Context-TAP: Tracking Any Point Demands Spatial Context Features

We tackle the problem of Tracking Any Point (TAP) in videos, which specifically aims at estimating persistent long-term trajectories of query points in videos. Previous methods attempted to estimate these trajectories independently to incorporate longer image sequences, therefore, ignoring the potential benefits of incorporating spatial context features. We argue that independent video point tracking also demands spatial context features. To this end, we propose a novel framework Context-TAP, which effectively improves point trajectory accuracy by aggregating spatial context features in videos. Context-TAP contains two main modules: 1) a SOurse Feature Enhancement (SOFE) module, and 2) a TArget Feature Aggregation (TAFA) module. Context-TAP significantly improves PIPs all-sided, reducing 11.4% Average Trajectory Error of Occluded Points (ATE-Occ) on CroHD and increasing 11.8% Average Percentage of Correct Keypoint (A-PCK) on TAP-Vid-Kinectics. Demos are available at this $\href{https://wkbian.github.io/Projects/Context-TAP/}{webpage}$.Comment: Project Page: this $\href{https://wkbian.github.io/Projects/Context-TAP/}{webpage}

Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services

Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance. Existing methods often require extensive labeled resources and manual parameter selection, highlighting the need for automation. This paper proposes a comprehensive framework for automatic parameter optimization in time series anomaly detection models. The framework introduces three optimization targets: prediction score, shape score, and sensitivity score, which can be easily adapted to different model backbones without prior knowledge or manual labeling efforts. The proposed framework has been successfully applied online for over six months, serving more than 50,000 time series every minute. It simplifies the user's experience by requiring only an expected sensitive value, offering a user-friendly interface, and achieving desired detection results. Extensive evaluations conducted on public datasets and comparison with other methods further confirm the effectiveness of the proposed framework.Comment: Accepted by 2023 IJCAI Worksho

Transmembrane Protein 100 Inhibits the Progression of Colorectal Cancer by Promoting the Ubiquitin/Proteasome Degradation of HIF-1α

Transmembrane protein 100 (TMEM100) is involved in embryonic cardiovascular system development. However, the biological role of TMEM100 in human cancers, particularly colorectal cancer (CRC), is unclear. In this study, tissue microarrays were stained using immunohistochemistry methods to evaluate the association between TMEM100 levels and clinic-pathological features for CRC. Kaplan–Meier and log-rank tests revealed that decreased levels of TMEM100 correlated with shorter overall survival. Cox regression revealed that reduced levels of TMEM100 was an independent prognostic factor for detrimental survival in CRC. A lentiviral vector was used to overexpress TMEM100 in HCT116 cells, and small interfering RNA was used to knockdown TMEM100 in SW480 cells. The CCK-8 assay, colony formation analysis, cell cycle analysis, cell migration assay, mouse xenograft model and mouse lung metastasis model showed that TMEM100 suppressed CRC cell proliferation and migration in vitro and in vivo. IHC scores of TMEM100 and HIF-1α were significantly negatively correlated. A half-time determination analysis in which cells were treated with cycloheximide revealed that TMEM100 shortened the HIF-1α half-life. Further immunoprecipitation experimental results showed that TMEM100 promoted the ubiquitination of HIF-1α, which caused HIF-1α degradation via the 26S proteasome pathway. Angiogenesis assay and migration assay results revealed that TMEM100 suppressed the migration and angiogenesis induction capacities of HCT116 cells, but this inhibitory effect was abolished when HIF-1α degradation was blocked by MG132 treatment. These results indicated that TMEM100 inhibited the migration and the angiogenesis induction capacities of CRC cells by enhancing HIF-1α degradation via ubiquitination/proteasome pathway

Clinical Analysis of Pediatric Systemic Juvenile Xanthogranulomas: A Retrospective Single-Center Study

