102 research outputs found
Empirical tests of Fama-French three-factor model and Principle Component Analysis on the Chinese stock market
Date: 2014-06-03 Authors: Kaiwen Wang Jingjing Guo [email protected] [email protected] Mobile: 0762063660 0762187877 Title: Empirical tests of Fama-French three-factor model and Principle Component Analysis on the Chinese stock market Tutor: Anders Vilhelmsson, Department of Business Administration, Lund University Purpose: This paper aim to verify that the Fama-French three factor model (FF) captures more cross-sectional variation in returns for the Chinese stock market than the CAPM, over the period January 2004 to December 2013. Furthermore, we construct statistically optimal factors by using the principal component analysis (PCA) for the Fama-French portfolios and test whether the FF model leaves anything significant that can be explained by the PCA factors. Method: Following the procedure in Fama and French (1993), first we construct FF factors and portfolios based on firm size and book-to-market equity, and then compare the performance between CAPM and FF models by applying time-series regressions. For deeper comparison, we continue to explain the return matrix (120*9) with principal component analysis, which produces several PCs for new time-series regressions and study the overall fitness and factor loadings of both FF and PCA models. To see which model captures the most variation, we run cross-sectional regressions with respect to all the three afore-mentioned models. Conclusion: Our results show that the FF model tends to be more powerful than CAPM for explaining the variations in cross-sectional returns. Yet within the FF model, our data contains one divergence from the US market, we actually find a reversal of book-to-market equity effect. Finally, our results suggest that the PCA model performs better than the FF model
Singular Trudinger--Moser inequality involving norm in bounded domain
In this paper, we use the method of blow-up analysis and capacity estimate to
derive the singular Trudinger--Moser inequality involving
-Finsler--Laplacian and norm, precisely, for any ,
and , we
have \begin{align} \sup_{u\in W_{0}^{1,N}(\Omega),\;\int_{\Omega}F^{N}(\nabla
u)dx-\gamma\|
u\|_p^N\leq1}\int_{\Omega}\frac{e^{\lambda_{N}(1-\frac{\beta}{N})\lvert
u\rvert^{\frac{N}{N-1}}}}{F^{o}(x)^{\beta}}\;\mathrm{d}x<+\infty\notag,
\end{align} where
and is the volume of a unit Wulff ball in ,
moreover, extremal functions for the inequality are also obtained. When
and , we can obtain the singular version of Tintarev
type inequality by the obove inequality, namely, for any
and , it holds
where and is the volume of unit ball in . Our results extend
many well-known Trudinger--Moser type inequalities to more general setting
Size Effect and Scaling in Quasi-static and Fatigue Fracture of Graphene Polymer Nanocomposites
This work investigated how the structure size affects the quasi-static and
fatigue behaviors of graphene polymer nanocomposites, a topic that has been
often overlooked. The results showed that both quasi-static and fatigue failure
of these materials scale nonlinearly with the structure size due to the
presence of a significant Fracture Process Zone (FPZ) ahead of the crack tip
induced by graphene nanomodification. Such a complicated size effect and
scaling in either quasi-static or fatigue scenario cannot be described by the
Linear Elastic Fracture Mechanics (LEFM), but can be well captured by the Size
Effect Law (SEL) which considers the FPZ.
Thanks to the SEL, the enhanced quasi-static and fatigue fracture properties
were properly characterized and shown to be independent of the structure size.
In addition, the differences on the morphological and mechanical behaviors
between quasi-static fracture and fatigue fracture were also identified and
clarified in this work.
The experimental data and analytical analyses reported in this paper are
important to deeply understand the mechanics of polymer-based nanocomposite
materials and even other quasi-brittle materials (e.g., fiber-reinforced
polymers or its hybrid with nanoparticles, etc.), and further advance the
development of computational models capable of capturing size-dependent
fracture of materials in various loading conditions.Comment: 41 pages, 18 figure
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto}
standard in graph representation learning. However, when it comes to link
prediction, they often struggle, surpassed by simple heuristics such as Common
Neighbor (CN). This discrepancy stems from a fundamental limitation: while
MPNNs excel in node-level representation, they stumble with encoding the joint
structural features essential to link prediction, like CN. To bridge this gap,
we posit that, by harnessing the orthogonality of input vectors, pure
message-passing can indeed capture joint structural features. Specifically, we
study the proficiency of MPNNs in approximating CN heuristics. Based on our
findings, we introduce the Message Passing Link Predictor (MPLP), a novel link
prediction model. MPLP taps into quasi-orthogonal vectors to estimate
link-level structural features, all while preserving the node-level
complexities. Moreover, our approach demonstrates that leveraging
message-passing to capture structural features could offset MPNNs'
expressiveness limitations at the expense of estimation variance. We conduct
experiments on benchmark datasets from various domains, where our method
consistently outperforms the baseline methods.Comment: preprin
Precheck Sequence Based False Base Station Detection During Handover: A Physical Layer Based Security Scheme
False Base Station (FBS) attack has been a severe security problem for the
cellular network since 2G era. During handover, the user equipment (UE)
periodically receives state information from surrounding base stations (BSs)
and uploads it to the source BS. The source BS compares the uploaded signal
power and shifts UE to another BS that can provide the strongest signal. An FBS
can transmit signal with the proper power and attract UE to connect to it. In
this paper, based on the 3GPP standard, a Precheck Sequence-based Detection
(PSD) Scheme is proposed to secure the transition of legal base station (LBS)
for UE. This scheme first analyzes the structure of received signals in blocks
and symbols. Several additional symbols are added to the current signal
sequence for verification. By designing a long table of symbol sequence, every
UE which needs handover will be allocated a specific sequence from this table.
