88 research outputs found
IS THERE DIVERSIFICATION BENEFIT BETWEEN EMERGING AND DEVELOPED STOCK MARKET: EVIDENCE FROM THE BRIC AND US STOCK MARKET
This paper seeks to investigate the linkage and co-movement relationships between the stock markets of US and BRIC, and determine the degree of diversification benefits among them within the sample period from January 2001 to September 2017. The entire sample period is divided into three phases: pre-crisis, during crisis and post-crisis in order to be more comparative. The empirical results show that there is a strong linkage and co-movement relationship between BRIC and US stock markets, especially after 2007 financial crisis. Also, the upward long run conditional correlations demonstrate that the diversification benefits are weakened substantially. However, there is not any evidence showing the existence of co-integration between BRIC and US market for all three phases, except for the stock market of China during the crisis. Moreover, most of the BRIC stock markets are appeared to have no short term causality to US market
MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field
We present MovingParts, a NeRF-based method for dynamic scene reconstruction
and part discovery. We consider motion as an important cue for identifying
parts, that all particles on the same part share the common motion pattern.
From the perspective of fluid simulation, existing deformation-based methods
for dynamic NeRF can be seen as parameterizing the scene motion under the
Eulerian view, i.e., focusing on specific locations in space through which the
fluid flows as time passes. However, it is intractable to extract the motion of
constituting objects or parts using the Eulerian view representation. In this
work, we introduce the dual Lagrangian view and enforce representations under
the Eulerian/Lagrangian views to be cycle-consistent. Under the Lagrangian
view, we parameterize the scene motion by tracking the trajectory of particles
on objects. The Lagrangian view makes it convenient to discover parts by
factorizing the scene motion as a composition of part-level rigid motions.
Experimentally, our method can achieve fast and high-quality dynamic scene
reconstruction from even a single moving camera, and the induced part-based
representation allows direct applications of part tracking, animation, 3D scene
editing, etc.Comment: 10 page
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
Uncertainty decomposition refers to the task of decomposing the total
uncertainty of a model into data (aleatoric) uncertainty, resulting from the
inherent complexity or ambiguity of the data, and model (epistemic)
uncertainty, resulting from the lack of knowledge in the model. Performing
uncertainty decomposition for large language models (LLMs) is an important step
toward improving the reliability, trustworthiness, and interpretability of
LLMs, but this research task is very challenging and remains unresolved. The
existing canonical method, Bayesian Neural Network (BNN), cannot be applied to
LLMs, because BNN requires training and ensembling multiple variants of models,
which is infeasible or prohibitively expensive for LLMs. In this paper, we
introduce an uncertainty decomposition framework for LLMs, called input
clarifications ensemble, which bypasses the need to train new models. Rather
than ensembling models with different parameters, our approach generates a set
of clarifications for the input, feeds them into the fixed LLMs, and ensembles
the corresponding predictions. We show that our framework shares a symmetric
decomposition structure with BNN. Empirical evaluations demonstrate that the
proposed framework provides accurate and reliable uncertainty quantification on
various tasks. Code will be made publicly available at
https://github.com/UCSB-NLP-Chang/llm_uncertainty .Comment: 15 pages, 3 figure
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
Self-supervised learning (SSL) for rich speech representations has achieved
empirical success in low-resource Automatic Speech Recognition (ASR) and other
speech processing tasks, which can mitigate the necessity of a large amount of
transcribed speech and thus has driven a growing demand for on-device ASR and
other speech processing. However, advanced speech SSL models have become
increasingly large, which contradicts the limited on-device resources. This gap
could be more severe in multilingual/multitask scenarios requiring
simultaneously recognizing multiple languages or executing multiple speech
processing tasks. Additionally, strongly overparameterized speech SSL models
tend to suffer from overfitting when being finetuned on low-resource speech
corpus. This work aims to enhance the practical usage of speech SSL models
towards a win-win in both enhanced efficiency and alleviated overfitting via
our proposed S-Router framework, which for the first time discovers that
simply discarding no more than 10\% of model weights via only finetuning model
connections of speech SSL models can achieve better accuracy over standard
weight finetuning on downstream speech processing tasks. More importantly,
S-Router can serve as an all-in-one technique to enable (1) a new
finetuning scheme, (2) an efficient multilingual/multitask solution, (3) a
state-of-the-art ASR pruning technique, and (4) a new tool to quantitatively
analyze the learned speech representation. We believe S-Router has provided
a new perspective for practical deployment of speech SSL models. Our codes are
available at: https://github.com/GATECH-EIC/S3-Router.Comment: Accepted at NeurIPS 202
The molecular basis of the genesis of basal tone in internal anal sphincter
Smooth muscle sphincters exhibit basal tone and control passage of contents through organs such as the gastrointestinal tract; loss of this tone leads to disorders such as faecal incontinence. However, the molecular mechanisms underlying this tone remain unknown. Here, we show that deletion of myosin light-chain kinases (MLCK) in the smooth muscle cells from internal anal sphincter (IAS-SMCs) abolishes basal tone, impairing defecation. Pharmacological regulation of ryanodine receptors (RyRs), L-type voltage-dependent Ca(2+) channels (VDCCs) or TMEM16A Ca(2+)-activated Cl(-) channels significantly changes global cytosolic Ca(2+) concentration ([Ca(2+)]i) and the tone. TMEM16A deletion in IAS-SMCs abolishes the effects of modulators for TMEM16A or VDCCs on a RyR-mediated rise in global [Ca(2+)]i and impairs the tone and defecation. Hence, MLCK activation in IAS-SMCs caused by a global rise in [Ca(2+)]i via a RyR-TMEM16A-VDCC signalling module sets the basal tone. Targeting this module may lead to new treatments for diseases like faecal incontinence
- …