1,100 research outputs found
Made in Italy (by the Chinese) : economic restructuring and the politics of migration
People around the world are on the move and settling in new, unexpected places. In Prato, Italy, Chinese immigrants now run most of the city’s textiles-apparel companies and even subcontract for such leading designers as Giorgio Armani and Dolce & Gabbana. Italian products once made by Italian workers are now increasingly made…by the Chinese! I argue that this development resulted from an uncanny synchronicity between their business approach and the demands of Italy’s local, familybased, small-batch production environment. In other words, the Chinese thrived because they fit in well with the unique makeup and demands of Italian industry.Personas de todo el mundo se desplazan y establecen en lugares nuevos e inesperados. En Prato, Italia, los inmigrantes chinos dirigen la mayorĂa de las empresas de confecciĂłn textil de la ciudad e incluso subcontratan la confecciĂłn de famosos diseñadores como Giorgio Armani o Dolce & Gabbana. Los productos italianos, antaño fabricados por trabajadores italianos, ahora cada vez están más hechos. . . ¡por chinos! Este desarrollo es el resultado de una extraordinaria sincronĂa entre su propia perspectiva empresarial y la estructura de producciĂłn local italiana a pequeña escala y basada en la familia. En definitiva, los chinos prosperan porque se adaptan bien al tipo de producciĂłn y a la demanda de la industria italiana
TCM Database@Taiwan: The World's Largest Traditional Chinese Medicine Database for Drug Screening In Silico
Rapid advancing computational technologies have greatly speeded up the development of computer-aided drug design (CADD). Recently, pharmaceutical companies have increasingly shifted their attentions toward traditional Chinese medicine (TCM) for novel lead compounds. Despite the growing number of studies on TCM, there is no free 3D small molecular structure database of TCM available for virtual screening or molecular simulation. To address this shortcoming, we have constructed TCM Database@Taiwan (http://tcm.cmu.edu.tw/) based on information collected from Chinese medical texts and scientific publications. TCM Database@Taiwan is currently the world's largest non-commercial TCM database. This web-based database contains more than 20,000 pure compounds isolated from 453 TCM ingredients. Both cdx (2D) and Tripos mol2 (3D) formats of each pure compound in the database are available for download and virtual screening. The TCM database includes both simple and advanced web-based query options that can specify search clauses, such as molecular properties, substructures, TCM ingredients, and TCM classification, based on intended drug actions. The TCM database can be easily accessed by all researchers conducting CADD. Over the last eight years, numerous volunteers have devoted their time to analyze TCM ingredients from Chinese medical texts as well as to construct structure files for each isolated compound. We believe that TCM Database@Taiwan will be a milestone on the path towards modernizing traditional Chinese medicine.National Science Council of Taiwan (NSC 99-2221-E-039-013-)China Medical UniversityAsia UniversityAsia University (CMU98-ASIA-09)Taiwan. Dept. of Health (Clinical Trial and Research Center of Excellence (DOH99-TD-B-111-004))Taiwan. Dept. of Health (Cancer Research Center of Excellence (DOH99-TD-C-111-005)
Towards A Unified Neural Architecture for Visual Recognition and Reasoning
Recognition and reasoning are two pillars of visual understanding. However,
these tasks have an imbalance in focus; whereas recent advances in neural
networks have shown strong empirical performance in visual recognition, there
has been comparably much less success in solving visual reasoning. Intuitively,
unifying these two tasks under a singular framework is desirable, as they are
mutually dependent and beneficial. Motivated by the recent success of
multi-task transformers for visual recognition and language understanding, we
propose a unified neural architecture for visual recognition and reasoning with
a generic interface (e.g., tokens) for both. Our framework enables the
principled investigation of how different visual recognition tasks, datasets,
and inductive biases can help enable spatiotemporal reasoning capabilities.
Noticeably, we find that object detection, which requires spatial localization
of individual objects, is the most beneficial recognition task for reasoning.
We further demonstrate via probing that implicit object-centric representations
emerge automatically inside our framework. Intriguingly, we discover that
certain architectural choices such as the backbone model of the visual encoder
have a significant impact on visual reasoning, but little on object detection.