The simulation results show that the performance of this PSD Scheme is better
than that of any existing ones, even when a specific transmit power is designed
for FBS
Grouped Knowledge Distillation for Deep Face Recognition
Compared with the feature-based distillation methods, logits distillation can
liberalize the requirements of consistent feature dimension between teacher and
student networks, while the performance is deemed inferior in face recognition.
One major challenge is that the light-weight student network has difficulty
fitting the target logits due to its low model capacity, which is attributed to
the significant number of identities in face recognition. Therefore, we seek to
probe the target logits to extract the primary knowledge related to face
identity, and discard the others, to make the distillation more achievable for
the student network. Specifically, there is a tail group with near-zero values
in the prediction, containing minor knowledge for distillation. To provide a
clear perspective of its impact, we first partition the logits into two groups,
i.e., Primary Group and Secondary Group, according to the cumulative
probability of the softened prediction. Then, we reorganize the Knowledge
Distillation (KD) loss of grouped logits into three parts, i.e., Primary-KD,
Secondary-KD, and Binary-KD. Primary-KD refers to distilling the primary
knowledge from the teacher, Secondary-KD aims to refine minor knowledge but
increases the difficulty of distillation, and Binary-KD ensures the consistency
of knowledge distribution between teacher and student. We experimentally found
that (1) Primary-KD and Binary-KD are indispensable for KD, and (2)
Secondary-KD is the culprit restricting KD at the bottleneck. Therefore, we
propose a Grouped Knowledge Distillation (GKD) that retains the Primary-KD and
Binary-KD but omits Secondary-KD in the ultimate KD loss calculation. Extensive
experimental results on popular face recognition benchmarks demonstrate the
superiority of proposed GKD over state-of-the-art methods.Comment: 9 pages, 2 figures, 7 tables, accepted by AAAI 202
Towards an Understanding of Large Language Models in Software Engineering Tasks
Large Language Models (LLMs) have drawn widespread attention and research due
to their astounding performance in tasks such as text generation and reasoning.
Derivative products, like ChatGPT, have been extensively deployed and highly
sought after. Meanwhile, the evaluation and optimization of LLMs in software
engineering tasks, such as code generation, have become a research focus.
However, there is still a lack of systematic research on the application and
evaluation of LLMs in the field of software engineering. Therefore, this paper
is the first to comprehensively investigate and collate the research and
products combining LLMs with software engineering, aiming to answer two
questions: (1) What are the current integrations of LLMs with software
engineering? (2) Can LLMs effectively handle software engineering tasks? To
find the answers, we have collected related literature as extensively as
possible from seven mainstream databases, and selected 123 papers for analysis.
We have categorized these papers in detail and reviewed the current research
status of LLMs from the perspective of seven major software engineering tasks,
hoping this will help researchers better grasp the research trends and address
the issues when applying LLMs. Meanwhile, we have also organized and presented
papers with evaluation content to reveal the performance and effectiveness of
LLMs in various software engineering tasks, providing guidance for researchers
and developers to optimize
Evidence of spin density waves in LaNiO
The recently discovered superconductivity with critical temperature
up to 80 K in the Ruddlesden-Popper phases LaNiO under
pressure has drawn great attention. Here we report the positive muon spin
relaxation (SR) study of polycrystalline LaNiO
under ambient pressure. The zero-field SR experiments reveal the
existence of static long range magnetic order in LaNiO,
and the the muon spin depolarization spectra are consistent with the spin
density wave internal field distribution. The weak transverse field SR
measurements suggest the bulk magnetic transition near K. This
is the first research which discovers the existence of the spin density wave in
LaNiO microscopically
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