Unsupervised Synthetic Image Refinement via Contrastive Learning and Consistent Semantic-Structural Constraints

Ensuring the realism of computer-generated synthetic images is crucial to deep neural network (DNN) training. Due to different semantic distributions between synthetic and real-world captured datasets, there exists semantic mismatch between synthetic and refined images, which in turn results in the semantic distortion. