11,034 research outputs found
The Halo Occupation Distribution of SDSS Quasars
We present an estimate of the projected two-point correlation function (2PCF)
of quasars in the Sloan Digital Sky Survey (SDSS) over the full range of one-
and two-halo scales, 0.02-120 Mpc/h. This was achieved by combining data from
SDSS DR7 on large scales and Hennawi et al. (2006; with appropriate statistical
corrections) on small scales. Our combined clustering sample is the largest
spectroscopic quasar clustering sample to date, containing ~48,000 quasars in
the redshift range 0.4<z<2.5 with median redshift 1.4. We interpret these
precise 2PCF measurements within the halo occupation distribution (HOD)
framework and constrain the occupation functions of central and satellite
quasars in dark matter halos. In order to explain the small-scale clustering,
the HOD modeling requires that a small fraction of z~1.4 quasars,
fsat=(7.4+/-1.4) 10^(-4), be satellites in dark matter halos. At z~1.4, the
median masses of the host halos of central and satellite quasars are
constrained to be Mcen=(4.1+0.3/-0.4) 10^12 Msun/h and Msat=(3.6+0.8/-1.0)
10^14 Msun/h, respectively. To investigate the redshift evolution of the
quasar-halo relationship, we also perform HOD modeling of the projected 2PCF
measured by Shen et al. (2007) for SDSS quasars with median redshift 3.2. We
find tentative evidence for an increase in the mass scale of quasar host
halos---the inferred median mass of halos hosting central quasars at z~3.2 is
Mcen=(14.1+5.8/-6.9) 10^12 Msun/h. The cutoff profiles of the mean occupation
functions of central quasars reveal that quasar luminosity is more tightly
correlated with halo mass at higher redshifts. The average quasar duty cycle
around the median host halo mass is inferred to be fq=(7.3+0.6/-1.5) 10^(-4) at
z~1.4 and fq=(8.6+20.4/-7.2) 10^(-2) at z~3.2. We discuss the implications of
our results for quasar evolution and quasar-galaxy co-evolution.Comment: matches the ApJ published versio
Crystal structure of human muscle creatine kinase
This is the publisher's version, also available electronically from "http://scripts.iucr.org".The crystal structure of human muscle creatine kinase has been determined by the molecular-replacement method and refined at 3.5 Å resolution. The structures of both the monomer and the dimer closely resemble those of the other known structures in the creatine kinase family. Two types of dimers, one with a non-crystallographic twofold symmetry axis and the other with a crystallographic twofold symmetry axis, were found to occur simultaneously in the crystal. These dimers form an infinite `double-helix'-like structure along an unusual long crystallographic 31 axis
MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection
In this paper, we propose a novel and effective Multi-Level Fusion network,
named as MLF-DET, for high-performance cross-modal 3D object DETection, which
integrates both the feature-level fusion and decision-level fusion to fully
utilize the information in the image. For the feature-level fusion, we present
the Multi-scale Voxel Image fusion (MVI) module, which densely aligns
multi-scale voxel features with image features. For the decision-level fusion,
we propose the lightweight Feature-cued Confidence Rectification (FCR) module
which further exploits image semantics to rectify the confidence of detection
candidates. Besides, we design an effective data augmentation strategy termed
Occlusion-aware GT Sampling (OGS) to reserve more sampled objects in the
training scenes, so as to reduce overfitting. Extensive experiments on the
KITTI dataset demonstrate the effectiveness of our method. Notably, on the
extremely competitive KITTI car 3D object detection benchmark, our method
reaches 82.89% moderate AP and achieves state-of-the-art performance without
bells and whistles
De novo protein design using geometric vector field networks
Innovations like protein diffusion have enabled significant progress in de
novo protein design, which is a vital topic in life science. These methods
typically depend on protein structure encoders to model residue backbone
frames, where atoms do not exist. Most prior encoders rely on atom-wise
features, such as angles and distances between atoms, which are not available
in this context. Thus far, only several simple encoders, such as IPA, have been
proposed for this scenario, exposing the frame modeling as a bottleneck. In
this work, we proffer the Vector Field Network (VFN), which enables network
layers to perform learnable vector computations between coordinates of
frame-anchored virtual atoms, thus achieving a higher capability for modeling
frames. The vector computation operates in a manner similar to a linear layer,
with each input channel receiving 3D virtual atom coordinates instead of scalar
values. The multiple feature vectors output by the vector computation are then
used to update the residue representations and virtual atom coordinates via
attention aggregation. Remarkably, VFN also excels in modeling both frames and
atoms, as the real atoms can be treated as the virtual atoms for modeling,
positioning VFN as a potential universal encoder. In protein diffusion (frame
modeling), VFN exhibits an impressive performance advantage over IPA, excelling
in terms of both designability (67.04% vs. 53.58%) and diversity (66.54% vs.
51.98%). In inverse folding (frame and atom modeling), VFN outperforms the
previous SoTA model, PiFold (54.7% vs. 51.66%), on sequence recovery rate. We
also propose a method of equipping VFN with the ESM model, which significantly
surpasses the previous ESM-based SoTA (62.67% vs. 55.65%), LM-Design, by a
substantial margin
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models
Transformer-based pre-trained language models (PLMs) mostly suffer from
excessive overhead despite their advanced capacity. For resource-constrained
devices, there is an urgent need for a spatially and temporally efficient model
which retains the major capacity of PLMs. However, existing statically
compressed models are unaware of the diverse complexities between input
instances, potentially resulting in redundancy and inadequacy for simple and
complex inputs. Also, miniature models with early exiting encounter challenges
in the trade-off between making predictions and serving the deeper layers.
Motivated by such considerations, we propose a collaborative optimization for
PLMs that integrates static model compression and dynamic inference
acceleration. Specifically, the PLM is slenderized in width while the depth
remains intact, complementing layer-wise early exiting to speed up inference
dynamically. To address the trade-off of early exiting, we propose a joint
training approach that calibrates slenderization and preserves contributive
structures to each exit instead of only the final layer. Experiments are
conducted on GLUE benchmark and the results verify the Pareto optimality of our
approach at high compression and acceleration rate with 1/8 parameters and 1/19
FLOPs of BERT.Comment: Accepted in EMNLP 2022 main conferenc
VisorGPT: Learning Visual Prior via Generative Pre-Training
Various stuff and things in visual data possess specific traits, which can be
learned by deep neural networks and are implicitly represented as the visual
prior, e.g., object location and shape, in the model. Such prior potentially
impacts many vision tasks. For example, in conditional image synthesis, spatial
conditions failing to adhere to the prior can result in visually inaccurate
synthetic results. This work aims to explicitly learn the visual prior and
enable the customization of sampling. Inspired by advances in language
modeling, we propose to learn Visual prior via Generative Pre-Training, dubbed
VisorGPT. By discretizing visual locations of objects, e.g., bounding boxes,
human pose, and instance masks, into sequences, VisorGPT can model visual prior
through likelihood maximization. Besides, prompt engineering is investigated to
unify various visual locations and enable customized sampling of sequential
outputs from the learned prior. Experimental results demonstrate that VisorGPT
can effectively model the visual prior, which can be employed for many vision
tasks, such as customizing accurate human pose for conditional image synthesis
models like ControlNet. Code will be released at
https://github.com/Sierkinhane/VisorGPT.Comment: Project web-page: https://sierkinhane.github.io/visor-gpt
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