230 research outputs found
Expression levels of microRNAs are not associated with their regulatory activities
MicroRNAs (miRNAs) regulate their targets by triggering mRNA degradation or translational repression. The negative relationship between miRNAs and their targets suggests that the regulatory effect of a miRNA could be determined from the expression levels of its targets. Here, we investigated the relationship between miRNA activities determined by computational programs and miRNA expression levels by using data in which both mRNA and miRNA expression from the same samples were measured. We found that different from the intuitive expectation one might have, miRNA activity shows very weak correlation with miRNA expression, which indicates complex regulating mechanisms between miRNAs and their target genes
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
The ability of graph neural networks (GNNs) to count certain graph
substructures, especially cycles, is important for the success of GNNs on a
wide range of tasks. It has been recently used as a popular metric for
evaluating the expressive power of GNNs. Many of the proposed GNN models with
provable cycle counting power are based on subgraph GNNs, i.e., extracting a
bag of subgraphs from the input graph, generating representations for each
subgraph, and using them to augment the representation of the input graph.
However, those methods require heavy preprocessing, and suffer from high time
and memory costs. In this paper, we overcome the aforementioned limitations of
subgraph GNNs by proposing a novel class of GNNs -- -Distance-Restricted
FWL(2) GNNs, or -DRFWL(2) GNNs. -DRFWL(2) GNNs use node pairs whose
mutual distances are at most as the units for message passing to balance
the expressive power and complexity. By performing message passing among
distance-restricted node pairs in the original graph, -DRFWL(2) GNNs avoid
the expensive subgraph extraction operations in subgraph GNNs, making both the
time and space complexity lower. We theoretically show that the discriminative
power of -DRFWL(2) GNNs strictly increases as increases. More
importantly, -DRFWL(2) GNNs have provably strong cycle counting power even
with : they can count all 3, 4, 5, 6-cycles. Since 6-cycles (e.g., benzene
rings) are ubiquitous in organic molecules, being able to detect and count them
is crucial for achieving robust and generalizable performance on molecular
tasks. Experiments on both synthetic datasets and molecular datasets verify our
theory. To the best of our knowledge, our model is the most efficient GNN model
to date (both theoretically and empirically) that can count up to 6-cycles
Dynamical properties of quantum many-body systems with long range interactions
Employing large-scale quantum Monte Carlo simulations, we systematically
compute the energy spectra of the 2D spin-1/2 Heisenberg model with long-range
interactions. With the ferromagnetic and staggered
antiferromagnetic interactions, we find the explicit range in for
{\color{black} the short-range Goldstone-type (gapless), anomalous
Goldstone-type (gapless) and Higgs-type (gapped) spectra. Accompanied by the
spin wave analysis, our numerical results vividly reveal how the long-range
interactions alter the usual linear and quadratic magnon dispersions in 2D
quantum magnets and give rise to anomalous dynamical exponents. Moreover, we
find explicit case where the gapped excitation exists even when the Hamiltonian
is extensive. This work provides the first set of unbiased dynamical data} of
long-range quantum many-body systems and suggests that many universally
accepted low-energy customs for short-range systems need to be substantially
modified for long-range ones which are of immediate relevance to the ongoing
experimental efforts from quantum simulators to 2D quantum moir\'e materials.Comment: 5 pages,3 figure
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the
presented items, plays a key role in online services. With two-decade rapid
development, nowadays the cumulated user behavior sequences on mature Internet
service platforms have become extremely long since the user's first
registration. Each user not only has intrinsic tastes, but also keeps changing
her personal interests during lifetime. Hence, it is challenging to handle such
lifelong sequential modeling for each individual user. Existing methodologies
for sequential modeling are only capable of dealing with relatively recent user
behaviors, which leaves huge space for modeling long-term especially lifelong
sequential patterns to facilitate user modeling. Moreover, one user's behavior
may be accounted for various previous behaviors within her whole online
activity history, i.e., long-term dependency with multi-scale sequential
patterns. In order to tackle these challenges, in this paper, we propose a
Hierarchical Periodic Memory Network for lifelong sequential modeling with
personalized memorization of sequential patterns for each user. The model also
adopts a hierarchical and periodical updating mechanism to capture multi-scale
