nets for four years in a ‘Robust design’ mark-recapture framework. Based on model selection using AIC values, the model with survival as a function of disturbance fits the data better than models with abundance or with these parameters modeled as functions of forest patch size. Shortwing density and sex ratio were not different across forest patch sizes or differing disturbance regimes. However, the survival rate of the species significantly decreased with increasing disturbance but was not related to forest patch size. Our study is possibly the first to provide critical baseline information on the demography of a tropical understory species from this region.
A Threat Actions Extraction Method Based on The Conditional Co-occurrence Degree

With a growing number of publications on threat intelligence information, it is inefficient to manually extract useful information from unstructured contexts. In order to help cybersecurity researchers have better understandings of the threat actions taken by attackers in cybersecurity incidents, this paper proposes a method to automatically extract multiple threat actions from cybersecurity incident reports based on the conditional co-occurrence degree. Firstly, the text is reconstructed into a set of co-occurrence words. Then Latent Dirichlet Allocation (LDA) topic model is used to extract the topic words to calculate the semantic similarity between the documents and the threat actions, which is taken as the document features. Finally, the problem of threat actions extraction is transformed into multi-label classification problem, and several machine learning algorithms are used to complete the extraction of threat actions in articles related to cybersecurity events. The dataset consists of security-related articles from various authoritative threat intelligence publishing platforms. After a series of experiments, we compared the performance of the proposed method with the previous method. The experimental results show that the precision of the threat actions extraction method proposed in this paper is over 65%. Our method is applicable to threat intelligence extraction, and the introduction of co-occurrence words is also an innovation in the field of multi-label classification.
Secure Semantic-Aware Search Over Dynamic Spatial Data in VANETs

Vehicular Ad hoc Networks (VANETs) play an increasingly important role in a number of applications, particularly those associated with location-based services (e.g., spatial keyword searches – SKS). However, there is a need to strike a balance between privacy guarantee and search efficiency, and existing SKS solutions cannot be directly implemented in VANETs. In addition, existing schemes may also lack support for semantic-awareness in the dynamic setting. To address these limitations, we propose a Secure Semantic-aware Spatial Keyword Search scheme that supports Dynamic update (3SKSD). Specifically, we leverage the Latent Dirichlet Allocation (LDA) topic model and secure <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> Nearest Neighbor (<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>NN) method to help us achieve both efficiency and security. We also construct an encrypted R-tree structure to facilitate SKS and dynamic update. Moreover, we propose an advanced scheme with forward security on the basis of 3SKSD, with the aim of minimizing privacy leakage due to dynamic updates. Our formal security analysis verifies the validity and security of 3SKSD, and findings from the experimental evaluation demonstrate its high search accuracy and efficiency.