246 research outputs found
Deep3DSketch+: Obtaining Customized 3D Model by Single Free-Hand Sketch through Deep Learning
As 3D models become critical in today's manufacturing and product design,
conventional 3D modeling approaches based on Computer-Aided Design (CAD) are
labor-intensive, time-consuming, and have high demands on the creators. This
work aims to introduce an alternative approach to 3D modeling by utilizing
free-hand sketches to obtain desired 3D models. We introduce Deep3DSketch+,
which is a deep-learning algorithm that takes the input of a single free-hand
sketch and produces a complete and high-fidelity model that matches the sketch
input. The neural network has view- and structural-awareness enabled by a Shape
Discriminator (SD) and a Stroke Enhancement Module (SEM), which overcomes the
limitations of sparsity and ambiguity of the sketches. The network design also
brings high robustness to partial sketch input in industrial applications.Our
approach has undergone extensive experiments, demonstrating its
state-of-the-art (SOTA) performance on both synthetic and real-world datasets.
These results validate the effectiveness and superiority of our method compared
to existing techniques. We have demonstrated the conversion of free-hand
sketches into physical 3D objects using additive manufacturing. We believe that
our approach has the potential to accelerate product design and democratize
customized manufacturing
Transforming Programs between APIs with Many-to-Many Mappings
Transforming programs between two APIs or different versions of
the same API is a common software engineering task. However,
existing languages supporting for such transformation cannot
satisfactorily handle the cases when the relations between
elements in the old API and the new API are many-to-many
mappings: multiple invocations to the old API are supposed to be
replaced by multiple invocations to the new API. Since the
multiple invocations of the original APIs may not appear
consecutively and the variables in these calls may have different
names, writing a tool correctly to cover all such invocation
cases is not an easy task. In this paper we propose a novel
guided-normalization approach to address this problem. Our core
insight is that programs in different forms can be
semantics-equivalently normalized into a basic form guided by
transformation goals, and developers only need to write rules for
the basic form to address the transformation. Based on this
approach, we design a declarative program transformation
language, PATL, for adapting Java programs between different
APIs. PATL has simple syntax and basic semantics to handle
transformations only considering consecutive statements inside
basic blocks, while with guided-normalization, it can be extended
to handle complex forms of invocations. Furthermore, PATL ensures
that the user-written rules would not accidentally break def-use
relations in the program. We formalize the semantics of PATL on
Middleweight Java and prove the semantics-preserving property of
guided-normalization. We also evaluated our language with three
non-trivial case studies: i.e. updating Google Calendar API,
switching from JDom to Dom4j, and switching from Swing to
SWT. The result is encouraging; it shows that our language allows
successful transformations of real world programs with a small
number of rules and little manual resolution
Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles
The single-particle cryo-EM field faces the persistent challenge of preferred
orientation, lacking general computational solutions. We introduce cryoPROS, an
AI-based approach designed to address the above issue. By generating the
auxiliary particles with a conditional deep generative model, cryoPROS
addresses the intrinsic bias in orientation estimation for the observed
particles. We effectively employed cryoPROS in the cryo-EM single particle
analysis of the hemagglutinin trimer, showing the ability to restore the
near-atomic resolution structure on non-tilt data. Moreover, the enhanced
version named cryoPROS-MP significantly improves the resolution of the membrane
protein NaX using the no-tilted data that contains the effects of micelles.
