Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning

Deep neural network based image compression has been extensively studied. Model robustness is largely overlooked, though it is crucial to service enabling. We perform the adversarial attack by injecting a small amount of noise perturbation to original source images, and then encode these adversarial examples using prevailing learnt image compression models. Experiments report severe distortion in the reconstruction of adversarial examples, revealing the general vulnerability of existing methods, regardless of the settings used in underlying compression model (e.g., network architecture, loss function, quality scale) and optimization strategy used for injecting perturbation (e.g., noise threshold, signal distance measurement). Later, we apply the iterative adversarial finetuning to refine pretrained models. In each iteration, random source images and adversarial examples are mixed to update underlying model. Results show the effectiveness of the proposed finetuning strategy by substantially improving the compression model robustness. Overall, our methodology is simple, effective, and generalizable, making it attractive for developing robust learnt image compression solution. All materials have been made publicly accessible at https://njuvision.github.io/RobustNIC for reproducible research.Comment: This paper has been completely rewritte

Systemic similarity analysis of compatibility drug-induced multiple pathway patterns _in vivo_

A major challenge in post-genomic research is to understand how physiological and pathological phenotypes arise from the networks of expressed genes and to develop powerful tools for translating the information exchanged between gene and the organ system networks. Although different expression modules may contribute independently to different phenotypes, it is difficult to interpret microarray experimental results at the level of single gene associations. The global effects and response pathways of small molecules in cells have been investigated, but the quantitative details of the activation mechanisms of multiple pathways _in vivo_ are not well understood. Similar response networks indicate similar modes of action, and gene networks may appear to be similar despite differences in the behaviour of individual gene groups. Here we establish the method for assessing global effect spectra of the complex signaling forms using Global Similarity Index (GSI) in cosines vector included angle. Our approach provides quantitative multidimensional measures of genes expression profile based on drug-dependent phenotypic alteration _in vivo_. These results make a starting point for identifying relationships between GSI at the molecular level and a step toward phenotypic outcomes at a system level to predict action of unknown compounds and any combination therapy

Non-local Attention Optimized Deep Image Compression

This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations in the encoders and decoders for both image and latent feature probability information (known as hyperprior) to capture both local and global correlations, and apply attention mechanism to generate masks that are used to weigh the features for the image and hyperprior, which implicitly adapt bit allocation for different features based on their importance. Furthermore, both hyperpriors and spatial-channel neighbors of the latent features are used to improve entropy coding. The proposed model outperforms the existing methods on Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and MS-SSIM distortion metrics

Spin susceptibility of Anderson impurities in arbitrary conduction bands

Spin susceptibility of Anderson impurities is a key quantity in understanding the physics of Kondo screening. Traditional numerical renormalization group (NRG) calculation of the impurity contribution $\chi_{\textrm{imp}}$ to susceptibility, defined originally by Wilson in a flat wide band, has been generalized before to structured conduction bands. The results brought about non-Fermi-liquid and diamagnetic Kondo behaviors in $\chi_{\textrm{imp}}$, even when the bands are not gapped at the Fermi energy. Here, we use the full density-matrix (FDM) NRG to present high-quality data for the local susceptibility $\chi_{\textrm{loc}}$ and to compare them with $\chi_{\textrm{imp}}$ obtained by the traditional NRG. Our results indicate that those exotic behaviors observed in $\chi_{\textrm{imp}}$ are unphysical. Instead, the low-energy excitations of the impurity in arbitrary bands only without gap at the Fermi energy are still a Fermi liquid and paramagnetic. We also demonstrate that unlike the traditional NRG yielding $\chi_{\textrm{loc}}$ less accurate than $\chi_{\textrm{imp}}$, the FDM method allows a high-precision dynamical calculation of $\chi_{\textrm{loc}}$ at much reduced computational cost, with an accuracy at least one order higher than $\chi_{\textrm{imp}}$. Moreover, artifacts in the FDM algorithm to $\chi_{\textrm{imp}}$, and origins of the spurious non-Fermi-liquid and diamagnetic features are clarified. Our work provides an efficient high-precision algorithm to calculate the spin susceptibility of impurity for arbitrary structured bands, while negating the applicability of Wilson's definition to such cases.Comment: the published versio

Isothiocyanate derivatives as anti-cancer agents

Master'sMASTER OF SCIENC

Finite-temperature vibronic spectra from the split-operator coherence thermofield dynamics

We present a numerically exact approach for evaluating vibrationally resolved electronic spectra at finite temperatures using the coherence thermofield dynamics. In this method, which avoids implementing an algorithm for solving the von Neumann equation for coherence, the thermal vibrational ensemble is first mapped to a pure-state wavepacket in an augmented space, and this wavepacket is then propagated by solving the standard, zero-temperature Schr\"odinger equation with the split-operator Fourier method. We show that the finite-temperature spectra obtained with the coherence thermofield dynamics in a Morse potential agree exactly with those computed by Boltzmann-averaging the spectra of individual vibrational levels. Because the split-operator thermofield dynamics on a full tensor-product grid is restricted to low-dimensional systems, we briefly discuss how the accessible dimensionality can be increased by various techniques developed for the zero-temperature split-operator Fourier method.Comment: 5 pages, 4 figure

Learning conservation laws in unknown quantum dynamics

We present a learning algorithm for discovering conservation laws given as sums of geometrically local observables in quantum dynamics. This includes conserved quantities that arise from local and global symmetries in closed and open quantum many-body systems. The algorithm combines the classical shadow formalism for estimating expectation values of observable and data analysis techniques based on singular value decompositions and robust polynomial interpolation to discover all such conservation laws in unknown quantum dynamics with rigorous performance guarantees. Our method can be directly realized in quantum experiments, which we illustrate with numerical simulations, using closed and open quantum system dynamics in a $\mathbb{Z}_2$-gauge theory and in many-body localized spin-chains.Comment: 22 pages, 3 figure