Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

High accuracy video label prediction (classification) models are attributed to large scale data. These data could be frame feature sequences extracted by a pre-trained convolutional-neural-network, which promote the efficiency for creating models. Unsupervised solutions such as feature average pooling, as a simple label-independent parameter-free based method, has limited ability to represent the video. While the supervised methods, like RNN, can greatly improve the recognition accuracy. However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased. In this paper, we proposes a novel video classification method based on a deep convolutional graph neural network(DCGN). The proposed method utilize the characteristics of the hierarchical structure of the video, and performed multi-level feature extraction on the video frame sequence through the graph network, obtained a video representation re ecting the event semantics hierarchically. We test our model on YouTube-8M Large-Scale Video Understanding dataset, and the result outperforms RNN based benchmarks.Comment: ECCV 201

Heuristics-Driven Link-of-Analogy Prompting: Enhancing Large Language Models for Document-Level Event Argument Extraction

In this study, we investigate in-context learning (ICL) in document-level event argument extraction (EAE). The paper identifies key challenges in this problem, including example selection, context length limitation, abundance of event types, and the limitation of Chain-of-Thought (CoT) prompting in non-reasoning tasks. To address these challenges, we introduce the Heuristic-Driven Link-of-Analogy (HD-LoA) prompting method. Specifically, we hypothesize and validate that LLMs learn task-specific heuristics from demonstrations via ICL. Building upon this hypothesis, we introduce an explicit heuristic-driven demonstration construction approach, which transforms the haphazard example selection process into a methodical method that emphasizes task heuristics. Additionally, inspired by the analogical reasoning of human, we propose the link-of-analogy prompting, which enables LLMs to process new situations by drawing analogies to known situations, enhancing their adaptability. Extensive experiments show that our method outperforms the existing prompting methods and few-shot supervised learning methods, exhibiting F1 score improvements of 4.53% and 9.38% on the document-level EAE dataset. Furthermore, when applied to sentiment analysis and natural language inference tasks, the HD-LoA prompting achieves accuracy gains of 2.87% and 2.63%, indicating its effectiveness across different tasks

A Novel Aptamer LL4A Specifically Targets Vemurafenib-Resistant Melanoma through Binding to the CD63 Protein.

Melanoma is a highly aggressive tumor with a poor prognosis, and half of all melanoma patients harbor BRAF mutations. A BRAF inhibitor, vemurafenib (PLX4032), has been approved by the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) to treat advanced melanoma patients with BRAFV600E mutation. However, the efficacy of vemurafenib is impeded by adaptive resistance in almost all patients. In this study, using a cell-based SELEX (systematic evolution of ligands by exponential enrichment) strategy, we obtained a DNA aptamer (named LL4) with high affinity and specificity against vemurafenib-resistant melanoma cells. Optimized truncated form (LL4A) specifically binds to vemurafenib-resistant melanoma cells with dissociation constants in the nanomolar range and with excellent stability and low toxicity. Meanwhile, fluorescence imaging confirmed that LL4A significantly accumulated in tumors formed by vemurafenib-resistant melanoma cells, but not in control tumors formed by their corresponding parental cells in vivo. Further, a transmembrane protein CD63 was identified as the binding target of aptamer LL4A using a pull-down assay combined with the liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. CD63 formed a supramolecular complex with TIMP1 and β1-integrin, activated the nuclear factor кB (NF-кB) and mitogen-activated protein kinase (MAPK) signaling pathways, and contributed to vemurafenib resistance. Potentially, the aptamer LL4A may be used diagnostically and therapeutically in humans to treat targeted vemurafenib-resistant melanoma

Tunable Atomically Wide Electrostatic Barriers Embedded in a Graphene WSe2 Heterostructure

Inducing and controlling electrostatic barriers in two-dimensional (2D) quantum materials has shown extraordinary promise to enable control of charge carriers, and is key for the realization of nanoscale electronic and optoelectronic devices1-10. Because of their atomically thin nature, the 2D materials have a congenital advantage to construct the thinnest possible p-n junctions1,3,7,9,10. To realize the ultimate functional unit for future nanoscale devices, creating atomically wide electrostatic barriers embedded in 2D materials is desired and remains an extremely challenge. Here we report the creation and manipulation of atomically wide electrostatic barriers embedded in graphene WSe2 heterostructures. By using a STM tip, we demonstrate the ability to generate a one-dimensional (1D) atomically wide boundary between 1T-WSe2 domains and continuously tune positions of the boundary because of ferroelasticity of the 1T-WSe2. Our experiment indicates that the 1D boundary introduces atomically wide electrostatic barriers in graphene above it. Then the 1D electrostatic barrier changes a single graphene WSe2 heterostructure quantum dot from a relativistic artificial atom to a relativistic artificial molecule

A nanocomposite of Au‐AgI core/shell dimer as a dual‐modality contrast agent for x‐ray computed tomography and photoacoustic imaging

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135097/1/mp9062.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135097/2/mp9062_am.pd

Bis[bis­(2-ethyl-5-methyl-1H-imidazol-4-yl-κN 3)methane](nitrato-κ2 O,O′)nickel(II) nitrate

In the title compound, [Ni(NO3)(C13H20N4)2]NO3, the NiII ion shows a distorted octa­hedral geometry formed by four N atoms from two bis­(2-ethyl-5-methyl-1H-imidazol-4-yl)methane ligands and two O atoms from a chelating nitrate anion. Three ethyl groups in the complex cation and the O atoms of the uncoordinated nitrate anion are disordered over two sets of positions [occupancy ratios of 0.52 (3):0.48 (3) and 0.63 (3):0.37 (3), respectively]. In the crystal, inter­molecular N—H⋯O hydrogen bonds connect the complex cations into a zigzag chain along [010] and further N—H⋯O hydrogen bonds between the chains and the uncoordinated nitrate anions lead to layers parallel to (100)