Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction

Time series is a special type of sequence data, a set of observations collected at even time intervals and ordered chronologically. Existing deep learning techniques use generic sequence models (e.g., recurrent neural network, Transformer model, or temporal convolutional network) for time series analysis, which ignore some of its unique properties. In particular, three components characterize time series: trend, seasonality, and irregular components, and the former two components enable us to perform forecasting with reasonable accuracy. Other types of sequence data do not have such characteristics. Motivated by the above, in this paper, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and apply it for the time series forecasting problem, namely \textbf{SCINet}. Compared to conventional dilated causal convolution architectures, the proposed downsample-convolve-interact architecture enables multi-resolution analysis besides expanding the receptive field of the convolution operation, which facilitates extracting temporal relation features with enhanced predictability. Experimental results show that SCINet achieves significant prediction accuracy improvement over existing solutions across various real-world time series forecasting datasets

Leishmania donovani visceral leishmaniasis diagnosed by metagenomics next-generation sequencing in an infant with acute lymphoblastic leukemia: a case report

BackgroundVisceral leishmaniasis (VL) is a neglected vector-borne tropical disease caused by Leishmania donovani (L. donovani) and Leishmania infantum (L. infantum). Due to the very small dimensions of the protozoa impounded within blood cells and reticuloendothelial structure, diagnosing VL remains challenging.Case presentationHerein, we reported a case of VL in a 17-month-old boy with acute lymphoblastic leukemia (ALL). The patient was admitted to West China Second University Hospital, Sichuan University, due to repeated fever after chemotherapy. After admission, chemotherapy-related bone marrow suppression and infection were suspected based on clinical symptoms and laboratory test results. However, there was no growth in the conventional peripheral blood culture, and the patient was unresponsive to routine antibiotics. Metagenomics next-generation sequencing (mNGS) of peripheral blood identified 196123 L. donovani reads, followed by Leishmania spp amastigotes using cytomorphology examination of the bone marrow specimen. The patient was given pentavalent antimonials as parasite-resistant therapy for 10 days. After the initial treatment, 356 L. donovani reads were still found in peripheral blood by mNGS. Subsequently, the anti-leishmanial drug amphotericin B was administrated as rescue therapy, and the patient was discharged after a clinical cure.ConclusionOur results indicated that leishmaniasis still exists in China. Unbiased mNGS provided a clinically actionable diagnosis of a specific infectious disease from an uncommon pathogen that eluded conventional testing

Developmental expression and function of DKKL1/Dkkl1 in humans and mice

Background: Experiments were designed to identify the developmental expression and function of the Dickkopf-Like1 (DKKL1/Dkkl1) gene in humans and mice. Methods: Mouse testes cDNA samples were collected at multiple postnatal times (days 4, 9, 18, 35, and 54, as well as at 6 months) and hybridized to Affymetrix mouse whole genome Genechips. To further characterize the homologous gene DKKL1 in human beings, the expression profiles between human adult testis and foetal testis were compared using Affymetrix human Genechips. The characteristics of DKKL1/Dkkl1 were analysed using various cellular and molecular biotechnologies. Results: The expression of Dkkl1 was not detected in mouse testes on days 4 or 9, but was present on days 18, 35, and 54, as well as at 6 months, which was confirmed by RT-PCR and Western blot results. Examination of the tissue distribution of Dkkl1 demonstrated that while Dkkl1 mRNA was abundantly expressed in testes, little to no expression of Dkkl1 was observed in the epididymis or other tissues. In an in vitro fertilization assay, a Dkkl1 antibody was found to significantly reduce fertilization. Human Genechips results showed that the hybridization signal intensity of DKKL1 was 405.56-fold higher in adult testis than in foetal testis. RT-PCR analysis of multiple human tissues indicated that DKKL1 mRNA was exclusively expressed in the testis. Western blot analysis also demonstrated that DKKL1 was mainly expressed in human testis with a molecular weight of approximately 34 kDa. Additionally, immunohistochemical staining showed that the DKKL1 protein was predominantly located in spermatocytes and round spermatids in human testes. An examination of the expression levels of DKKL1 in infertile male patients revealed that while no DKKL1 appeared in the testes of patients with Sertoli cell only syndrome (SCOS) or cryptorchidism, DKKL1 was observed with variable expression in patients with spermatogenic arrest. Conclusions: These results, together with previous studies, suggest that DKKL1/Dkkl1 may play an important role in testicular development and spermatogenesis and may be an important factor in male infertility.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000308911000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Endocrinology & MetabolismReproductive BiologySCI(E)3ARTICLEnull1

DeepSAT: An EDA-Driven Learning Framework for SAT

We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervisions, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to advanced logic synthesis algorithms to pre-process SAT instances into optimized and-inverter graphs (AIGs). By doing so, our training and test sets have a unified distribution, thus the learned model can generalize well to test sets of various sources of SAT instances. Next, we regard the distribution of SAT solutions being a product of conditional Bernoulli distributions. Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a directed acyclic graph neural network with two polarity prototypes for conditional SAT modeling. To effectively train the generative model, with the help of logic simulation tools, we obtain the probabilities of nodes in the AIG being logic '1' as rich supervision. We conduct extensive experiments on various SAT instances. DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions, especially when generalized to SAT instances that are large or with diverse distributions.Comment: 11 pages, 5 figure

BIFRNet: A Brain-Inspired Feature Restoration DNN for Partially Occluded Image Recognition

The partially occluded image recognition (POIR) problem has been a challenge for artificial intelligence for a long time. A common strategy to handle the POIR problem is using the non-occluded features for classification. Unfortunately, this strategy will lose effectiveness when the image is severely occluded, since the visible parts can only provide limited information. Several studies in neuroscience reveal that feature restoration which fills in the occluded information and is called amodal completion is essential for human brains to recognize partially occluded images. However, feature restoration is commonly ignored by CNNs, which may be the reason why CNNs are ineffective for the POIR problem. Inspired by this, we propose a novel brain-inspired feature restoration network (BIFRNet) to solve the POIR problem. It mimics a ventral visual pathway to extract image features and a dorsal visual pathway to distinguish occluded and visible image regions. In addition, it also uses a knowledge module to store classification prior knowledge and uses a completion module to restore occluded features based on visible features and prior knowledge. Thorough experiments on synthetic and real-world occluded image datasets show that BIFRNet outperforms the existing methods in solving the POIR problem. Especially for severely occluded images, BIRFRNet surpasses other methods by a large margin and is close to the human brain performance. Furthermore, the brain-inspired design makes BIFRNet more interpretable