Compared to the classical approaches, cryoPROS does not need special
experimental or image acquisition techniques, providing a purely computational
yet effective solution for the preferred orientation problem. Finally, we
conduct extensive experiments that establish the low risk of model bias and the
high robustness of cryoPROS
Changing Patterns of Spatial Clustering of Schistosomiasis in Southwest China between 1999–2001 and 2007–2008: Assessing Progress toward Eradication after the World Bank Loan Project
We compared changes in the spatial clustering of schistosomiasis in Southwest China at the conclusion of and six years following the end of the World Bank Loan Project (WBLP), the control strategy of which was focused on the large-scale use of chemotherapy. Parasitological data were obtained through standardized surveys conducted in 1999–2001 and again in 2007–2008. Two alternate spatial cluster methods were used to identify spatial clusters of cases: Anselin’s Local Moran’s I test and Kulldorff’s spatial scan statistic. Substantial reductions in the burden of schistosomiasis were found after the end of the WBLP, but the spatial extent of schistosomiasis was not reduced across the study area. Spatial clusters continued to occur in three regions: Chengdu Plain, Yangtze River Valley, and Lancang River Valley during the two periods, and regularly involved five counties. These findings suggest that despite impressive reductions in burden, the hilly and mountainous regions of Southwest China remain at risk of schistosome re-emergence. Our results help to highlight specific locations where integrated control programs can focus to speed the elimination of schistosomiasis in China
On the Propagation of a Geoeffective Coronal Mass Ejection during March 15 -- 17, 2015
The largest geomagnetic storm so far in the solar cycle 24 was produced by a
fast coronal mass ejection (CME) originating on 2015 March 15. It was an
initially west-oriented CME and expected to only cause a weak geomagnetic
disturbance. Why did this CME finally cause such a large geomagnetic storm? We
try to find some clues by investigating its propagation from the Sun to 1 AU.
First, we reconstruct the CME's kinematic properties in the corona from the
SOHO and SDO imaging data with the aid of the graduated cylindrical shell (GCS)
model. It is suggested that the CME propagated to the west
away from the Sun-Earth line with a speed of
about 817 km s before leaving the field of view of the SOHO/LASCO C3
camera. A magnetic cloud (MC) corresponding to this CME was measured in-situ by
the Wind spacecraft two days later. By applying two MC reconstruction methods,
we infer the configuration of the MC as well as some kinematic information,
which implies that the CME possibly experienced an eastward deflection on its
way to 1 AU. However, due to the lack of observations from the STEREO
spacecraft, the CME's kinematic evolution in interplanetary space is not clear.
In order to fill this gap, we utilize numerical MHD simulation, drag-based CME
propagation model (DBM) and the model for CME deflection in interplanetary
space (DIPS) to recover the propagation process, especially the trajectory, of
the CME from to 1 AU. It is suggested that the trajectory of the CME
was deflected toward the Earth by about , consistent with the
implication from the MC reconstruction at 1 AU. This eastward deflection
probably contributed to the CME's unexpected geoeffectiveness by pushing the
center of the initially west-oriented CME closer to the Earth.Comment: 10 pages, 5 figures, 1 table, accepted by JGR - Space Physic
Surface electrocardiographic characteristics in coronavirus disease 2019: repolarization abnormalities associated with cardiac involvement
AIMS
The coronavirus disease 2019 (COVID-19) has spread rapidly around the globe, causing significant morbidity and mortality. This study aims to describe electrocardiographic (ECG) characteristics of COVID-19 patients and to identify ECG parameters that are associated with cardiac involvement.
METHODS AND RESULTS
The study included patients who were hospitalized with COVID-19 diagnosis and had cardiac biomarker assessments and simultaneous 12-lead surface ECGs. Sixty-three hospitalized patients (median 53 [inter-quartile range, 43-65] years, 76.2% male) were enrolled, including patients with (n = 23) and without (n = 40) cardiac injury. Patients with cardiac injury were older, had more pre-existing co-morbidities, and had higher mortality than those without cardiac injury. They also had prolonged QTc intervals and more T wave changes. Logistic regression model identified that the number of abnormal T waves (odds ratio (OR), 2.36 [95% confidence interval (CI), 1.38-4.04], P = 0.002) and QTc interval (OR, 1.31 [95% CI, 1.03-1.66], P = 0.027) were independent indicators for cardiac injury. The combination model of these two parameters along with age could well discriminate cardiac injury (area the under curve 0.881, P < 0.001) by receiver operating characteristic analysis. Cox regression model identified that the presence of T wave changes was an independent predictor of mortality (hazard ratio, 3.57 [1.40, 9.11], P = 0.008) after adjustment for age.
CONCLUSIONS
In COVID-19 patients, presence of cardiac injury at admission is associated with poor clinical outcomes. Repolarization abnormalities on surface ECG such as abnormal T waves and prolonged QTc intervals are more common in patients with cardiac involvement and can help in further risk stratification
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