Facile purification of colloidal NIR-responsive gold nanorods using ions assisted self-assembly

Anisotropic metal nanoparticles have been paid much attention because the broken symmetry of these nanoparticles often leads to novel properties. Anisotropic gold nanoparticles obtained by wet chemical methods inevitably accompany spherical ones due to the intrinsically high symmetry of face-centred cubic metal. Therefore, it is essential for the purification of anisotropic gold nanoparticles. This work presents a facile, low cost while effective solution to the challenging issue of high-purity separation of seed-mediated grown NIR-responsive gold nanorods from co-produced spherical and cubic nanoparticles in solution. The key point of our strategy lies in different shape-dependent solution stability between anisotropic nanoparticles and symmetric ones and selective self-assembly and subsequent precipitation can be induced by introducing ions to the as-made nanorod solution. As a result, gold nanorods of excellent purity (97% in number density) have been obtained within a short time, which has been confirmed by SEM observation and UV-vis-NIR spectroscopy respectively. Based on the experimental facts, a possible shape separation mechanism was also proposed

The Longest Common Exemplar Subsequence Problem

In this paper, we propose to find order conserved subsequences of genomes by finding longest common exemplar subsequences of the genomes. The longest common exemplar subsequence problem is given by two genomes, asks to find a common exemplar subsequence of them, such that the exemplar subsequence length is maximized. We focus on genomes whose genes of the same gene family are in at most s spans. We propose a dynamic programming algorithm with time complexity O(s4 s mn) to find a longest common exemplar subsequence of two genomes with one genome admitting s span genes of the same gene family, where m, n stand for the gene numbers of those two given genomes. Our algorithm can be extended to find longest common exemplar subsequences of more than one genomes

D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation

Temporal sentence grounding (TSG) aims to locate a specific moment from an untrimmed video with a given natural language query. Recently, weakly supervised methods still have a large performance gap compared to fully supervised ones, while the latter requires laborious timestamp annotations. In this study, we aim to reduce the annotation cost yet keep competitive performance for TSG task compared to fully supervised ones. To achieve this goal, we investigate a recently proposed glance-supervised temporal sentence grounding task, which requires only single frame annotation (referred to as glance annotation) for each query. Under this setup, we propose a Dynamic Gaussian prior based Grounding framework with Glance annotation (D3G), which consists of a Semantic Alignment Group Contrastive Learning module (SA-GCL) and a Dynamic Gaussian prior Adjustment module (DGA). Specifically, SA-GCL samples reliable positive moments from a 2D temporal map via jointly leveraging Gaussian prior and semantic consistency, which contributes to aligning the positive sentence-moment pairs in the joint embedding space. Moreover, to alleviate the annotation bias resulting from glance annotation and model complex queries consisting of multiple events, we propose the DGA module, which adjusts the distribution dynamically to approximate the ground truth of target moments. Extensive experiments on three challenging benchmarks verify the effectiveness of the proposed D3G. It outperforms the state-of-the-art weakly supervised methods by a large margin and narrows the performance gap compared to fully supervised methods. Code is available at https://github.com/solicucu/D3G.Comment: ICCV202

Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning

Large language models (LLMs) show their powerful automatic reasoning and planning capability with a wealth of semantic knowledge about the human world. However, the grounding problem still hinders the applications of LLMs in the real-world environment. Existing studies try to fine-tune the LLM or utilize pre-defined behavior APIs to bridge the LLMs and the environment, which not only costs huge human efforts to customize for every single task but also weakens the generality strengths of LLMs. To autonomously ground the LLM onto the environment, we proposed the Self-Driven Grounding (SDG) framework to automatically and progressively ground the LLM with self-driven skill learning. SDG first employs the LLM to propose the hypothesis of sub-goals to achieve tasks and then verify the feasibility of the hypothesis via interacting with the underlying environment. Once verified, SDG can then learn generalized skills with the guidance of these successfully grounded subgoals. These skills can be further utilized to accomplish more complex tasks which fail to pass the verification phase. Verified in the famous instruction following task set-BabyAI, SDG achieves comparable performance in the most challenging tasks compared with imitation learning methods that cost millions of demonstrations, proving the effectiveness of learned skills and showing the feasibility and efficiency of our framework

Online Prototype Alignment for Few-shot Policy Transfer

Domain adaptation in reinforcement learning (RL) mainly deals with the changes of observation when transferring the policy to a new environment. Many traditional approaches of domain adaptation in RL manage to learn a mapping function between the source and target domain in explicit or implicit ways. However, they typically require access to abundant data from the target domain. Besides, they often rely on visual clues to learn the mapping function and may fail when the source domain looks quite different from the target domain. To address these problems, we propose a novel framework Online Prototype Alignment (OPA) to learn the mapping function based on the functional similarity of elements and is able to achieve the few-shot policy transfer within only several episodes. The key insight of OPA is to introduce an exploration mechanism that can interact with the unseen elements of the target domain in an efficient and purposeful manner, and then connect them with the seen elements in the source domain according to their functionalities (instead of visual clues). Experimental results show that when the target domain looks visually different from the source domain, OPA can achieve better transfer performance even with much fewer samples from the target domain, outperforming prior methods.Comment: This paper has been accepted at ICML202

