ABJM Amplitudes in U-gauge and a Soft Theorem

We report progress in computing and analyzing all tree amplitudes in ABJM theory. Inspired by the isomorphism between the orthogonal Grassmannian and the pure spinor geometries, we adopt a new gauge, called u-gauge, for evaluating the orthogonal Grassmannian integral for ABJM amplitudes. We carry out the integral explicitly for the 8-point amplitude and obtain the complete supersymmetric amplitude. The physical and spurious poles arise from the integral as expected from on-shell diagrams. We also derive a double scalar soft theorem of ABJM amplitudes and verify it for known amplitudes.Comment: 35 pages, 6 figures; v2. minor correction

Internalized Model Minority Myth, Asian Values, and Help-Seeking Attitudes among Asian American Students

The present study examined cultural factors underlying help-seeking attitudes of Asian American college students (N = 106). Specifically, we explored internalized model minority myth as a predictor of help-seeking attitudes and tested an intrapersonal-interpersonal framework of Asian values as a mechanism by which the two are related. Results indicated that internalized model minority myth significantly predicted unfavorable help-seeking attitudes, and emotional self-control mediated this relationship. Interpersonal values and humility were nonsignificant mediators, contrary to our hypotheses. The findings suggest that the investigation of internalized model minority myth in help-seeking research is a worthwhile endeavor, and they also highlight emotional self-control as an important explanatory variable in help-seeking attitudes of Asian American college students

NFTs to MARS: Multi-Attention Recommender System for NFTs

Recommender systems have become essential tools for enhancing user experiences across various domains. While extensive research has been conducted on recommender systems for movies, music, and e-commerce, the rapidly growing and economically significant Non-Fungible Token (NFT) market remains underexplored. The unique characteristics and increasing prominence of the NFT market highlight the importance of developing tailored recommender systems to cater to its specific needs and unlock its full potential. In this paper, we examine the distinctive characteristics of NFTs and propose the first recommender system specifically designed to address NFT market challenges. In specific, we develop a Multi-Attention Recommender System for NFTs (NFT-MARS) with three key characteristics: (1) graph attention to handle sparse user-item interactions, (2) multi-modal attention to incorporate feature preference of users, and (3) multi-task learning to consider the dual nature of NFTs as both artwork and financial assets. We demonstrate the effectiveness of NFT-MARS compared to various baseline models using the actual transaction data of NFTs collected directly from blockchain for four of the most popular NFT collections. The source code and data are available at https://anonymous.4open.science/r/RecSys2023-93ED

Temporal Graph Networks for Graph Anomaly Detection in Financial Networks

This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions. We present a comprehensive framework that leverages TGN, capable of capturing dynamic changes in edges within financial networks, for fraud detection. Our study compares TGN's performance against static Graph Neural Network (GNN) baselines, as well as cutting-edge hypergraph neural network baselines using DGraph dataset for a realistic financial context. Our results demonstrate that TGN significantly outperforms other models in terms of AUC metrics. This superior performance underlines TGN's potential as an effective tool for detecting financial fraud, showcasing its ability to adapt to the dynamic and complex nature of modern financial systems. We also experimented with various graph embedding modules within the TGN framework and compared the effectiveness of each module. In conclusion, we demonstrated that, even with variations within TGN, it is possible to achieve good performance in the anomaly detection task.Comment: Presented at the AAAI 2024 Workshop on AI in Finance for Social Impact (https://sites.google.com/view/aifin-aaai2024

A Recommender System for NFT Collectibles with Item Feature

Recommender systems have been actively studied and applied in various domains to deal with information overload. Although there are numerous studies on recommender systems for movies, music, and e-commerce, comparatively less attention has been paid to the recommender system for NFTs despite the continuous growth of the NFT market. This paper presents a recommender system for NFTs that utilizes a variety of data sources, from NFT transaction records to external item features, to generate precise recommendations that cater to individual preferences. We develop a data-efficient graph-based recommender system to efficiently capture the complex relationship between each item and users and generate node(item) embeddings which incorporate both node feature information and graph structure. Furthermore, we exploit inputs beyond user-item interactions, such as image feature, text feature, and price feature. Numerical experiments verify the performance of the graph-based recommender system improves significantly after utilizing all types of item features as side information, thereby outperforming all other baselines.Comment: Presented at the AAAI 2023 Bridge on AI for Financial Services (https://sites.google.com/view/aaai-ai-fin/home

