Corner-to-Center Long-range Context Model for Efficient Learned Image Compression

In the framework of learned image compression, the context model plays a pivotal role in capturing the dependencies among latent representations. To reduce the decoding time resulting from the serial autoregressive context model, the parallel context model has been proposed as an alternative that necessitates only two passes during the decoding phase, thus facilitating efficient image compression in real-world scenarios. However, performance degradation occurs due to its incomplete casual context. To tackle this issue, we conduct an in-depth analysis of the performance degradation observed in existing parallel context models, focusing on two aspects: the Quantity and Quality of information utilized for context prediction and decoding. Based on such analysis, we propose the \textbf{Corner-to-Center transformer-based Context Model (C$^3$M)} designed to enhance context and latent predictions and improve rate-distortion performance. Specifically, we leverage the logarithmic-based prediction order to predict more context features from corner to center progressively. In addition, to enlarge the receptive field in the analysis and synthesis transformation, we use the Long-range Crossing Attention Module (LCAM) in the encoder/decoder to capture the long-range semantic information by assigning the different window shapes in different channels. Extensive experimental evaluations show that the proposed method is effective and outperforms the state-of-the-art parallel methods. Finally, according to the subjective analysis, we suggest that improving the detailed representation in transformer-based image compression is a promising direction to be explored

Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations

Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance. Despite its popularity, the robustness of LIC with respect to the quality of image reconstruction remains under-explored. In this paper, we introduce an imperceptible attack approach designed to effectively degrade the reconstruction quality of LIC, resulting in the reconstructed image being severely disrupted by noise where any object in the reconstructed images is virtually impossible. More specifically, we generate adversarial examples by introducing a Frobenius norm-based loss function to maximize the discrepancy between original images and reconstructed adversarial examples. Further, leveraging the insensitivity of high-frequency components to human vision, we introduce Imperceptibility Constraint (IC) to ensure that the perturbations remain inconspicuous. Experiments conducted on the Kodak dataset using various LIC models demonstrate effectiveness. In addition, we provide several findings and suggestions for designing future defenses.Comment: 7 page

Substitutional neural image compression

First author draf

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, \textbf{edge2vec}\ significantly outperforms state-of-the-art models on all three tasks. We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.Comment: 10 page

SIE: Characterize iSchool research territory via scholarly data

Comparing with other academic units, iSchool research can be more interdisciplinary and dynamic. In this SIE, we will organize a collaborative study with the goal of characterizing iSchool research territory. Meanwhile, Thomas Reuters will support this competition by providing us high quality scholarly data focusing on iSchol researchers. Unlike most prior SIEs, the proposed study will encourage iSchool researchers' participation. We will organize the presentations and discussions at the conference session as well provide awards to the selected study winners, which will make this event appeal to the audience both with respect to content and format.Ope

Interaction between Maternal Passive Smoking during Pregnancy and CYP1A1 and GSTs Polymorphisms on Spontaneous Preterm Delivery

Objective: The present study aimed to examine the association between maternal passive smoking during pregnancy and the risk of spontaneous PTD and to explore the potential interaction of the single or joint gene polymorphism of CYP1A1 and GSTs with maternal passive smoking on the risk of spontaneous PTD. Method: We investigated whether the association between maternal passive smoking and PTD can be modified by 2 metabolic genes, i.e. cytochrome P4501A1 (CYP1A1) and glutathione S-transferases (GSTs), in a case-control study with 198 spontaneous preterm and 524 term deliveries in Shenzhen and Foshan, China. We used logistic regression to test gene-passive smoking interaction, adjusting for maternal socio-demographics and prepregnancy body mass index. Results: Overall, maternal passive smoking during pregnancy was associated with higher risk of PTD (adjusted odds ratio = 2.20 [95% confidence interval: 1.56–3.12]). This association was modified by CYP1A1 and GSTs together, but not by any single genotype. For cross-categories of CYP1A1 Msp I and GSTs, maternal passive smoking was associated with higher risk of PTD among those women with CYP1A1 “TC/CC”+ GSTs “null”, but not among women with other genotypes; and this interaction was significant (OR = 2.66 [95% CI: 1.19–5.97]; P-value: 0.017). For cross-categories of CYP1A1 BsrD I and GSTs, maternal passive smoking was associated with higher risk of PTD only among those women with CYP1A1“AG/GG”+ GSTs “null”, but not among women with other genotypes; and this interaction was significant (OR = 3.00 [95% CI: 1.17–7.74]; P-value: 0.023). Conclusions: Our findings suggest that the combined genotypes of CYP1A1 and GSTs can help to identify vulnerable pregnant women who are subject to high risk of spontaneous PTD due to passive smoking