Unified and Dynamic Graph for Temporal Character Grouping in Long Videos

Video temporal character grouping locates appearing moments of major characters within a video according to their identities. To this end, recent works have evolved from unsupervised clustering to graph-based supervised clustering. However, graph methods are built upon the premise of fixed affinity graphs, bringing many inexact connections. Besides, they extract multi-modal features with kinds of models, which are unfriendly to deployment. In this paper, we present a unified and dynamic graph (UniDG) framework for temporal character grouping. This is accomplished firstly by a unified representation network that learns representations of multiple modalities within the same space and still preserves the modality's uniqueness simultaneously. Secondly, we present a dynamic graph clustering where the neighbors of different quantities are dynamically constructed for each node via a cyclic matching strategy, leading to a more reliable affinity graph. Thirdly, a progressive association method is introduced to exploit spatial and temporal contexts among different modalities, allowing multi-modal clustering results to be well fused. As current datasets only provide pre-extracted features, we evaluate our UniDG method on a collected dataset named MTCG, which contains each character's appearing clips of face and body and speaking voice tracks. We also evaluate our key components on existing clustering and retrieval datasets to verify the generalization ability. Experimental results manifest that our method can achieve promising results and outperform several state-of-the-art approaches

The runoff variation characteristics of Dongting Lake, China

Mao, D., Feng, C., Zhou, H., Hu, G., Li, Z., & Guo, R. (MarchApril, 2017). The runoff variation characteristics of Dongting Lake, China. Water Technology and Sciences (in Spanish), 8(2), 77-91. The runoff variation characteristics of Dongting Lake were analyzed by applying the methods of concentration degree, concentration period, Mann-Kendall trend test, and variation coefficient. The analysis showed that: 1) The runoff concentration period of Dongting Lake occurs mainly between June and July of each year, with the peak time in late June–early July, and the composite vector directions in concentration period range from 103.2° to 190.2°; 2) The runoff variation coefficient ranges from 0.194 to 0.761, which indicates the instability of runoff. Extreme ratios of inflow and outflow are over 0.6 with an obvious attenuation; 3) The alternating pattern between wet years and dry years showed that the water distribution of the four rivers is relatively equal, while Ouchikou from three bayous is more violent, accounting for 32.79% of wet years and 57.38% of dry years respectively. The drastic change of annual water allocation is adverse to rational utilization of water resources

A Chinese Herbal Preparation Containing Radix Salviae Miltiorrhizae, Radix Notoginseng and Borneolum Syntheticum Reduces Circulating Adhesion Molecules

Circulating adhesion molecules (CAMs), surface proteins expressed in the vascular endothelium, have emerged as risk factors for cardiovascular disease (CVD). CAMs are involved in intercellular communication that are believed to play a role in atherosclerosis. A Chinese medicine, the “Dantonic Pill” (DP) (also known as the “Cardiotonic Pill”), containing three Chinese herbal material medica, Radix Salviae Miltiorrhizae, Radix Notoginseng and Borneolum Syntheticum, has been used in China for the prevention and management of CVD. Previous laboratory and animal studies have suggested that this preparation reduces both atherogenesis and adhesion molecule expression. A parallel double blind randomized placebo-controlled study was conducted to assess the effects of the DP on three species of CAM (intercellular cell adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 and endothelial cell selectin (E-selectin)) in participants with mild-moderate hypercholesterolemia. Secondary endpoints included biochemical and hematological variables and clinical effects. Forty participants were randomized to either treatment or control for 12 weeks. Treatment with DP was associated with a statistically significant decrease in ICAM-1 (9% decrease, P = .03) and E-Selectin (15% decrease, P = .004). There was no significant change in renal function tests, liver function tests, glucose, lipids or C-reactive protein levels and clinical adverse effects did not differ between the active and the control groups. There were no relevant changes in participants receiving placebo. These results suggest that this herbal medicine may contribute to the development of a novel approach to cardiovascular risk reduction

Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning

In the field of multi-task reinforcement learning, the modular principle, which involves specializing functionalities into different modules and combining them appropriately, has been widely adopted as a promising approach to prevent the negative transfer problem that performance degradation due to conflicts between tasks. However, most of the existing multi-task RL methods only combine shared modules at the task level, ignoring that there may be conflicts within the task. In addition, these methods do not take into account that without constraints, some modules may learn similar functions, resulting in restricting the model's expressiveness and generalization capability of modular methods. In this paper, we propose the Contrastive Modules with Temporal Attention(CMTA) method to address these limitations. CMTA constrains the modules to be different from each other by contrastive learning and combining shared modules at a finer granularity than the task level with temporal attention, alleviating the negative transfer within the task and improving the generalization ability and the performance for multi-task RL. We conducted the experiment on Meta-World, a multi-task RL benchmark containing various robotics manipulation tasks. Experimental results show that CMTA outperforms learning each task individually for the first time and achieves substantial performance improvements over the baselines.Comment: This paper has been accepted at NeurIPS 2023 as a poste

Accelerating O-redox kinetics with carbon nanotubes for stable lithium-rich cathodes

Lithium-rich cathodes (LRCs) show great potential to improve the energy density of commercial lithium-ion batteries owing to their cationic and anionic redox characteristics. Herein, a complete conductive network using carbon nanotubes (CNTs) additives to improve the poor kinetics of LRCs is fabricated. Ex situ X-ray photoelectron spectroscopy first demonstrates that the slope at a low potential and the following long platform can be assigned to the transition metal and oxygen redox, respectively. The combination of galvanostatic intermittent titration technique and electrochemical impedance spectroscopy further reveal that a battery with CNTs exhibited accelerated kinetics, especially for the O-redox process. Consequently, LRCs with CNTs exhibit a much better rate and cycling performance (approximate to 89% capacity retention at 2 C for over 200 cycles) than the Super P case. Eventually, TEM results imply that the improved electrochemical performance of the CNTs case also benefits from its more stable bulk and surface structures. Such a facile conductive additive modification strategy also provides a universal approach for the enhancement of the electron diffusion properties of other electrode materials.Web of Science67art. no. 220044