290 research outputs found
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs
Graph neural networks (GNNs), as the de-facto model class for representation
learning on graphs, are built upon the multi-layer perceptrons (MLP)
architecture with additional message passing layers to allow features to flow
across nodes. While conventional wisdom commonly attributes the success of GNNs
to their advanced expressivity, we conjecture that this is not the main cause
of GNNs' superiority in node-level prediction tasks. This paper pinpoints the
major source of GNNs' performance gain to their intrinsic generalization
capability, by introducing an intermediate model class dubbed as
P(ropagational)MLP, which is identical to standard MLP in training, but then
adopts GNN's architecture in testing. Intriguingly, we observe that PMLPs
consistently perform on par with (or even exceed) their GNN counterparts, while
being much more efficient in training. This finding sheds new insights into
understanding the learning behavior of GNNs, and can be used as an analytic
tool for dissecting various GNN-related research problems. As an initial step
to analyze the inherent generalizability of GNNs, we show the essential
difference between MLP and PMLP at infinite-width limit lies in the NTK feature
map in the post-training stage. Moreover, by examining their extrapolation
behavior, we find that though many GNNs and their PMLP counterparts cannot
extrapolate non-linear functions for extremely out-of-distribution samples,
they have greater potential to generalize to testing samples near the training
data range as natural advantages of GNN architectures.Comment: Accepted to ICLR 2023. Codes in https://github.com/chr26195/PML
Quantum Interference of Stored Coherent Spin-wave Excitations in a Two-channel Memory
Quantum memories are essential elements in long-distance quantum networks and
quantum computation. Significant advances have been achieved in demonstrating
relative long-lived single-channel memory at single-photon level in cold atomic
media. However, the qubit memory corresponding to store two-channel spin-wave
excitations (SWEs) still faces challenges, including the limitations resulting
from Larmor procession, fluctuating ambient magnetic field, and
manipulation/measurement of the relative phase between the two channels. Here,
we demonstrate a two-channel memory scheme in an ideal tripod atomic system, in
which the total readout signal exhibits either constructive or destructive
interference when the two-channel SWEs are retrieved by two reading beams with
a controllable relative phase. Experimental result indicates quantum coherence
between the stored SWEs. Based on such phase-sensitive storage/retrieval
scheme, measurements of the relative phase between the two SWEs and Rabi
oscillation, as well as elimination of the collapse and revival of the readout
signal, are experimentally demonstrated
Effect of Initial Backfill Temperature on the Deformation Behavior of Early Age Cemented Paste Backfill That Contains Sodium Silicate
Enhancing the knowledge on the deformation behavior of cemented paste backfill (CPB) in terms of stress-strain relations and modulus of elasticity is significant for economic and safety reasons. In this paper, the effect of the initial backfill temperature on the CPB’s stress-strain behavior and modulus of elasticity is investigated. Results show that the stress-strain relationship and the modulus of elasticity behavior of CPB are significantly affected by the curing time and initial temperature of CPB. Additionally, the relationship between the modulus of elasticity and unconfined compressive strength (UCS) and the degree of hydration was evaluated and discussed. The increase of UCS and hydration degree leads to an increase in the modulus of elasticity, which is not significantly affected by the initial temperature
Deformed Two-Mode Quadrature Operators in Noncommutative Space
Starting from noncommutative quantum mechanics algebra, we investigate the
variances of the deformed two-mode quadrature operators under the evolution of
three types of two-mode squeezed states in noncommutative space. A novel
conclusion can be found and it may associate the checking of the variances in
noncommutative space with homodyne detecting technology. Moreover, we analyze
the influence of the scaling parameter on the degree of squeezing for the
deformed level and the corresponding consequences.Comment: 11 pages, no figure
Excellent performance of Pt-C/TiO2 for methanol oxidation:contribution of mesopores and partially coated carbon
Partial deposition of carbon onto mesoporous TiO2 (C/TiO2) were prepared as supporting substrate for Pt catalyst development. Carbon deposition is achieved by in-situ carbonization of furfuryl alcohol. The hybrid catalysts were characterized by XRD, Raman, SEM and TEM and exhibited outstanding catalytic activity and stability in methanol oxidation reaction. The heterogeneous carbon coated on mesoporous TiO2 fibers provided excellent electrical conductivity and strong interfacial interaction between TiO2 support and Pt metal nanoparticles. Methanol oxidation reaction results showed that the activity of Pt-C/TiO2 is 3.0 and 1.5 times higher than that of Pt-TiO2 and Pt-C, respectively. In addition, the Pt-C/TiO2 exhibited a 6.7 times enhanced stability compared with Pt-C after 2000 cycles. The synergistic effect of C/TiO2 is responsible for the enhanced activity of Pt-C/TiO2, and its excellent durability could be ascribed to the strong interfacial interaction between Pt nanoparticles and C/TiO2 support
A simple vector system to improve performance and utilisation of recombinant antibodies