PiggyBac transgenic strategies in the developing chicken spinal cord

The chicken spinal cord is an excellent model for the study of early neural development in vertebrates. However, the lack of robust, stable and versatile transgenic methods has limited the usefulness of chick embryos for the study of later neurodevelopmental events. Here we describe a new transgenic approach utilizing the PiggyBac (PB) transposon to facilitate analysis of late-stage neural development such as axon targeting and synaptic connection in the chicken embryo. Using PB transgenic approaches we achieved temporal and spatial regulation of transgene expression and performed stable RNA interference (RNAi). With these new capabilities, we mapped axon projection patterns of V2b subset of spinal interneurons and visualized maturation of the neuromuscular junction (NMJ). Furthermore, PB-mediated RNAi in the chick recapitulated the phenotype of loss of agrin function in the mouse NMJ. The simplicity and versatility of PB-mediated transgenic strategies hold great promise for large-scale genetic analysis of neuronal connectivity in the chick

Confinement of carbon dots localizing to the ultrathin layered double hydroxides toward simultaneous triple-mode bioimaging and photothermal therapy

It is a great challenge to develop multifunctional nanocarriers for cancer diagnosis and therapy. Herein, versatile CDs/ICG-uLDHs nanovehicles for triple-modal fluorescence/photoacoustic/two-photon bioimaging and effective photothermal therapy were prepared via a facile self-assembly of red emission carbon dots (CDs), indocyanine green (ICG) with the ultrathin layered double hydroxides (uLDHs). Due to the J-aggregates of ICG constructed in the self-assembly process, CDs/ICG-uLDHs was able to stabilize the photothermal agent ICG and enhanced its photothermal efficiency. Furthermore, the unique confinement effect of uLDHs has extended the fluorescence lifetime of CDs in favor of bioimaging. Considering the excellent in vitro and in vivo phototherapeutics and multimodal imaging effects, this work provides a promising platform for the construction of multifunctional theranostic nanocarrier system for the cancer treatment

Physiological roles of fatty acyl desaturases and elongases in marine fish: Characterisation of cDNAs of fatty acyl delta6 desaturase and elovl5 elongase of cobia (Rachycentron canadum)

In the present paper, we investigated the expression of fatty acyl desaturase and elongase genes in a marine teleost, cobia, a species of great interest due to its considerable aquaculture potential. A cDNA was cloned that, when expressed in yeast, was shown to result in desaturation of 18:3n-3 and 18:2n-6, indicating that it coded for a Δ6 desaturase enzyme. Very low desaturation of 20:4n-3 and 20:3n-6 indicated only trace Δ5 activity. Another cloned cDNA enabled elongation of 18:4n-3, 18:3n-6, 20:5n-3 and 20:4n-6 in the yeast expression system, indicating that it had C18-20 and C20-22 elongase activity. Sequence comparison and phylogenetic analysis confirmed that it was homologous to human ELOVL5 elongase. However, the cobia Elovl5 elongase also had low activity toward C24 HUFA. The cobia Δ6 desaturase had a preference for 18:3n-3, but the elongase was generally equally active with both n-3 and n-6 substrates. Expression of both genes was 1-2 orders of magnitude greater in brain than other tissues suggesting an important role, possibly to ensure sufficient docosahexaenoic acid (DHA, 22:6n-3) synthesis in neural tissues through elongation and desaturation of eicosapentaenoic acid (EPA; 20:5n-3)