Ultrahigh Hot Carrier Transient Photocurrent in Nanocrystal Arrays by Auger Recombination.

In this report, we show that a new mechanism for carrier transport in solution-processed colloidal semiconductor nanocrystal arrays exists at high excitation intensity on ultrafast time scales and allows for facile intrinsic transport between as-prepared nanocrystals over long distances. By combining a high speed photoconductive switch with an ultrafast laser excitation in a sub-40 ps photoconductor, we observed transient photocurrents with peak densities of 3 × 104 - 106 mA/cm2 in self-assembled PbSe nanocrystals capped with long native oleic acid ligands. The ratio between the transient photocurrent peak and the steady-state dark current is 10 orders of magnitude. The transient mobility at the peak current is estimated to range between 0.5-17.5 cm2/(V s) for the various nanocrystal sizes studied, which is 6 to 9 orders of magnitude higher than the dark current steady-state mobility in PbSe, CdSe, and CdTe nanocrystals capped with native ligands. The results are analyzed using a kinetic model which attributes the ultrahigh transient photocurrent to multiple photogenerated excitons undergoing on-particle Auger recombination, followed by rapid tunneling at high energies. This mechanism is demonstrated for a wide range of PbSe nanocrystals sizes (diameters from 2.7 to 7.1 nm) and experimental parameters. Our observations indicate that native ligand-capped nanocrystal arrays are promising for optoelectronics applications wherein multiple carriers are photoinjected to interband states

Language-Based Image Editing with Recurrent Attentive Models

We investigate the problem of Language-Based Image Editing (LBIE). Given a source image and a natural language description, we want to generate a target image by editing the source image based on the description. We propose a generic modeling framework for two sub-tasks of LBIE: language-based image segmentation and image colorization. The framework uses recurrent attentive models to fuse image and language features. Instead of using a fixed step size, we introduce for each region of the image a termination gate to dynamically determine after each inference step whether to continue extrapolating additional information from the textual description. The effectiveness of the framework is validated on three datasets. First, we introduce a synthetic dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE system. Second, we show that the framework leads to state-of-the-art performance on image segmentation on the ReferIt dataset. Third, we present the first language-based colorization result on the Oxford-102 Flowers dataset.Comment: Accepted to CVPR 2018 as a Spotligh

EASYFLOW: Keep Ethereum Away From Overflow

While Ethereum smart contracts enabled a wide range of blockchain applications, they are extremely vulnerable to different forms of security attacks. Due to the fact that transactions to smart contracts commonly involve cryptocurrency transfer, any successful attacks can lead to money loss or even financial disorder. In this paper, we focus on the overflow attacks in Ethereum , mainly because they widely rooted in many smart contracts and comparatively easy to exploit. We have developed EASYFLOW , an overflow detector at Ethereum Virtual Machine level. The key insight behind EASYFLOW is a taint analysis based tracking technique to analyze the propagation of involved taints. Specifically, EASYFLOW can not only divide smart contracts into safe contracts, manifested overflows, well-protected overflows and potential overflows, but also automatically generate transactions to trigger potential overflows. In our preliminary evaluation, EASYFLOW managed to find potentially vulnerable Ethereum contracts with little runtime overhead.Comment: Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings. IEEE Press, 201

Tracking the Consumption Junction: Temporal Dependencies between Articles and Advertisements in Dutch Newspapers

Historians have regularly debated whether advertisements can be used as a viable source to study the past. Their main concern centered on the question of agency. Were advertisements a reflection of historical events and societal debates, or were ad makers instrumental in shaping society and the ways people interacted with consumer goods? Using techniques from econometrics (Granger causality test) and complexity science (Adaptive Fractal Analysis), this paper analyzes to what extent advertisements shaped or reflected society. We found evidence that indicate a fundamental difference between the dynamic behavior of word use in articles and advertisements published in a century of Dutch newspapers. Articles exhibit persistent trends that are likely to be reflective of communicative memory. Contrary to this, advertisements have a more irregular behavior characterized by short bursts and fast decay, which, in part, mirrors the dynamic through which advertisers introduced terms into public discourse. On the issue of whether advertisements shaped or reflected society, we found particular product types that seemed to be collectively driven by a causality going from advertisements to articles. Generally, we found support for a complex interaction pattern dubbed the consumption junction. Finally, we discovered noteworthy patterns in terms of causality and long-range dependencies for specific product groups. All in, this study shows how methods from econometrics and complexity science can be applied to humanities data to improve our understanding of complex cultural-historical phenomena such as the role of advertising in society

Transcriptomic Analysis of Rice (Oryza sativa) Developing Embryos Using the RNA-Seq Technique