Given the results of our experiments, we believe that visual reasoning should
be considered as a first-class citizen alongside visual recognition, as they
are strongly correlated but benefit from potentially different design choices
Node and Edge Differential Privacy for Graph Laplacian Spectra: Mechanisms and Scaling Laws
This paper develops a framework for privatizing the spectrum of the graph
Laplacian of an undirected graph using differential privacy. We consider two
privacy formulations. The first obfuscates the presence of edges in the graph
and the second obfuscates the presence of nodes. We compare these two privacy
formulations and show that the privacy formulation that considers edges is
better suited to most engineering applications. We use the bounded Laplace
mechanism to provide differential privacy to the eigenvalues of a graph
Laplacian, and we pay special attention to the algebraic connectivity, which is
the Laplacian's second smallest eigenvalue. Analytical bounds are presented on
the the accuracy of the mechanisms and on certain graph properties computed
with private spectra. A suite of numerical examples confirms the accuracy of
private spectra in practice.Comment: arXiv admin note: text overlap with arXiv:2104.0065
Does Visual Pretraining Help End-to-End Reasoning?
We aim to investigate whether end-to-end learning of visual reasoning can be
achieved with general-purpose neural networks, with the help of visual
pretraining. A positive result would refute the common belief that explicit
visual abstraction (e.g. object detection) is essential for compositional
generalization on visual reasoning, and confirm the feasibility of a neural
network "generalist" to solve visual recognition and reasoning tasks. We
propose a simple and general self-supervised framework which "compresses" each
video frame into a small set of tokens with a transformer network, and
reconstructs the remaining frames based on the compressed temporal context. To
minimize the reconstruction loss, the network must learn a compact
representation for each image, as well as capture temporal dynamics and object
permanence from temporal context. We perform evaluation on two visual reasoning
benchmarks, CATER and ACRE. We observe that pretraining is essential to achieve
compositional generalization for end-to-end visual reasoning. Our proposed
framework outperforms traditional supervised pretraining, including image
classification and explicit object detection, by large margins.Comment: NeurIPS 202
Hybrid neural network approaches to predict drug–target binding affinity for drug repurposing: screening for potential leads for Alzheimer’s disease
Alzheimer’s disease (AD) is a neurodegenerative disease that primarily affects elderly individuals. Recent studies have found that sigma-1 receptor (S1R) agonists can maintain endoplasmic reticulum stress homeostasis, reduce neuronal apoptosis, and enhance mitochondrial function and autophagy, making S1R a target for AD therapy. Traditional experimental methods are costly and inefficient, and rapid and accurate prediction methods need to be developed, while drug repurposing provides new ways and options for AD treatment. In this paper, we propose HNNDTA, a hybrid neural network for drug–target affinity (DTA) prediction, to facilitate drug repurposing for AD treatment. The study combines protein–protein interaction (PPI) network analysis, the HNNDTA model, and molecular docking to identify potential leads for AD. The HNNDTA model was constructed using 13 drug encoding networks and 9 target encoding networks with 2506 FDA-approved drugs as the candidate drug library for S1R and related proteins. Seven potential drugs were identified using network pharmacology and DTA prediction results of the HNNDTA model. Molecular docking simulations were further performed using the AutoDock Vina tool to screen haloperidol and bromperidol as lead compounds for AD treatment. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation results indicated that both compounds had good pharmacokinetic properties and were virtually non-toxic. The study proposes a new approach to computer-aided drug design that is faster and more economical, and can improve hit rates for new drug compounds. The results of this study provide new lead compounds for AD treatment, which may be effective due to their multi-target action. HNNDTA is freely available at https://github.com/lizhj39/HNNDTA
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Efficient Parallel Algorithms and Data Structures Related to Trees
The main contribution of this dissertation proposes a new paradigm, called the parentheses matching paradigm. It claims that this paradigm is well suited for designing efficient parallel algorithms for a broad class of nonnumeric problems. To demonstrate its applicability, we present three cost-optimal parallel algorithms for breadth-first traversal of general trees, sorting a special class of integers, and coloring an interval graph with the minimum number of colors
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