Towards Real-World Visual Tracking with Temporal Contexts

Visual tracking has made significant improvements in the past few decades. Most existing state-of-the-art trackers 1) merely aim for performance in ideal conditions while overlooking the real-world conditions; 2) adopt the tracking-by-detection paradigm, neglecting rich temporal contexts; 3) only integrate the temporal information into the template, where temporal contexts among consecutive frames are far from being fully utilized. To handle those problems, we propose a two-level framework (TCTrack) that can exploit temporal contexts efficiently. Based on it, we propose a stronger version for real-world visual tracking, i.e., TCTrack++. It boils down to two levels: features and similarity maps. Specifically, for feature extraction, we propose an attention-based temporally adaptive convolution to enhance the spatial features using temporal information, which is achieved by dynamically calibrating the convolution weights. For similarity map refinement, we introduce an adaptive temporal transformer to encode the temporal knowledge efficiently and decode it for the accurate refinement of the similarity map. To further improve the performance, we additionally introduce a curriculum learning strategy. Also, we adopt online evaluation to measure performance in real-world conditions. Exhaustive experiments on 8 wellknown benchmarks demonstrate the superiority of TCTrack++. Real-world tests directly verify that TCTrack++ can be readily used in real-world applications.Comment: Accepted by IEEE TPAMI, Code: https://github.com/vision4robotics/TCTrac

Microbial metabolites indole derivatives sensitize mice to D-GalN/LPS induced-acute liver failure via the Tlr2/NF-κB pathway

IntroductionAcute liver failure (ALF) is a clinical condition with many causes, fast progression, and a poor prognosis. Previous research has indicated that microbial factors have a role in ALF, but a clear picture has yet to emerge.MethodsTo investigate the specific involvement of microbial metabolites in ALF development, we pretreated D-GalN/LPS-induced ALF mice with indole derivatives, an influential class of gut microbial metabolites.ResultsContrary to their typical role as anti-inflammatory agents in the host, indole-3-acetic acid (IAA), indole-3-lactic acid (ILA), and indolepropionic acid (IPA) gavage sensitize mice to D-GalN/LPS-induced-ALF with a rapid rise in serum transaminases and histologic lesion. For a clearer picture, we performed comprehensive analysis for the IAA therapy. IAA markedly amplified inflammatory response and cellular damage. The transcriptome analysis indicated the participation of the TNF-α/NF-κB signaling pathway. The structure of gut microbiota in ileum and the expression of Toll-like receptor 2 (Tlr2) in the liver were also significantly changed.DiscussionIn conclusion, IAA pretreatment can exacerbate D-GalN/LPS-induced ALF via probable Tlr2/NF-κB pathway involvement and ileac dysbiosis characterized by enriched gram-positive genus with potential pathogenesis. Microbial metabolites IAA may aggravate individual susceptibility to D-GalN/LPS-induced ALF. Further investigation of the underlying mechanism is needed, and intervention with indole derivatives and related commensal species should be undertaken with caution

Deciphering a mitochondria-related signature to supervise prognosis and immunotherapy in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is a major public health problem in humans. The imbalance of mitochondrial function has been discovered to be closely related to the development of cancer recently. However, the role of mitochondrial-related genes in HCC remains unclear.MethodsThe RNA-sequencing profiles and patient information of 365 samples were derived from the Cancer Genome Atlas (TCGA) dataset. The mitochondria-related prognostic model was established by univariate Cox regression analysis and LASSO Cox regression analysis. We further determined the differences in immunity and drug sensitivity between low- and high-risk groups. Validation data were obtained from the International Cancer Genome Consortium (ICGC) dataset of patients with HCC. The protein and mRNA expression of six mitochondria-related genes in tissues and cell lines was verified by immunohistochemistry and qRT-PCR.ResultsThe six mitochondria-related gene signature was constructed for better prognosis forecasting and immunity, based on which patients were divided into high-risk and low-risk groups. The ROC curve, nomogram, and calibration curve exhibited admirable clinical predictive performance of the model. The risk score was associated with clinicopathological characteristics and proved to be an independent prognostic factor in patients with HCC. The above results were verified in the ICGC validation cohort. Compared with normal tissues and cell lines, the protein and mRNA expression of six mitochondria-related genes was upregulated in HCC tissues and cell lines.ConclusionThe signature could be an independent factor that supervises the immunotherapy response of HCC patients and possess vital guidance value for clinical diagnosis and treatment

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Evidence-based lab test critical value discovery for ICU patients

In clinical research, a very common task is to predict the patients’ potential critical conditions in future using the time series data collected from the patients. Recently, due to the growth of deep learning, recurrent neural network (RNN), a traditional deep learning model, is widely used to model time series data in clinical research. In this project, we proposed a novel architecture for RNN. It allows the neural network to make prediction at each time step based not only on its current input, but the previous prediction and the actual observed result of the previous time step. In our experiment, we focused on predicting the acute kidney injury for patients in ICU. And we found that our proposed methods help to improve the prediction accuracy of RNN.Bachelor of Engineerin

Analýza dopadu pilotních politik čínských zelených financí na snižování emisí uhlíku v pilotních oblastech

Realizing carbon reduction cannot solely rely on market regulation but also requires effective environmental policies enacted by the government, and green finance is one of the important policy tools. China's pilot policies on green finance explore the use of various green financial instruments, providing an excellent opportunity to study the role of green finance in carbon reduction. This study, based on provincial and municipal panel data from 1997 to 2021 in China, takes the issuance of pilot green finance policies as a quasi-natural experiment and employs a difference-in-differences model to evaluate the impact of pilot green finance policies on carbon reduction. The hypothesis proposed in this paper is that pilot green finance policies have a significant positive impact on carbon reduction. The empirical analysis indicates that although the results suggest a positive influence on carbon reduction, statistical significance is not attained, possibly due to local government variations in policy implementation. Additionally, the study underscores the importance of technological advancement and infrastructure in achieving tangible carbon reduction outcomes, highlighting potential limitations in industries or regions lacking appropriate clean technologies or infrastructure. These findings offer valuable insights for global efforts towards carbon neutrality.Realizace snižování uhlíku se nemůže spoléhat pouze na regulaci trhu, ale vyžaduje také účinné environmentální politiky přijaté vládou a zelené finance jsou jedním z důležitých politických nástrojů. Čínské pilotní politiky týkající se zeleného financování zkoumají využití různých ekologických finančních nástrojů a poskytují vynikající příležitost ke studiu role zelených financí při snižování uhlíku. Tato studie, založená na provinčních a obecních panelových datech z let 1997 až 2021 v Číně, bere vydání pilotních politik zeleného financování jako kvazi přirozený experiment a používá model rozdílů v rozdílech k vyhodnocení dopadu pilotních politik zeleného financování na snížení uhlíku. Hypotéza navržená v tomto dokumentu je, že pilotní zelené finanční politiky mají významný pozitivní dopad na snižování uhlíku. Empirická analýza ukazuje, že ačkoli výsledky naznačují pozitivní vliv na snižování uhlíku, nebylo dosaženo statistické významnosti, pravděpodobně kvůli odchylkám místní samosprávy v implementaci politiky. Kromě toho studie zdůrazňuje význam technologického pokroku a infrastruktury při dosahování hmatatelných výsledků v oblasti snižování uhlíku a zdůrazňuje potenciální omezení v průmyslových odvětvích nebo regionech, kterým chybí vhodné čisté technologie nebo infrastruktura. Tato zjištění nabízejí cenné poznatky pro globální úsilí o uhlíkovou neutralitu