222 research outputs found
Using Long-Short Term Memory Network to Train Machine Composing Baroque Fugue/Canon
The goal of this project is to train a machine to compose Baroque Fugue/Canon by using Long-Short Term Memory architecture (LSTM). LSTM is a type of artificial recursive neural network (RNN), which excels at learning patterns at both long and short time periods. By limiting to particular “styles” of structures and patterns, the problem becomes more tractable. In our study, we focus on the Baroque Fugue/Canons as they are polyphonic music with standard rules and structures to regulate the composing process. A 2-layer bi-directional LSTM network has been designed. With training data of midi files of Fugue/Canon primarily composed by J.S Bach, have been collected from internet and translated into a text file. Length, frequency, intensity and timing will be considered as training features. The checkpoint file with best validation loss will be used to generate output file, and converted back into midi file. The result will be announced at the conference
Prevalence of osteoporosis in China: a meta-analysis and systematic review
Abstract Background We conducted a systematic review and meta-analysis to obtain a reliable estimation of the prevalence of osteoporosis in China and to characterize its epidemiology. Methods We identified relevant studies via a search of literature published from 2003 to October 2015 in the PubMed, Web of Science, China National Knowledge Infrastructure, Wanfang and Weipu databases. Both Chinese and WHO criteria were considered acceptable for the diagnosis of osteoporosis. Prevalence estimates were obtained using random effects models. Meta-regression analysis was used to explore the sources of heterogeneity, and publication bias was evaluated by visually inspecting funnel plots. Results Overall, 69 articles were included in this study. An obvious increase in the prevalence of osteoporosis was identified over the past 12 years (prevalence of 14.94 % before 2008 and 27.96 % during the period spanning 2012–2015). The prevalence of osteoporosis was higher in females than in males (25.41 % vs. 15.33 %) and increased with age. Osteoporosis prevalence was higher in rural than in urban areas (20.87 % vs. 23.92 %) and higher in southern than in northern areas (23.17 % vs. 20.13 %). At present, the pooled prevalence of osteoporosis in people aged 50 years and older was more than twice the pooled prevalence identified in 2006 (34.65 % vs. 15.7 %). The application of different diagnostic criteria could have an impact on prevalence estimation (19.7 % vs. 29.3 %). Meta-regression suggested that study setting also influenced the estimation of point prevalence (P = 0.022). Conclusions The prevalence of osteoporosis in China has increased over the past 12 years, affecting more than one-third of people aged 50 years and older. The prevalence of osteoporosis increased with age and was higher in females than in males. Prevention and control measures have become all the more important given the increase in osteoporosis prevalence, and three-step prevention programmes should be implemented
SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search
The -Nearest Neighbor Search (-NNS) is the backbone of several
cloud-based services such as recommender systems, face recognition, and
database search on text and images. In these services, the client sends the
query to the cloud server and receives the response in which case the query and
response are revealed to the service provider. Such data disclosures are
unacceptable in several scenarios due to the sensitivity of data and/or privacy
laws.
In this paper, we introduce SANNS, a system for secure -NNS that keeps
client's query and the search result confidential. SANNS comprises two
protocols: an optimized linear scan and a protocol based on a novel sublinear
time clustering-based algorithm. We prove the security of both protocols in the
standard semi-honest model. The protocols are built upon several
state-of-the-art cryptographic primitives such as lattice-based additively
homomorphic encryption, distributed oblivious RAM, and garbled circuits. We
provide several contributions to each of these primitives which are applicable
to other secure computation tasks. Both of our protocols rely on a new circuit
for the approximate top- selection from numbers that is built from comparators.
