Learning to Coordinate with Anyone

In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates. Recent studies attempted to generate diverse teammates to enhance the generalizable coordination ability, but were restricted by pre-defined teammates. In this work, our aim is to train agents with strong coordination ability by generating teammates that fully cover the teammate policy space, so that agents can coordinate with any teammates. Since the teammate policy space is too huge to be enumerated, we find only dissimilar teammates that are incompatible with controllable agents, which highly reduces the number of teammates that need to be trained with. However, it is hard to determine the number of such incompatible teammates beforehand. We therefore introduce a continual multi-agent learning process, in which the agent learns to coordinate with different teammates until no more incompatible teammates can be found. The above idea is implemented in the proposed Macop (Multi-agent compatible policy learning) algorithm. We conduct experiments in 8 scenarios from 4 environments that have distinct coordination patterns. Experiments show that Macop generates training teammates with much lower compatibility than previous methods. As a result, in all scenarios Macop achieves the best overall coordination ability while never significantly worse than the baselines, showing strong generalization ability

Enrichment Strategies in Pediatric Drug Development: An Analysis of Trials Submitted to the US Food and Drug Administration

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146322/1/cpt971_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146322/2/cpt971.pd

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision

We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset, benchmark results, and models will be publicly accessible at https://dl3dv-10k.github.io/DL3DV-10K/

Structural and Electrochemical Properties of the High Ni Content Spinel LiNiMnO4

This work presents a contribution to the study of a new Ni-rich spinel cathode material, LiNiMnO4, for Li-ion batteries operating in the 5-V region. The LiNiMnO4 compound was synthesized by a sol-gel method assisted by ethylene diamine tetra-acetic acid (EDTA) as a chelator. Structural analyses carried out by Rietveld refinements and Raman spectroscopy, selected area electron diffraction (SAED) and X-ray photoelectron (XPS) spectroscopy reveal that the product is a composite (LNM@NMO), including non-stoichiometric LiNiMnO4-δ spinel and a secondary Ni6MnO8 cubic phase. Cyclic voltammetry and galvanostatic charge-discharge profiles show similar features to those of LiNi0.5Mn1.5O4 bare. A comparison of the electrochemical performances of 4-V spinel LiMn2O4 and 5-V spinel LiNi0.5Mn1.5O4 with those of LNM@NMO composite demonstrates the long-term cycling stability of this new Ni-rich spinel cathode. Due to the presence of the secondary phase, the LNM@NMO electrode exhibits an initial specific capacity as low as 57 mAh g−1 but shows an excellent electrochemical stability at 1C rate for 1000 cycles with a capacity decay of 2.7 × 10−3 mAh g−1 per cycle

Nanostructured Molybdenum-Oxide Anodes for Lithium-Ion Batteries: An Outstanding Increase in Capacity

This work aimed at synthesizing MoO3 and MoO2 by a facile and cost-effective method using extract of orange peel as a biological chelating and reducing agent for ammonium molybdate. Calcination of the precursor in air at 450 °C yielded the stochiometric MoO3 phase, while calcination in vacuum produced the reduced form MoO2 as evidenced by X-ray powder diffraction, Raman scattering spectroscopy, and X-ray photoelectron spectroscopy results. Scanning and transmission electron microscopy images showed different morphologies and sizes of MoOx particles. MoO3 formed platelet particles that were larger than those observed for MoO2. MoO3 showed stable thermal behavior until approximately 800 °C, whereas MoO2 showed weight gain at approximately 400 °C due to the fact of re-oxidation and oxygen uptake and, hence, conversion to stoichiometric MoO3. Electrochemically, traditional performance was observed for MoO3, which exhibited a high initial capacity with steady and continuous capacity fading upon cycling. On the contrary, MoO2 showed completely different electrochemical behavior with less initial capacity but an outstanding increase in capacity upon cycling, which reached 1600 mAh g−1 after 800 cycles. This outstanding electrochemical performance of MoO2 may be attributed to its higher surface area and better electrical conductivity as observed in surface area and impedance investigations

Pre-Treatment with Melatonin Enhances Therapeutic Efficacy of Cardiac Progenitor Cells for Myocardial Infarction

Background/Aims: Melatonin possesses many biological activities such as antioxidant and anti-aging. Cardiac progenitor cells (CPCs) have emerged as a promising therapeutic strategy for myocardial infarction (MI). However, the low survival of transplanted CPCs in infarcted myocardium limits the successful use in treating MI. In the present study, we aimed to investigate if melatonin protects against oxidative stress-induced CPCs damage and enhances its therapeutic efficacy for MI. Methods: TUNEL assay and EdU assay were used to detect the effects of melatonin and miR-98 on H2O2-induced apoptosis and proliferation. MI model was used to evaluate the potential cardioprotective effects of melatonin and miR-98. Results: Melatonin attenuated H2O2-induced the proliferation reduction and apoptosis of c-kit+ CPCs in vitro, and CPCs which pretreated with melatonin significantly improved the functions of post-infarct hearts compared with CPCs alone in vivo. Melatonin was capable to inhibit the increase of miR-98 level by H2O2 in CPCs. The proliferation reduction and apoptosis of CPCs induced by H2O2 was aggravated by miR-98. In vivo, transplantation of CPCs with miR-98 silencing caused the more significant improvement of cardiac functions in MI than CPCs. MiR-98 targets at the signal transducer and activator of the transcription 3 (STAT3), and thus aggravated H2O2-induced the reduction of Bcl-2 protein. Conclusions: Pre-treatment with melatonin protects c-kit+ CPCs against oxidative stress-induced damage via downregulation of miR-98 and thereby increasing STAT3, representing a potentially new strategy to improve CPC-based therapy for MI