Securing the Wireless Emergency Alerts System

Modern cell phones are required to receive and display alerts via the Wireless Emergency Alert (WEA) program, under the mandate of the Warning, Alert, and Response Act of 2006. These alerts include AMBER alerts, severe weather alerts, and (unblockable) Presidential Alerts, intended to inform the public of imminent threats. Recently, a test Presidential Alert was sent to all capable phones in the U.S., prompting concerns about how the underlying WEA protocol could be misused or attacked. In this paper, we investigate the details of this system and develop and demonstrate the first practical spoofing attack on Presidential Alerts, using commercially available hardware and modified open source software. Our attack can be performed using a commercially available software-defined radio, and our modifications to the open source software libraries. We find that with only four malicious portable base stations of a single Watt of transmit power each, almost all of a 50,000-seat stadium can be attacked with a 90% success rate. The real impact of such an attack would, of course, depend on the density of cellphones in range; fake alerts in crowded cities or stadiums could potentially result in cascades of panic. Fixing this problem will require a large collaborative effort between carriers, government stakeholders, and cellphone manufacturers. To seed this effort, we also propose three mitigation solutions to address this threat

30 inch Roll-Based Production of High-Quality Graphene Films for Flexible Transparent Electrodes

We report that 30-inch scale multiple roll-to-roll transfer and wet chemical doping considerably enhance the electrical properties of the graphene films grown on roll-type Cu substrates by chemical vapor deposition. The resulting graphene films shows a sheet resistance as low as ~30 Ohm/sq at ~90 % transparency which is superior to commercial transparent electrodes such as indium tin oxides (ITO). The monolayer of graphene shows sheet resistances as low as ~125 Ohm/sq with 97.4% optical transmittance and half-integer quantum Hall effect, indicating the high-quality of these graphene films. As a practical application, we also fabricated a touch screen panel device based on the graphene transparent electrodes, showing extraordinary mechanical and electrical performances

Lifespan Differences in Cortico-Striatal Resting State Connectivity

Distinctive cortico-striatal circuits that serve motor and cognitive functions have been recently mapped based on resting state connectivity. It has been reported that age differences in cortico-striatal connectivity relate to cognitive declines in aging. Moreover, children in their early teens (i.e., youth) already show mature motor network patterns while their cognitive networks are still developing. In the current study, we examined age differences in the frontal-striatal ?cognitive? and ?motor? circuits in children and adolescence, young adults (YAs), and older adults (OAs). We predicted that the strength of the ?cognitive? frontal-striatal circuits would follow an inverted ?U? pattern across age; children and OAs would have weaker connectivity than YAs. However, we predicted that the ?motor? circuits would show less variation in connectivity strength across the lifespan. We found that most areas in both the ?cognitive? and ?motor? circuits showed higher connectivity in YAs than children and OAs, suggesting general inverted ?U?-shaped changes across the lifespan for both the cognitive and motor frontal-striatal networks.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140317/1/brain.2013.0155.pd

Ultra-Sharp Nanowire Arrays Natively Permeate, Record, and Stimulate Intracellular Activity in Neuronal and Cardiac Networks

Intracellular access with high spatiotemporal resolution can enhance our understanding of how neurons or cardiomyocytes regulate and orchestrate network activity, and how this activity can be affected with pharmacology or other interventional modalities. Nanoscale devices often employ electroporation to transiently permeate the cell membrane and record intracellular potentials, which tend to decrease rapidly to extracellular potential amplitudes with time. Here, we report innovative scalable, vertical, ultra-sharp nanowire arrays that are individually addressable to enable long-term, native recordings of intracellular potentials. We report large action potential amplitudes that are indicative of intracellular access from 3D tissue-like networks of neurons and cardiomyocytes across recording days and that do not decrease to extracellular amplitudes for the duration of the recording of several minutes. Our findings are validated with cross-sectional microscopy, pharmacology, and electrical interventions. Our experiments and simulations demonstrate that individual electrical addressability of nanowires is necessary for high-fidelity intracellular electrophysiological recordings. This study advances our understanding of and control over high-quality multi-channel intracellular recordings, and paves the way toward predictive, high-throughput, and low-cost electrophysiological drug screening platforms.Comment: Main manuscript: 33 pages, 4 figures, Supporting information: 43 pages, 27 figures, Submitted to Advanced Material