Universal condition for critical percolation thresholds of kagome-like lattices

Lattices that can be represented in a kagome-like form are shown to satisfy a universal percolation criticality condition, expressed as a relation between P_3, the probability that all three vertices in the triangle connect, and P_0, the probability that none connect. A linear approximation for P_3(P_0) is derived and appears to provide a rigorous upper bound for critical thresholds. A numerically determined relation for P_3(P_0) gives thresholds for the kagome, site-bond honeycomb, (3-12^2), and "stack-of-triangle" lattices that compare favorably with numerical results.Comment: Several new figures and small change

Protein structural class prediction based on an improved statistical strategy

<p>Abstract</p> <p>Background</p> <p>A protein structural class (PSC) belongs to the most basic but important classification in protein structures. The prediction technique of protein structural class has been developing for decades. Two popular indices are the amino-acid-frequency (AAF) based, and amino-acid-arrangement (AAA) with long-term correlation (LTC) – based indices. They were proposed in many works. Both indices have its pros and cons. For example, the AAF index focuses on a statistical analysis, while the AAA-LTC emphasizes the long-term, biological significance. Unfortunately, the datasets used in previous work were not very reliable for a small number of sequences with a high-sequence similarity.</p> <p>Results</p> <p>By modifying a statistical strategy, we proposed a new index method that combines probability and information theory together with a long-term correlation. We also proposed a numerically and biologically reliable dataset included more than 5700 sequences with a low sequence similarity. The results showed that the proposed approach has its high accuracy. Comparing with amino acid composition (AAC) index using a distance method, the accuracy of our approach has a 16–20% improvement for re-substitution test and about 6–11% improvement for cross-validation test. The values were about 23% and 15% for the component coupled method (CCM).</p> <p>Conclusion</p> <p>A new index method, combining probability and information theory together with a long-term correlation was proposed in this paper. The statistical method was improved significantly based on our new index. The cross validation test was conducted, and the result show the proposed method has a great improvement.</p

Magnetoresistance and Doping Effects in Conjugated Polymer-Based Organic Light Emitting Diodes.

PhDMagnetoresistance (MR) and doping effects have been investigated in a poly(3-hexylthiophene-2,5-diyl) (P3HT) based organic light emitting diodes. In single device of fixed composition (Au/P3HT/Al as spun and processed in air), the measured MR strongly depends on the drive conditions. The magnetoconductance (MC) varies from negative to positive (-0.4% ≤ MC ≤ 0.4%) with increasing current density, depending on which microscopic mechanism dominates. The negative MC is due to bipolaron based interactions and the positive MC to triplet-polaron based interactions (as confirmed by light emission). Oxygen doping is prevalent in P3HT devices processed in air and the effect of de-doping (by annealing above the glass transition temperature) is investigated on the MC of an Au/P3HT/Al diode. De-doping reduces the current through the device under forward bias by ~3 orders of magnitude, but increases the negative (low current) MC from a maximum of -0.5% pre-annealing to -3% post-annealing. This increased negative MC is consistent with bipolaron theory predictions based on Fermi level shifts and density of states (DoS) changes due to de-doping. The decrease in current density is explained by increased injection barriers at both electrodes also resulting from de-doping. Deliberate chemical doping of P3HT is carried out using pentacene as a hole trap centre. The trapping effect of pentacene is confirmed by reproducible and significant hole mobility-pentacene concentration behaviour, as measured by dark injection (DI) transient measurements. The enhanced carrier injection resulting from the pentacene doping also leads to increased electroluminescence (EL). The resultant MC in pentacene doped devices is strongly dependent on carrier injection and can be significantly enhanced by doping, for example from -0.2% to -0.6% depending on device and drive conditions. Throughout this thesis Lorentzian and non-Lorentzian function fitting is carried out on the measured MC, although the underlying microscopic mechanisms cannot always be discerned.China Scholars Council (CSC); Queen Mary University of Londo

Sharp bounds for a class of integral operators in weighted-type spaces on Heisenberg group

In this paper, we will use the conclusions and methods in \cite{1} to obtain the sharp bounds for a class of integral operators with the nonnegative kernels in weighted-type spaces on Heisenberg group. As promotions, the sharp bounds of Hardy operator , Hardy Littlewood-P\'{o}lya operator and Hilbert operator are also obtained

Do DALL-E and Flamingo Understand Each Other?

The field of multimodal research focusing on the comprehension and creation of both images and text has witnessed significant strides. This progress is exemplified by the emergence of sophisticated models dedicated to image captioning at scale, such as the notable Flamingo model and text-to-image generative models, with DALL-E serving as a prominent example. An interesting question worth exploring in this domain is whether Flamingo and DALL-E understand each other. To study this question, we propose a reconstruction task where Flamingo generates a description for a given image and DALL-E uses this description as input to synthesize a new image. We argue that these models understand each other if the generated image is similar to the given image. Specifically, we study the relationship between the quality of the image reconstruction and that of the text generation. We find that an optimal description of an image is one that gives rise to a generated image similar to the original one. The finding motivates us to propose a unified framework to finetune the text-to-image and image-to-text models. Concretely, the reconstruction part forms a regularization loss to guide the tuning of the models. Extensive experiments on multiple datasets with different image captioning and image generation models validate our findings and demonstrate the effectiveness of our proposed unified framework. As DALL-E and Flamingo are not publicly available, we use Stable Diffusion and BLIP in the remaining work. Project website: https://dalleflamingo.github.io.Comment: Accepted to ICCV 202

Lightweight Adapter Tuning for Multilingual Speech Translation

Adapter modules were recently introduced as an efficient alternative to fine-tuning in NLP. Adapter tuning consists in freezing pretrained parameters of a model and injecting lightweight modules between layers, resulting in the addition of only a small number of task-specific trainable parameters. While adapter tuning was investigated for multilingual neural machine translation, this paper proposes a comprehensive analysis of adapters for multilingual speech translation (ST). Starting from different pre-trained models (a multilingual ST trained on parallel data or a multilingual BART (mBART) trained on non-parallel multilingual data), we show that adapters can be used to: (a) efficiently specialize ST to specific language pairs with a low extra cost in terms of parameters, and (b) transfer from an automatic speech recognition (ASR) task and an mBART pre-trained model to a multilingual ST task. Experiments show that adapter tuning offer competitive results to full fine-tuning, while being much more parameter-efficient.Comment: Accepted at ACL-IJCNLP 202

Robust unsupervised small area change detection from SAR imagery using deep learning

Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection