Low Rank Directed Acyclic Graphs and Causal Structure Learning

Despite several important advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse. In particular, the recent formulation of structure learning as a continuous optimization problem proved to have considerable advantages over the traditional combinatorial formulation, but the performance of the resulting algorithms is still wanting when the target graph is relatively large and dense. In this paper we propose a novel approach to mitigate this problem, by exploiting a low rank assumption regarding the (weighted) adjacency matrix of a DAG causal model. We establish several useful results relating interpretable graphical conditions to the low rank assumption, and show how to adapt existing methods for causal structure learning to take advantage of this assumption. We also provide empirical evidence for the utility of our low rank algorithms, especially on graphs that are not sparse. Not only do they outperform state-of-the-art algorithms when the low rank condition is satisfied, the performance on randomly generated scale-free graphs is also very competitive even though the true ranks may not be as low as is assumed

Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks. These preliminary models often rely on off-the-shelf learning models to predict from historical statistics while disregarding the physics governing the generating behaviors of these flows. This paper instead introduces Flow Neural Network (FlowNN) to improve the feature representation with learned physical bias. This is implemented by an induction layer, working upon the embedding layer, to impose the physics connected data correlations, and a self-supervised learning strategy with stop-gradient to make the learned physics universal. For the short-timescale network prediction tasks, FlowNN achieves 17% - 71% of loss decrease than the state-of-the-art baselines on both synthetic and real-world networking datasets, which shows the strength of this new approach. Code will be made available.Comment: re-organize the presentatio

Establishment of Absolute Gravity Datum in CMONOC and Its Application

The high accuracy absolute gravity datum covered the Chinese mainland area is established by using absolute gravity measurement data of one hundred stations in CMONOC(Crustal Movement Observation Network of China), the accuracy of each station is better than 5.0 μGal/a. The high accuracy gravity datum can be used for relative gravity measurements in adjustment, and the real gravity value can be determined from relative gravity measurement data of adjustment by using the gravity datum to avoid distortion of the real figure of gravity change. The trend of absolute gravity change from several observations at Chengdu station shows that the secular trend of gravity change is larger than 5.01±0.7 μGal/a, which is probably caused by the distribution of mass below the earth. The rate of land subsidence of Wuhan area is 3.27±0.65 mm/a determined from combined long-term absolute gravity measurements and GRACE satellite data according to the two system difference

Model-free inference of diffusion networks using RKHS embeddings

International audienc

The absolute gravity measurement by FG5 gravimeter at Great Wall Station, Antarctica

Gravity measurement is of great importance to the height datum in Antarctica. The absolute gravity measurement was carried out at Great Wall Station, Antarctica, using FG5 absolute gravity instrument. The gravity data was processed with corrections of earth tide, ocean tide, polar motion and the atmosphere, and the RMS is within ± 3 x 10(-8) ms(-2). The vertical and horizontal gravity gradients were measured using 2 La Coaste & Romberg (LCR) gravimeters. The absolute gravity measurement provides the fundamental data for the validation and calibration of the satellite gravity projects such as CHAMP, GRACE and GOCE, and for the high accuracy geo id model

A Fast-Transient-Response NMOS LDO with Wide Load-Capacitance Range for Cross-Point Memory

In this paper, a fast-transient-response NMOS low-dropout regulator (LDO) with a wide load-capacitance range was presented to provide a V/2 read bias for cross-point memory. To utilize the large dropout voltage in the V/2 bias scheme, a fast loop consisting of NMOS and flipped voltage amplifier (FVA) topology was adopted with a fast transient response. This design is suitable to provide a V/2 read bias with 3.3 V input voltage and 1.65 V output voltage for different cross-point memories. The FVA-based LDO designed in the 110 nm CMOS process remained stable under a wide range of load capacitances from 0 to 10 nF and equivalent series resistance (ESR) conditions. At the capacitor-less condition, it exhibited a unity-gain bandwidth (UGB) of approximately 400 MHz at full load. For load current changes from 0 to 10 mA within an edge time of 10 ps, the simulated undershoot and settling time were only 144 mV and 50 ns, respectively. The regulator consumed 70 µA quiescent current and achieved a remarkable figure-of-merit (FOM) of 1.01 mV. At the ESR condition of a 1 µF off-chip capacitor, the simulated quiescent current, on-chip capacitor consumption, and current efficiency at full load were 8.5 µA, 2 pF, and 99.992%, respectively. The undershoot voltage was 20 mV with 800 ns settling time for a load step from 0 to 100 mA within the 10 ps edge time

Comparison of gut microbiota in autism spectrum disorders and neurotypical boys in China: A case-control study

Background: Autism spectrum disorders (ASDs) are a set of complex neurobiological disorders. Growing evidence has shown that the microbiota that resides in the gut can modulate brain development via the gut–brain axis. However, direct clinical evidence of the role of the microbiota–gut–brain axis in ASD is relatively limited. Methods: A case-control study of 71 boys with ASD and 18 neurotypical controls was conducted at China-Japan Friendship Hospital. Demographic information and fecal samples were collected, and the gut microbiome was evaluated and compared by 16S ribosomal RNA gene sequencing and metagenomic sequencing. Results: A higher abundance of operational taxonomic units (OTUs) based on fecal bacterial profiling was observed in the ASD group. Significantly different microbiome profiles were observed between the two groups. At the genus level, we observed a decrease in the relative abundance of Escherichia, Shigella, Veillonella, Akkermansia, Provindencia, Dialister, Bifidobacterium, Streptococcus, Ruminococcaceae UCG_002, Megasphaera, Eubacterium_coprostanol, Citrobacter, Ruminiclostridium_5, and Ruminiclostridium_6 in the ASD cohort, while Eisenbergiella, Klebsiella, Faecalibacterium, and Blautia were significantly increased. Ten bacterial strains were selected for clinical discrimination between those with ASD and the neurotypical controls. The highest AUC value of the model was 0.947. Conclusion: Significant differences were observed in the composition of the gut microbiome between boys with ASD and neurotypical controls. These findings contribute to the knowledge of the alteration of the gut microbiome in ASD patients, which opens the possibility for early identification of this disease