145 research outputs found
Government affiliation, real earnings management, and firm performance : the case of privately held firms
Using a moderated mediation model, we investigate the effects of government affiliation on the performance and real earnings management of privately held firms in China between 1998 and 2012. We find that politically affiliated firms tend to have superior accounting performance. The findings also suggest that politically affiliated firms are more likely than non-affiliated firms to engage in real activities to manipulate earnings. Furthermore, regional economic development moderates the relationships between political affiliation and real earnings management as well as firm performance. Finally, real earnings management mediates the effect of political affiliation on firm performance among privately held firms
Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
Unsupervised pre-training methods utilizing large and diverse datasets have
achieved tremendous success across a range of domains. Recent work has
investigated such unsupervised pre-training methods for model-based
reinforcement learning (MBRL) but is limited to domain-specific or simulated
data. In this paper, we study the problem of pre-training world models with
abundant in-the-wild videos for efficient learning of downstream visual control
tasks. However, in-the-wild videos are complicated with various contextual
factors, such as intricate backgrounds and textured appearance, which precludes
a world model from extracting shared world knowledge to generalize better. To
tackle this issue, we introduce Contextualized World Models (ContextWM) that
explicitly model both the context and dynamics to overcome the complexity and
diversity of in-the-wild videos and facilitate knowledge transfer between
distinct scenes. Specifically, a contextualized extension of the latent
dynamics model is elaborately realized by incorporating a context encoder to
retain contextual information and empower the image decoder, which allows the
latent dynamics model to concentrate on essential temporal variations. Our
experiments show that in-the-wild video pre-training equipped with ContextWM
can significantly improve the sample-efficiency of MBRL in various domains,
including robotic manipulation, locomotion, and autonomous driving
Real-Time And Robust 3D Object Detection with Roadside LiDARs
This work aims to address the challenges in autonomous driving by focusing on
the 3D perception of the environment using roadside LiDARs. We design a 3D
object detection model that can detect traffic participants in roadside LiDARs
in real-time. Our model uses an existing 3D detector as a baseline and improves
its accuracy. To prove the effectiveness of our proposed modules, we train and
evaluate the model on three different vehicle and infrastructure datasets. To
show the domain adaptation ability of our detector, we train it on an
infrastructure dataset from China and perform transfer learning on a different
dataset recorded in Germany. We do several sets of experiments and ablation
studies for each module in the detector that show that our model outperforms
the baseline by a significant margin, while the inference speed is at 45 Hz (22
ms). We make a significant contribution with our LiDAR-based 3D detector that
can be used for smart city applications to provide connected and automated
vehicles with a far-reaching view. Vehicles that are connected to the roadside
sensors can get information about other vehicles around the corner to improve
their path and maneuver planning and to increase road traffic safety.Comment: arXiv admin note: substantial text overlap with arXiv:2204.0013
Vaccine Adjuvant Delivery Systems Constructed Using Biocompatible Nanoparticles Formed through Self-Assembly of Small Molecules
Subunit vaccines are playing a critical role in controlling numerous diseases and attracting more and more research interests due to their numerous advantages over conventional whole microbe-based vaccines. However, subunit vaccines are weak immunogens and thus have limited capacity in eliciting the humoral and cellular immunity against pathogens. Recently, nanoparticles (NPs) formed with certain small molecules through self-assembly have been employed as an effective carrier for subunit vaccines to play roles of adjuvant, delivery and stabilization of antigens, thus engendering a vaccine adjuvant-delivery system (VADS), which shows promises to overcome the hurdles in developing subunit vaccines. In particular, the small molecule-self-assembled NPs as a VADS can not only deliver vaccine ingredients to immune cells but also influence the immunoresponse toward a Th1 (type 1 T helper cell) and Th2 balanced pathway to establish both humoral and cellular immunity. This chapter describes the innovative VADSs based on the small molecule-self-assembled NPs, such as metal NPs (mNPs), emulsions, liposomes, and ISCOMs, which are elaborately designed for the development of subunit vaccines
