55 research outputs found
Learning Delaunay Triangulation using Self-attention and Domain Knowledge
Delaunay triangulation is a well-known geometric combinatorial optimization
problem with various applications. Many algorithms can generate Delaunay
triangulation given an input point set, but most are nontrivial algorithms
requiring an understanding of geometry or the performance of additional
geometric operations, such as the edge flip. Deep learning has been used to
solve various combinatorial optimization problems; however, generating Delaunay
triangulation based on deep learning remains a difficult problem, and very few
research has been conducted due to its complexity. In this paper, we propose a
novel deep-learning-based approach for learning Delaunay triangulation using a
new attention mechanism based on self-attention and domain knowledge. The
proposed model is designed such that the model efficiently learns
point-to-point relationships using self-attention in the encoder. In the
decoder, a new attention score function using domain knowledge is proposed to
provide a high penalty when the geometric requirement is not satisfied. The
strength of the proposed attention score function lies in its ability to extend
its application to solving other combinatorial optimization problems involving
geometry. When the proposed neural net model is well trained, it is simple and
efficient because it automatically predicts the Delaunay triangulation for an
input point set without requiring any additional geometric operations. We
conduct experiments to demonstrate the effectiveness of the proposed model and
conclude that it exhibits better performance compared with other
deep-learning-based approaches
Synthesis and Characterization of Polycarbonate Copolymers Containing Benzoyl Groups on the Side Chain for Scratch Resistance
The purpose of this study was to enhance the scratch resistance of polycarbonate copolymer by using 3,3âČ-dibenzoyl-4,4âČ-dihydroxybiphenyl (DBHP) monomer, containing benzoyl moieties on the ortho positions. DBHP monomer was synthesized from 4,4âČ-dihydroxybiphenyl and benzoyl chloride, followed by the Friedel-Craft rearrangement reaction with AlCl3. The polymerizations were conducted following the low-temperature procedure, which is carried out in methylene chloride by using triphosgene, triethylamine, bisphenol-A, and DBHP. The chemical structures of the polycarbonate copolymers were confirmed by 1H-NMR. The thermal properties of copolymers were investigated by thermogravimetric analysis and differential scanning calorimetry, and also surface morphologies were assessed by atomic force microscopy. The scratch resistance of homopolymer film (100âÎŒm) changed from 6B to 1B, and the contact angle of a sessile water drop onto the homopolymer film also increased
Harvey: A Greybox Fuzzer for Smart Contracts
We present Harvey, an industrial greybox fuzzer for smart contracts, which
are programs managing accounts on a blockchain. Greybox fuzzing is a
lightweight test-generation approach that effectively detects bugs and security
vulnerabilities. However, greybox fuzzers randomly mutate program inputs to
exercise new paths; this makes it challenging to cover code that is guarded by
narrow checks, which are satisfied by no more than a few input values.
Moreover, most real-world smart contracts transition through many different
states during their lifetime, e.g., for every bid in an auction. To explore
these states and thereby detect deep vulnerabilities, a greybox fuzzer would
need to generate sequences of contract transactions, e.g., by creating bids
from multiple users, while at the same time keeping the search space and test
suite tractable. In this experience paper, we explain how Harvey alleviates
both challenges with two key fuzzing techniques and distill the main lessons
learned. First, Harvey extends standard greybox fuzzing with a method for
predicting new inputs that are more likely to cover new paths or reveal
vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in
a targeted and demand-driven way. We have evaluated our approach on 27
real-world contracts. Our experiments show that the underlying techniques
significantly increase Harvey's effectiveness in achieving high coverage and
detecting vulnerabilities, in most cases orders-of-magnitude faster; they also
reveal new insights about contract code.Comment: arXiv admin note: substantial text overlap with arXiv:1807.0787
Topological Cluster Analysis Reveals the Systemic Organization of the Caenorhabditis elegans Connectome
The modular organization of networks of individual neurons interwoven through synapses has not been fully explored due to the incredible complexity of the connectivity architecture. Here we use the modularity-based community detection method for directed, weighted networks to examine hierarchically organized modules in the complete wiring diagram (connectome) of Caenorhabditis elegans (C. elegans) and to investigate their topological properties. Incorporating bilateral symmetry of the network as an important cue for proper cluster assignment, we identified anatomical clusters in the C. elegans connectome, including a body-spanning cluster, which correspond to experimentally identified functional circuits. Moreover, the hierarchical organization of the five clusters explains the systemic cooperation (e.g., mechanosensation, chemosensation, and navigation) that occurs among the structurally segregated biological circuits to produce higher-order complex behaviors
Time-Resolved in Situ Visualization of the Structural Response of Zeolites During Catalysis
Zeolites are three-dimensional aluminosilicates having unique properties from the size and connectivity of their sub-nanometer pores, the Si/Al ratio of the anionic framework, and the charge-balancing cations. The inhomogeneous distribution of the cations affects their catalytic performances because it influences the intra-crystalline diffusion rates of the reactants and products. However, the structural deformation regarding inhomogeneous active regions during the catalysis is not yet observed by conventional analytical tools. Here we employ in situ X-ray free electron laser-based time-resolved coherent X-ray diffraction imaging to investigate the internal deformations originating from the inhomogeneous Cu ion distributions in Cu-exchanged ZSM-5 zeolite crystals during the deoxygenation of nitrogen oxides with propene. We show that the interactions between the reactants and the active sites lead to an unusual strain distribution, confirmed by density functional theory simulations. These observations provide insights into the role of structural inhomogeneity in zeolites during catalysis and will assist the future design of zeolites for their applications
The IPIN 2019 Indoor Localisation CompetitionâDescription and Results
IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks
Discovery of Q203, a potent clinical candidate for the treatment of tuberculosis
New therapeutic strategies are needed to combat the tuberculosis pandemic and the spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) forms of the disease, which remain a serious public health challenge worldwide1, 2. The most urgent clinical need is to discover potent agents capable of reducing the duration of MDR and XDR tuberculosis therapy with a success rate comparable to that of current therapies for drug-susceptible tuberculosis. The last decade has seen the discovery of new agent classes for the management of tuberculosis3, 4, 5, several of which are currently in clinical trials6, 7, 8. However, given the high attrition rate of drug candidates during clinical development and the emergence of drug resistance, the discovery of additional clinical candidates is clearly needed. Here, we report on a promising class of imidazopyridine amide (IPA) compounds that block Mycobacterium tuberculosis growth by targeting the respiratory cytochrome bc1 complex. The optimized IPA compound Q203 inhibited the growth of MDR and XDR M. tuberculosis clinical isolates in culture broth medium in the low nanomolar range and was efficacious in a mouse model of tuberculosis at a dose less than 1 mg per kg body weight, which highlights the potency of this compound. In addition, Q203 displays pharmacokinetic and safety profiles compatible with once-daily dosing. Together, our data indicate that Q203 is a promising new clinical candidate for the treatment of tuberculosis
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