206 research outputs found
Mechanism and Interface Study of One-to-one Metal NP/Metal Organic Framework Core-shell Structure
Thesis advisor: Chia-Kuang (Frank) TsungThe core-shell hybrid structure is the simplest motif of two-component systems which consists of an inner core coated by an outer shell. Core-shell composite materials are attractive for their biomedical, electronic and catalytic applications in which interface between core and shell is critical for various functionalities. However, it is still challenging to study the exact role that interface plays during the formation of the core-shell structures and in the properties of the resulted materials. By studying the formation mechanism of a well interface controlled one-to-one metal nanoparticle (NP)@zeolite imidazolate framework-8 (ZIF-8) core-shell material, we found that the dissociation of capping agents on the NP surface results in direct contact between NP and ZIF-8, which is essential for the formation of core-shell structure. And the amount of capping agents on the NP surface has a significant effect to the crystallinity and stability of ZIF-8 coating shell. Guided by our understanding to the interface, one-to-one NP@UiO-66 core-shell structure has also been achieved for the first time. We believe that our research will help the development of rational design and synthesis of core-shell structures, particularly in those requiring good interface controls.Thesis (MS) â Boston College, 2017.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Chemistry
Specify Robust Causal Representation from Mixed Observations
Learning representations purely from observations concerns the problem of
learning a low-dimensional, compact representation which is beneficial to
prediction models. Under the hypothesis that the intrinsic latent factors
follow some casual generative models, we argue that by learning a causal
representation, which is the minimal sufficient causes of the whole system, we
can improve the robustness and generalization performance of machine learning
models. In this paper, we develop a learning method to learn such
representation from observational data by regularizing the learning procedure
with mutual information measures, according to the hypothetical factored causal
graph. We theoretically and empirically show that the models trained with the
learned causal representations are more robust under adversarial attacks and
distribution shifts compared with baselines. The supplementary materials are
available at https://github.com/ymy .Comment: arXiv admin note: substantial text overlap with arXiv:2202.0838
Disentangled Generative Causal Representation Learning
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR)
learning method. Unlike existing disentanglement methods that enforce
independence of the latent variables, we consider the general case where the
underlying factors of interests can be causally correlated. We show that
previous methods with independent priors fail to disentangle causally
correlated factors. Motivated by this finding, we propose a new disentangled
learning method called DEAR that enables causal controllable generation and
causal representation learning. The key ingredient of this new formulation is
to use a structural causal model (SCM) as the prior for a bidirectional
generative model. The prior is then trained jointly with a generator and an
encoder using a suitable GAN loss incorporated with supervision. We provide
theoretical justification on the identifiability and asymptotic consistency of
the proposed method, which guarantees disentangled causal representation
learning under appropriate conditions. We conduct extensive experiments on both
synthesized and real data sets to demonstrate the effectiveness of DEAR in
causal controllable generation, and the benefits of the learned representations
for downstream tasks in terms of sample efficiency and distributional
robustness
Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic Learning
Simulation-based Medical Education (SBME) has been developed as a
cost-effective means of enhancing the diagnostic skills of novice physicians
and interns, thereby mitigating the need for resource-intensive
mentor-apprentice training. However, feedback provided in most SBME is often
directed towards improving the operational proficiency of learners, rather than
providing summative medical diagnoses that result from experience and time.
Additionally, the multimodal nature of medical data during diagnosis poses
significant challenges for interns and novice physicians, including the
tendency to overlook or over-rely on data from certain modalities, and
difficulties in comprehending potential associations between modalities. To
address these challenges, we present DiagnosisAssistant, a visual analytics
system that leverages historical medical records as a proxy for multimodal
modeling and visualization to enhance the learning experience of interns and
novice physicians. The system employs elaborately designed visualizations to
explore different modality data, offer diagnostic interpretive hints based on
the constructed model, and enable comparative analyses of specific patients.