Constituency Parsing using LLMs
Constituency parsing is a fundamental yet unsolved natural language
processing task. In this paper, we explore the potential of recent large
language models (LLMs) that have exhibited remarkable performance across
various domains and tasks to tackle this task. We employ three linearization
strategies to transform output trees into symbol sequences, such that LLMs can
solve constituency parsing by generating linearized trees. We conduct
experiments using a diverse range of LLMs, including ChatGPT, GPT-4, OPT,
LLaMA, and Alpaca, comparing their performance against the state-of-the-art
constituency parsers. Our experiments encompass zero-shot, few-shot, and
full-training learning settings, and we evaluate the models on one in-domain
and five out-of-domain test datasets. Our findings reveal insights into LLMs'
performance, generalization abilities, and challenges in constituency parsing
Surface Crack Detection for Carbon Fiber Reinforced Plastic Materials Using Pulsed Eddy Current Based on Rectangular Differential Probe
Aiming at the surface defect inspection of carbon fiber reinforced composite, the differential and the direct measurement finite element simulation models of pulsed eddy current flaw detection were built. The principle of differential pulsed eddy current detection was analyzed and the sensitivity of defect detection was compared through two kinds of measurements. The validity of simulation results was demonstrated by experiments. The simulation and experimental results show that the pulsed eddy current detection method based on rectangular differential probe can effectively improve the sensitivity of surface defect detection of carbon fiber reinforced composite material
CLIPood: Generalizing CLIP to Out-of-Distributions
Out-of-distribution (OOD) generalization, where the model needs to handle
distribution shifts from training, is a major challenge of machine learning.
Contrastive language-image pre-training (CLIP) models have shown impressive
zero-shot ability, but the further adaptation of CLIP on downstream tasks
undesirably degrades OOD performances. This paper aims at generalizing CLIP to
out-of-distribution test data on downstream tasks. We propose CLIPood, a
fine-tuning method that can adapt CLIP models to OOD situations where both
domain shifts and open classes may occur on the unseen test data. To exploit
the semantic relations between classes from the text modality, CLIPood
introduces a new training objective, margin metric softmax (MMS), with class
adaptive margins for fine-tuning. To incorporate both pre-trained zero-shot
model and fine-tuned task-adaptive model, CLIPood leverages a new optimization
strategy, Beta moving average (BMA), to maintain a temporal ensemble weighted
by Beta distribution. Experiments on diverse datasets with different OOD
scenarios show that CLIPood consistently outperforms existing generalization
techniques.Comment: Accepted by ICML 202
DeepC2: AI-powered Covert Botnet Command and Control on OSNs
Botnets are one of the major threats to computer security. In previous botnet
command and control (C&C) scenarios using online social networks (OSNs),
methods for addressing (e.g., IDs, links, or DGAs) are hardcoded into bots.
Once a bot is reverse engineered, the botmaster and C&C infrastructure will be
exposed. Additionally, abnormal content from explicit commands may expose
botmasters and raise anomalies on OSNs. To overcome these deficiencies, we
proposed DeepC2, an AI-powered covert C&C method on OSNs. By leveraging neural
networks, bots can find botmasters by avatars, which are converted into feature
vectors and embedded into bots. Adversaries cannot infer botmasters' accounts
from the vectors. Commands are embedded into normal contents (e.g., tweets and
comments) using text data augmentation and hash collision. Experiments on
Twitter show that command-embedded contents can be generated efficiently, and
bots can find botmasters and obtain commands accurately. Security analysis on
different scenarios show that DeepC2 is robust and hard to be shut down. By
demonstrating how AI may help promote covert communication on OSNs, this work
provides a new perspective on botnet detection and confrontation.Comment: 13 pages, 15 figures, 7 tables. Discussion on possible
countermeasures update
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