sequential patterns of user interests while supporting the evolving user
behavior logs. The experimental results over three large-scale real-world
datasets have demonstrated the advantages of our proposed model with
significant improvement in user response prediction performance against the
state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets:
https://github.com/alimamarankgroup/HPM
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to tradeoff between
immediate and future returns are fundamental questions for a typical
ride-hailing platform. We model ride-hailing as a large-scale parallel ranking
problem and study the joint decision-making task of order dispatching and fleet
management in online ride-hailing platforms. This task brings unique challenges
in the following four aspects. First, to facilitate a huge number of vehicles
to act and learn efficiently and robustly, we treat each region cell as an
agent and build a multi-agent reinforcement learning framework. Second, to
coordinate the agents from different regions to achieve long-term benefits, we
leverage the geographical hierarchy of the region grids to perform hierarchical
reinforcement learning. Third, to deal with the heterogeneous and variant
action space for joint order dispatching and fleet management, we design the
action as the ranking weight vector to rank and select the specific order or
the fleet management destination in a unified formulation. Fourth, to achieve
the multi-scale ride-hailing platform, we conduct the decision-making process
in a hierarchical way where a multi-head attention mechanism is utilized to
incorporate the impacts of neighbor agents and capture the key agent in each
scale. The whole novel framework is named as CoRide. Extensive experiments
based on multiple cities real-world data as well as analytic synthetic data
demonstrate that CoRide provides superior performance in terms of platform
revenue and user experience in the task of city-wide hybrid order dispatching
and fleet management over strong baselines.Comment: CIKM 201
Dynamic alterations in the amplitude of low-frequency fluctuation in patients with cerebral small vessel disease
Background and purposePrevious studies have focused on the changes of dynamic and static functional connections in cerebral small vessel disease (CSVD). However, the dynamic characteristics of local brain activity are poorly understood. The purpose of this study was to investigate the dynamic cerebral activity changes in patients with CSVD using the dynamic amplitude of low-frequency fluctuation (d-ALFF).MethodsA total of 104 CSVD patients with cognitive impairment (CSVD-CI, nβ=β52) or normal cognition (CSVD-NC, nβ=β52) and 63 matched healthy controls (HCs) were included in this study. Every participant underwent magnetic resonance imaging scans and a battery of neuropsychological examinations. The dynamics of spontaneous brain activity were assessed using dynamic changes in the amplitude of low-frequency fluctuation (ALFF) with the sliding-window method. We used voxel-wise one-way analysis of variance (ANOVA) to compare dynamic ALFF variability among the three groups. Post-hoc t-tests were used to evaluate differences between each group pair. Finally, the brain regions with d-ALFF values with differences between CSVD subgroups were taken as regions of interest (ROI), and the d-ALFF values corresponding to the ROI were extracted for partial correlation analysis with memory.Results(1) There was no significant difference in age (pβ=β0.120), sex (pβ=β0.673) and education (pβ=β0.067) among CSVD-CI, CSVD-NC and HC groups, but there were significant differences Prevalence of hypertension and diabetes mellitus among the three groups (pβ<β10β3). There were significant differences in scores of several neuropsychological scales among the three groups (pβ<β10β3). (2) ANOVA and post-hoc t-test showed that there were dynamic abnormalities of spontaneous activity in several brain regions in three groups, mainly located in bilateral parahippocampal gyrus and bilateral hippocampus, bilateral insular and frontal lobes, and the static activity abnormalities in bilateral parahippocampal gyrus and bilateral hippocampal regions were observed at the same time, suggesting that bilateral parahippocampal gyrus and bilateral hippocampus may be the key brain regions for cognitive impairment caused by CSVD. (3) The correlation showed that d-ALFF in the bilateral insular was slightly correlated with the Mini-Mental State Examination (MMSE) score and disease progression rate. The d-ALFF value of the left postcentral gyrus was negatively correlated with the Clock Drawing Test (CDT) score (rβ=ββ0.416, pβ=β0.004), and the d-ALFF value of the right postcentral gyrus was negatively correlated with the Reyβs Auditory Verbal Learning Test (RAVLT) word recognition (rβ=ββ0.320, pβ=β0.028).ConclusionThere is a wide range of dynamic abnormalities of spontaneous brain activity in patients with CSVD, in which the abnormalities of this activity in specific brain regions are related to memory and execution or emotion
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