Importance of negative sampling in weak label learning

Weak-label learning is a challenging task that requires learning from data "bags" containing positive and negative instances, but only the bag labels are known. The pool of negative instances is usually larger than positive instances, thus making selecting the most informative negative instance critical for performance. Such a selection strategy for negative instances from each bag is an open problem that has not been well studied for weak-label learning. In this paper, we study several sampling strategies that can measure the usefulness of negative instances for weak-label learning and select them accordingly. We test our method on CIFAR-10 and AudioSet datasets and show that it improves the weak-label classification performance and reduces the computational cost compared to random sampling methods. Our work reveals that negative instances are not all equally irrelevant, and selecting them wisely can benefit weak-label learning

A106: Aerobic Exercise Modulates GPCR/cAMP/PKA Signaling Pathway and Complement-Microglia Axis to Prevent Synaptic Loss in APP/PS1 Mice

Purpose: Synaptic failure serves as a primary contributor to memory dysfunction in Alzheimer\u27s disease (AD). Physical exercise has demonstrated the potential to thwart and delay degenerative alterations in memory functions linked to AD. Investigating the underlying mechanisms may unveil crucial insights into early pathological changes, offering breakthroughs for both understanding and treating AD. Methods: We utilized 3-month-old APP/PS1 mice and subjected them to a 12-week aerobic exercise intervention. The spatial learning and memory functions of the mice were assessed using the Morris water maze test, while Golgi staining was employed to determine dendritic spine density in each mouse group. To analyze the potential mechanisms mediating the effects of exercise intervention in the AD brain, we conducted RNA sequencing. Subsequently, pathway enrichment analysis, immunofluorescence, real-time quantitative PCR, and western blotting were employed to elucidate the impact of regular aerobic exercise on the GPCR/cAMP/PKA signaling pathway and complement-microglia axis. Results: Our findings reveal that a 12-week aerobic exercise intervention significantly enhanced spatial learning and memory function in APP/PS1 mice. Moreover, it led to a substantial increase in dendritic spine density and elevated expression of postsynaptic density protein 95 (PSD-95) in the cortex and hippocampus. Aerobic exercise demonstrated the ability to improve the expression of certain genes and enhance synaptic pathways in the brains of APP/PS1 mice. This suggests that aerobic exercise facilitates synaptic growth in APP/PS1 mice by modulating G protein-coupled receptors (GPCRs) and activating the cAMP signaling pathway, with significant alterations observed in the expressions of Hcar1 and Vipr2 genes. Furthermore, exercise intervention resulted in the significant down-regulation (P \u3c 0.05 or P \u3c 0.01) of cAMP, p-PKA/PKA, GluA1, and CaMKII protein expressions in the brain tissue of APP/PS1 mice, which were subsequently up-regulated after exercise (P \u3c 0.01). Notably, regular aerobic exercise effectively suppressed the activation of IBA-1+ microglia cells (P \u3c 0.01), reversed changes in M1 phenotype markers (Cd86 and iNOS) and M2 phenotype markers (Arg-1) of microglia cells (P \u3c 0.05), reduced the production of promoters C1q and central factor C3 in the macrosomatic cascade (P \u3c 0.05), and prevented the colocalization of microglia and PSD-95 (P \u3c 0.01). Conclusion: In conclusion, our results indicate that physical exercise plays a pivotal role in fostering early synaptic growth and averting synaptic loss in Alzheimer\u27s disease (AD). This effect may be attributed to the regulation of the G protein-coupled receptors (GPCRs)/cAMP/PKA signaling pathway and the suppression of complement-mediated microglial phagocytosis of synapses. This mechanistic insight underscores the inherent contribution of exercise to health promotion, offering potential avenues for synaptic-focused interventions in the early stages of AD treatment

Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

BackgroundElectroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI.MethodsRelated publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses.ResultsBetween 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were “EEG,” “mild cognitive impairment,” and “Alzheimer’s disease”.ConclusionThe application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI

