51 research outputs found
Predictability of extreme daily returns and Preference for lottery-like stocks in an emerging market
This study investigates the presence of the MAX effect – stocks
with extreme daily (positive) return in the current month perform
poorly in the following month – in the Pakistani stock market
(PSX). Similar to the US, Europe, and Chinese stock markets, we
find a negative effect of MAX on risk-adjusted returns.
Furthermore, we find that the MAX effect persists even if we
extend the holding period to three- and six-month. Our results
are robust for both portfolio-level and firm-level cross-sectional
analyses and across subperiods, size groups, and alternative factor
definitions and models. Interestingly, contrary to findings reported
elsewhere, we find that the MAX effect in Pakistan exists only
when the overall economy is in an expansion state. A battery of
tests suggests that triviality in MAX effect during economic contraction in Pakistan is driven by the more negative subsequent
performance of low-MAX stocks (short-leg), whereas, in other markets, more negative subsequent performance of high-MAX stocks
(long-leg) is evident during economic downturns. Our potential
explanation is partially supported by the theoretical model of
Palfrey & Wang, who find that demand for speculative stocks (i.e.
lottery-like stocks) is higher during ‘good’ economic news (expansion) than ‘bad’ economic news (contraction)
Tunable Soft Prompts are Messengers in Federated Learning
Federated learning (FL) enables multiple participants to collaboratively
train machine learning models using decentralized data sources, alleviating
privacy concerns that arise from directly sharing local data. However, the lack
of model privacy protection in FL becomes an unneglectable challenge,
especially when people want to federally finetune models based on a proprietary
large language model. In this study, we propose a novel FL training approach
that accomplishes information exchange among participants via tunable soft
prompts. These soft prompts, updated and transmitted between the server and
clients, assume the role of the global model parameters and serve as messengers
to deliver useful knowledge from the local data and global model. As the global
model itself is not required to be shared and the local training is conducted
based on an auxiliary model with fewer parameters than the global model, the
proposed approach provides protection for the global model while reducing
communication and computation costs in FL. Extensive experiments show the
effectiveness of the proposed approach compared to several baselines. We have
released the source code at
\url{https://github.com/alibaba/FederatedScope/tree/fedsp/federatedscope/nlp/fedsp}.Comment: Accepted by EMNLP-2
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
During X-ray computed tomography (CT) scanning, metallic implants carrying
with patients often lead to adverse artifacts in the captured CT images and
then impair the clinical treatment. Against this metal artifact reduction (MAR)
task, the existing deep-learning-based methods have gained promising
reconstruction performance. Nevertheless, there is still some room for further
improvement of MAR performance and generalization ability, since some important
prior knowledge underlying this specific task has not been fully exploited.
Hereby, in this paper, we carefully analyze the characteristics of metal
artifacts and propose an orientation-shared convolution representation strategy
to adapt the physical prior structures of artifacts, i.e., rotationally
symmetrical streaking patterns. The proposed method rationally adopts
Fourier-series-expansion-based filter parametrization in artifact modeling,
which can better separate artifacts from anatomical tissues and boost the model
generalizability. Comprehensive experiments executed on synthesized and
clinical datasets show the superiority of our method in detail preservation
beyond the current representative MAR methods. Code will be available at
\url{https://github.com/hongwang01/OSCNet
FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs
Efficient detectors for edge devices are often optimized for parameters or
speed count metrics, which remain in weak correlation with the energy of
detectors.
However, some vision applications of convolutional neural networks, such as
always-on surveillance cameras, are critical for energy constraints.
This paper aims to serve as a baseline by designing detectors to reach
tradeoffs between energy and performance from two perspectives:
1) We extensively analyze various CNNs to identify low-energy architectures,
including selecting activation functions, convolutions operators, and feature
fusion structures on necks. These underappreciated details in past work
seriously affect the energy consumption of detectors;
2) To break through the dilemmatic energy-performance problem, we propose a
balanced detector driven by energy using discovered low-energy components named
\textit{FemtoDet}.
In addition to the novel construction, we improve FemtoDet by considering
convolutions and training strategy optimizations.
Specifically, we develop a new instance boundary enhancement (IBE) module for
convolution optimization to overcome the contradiction between the limited
capacity of CNNs and detection tasks in diverse spatial representations, and
propose a recursive warm-restart (RecWR) for optimizing training strategy to
escape the sub-optimization of light-weight detectors by considering the data
shift produced in popular augmentations.
As a result, FemtoDet with only 68.77k parameters achieves a competitive
score of 46.3 AP50 on PASCAL VOC and 1.11 W 64.47 FPS on Qualcomm
Snapdragon 865 CPU platforms.
Extensive experiments on COCO and TJU-DHD datasets indicate that the proposed
method achieves competitive results in diverse scenes.Comment: ICCV 202
Open-World Weakly-Supervised Object Localization
While remarkable success has been achieved in weakly-supervised object
localization (WSOL), current frameworks are not capable of locating objects of
novel categories in open-world settings. To address this issue, we are the
first to introduce a new weakly-supervised object localization task called
OWSOL (Open-World Weakly-Supervised Object Localization). During training, all
labeled data comes from known categories and, both known and novel categories
exist in the unlabeled data. To handle such data, we propose a novel paradigm
of contrastive representation co-learning using both labeled and unlabeled data
to generate a complete G-CAM (Generalized Class Activation Map) for object
localization, without the requirement of bounding box annotation. As no class
label is available for the unlabelled data, we conduct clustering over the full
training set and design a novel multiple semantic centroids-driven contrastive
loss for representation learning. We re-organize two widely used datasets,
i.e., ImageNet-1K and iNatLoc500, and propose OpenImages150 to serve as
evaluation benchmarks for OWSOL. Extensive experiments demonstrate that the
proposed method can surpass all baselines by a large margin. We believe that
this work can shift the close-set localization towards the open-world setting
and serve as a foundation for subsequent works. Code will be released at
https://github.com/ryylcc/OWSOL
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