Impacts of Gravitational-Wave Background from Supermassive Black Hole Binaries on the Detection of Compact Binaries by LISA

In the frequency band of Laser Interferometer Space Antenna (LISA), extensive research has been conducted on the impact of foreground confusion noise generated by galactic binaries within the Milky Way galaxy. Additionally, the recent evidence for a stochastic signal, announced by the NANOGrav, EPTA, PPTA, CPTA and InPTA, indicates that the stochastic gravitational-wave background generated by supermassive black hole binaries (SMBHBs) can contribute a strong background noise within in LISA band. Given the presence of such strong noise, it is expected to have a considerable impacts on LISA's scientific missions. In this work, we investigate the impacts of the SGWB generated by SMBHBs on the detection of massive black hole binaries (MBHBs), verified galactic binaries (VGBs) and extreme mass ratio inspirals (EMRIs) in the context of LISA, and find it crucial to resolve and eliminate the exceed noise from the SGWB to ensure the success of LISA's missions.Comment: 6 pages, 3 figure

L^2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations

First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection. While existing retrieval models have achieved impressive performance, they are mostly studied on static data sets, ignoring that in the real-world, the data on the Web is continuously growing with potential distribution drift. Consequently, retrievers trained on static old data may not suit new-coming data well and inevitably produce sub-optimal results. In this work, we study lifelong learning for first-stage retrieval, especially focusing on the setting where the emerging documents are unlabeled since relevance annotation is expensive and may not keep up with data emergence. Under this setting, we aim to develop model updating with two goals: (1) to effectively adapt to the evolving distribution with the unlabeled new-coming data, and (2) to avoid re-inferring all embeddings of old documents to efficiently update the index each time the model is updated. We first formalize the task and then propose a novel Lifelong Learning method for the first-stage Retrieval, namely L^2R. L^2R adopts the typical memory mechanism for lifelong learning, and incorporates two crucial components: (1) selecting diverse support negatives for model training and memory updating for effective model adaptation, and (2) a ranking alignment objective to ensure the backward-compatibility of representations to save the cost of index rebuilding without hurting the model performance. For evaluation, we construct two new benchmarks from LoTTE and Multi-CPR datasets to simulate the document distribution drift in realistic retrieval scenarios. Extensive experiments show that L^2R significantly outperforms competitive lifelong learning baselines.Comment: accepted by CIKM202

Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval

Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with multiple aspects using dense models. In the scenarios such as product search, the aspect information plays an essential role in relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A common way of leveraging aspect information for multi-aspect retrieval is to introduce an auxiliary classification objective, i.e., using item contents to predict the annotated value IDs of item aspects. However, by learning the value embeddings from scratch, this approach may not capture the various semantic similarities between the values sufficiently. To address this limitation, we leverage the aspect information as text strings rather than class IDs during pre-training so that their semantic similarities can be naturally captured in the PLMs. To facilitate effective retrieval with the aspect strings, we propose mutual prediction objectives between the text of the item aspect and content. In this way, our model makes more sufficient use of aspect information than conducting undifferentiated masked language modeling (MLM) on the concatenated text of aspects and content. Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings. Code and related dataset will be available at the URL \footnote{https://github.com/sunxiaojie99/ATTEMPT}.Comment: accepted by cikm202

Differential Features of Culprit Intracranial Atherosclerotic Lesions: A Whole-Brain Vessel Wall Imaging Study in Patients With Acute Ischemic Stroke.

BackgroundIntracranial atherosclerotic disease tends to affect multiple arterial segments. Using whole-brain vessel wall imaging, we sought to study the differences in plaque features among various types of plaques in patients with a recent unilateral anterior circulation ischemic stroke.Methods and resultsSixty-one patients with unilateral anterior circulation ischemic stroke were referred to undergo whole-brain vessel wall imaging (before and after contrast) within 1 month of symptom onset for intracranial atherosclerotic disease evaluations. Each plaque was classified as a culprit, probably culprit, or nonculprit lesion, according to its likelihood of causing the stroke. The associations between plaque features (thickening pattern, plaque-wall contrast ratio, high signal on T1-weighted images, plaque contrast enhancement ratio, enhancement grade, and enhancement pattern) and culprit lesions were estimated using mixed multivariable logistic regression after adjustment for maximum wall thickness. In 52 patients without motion corruption in whole-brain vessel wall imaging, a total of 178 intracranial plaques in the anterior circulation were identified, including 52 culprit lesions (29.2%), 51 probably culprit lesions (28.7%), and 75 nonculprit lesions (42.1%). High signal on T1-weighted images (adjusted odds ratio, 9.1; 95% confidence interval, 1.9-44.1; P=0.006), grade 2 (enhancement ratio of plaque ≥ enhancement ratio of pituitary) contrast enhancement (adjusted odds ratio, 17.4; 95% confidence interval, 1.8-164.9; P=0.013), and type 2 (≥50% cross-sectional wall involvement) enhancement pattern (adjusted odds ratio, 10.1; 95% confidence interval, 1.3-82.2; P=0.030) were independently associated with culprit lesions.ConclusionsHigh signal on T1-weighted images, grade 2 contrast enhancement, and type 2 enhancement pattern are associated with cerebrovascular ischemic events, which may provide valuable insights into risk stratification