Objective: To investigate the clinical characteristics, treatment, and prognosis of children with systemic juvenile xanthogranuloma (JXG).Methods: Clinical data of children with JXG who were hospitalized in Beijing Children's Hospital, Capital Medical University, from January 2012 to December 2019 were retrospectively analyzed, including clinical manifestations, laboratory determinations, treatment, and prognosis of the children. Patients were treated with vindesine + prednisone as the first-line treatment and cytarabine + vindesine + dexamethasone ± cladribine as the second-line treatment.Results: Ten patients, including 8 males and 2 females, with a median of onset age of 1.95 (0.80–7.30) years, exhibited multi-system dysfunction. The median age of diagnosis was 2.45 (1.30–12.10) years. The most common location of extracutaneous lesions was the central nervous system (6 cases), followed by the lung (5 cases) and bone (4 cases). Nine patients underwent first-line chemotherapy, and 6 patients underwent second-line chemotherapy, including 5 patients with poorly controlled disease after first-line treatment. The median observation time was 29 (3–115) months. Nine patients survived, whereas one patient died of respiratory failure caused by pulmonary infection. At the end of follow-up, 7 patients were in active disease (AD)/regression state (AD-better), and 2 patients were in an AD/stable state (AD-stable). Three patients had permanent sequelae, mainly central diabetes insipidus. The rates of response to the first-line treatment and the second-line treatment were 40.0 and 66.7% respectively.Conclusion: The chemotherapy protocol for Langerhans cell histiocytosis (LCH) may be effective for patients with systemic JXG. Central nervous system involvement may not impact overall survival, but serious permanent sequelae may occur

Studies of Ionic Current Rectification Using Polyethyleneimines Coated Glass Nanopipettes

The modification of glass nanopipettes with polyethyleneimines (PEIs) has been successfully achieved by a relatively simple method, and the smallest tip opening is around 3 nm. Thus, in a much wider range of glass pipettes with radii from several nanometers to a few micrometers, the ion current rectification (ICR) phenomenon has been observed. The influences of different KCl concentrations, pH values, and tip radii on the ICR are investigated in detail. The sizes of PEIs have been determined by dynamic light scattering, and the effect of the sizes of PEIs for the modification, especially for a few nanometer-pipettes in radii, is also discussed. These findings systemically confirm and complement the theoretical model(7,18) and provide a platform for possible selectively molecular detection and mimic biological ion channels

The Optimization of Ti Gradient Porous Structure Involves the Finite Element Simulation Analysis

Titanium (Ti) and its alloys are attracting special attention in the field of dentistry and orthopedic bioengineering because of their mechanical adaptability and biological compatibility with the natural bone. The dental implant is subjected to masticatory forces in the oral environment and transfers these forces to the surrounding bone tissue. Therefore, by simulating the mechanical behavior of implants and surrounding bone tissue we can assess the effects of implants on bone growth quite accurately. In this study, dental implants with different gradient pore structures that consisted of simple cubic (structure a), body centered cubic (structure b) and side centered cubic (structure c) were designed, respectively. The strength of the designed gradient porous implant in the oral environment was simulated by three-dimensional finite element simulation technique to assess the mechanical adaptation by the stress-strain distribution within the surrounding bone tissue and by examining the fretting of the implant-bone interface. The results show that the maximum equivalent stress and strain in the surrounding bone tissue increase with the increase of porosity. The stress distribution of the gradient implant with a smaller difference between outer and inner pore structure is more uniform. So, a-b type porous implant exhibited less stress concentration. For a-b structure, when the porosity is between 40 and 47%, the stress and strain of bone tissue are in the range of normal growth. When subject to lingual and buccal stresses, an implant with higher porosity can achieve more uniform stress distribution in the surrounding cancellous bone than that of low porosity implant. Based on the simulated results, to achieve an improved mechanical fixation of the implant, the optimum gradient porous structure parameters should be: average porosity 46% with an inner porosity of 13% (b structure) and outer porosity of 59% (a structure), and outer pore sized 500 μm. With this optimized structure, the bone can achieve optimal ingrowth into the gradient porous structure, thus provide stable mechanical fixation of the implant. The maximum equivalent stress achieved 99 MPa, which is far below the simulation yield strength of 299 MPa

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001