BACKGROUND: Isolation of recombinant antibody fragments from antibody libraries is well established using technologies such as phage display. Phage display vectors are ideal for efficient display of antibody fragments on the surface of bacteriophage particles. However, they are often inefficient for expression of soluble antibody fragments, and sub-cloning of selected antibody populations into dedicated soluble antibody fragment expression vectors can enhance expression. RESULTS: We have developed a simple vector system for expression, dimerisation and detection of recombinant antibody fragments in the form of single chain Fvs (scFvs). Expression is driven by the T7 RNA polymerase promoter in conjunction with the inducible lysogen strain BL21 (DE3). The system is compatible with a simple auto-induction culture system for scFv production. As an alternative to periplasmic expression, expression directly in the cytoplasm of a mutant strain with a more oxidising cytoplasmic environment (Origami 2â„¢ (DE3)) was investigated and found to be inferior to periplasmic expression in BL21 (DE3) cells. The effect on yield and binding activity of fusing scFvs to the N terminus of maltose binding protein (a solubility enhancing partner), bacterial alkaline phosphatase (a naturally dimeric enzymatic reporter molecule), or the addition of a free C-terminal cysteine was determined. Fusion of scFvs to the N-terminus of maltose binding protein increased scFv yield but binding activity of the scFv was compromised. In contrast, fusion to the N-terminus of bacterial alkaline phosphatase led to an improved performance. Alkaline phosphatase provides a convenient tag allowing direct enzymatic detection of scFv fusions within crude extracts without the need for secondary reagents. Alkaline phosphatase also drives dimerisation of the scFv leading to an improvement in performance compared to monovalent constructs. This is illustrated by ELISA, western blot and immunohistochemistry. CONCLUSION: Nine scFv expression vectors have been generated and tested. Three vectors showed utility for expression of functional scFv fragments. One vector, pSANG14-3F, produces scFv-alkaline phosphatase fusion molecules which offers a simple, convenient and sensitive way of determining the reactivity of recombinant antibody fragments in a variety of common assay systems
Doping inorganic ions to regulate bioactivity of Ca–P coating on bioabsorbable high purity magnesium
AbstractPerformance of biomaterials was strongly affected by their surface properties and could be designed artificially to meet specific biomedical requirements. In this study, F−(F), SiO42−(Si), or HCO3−(C)-doped Ca–P coatings were fabricated by biomimetic deposition on the surface of biodegradable high-purity magnesium (HP Mg). The crystalline phases, morphologies and compositions of Ca–P coatings had been characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The biomineralization and corrosion resistance of doped Ca–P coatings had also been investigated. The results showed that the Ca–P coating with or without doped elements mainly contained the plate-like dicalcium phosphate dehydrate (DCPD) phase. The doped F, Si, or C changed the surface morphology of Ca–P coatings after mineralization. Doped F enhanced the mineralization of Ca–P coating, and doped Si retarded the mineralization of Ca–P coating. However, H2 evolution of HP Mg discs with different Ca–P coatings was close to 0.4–0.7ml/cm2 after two-week immersion. That meant that the corrosion resistance of the Ca–P coatings with different or without doped elements did not change significantly
Satellite-based estimate of the variability of warm cloud properties associated with aerosol and meteorological conditions
Aerosol-cloud interaction (ACI) is examined using 10 years of data from the MODIS/Terra (morning orbit) and MODIS/Aqua (afternoon orbit) satellites. Aerosol optical depth (AOD) and cloud properties retrieved from both sensors are used to explore in a statistical sense the morning-to-afternoon variation of cloud properties in conditions with low and high AOD, over both land and ocean. The results show that the interaction between aerosol particles and clouds is more complex and of greater uncertainty over land than over ocean. The variation in d(Cloud_X), defined as the mean change in cloud property Cloud_X between the morning and afternoon overpasses in high-AOD conditions minus that in low-AOD conditions, is different over land and ocean. This applies to cloud droplet effective radius (CDR), cloud fraction (CF) and cloud top pressure (CTP), but not to cloud optical thickness (COT) and cloud liquid water path (CWP). Both COT and CWP increase over land and ocean after the time step, irrespective of the AOD. However, the initial AOD conditions can affect the amplitude of variation of COT and CWP. The effects of initial cloud fraction and meteorological conditions on the change in CF under lowand high-AOD conditions after the 3 h time step over land are also explored. Two cases are considered: (1) when the cloud cover increases and (2) when the cloud cover decreases. For both cases, we find that almost all values of d(CF) are positive, indicating that the variations of CF are larger in high AOD than that in low AOD after the 3 h time step. The results also show that a large increase in cloud fraction occurs when scenes experience large AOD and stronger upward motion of air parcels. Furthermore, the increase rate of cloud cover is larger for high AOD with increasing relative humidity (RH) when RH is larger than 20 %. We also find that a smaller increase in cloud fraction occurs when scenes experience larger AOD and larger initial cloud cover. Overall, the analysis of the diurnal variation of cloud properties provides a better understanding of aerosol-cloud interaction over land and ocean.Peer reviewe
YUAN 2.0: A Large Language Model with Localized Filtering-based Attention
In this work, we develop and release Yuan 2.0, a series of large language
models with parameters ranging from 2.1 billion to 102.6 billion. The Localized
Filtering-based Attention (LFA) is introduced to incorporate prior knowledge of
local dependencies of natural language into Attention. A data filtering and
generating system is presented to build pre-training and fine-tuning dataset in
high quality. A distributed training method with non-uniform pipeline parallel,
data parallel, and optimizer parallel is proposed, which greatly reduces the
bandwidth requirements of intra-node communication, and achieves good
performance in large-scale distributed training. Yuan 2.0 models display
impressive ability in code generation, math problem-solving, and chatting
compared with existing models. The latest version of YUAN 2.0, including model
weights and source code, is accessible at Github
- …