Rice (Oryza sativa) is an excellent model monocot with a known genome sequence for studying embryogenesis. Here we report the transcriptome profiling analysis of rice developing embryos using RNA-Seq as an attempt to gain insight into the molecular and cellular events associated with rice embryogenesis. RNA-Seq analysis generated 17,755,890 sequence reads aligned with 27,190 genes, which provided abundant data for the analysis of rice embryogenesis. A total of 23,971, 23,732, and 23,592 genes were identified from embryos at three developmental stages (3–5, 7, and 14 DAP), while an analysis between stages allowed the identification of a subset of stage-specific genes. The number of genes expressed stage-specifically was 1,131, 1,443, and 1,223, respectively. In addition, we investigated transcriptomic changes during rice embryogenesis based on our RNA-Seq data. A total of 1,011 differentially expressed genes (DEGs) (log2Ratio ≥1, FDR ≤0.001) were identified; thus, the transcriptome of the developing rice embryos changed considerably. A total of 672 genes with significant changes in expression were detected between 3–5 and 7 DAP; 504 DEGs were identified between 7 and 14 DAP. A large number of genes related to metabolism, transcriptional regulation, nucleic acid replication/processing, and signal transduction were expressed predominantly in the early and middle stages of embryogenesis. Protein biosynthesis-related genes accumulated predominantly in embryos at the middle stage. Genes for starch/sucrose metabolism and protein modification were highly expressed in the middle and late stages of embryogenesis. In addition, we found that many transcription factor families may play important roles at different developmental stages, not only in embryo initiation but also in other developmental processes. These results will expand our understanding of the complex molecular and cellular events in rice embryogenesis and provide a foundation for future studies on embryo development in rice and other cereal crops

Protein Coding Sequence Identification by Simultaneously Characterizing the Periodic and Random Features of DNA Sequences

Most codon indices used today are based on highly biased nonrandom usage of codons in coding regions. The background of a coding or noncoding DNA sequence, however, is fairly random, and can be characterized as a random fractal. When a gene-finding algorithm incorporates multiple sources of information about coding regions, it becomes more successful. It is thus highly desirable to develop new and efficient codon indices by simultaneously characterizing the fractal and periodic features of a DNA sequence. In this paper, we describe a novel way of achieving this goal. The efficiency of the new codon index is evaluated by studying all of the 16 yeast chromosomes. In particular, we show that the method automatically and correctly identifies which of the three reading frames is the one that contains a gene

Fractal Behavior in the Clarification Process of Cane Sugar Production

Cane sugar production is an important industrial process. One of the most important steps in cane sugar production is the clarification process, which provides high-quality, concentrated sugar syrup crystal for further processing. To gain fundamental understanding of the physical and chemical processes associated with the clarification process and help design better approaches to improve the clarification of the mixed juice, we explore the fractal behavior of the variables pertinent to the clarification process. We show that the major variables in this key process all show persistent long-range correlations, for time scales up to at least a few days. Persistent long-range correlations amount to unilateral deviations from a preset target. This means that when the process is in a desired mode such that the target variables, color of the produced sugar and its clarity degree, both satisfy preset conditions, they will remain so for a long period of time. However, adversity could happen, in the sense that when they do not satisfy the requirements, the adverse situation may last quite long. These findings have to be explicitly accounted for when designing active controlling strategies to improve the quality of the produced sugar

Surface-SOS:Self-Supervised Object Segmentation via Neural Surface Representation

Self-supervised Object Segmentation (SOS) aims to segment objects without any annotations. Under conditions of multi-camera inputs, the structural, textural and geometrical consistency among each view can be leveraged to achieve fine-grained object segmentation. To make better use of the above information, we propose Surface representation based Self-supervised Object Segmentation (Surface-SOS), a new framework to segment objects for each view by 3D surface representation from multi-view images of a scene. To model high-quality geometry surfaces for complex scenes, we design a novel scene representation scheme, which decomposes the scene into two complementary neural representation modules respectively with a Signed Distance Function (SDF). Moreover, Surface-SOS is able to refine single-view segmentation with multi-view unlabeled images, by introducing coarse segmentation masks as additional input. To the best of our knowledge, Surface-SOS is the first self-supervised approach that leverages neural surface representation to break the dependence on large amounts of annotated data and strong constraints. These constraints typically involve observing target objects against a static background or relying on temporal supervision in videos. Extensive experiments on standard benchmarks including LLFF, CO3D, BlendedMVS, TUM and several real-world scenes show that Surface-SOS always yields finer object masks than its NeRF-based counterparts and surpasses supervised single-view baselines remarkably.</p