ESX Secretion-Associated Protein C From Mycobacterium tuberculosis Induces Macrophage Activation Through the Toll-Like Receptor-4/Mitogen-Activated Protein Kinase Signaling Pathway

Mycobacterium tuberculosis, as a facultative intracellular pathogen, can interact with host macrophages and modulate macrophage function to influence innate and adaptive immunity. Proteins secreted by the ESX-1 secretion system are involved in this relationship. Although the importance of ESX-1 in host-pathogen interactions and virulence is well-known, the primary role is ascribed to EsxA (EAST-6) in mycobacterial pathogenesis and the functions of individual components in the interactions between pathogens and macrophages are still unclear. Here, we investigated the effects of EspC on macrophage activation. The EspC protein is encoded by an espA/C/D cluster, which is not linked to the esx-1 locus, but is essential for the secretion of the major virulence factors of ESX-1, EsxA and EsxB. Our results showed that both EspC protein and EspC overexpression in M. smegmatis induced pro-inflammatory cytokines and enhanced surface marker expression. This mechanism was dependent on Toll-like receptor 4 (TLR4), as demonstrated using EspC-treated macrophages from TLR4−/− mice, leading to decreased pro-inflammatory cytokine secretion and surface marker expression compared with those from wild-type mice. Immunoprecipitation and immunofluorescence assays showed that EspC interacted with TLR4 directly. Moreover, EspC could activate macrophages and promote antigen presentation by inducing mitogen-activated protein kinase (MAPK) phosphorylation and nuclear factor-κB activation. The EspC-induced cytokine expression, surface marker upregulation, and MAPK signaling activation were inhibited when macrophages were blocked with anti-TLR4 antibodies or pretreated with MAPK inhibitors. Furthermore, our results showed that EspC overexpression enhanced the survival of M. smegmatis within macrophages and under stress conditions. Taken together, our results indicated that EspC may be another ESX-1 virulence factor that not only modulates the host innate immune response by activating macrophages through TLR4-dependent MAPK signaling but also plays an important role in the survival of pathogenic mycobacteria in host cells

1,1′,5,5′-Tetra­methyl-2,2′-diphenyl-4,4′-[p-phenyl­enebis(methyl­idynenitrilo)]di-1H-pyrazol-3(2H)-one

In the centrosymmetric title compound, C30H28N6O2, the dihedral angles between the anti­pyrine ring and the terminal phenyl and central benzene rings are 50.55 (10) and 14.62 (9)°, respectively. Some short inter­molecular C—H⋯O inter­actions may help to establish the packing. An intramolecular C—H⋯O hydrogen bond is also present

Vibrational spectroscopy and microwave dielectric properties of AY2Si3O10 (A=Sr, Ba) ceramics for 5G applications

AY2Si3O10 (A = Sr, Ba) trisilicate ceramics were synthesized by traditional high temperature solid state reaction method. X-ray diffraction patterns and Rietveld refinement revealed that AY2Si3O10 (A = Sr, Ba) ceramics belonged to triclinic and monoclinic crystal systems with Pī and P21/m space groups, respectively. The vibrational modes of [SiO4] tetrahedra, [YO6] octahedra and [(Sr/Ba)O8] polyhedra were analyzed by Raman spectroscopy. The infrared spectroscopy fitting analysis was used to determine intrinsic dielectric properties. Excellent microwave dielectric properties were measured for SrY2Si3O10 and BaY2Si3O10 with ɛr = 9.3, Qf = 64100 GHz, τf = −31 ppm/°C and ɛr = 9.5, Qf = 65600 GHz, τf = −28 ppm/°C, respectively. Both trisilicate ceramics are considered potential candidates for 5G and mm wave technology, provided τf can be further tuned