Our approach is validated through two case studies and expert interviews,
demonstrating its effectiveness in enhancing medical training.Comment: Accepted by IEEE VIS 202
Learning to Select Cuts for Efficient Mixed-Integer Programming
Cutting plane methods play a significant role in modern solvers for tackling
mixed-integer programming (MIP) problems. Proper selection of cuts would remove
infeasible solutions in the early stage, thus largely reducing the
computational burden without hurting the solution accuracy. However, the major
cut selection approaches heavily rely on heuristics, which strongly depend on
the specific problem at hand and thus limit their generalization capability. In
this paper, we propose a data-driven and generalizable cut selection approach,
named Cut Ranking, in the settings of multiple instance learning. To measure
the quality of the candidate cuts, a scoring function, which takes the
instance-specific cut features as inputs, is trained and applied in cut ranking
and selection. In order to evaluate our method, we conduct extensive
experiments on both synthetic datasets and real-world datasets. Compared with
commonly used heuristics for cut selection, the learning-based policy has shown
to be more effective, and is capable of generalizing over multiple problems
with different properties. Cut Ranking has been deployed in an industrial
solver for large-scale MIPs. In the online A/B testing of the product planning
problems with more than variables and constraints daily, Cut Ranking has
achieved the average speedup ratio of 12.42% over the production solver without
any accuracy loss of solution.Comment: Paper accepted at Pattern Recognition journa
Effect of lateral tilt angle on the volume of the abdominal aorta and inferior vena cava in pregnant and nonpregnant women determined by magnetic resonance imaging
Effect of lateral tilt angle on the volume of the abdominal aorta and inferior vena cava in pregnant and nonpregnant women determined by magnetic resonance imaging
Efficient organic solar cells enabled by simple non-fused electron donors with low synthetic complexity
Abstract Fusedâring electron donors boost the efficiency of organic solar cells (OSCs), but they suffer from high cost and low yield for their large synthetic complexity (SC > 30%). Herein, the authors develop a series of simple nonâfusedâring electron donors, PF1 and PF2, which alternately consist of furanâ3âcarboxylate and 2,2âČâbithiophene. Note that PF1 and PF2 present very small SC of 9.7% for their inexpensive raw materials, facile synthesis, and high synthetic yield. Compared to their allâthiopheneâbackbone counterpart PTâE, two new polymers feature larger conjugated plane, resulting in higher hole mobility for them, especially a value up to â10 â4 cm 2 V â1 ·s for PF2 with longer alkyl side chain. Meanwhile, PF1 and PF2 exhibit larger dielectric constant and deeper electronic energy level versus PTâE. Benefiting from the better physicochemical properties, the efficiencies of PF1â and PF2âbased devices are improved by â16.7% and â71.3% relative to that PTâEâbased devices, respectively. Furthermore, the optimized PF2âbased devices with introducing PC 71 BM as the third component deliver a higher efficiency of 12.40%. The work not only indicates that furanâ3âcarboxylate is a simple yet efficient building block for constructing nonâfusedâring polymers but also provides a promising electron donor PF2 for the lowâcost production of OSCs.A simple structure nonâfusedâring electron donor PF2 alternately consisting of furanâ3âcarboxylate and 2,2âČâbithiophene presents very small synthetic complexity of 9.7% as well as low material cost of â19.0 $ g â1 . More importantly, PF2 delivers a high efficiency of 12.4% coupled with strong operational stability. imag
TELEX HEBDOMADAIRE NR 95 DU 17.09.82 DESTINE A L'ENSEMBLE DES DELEGATIONS EXTERIEURES ET BUREAUX DE PRESS ET D'INFORMATION INDEPENDANTS DANS LES PAYS TIERS = WEEKLY MEMO NO. 95 FOR 17.09.82 TO FOREIGN DELEGATIONS AND PRESS BUREAUS OF THIRD COUNTRIES
<p>High-performance liquid chromatography (HPLC) results of (A) commercial surfactin sample, and (B) our extract surfactin of <i>B</i>. <i>subtilis</i> HH2 in LB medium. There were three main peaks (Peak A-C) of the extract and the surfactin standard in the same location.</p
The chirally improved quark propagator and restoration of chiral symmetry
The chirally improved (CI) quark propagator in Landau gauge is calculated in
two flavor lattice Quantum Chromodynamics. Its wave-function renormalization
function and mass function are studied. To minimize lattice
artifacts, tree-level improvement of the propagator and tree-level correction
of the lattice dressing functions is applied. Subsequently the CI quark
propagator under Dirac operator low-mode removal is investigated. The
dynamically generated mass in the infrared domain of the mass function is found
to dissolve continuously as a function of the reduction level and strong
suppression of for small momenta is observed.Comment: 9 pages, 8 figures; accepted at Phys. Lett.
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