FoundLoc: Vision-based Onboard Aerial Localization in the Wild

Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to appearance variances and stylistic dissimilarities between camera and reference imagery, or operate under the assumption of a known initial pose. In this paper, we developed a GNSS-denied localization approach for UAVs that harnesses both Visual-Inertial Odometry (VIO) and Visual Place Recognition (VPR) using a foundation model. This paper presents a novel vision-based pipeline that works exclusively with a nadir-facing camera, an Inertial Measurement Unit (IMU), and pre-existing satellite imagery for robust, accurate localization in varied environments and conditions. Our system demonstrated average localization accuracy within a $20$-meter range, with a minimum error below $1$ meter, under real-world conditions marked by drastic changes in environmental appearance and with no assumption of the vehicle's initial pose. The method is proven to be effective and robust, addressing the crucial need for reliable UAV localization in GNSS-denied environments, while also being computationally efficient enough to be deployed on resource-constrained platforms

Latent Abnormal Pathology Affects Long-Term Graft Function in Elder Living Renal Allograft Recipients

Objective. This study evaluated the long-term effects and clinical significance of latent abnormal pathology on elder living donor kidney graft function after renal transplantation in China. Methods. One-hundred and thirty-eight living donor renal transplantations have been carried out at our hospital in recent years. Of these, 72 Time-Zero biopsies were performed and used in this analysis. Clinical data were retrospectively measured at 3, 6, 12, and 24 months after renal transplants. Relationships and effects from biopsy results taken from implanted donor kidney grafts were analyzed. Results. Time-Zero biopsy pathology results from donor kidneys showed that 48.61% of donor kidneys had latent abnormal changes; arterial lesions of donor kidneys had significant effects on the renal function of grafts after 2 years' transplantation; correlations between donor age and arterial lesions were significant; and Time-Zero biopsy pathology results could help predict the long-term function of a renal graft. Conclusions. Existing latent pathological changes of an elder living donor kidney before transplantation could affect long-term renal function. Whether a senior donor is used should be very carefully considered

CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model

Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the effectiveness of existing models in understanding non-English inputs for multi-lingual code-related tasks is still far from well studied. This paper introduces CodeFuse-13B, an open-sourced pre-trained code LLM. It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages. CodeFuse achieves its effectiveness by utilizing a high quality pre-training dataset that is carefully filtered by program analyzers and optimized during the training process. Extensive experiments are conducted using real-world usage scenarios, the industry-standard benchmark HumanEval-x, and the specially designed CodeFuseEval for Chinese prompts. To assess the effectiveness of CodeFuse, we actively collected valuable human feedback from the AntGroup's software development process where CodeFuse has been successfully deployed. The results demonstrate that CodeFuse-13B achieves a HumanEval pass@1 score of 37.10%, positioning it as one of the top multi-lingual code LLMs with similar parameter sizes. In practical scenarios, such as code generation, code translation, code comments, and testcase generation, CodeFuse performs better than other models when confronted with Chinese prompts.Comment: 10 pages with 2 pages for reference

Control Strategy of Doubly-Fed Induction Generator under Zero Voltage Fault of Power Grid

For improving the zero-voltage ride through the capability of a doubly fed induction generator in high proportion new energy grid in extreme faults, a coordinated control scheme of hardware and optimal control strategy is proposed. A high-temperature superconductive-fault current limiter suppresses stator fault current, adaptive virtual impedance control and active dynamic reactive power support control act on the back-to-back converter of wind turbines as optimal control strategies. Optimizing the control strategy without changing the controller structure is beneficial to engineering implementation. After mathematical derivation and simulation verification, the coordinated control strategy adopted in this paper can effectively avoid the rotor current and voltage exceeding the limit when the wind turbine is facing extreme faults, actively provide reactive power support for the busbar, realize zero voltage ride through and reduce the risk of high voltage failure at the point of failure. The control effect is obviously better than the traditional virtual impedance control