We have implemented our proposed system and performed extensive experimental
results on four datasets in two different computation environments,
demonstrating more than faster response time compared to
optimally implemented protocols from the prior work. Moreover, SANNS is the
first work that scales to the database of 10 million entries, pushing the limit
by more than two orders of magnitude.Comment: 18 pages, to appear at USENIX Security Symposium 202
Attapulgite Nanofiber-Cellulose Nanocomposite with Core-Shell Structure for Dye Adsorption
Nanocomposite particle used for adsorption has attracted continuous attention because of large specific surface area and adjustable properties from nanocomponent. Herein nanocomposite particle with cellulose core and attapulgite nanofibers shell was prepared. The size of cellulose core was about 2 mm and the thickness of nanofibers shell is about 300 μm. Adsorption capacity of nanocomposite particle to methylene blue can reach up to 11.07 mg L−1 and the best adsorption effect occurs at pH = 8; pseudo-first-order equation and the Langmuir equation best describe the adsorption kinetic and isotherm, respectively; repeated adsorption-desorption experimental results show that 94.64% of the original adsorption capacity can be retained after being reused three times
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Chronic Dry Eye Disease is Principally Mediated by Effector Memory Th17 Cells
Recent experimental and clinical data suggest that there is a link between dry eye disease (DED) and T cell-mediated immunity. However, whether these immune responses are a consequence or cause of ocular surface inflammation remains to be determined. Thus far, only models of acute DED have been used to derive experimental data. This is in contrast to clinical DED which usually presents as a chronic disease. In the present study, using a murine model of chronic DED, it was established that the chronic phase of the disease is accompanied by Th17 responses at the ocular surface, and that a significant memory T cell population can be recovered from chronic DED. This memory response is predominantly mediated by Th17 cells. Moreover, adoptive transfer of this memory T cell population was shown to induce more severe and rapidly progressing DED than did the adoptive transfer of its effector or naïve counterparts. Not only do these results clearly demonstrate that effector memory Th17 cells are primarily responsible for maintaining the chronic and relapsing course of DED, but they also highlight a potentially novel therapeutic strategy for targeting memory immune responses in patients with DED
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
Deep cooperative multi-agent reinforcement learning has demonstrated its
remarkable success over a wide spectrum of complex control tasks. However,
recent advances in multi-agent learning mainly focus on value decomposition
while leaving entity interactions still intertwined, which easily leads to
over-fitting on noisy interactions between entities. In this work, we introduce
a novel interactiOn Pattern disenTangling (OPT) method, to disentangle not only
the joint value function into agent-wise value functions for decentralized
execution, but also the entity interactions into interaction prototypes, each
of which represents an underlying interaction pattern within a subgroup of the
entities. OPT facilitates filtering the noisy interactions between irrelevant
entities and thus significantly improves generalizability as well as
interpretability. Specifically, OPT introduces a sparse disagreement mechanism
to encourage sparsity and diversity among discovered interaction prototypes.
Then the model selectively restructures these prototypes into a compact
interaction pattern by an aggregator with learnable weights. To alleviate the
training instability issue caused by partial observability, we propose to
maximize the mutual information between the aggregation weights and the history
behaviors of each agent. Experiments on both single-task and multi-task
benchmarks demonstrate that the proposed method yields results superior to the
state-of-the-art counterparts. Our code is available at
https://github.com/liushunyu/OPT
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?
Centralized Training with Decentralized Execution (CTDE) has recently emerged
as a popular framework for cooperative Multi-Agent Reinforcement Learning
(MARL), where agents can use additional global state information to guide
training in a centralized way and make their own decisions only based on
decentralized local policies. Despite the encouraging results achieved, CTDE
makes an independence assumption on agent policies, which limits agents to
adopt global cooperative information from each other during centralized
training. Therefore, we argue that existing CTDE methods cannot fully utilize
global information for training, leading to an inefficient joint-policy
exploration and even suboptimal results. In this paper, we introduce a novel
Centralized Advising and Decentralized Pruning (CADP) framework for multi-agent
reinforcement learning, that not only enables an efficacious message exchange
among agents during training but also guarantees the independent policies for
execution. Firstly, CADP endows agents the explicit communication channel to
seek and take advices from different agents for more centralized training. To
further ensure the decentralized execution, we propose a smooth model pruning
mechanism to progressively constraint the agent communication into a closed one
without degradation in agent cooperation capability. Empirical evaluations on
StarCraft II micromanagement and Google Research Football benchmarks
demonstrate that the proposed framework achieves superior performance compared
with the state-of-the-art counterparts. Our code will be made publicly
available
Study on Oil Pressure Characteristics and Trajectory Tracking Control in Shift Process of Wet-Clutch for Electric Vehicles
Accurate control of oil pressure of wet-clutch is of great importance for improving shift quality. Based on dynamic models of two-gear planetary transmission and hydraulic control system, a trajectory tracking model of oil pressure was built by sliding mode control method. An experiment was designed to verify the validity of hydraulic control system, through which the relationship between duty cycle of on-off valve and oil pressure of clutch was determined. The tracking effect was analyzed by simulation. Results showed that oil pressure could follow well the optimal trajectory and the shift quality was effectively improved
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Value Decomposition (VD) aims to deduce the contributions of agents for
decentralized policies in the presence of only global rewards, and has recently
emerged as a powerful credit assignment paradigm for tackling cooperative
Multi-Agent Reinforcement Learning (MARL) problems. One of the main challenges
in VD is to promote diverse behaviors among agents, while existing methods
directly encourage the diversity of learned agent networks with various
strategies. However, we argue that these dedicated designs for agent networks
are still limited by the indistinguishable VD network, leading to homogeneous
agent behaviors and thus downgrading the cooperation capability. In this paper,
we propose a novel Contrastive Identity-Aware learning (CIA) method, explicitly
boosting the credit-level distinguishability of the VD network to break the
bottleneck of multi-agent diversity. Specifically, our approach leverages
contrastive learning to maximize the mutual information between the temporal
credits and identity representations of different agents, encouraging the full
expressiveness of credit assignment and further the emergence of
individualities. The algorithm implementation of the proposed CIA module is
simple yet effective that can be readily incorporated into various VD
architectures. Experiments on the SMAC benchmarks and across different VD
backbones demonstrate that the proposed method yields results superior to the
state-of-the-art counterparts. Our code is available at
https://github.com/liushunyu/CIA
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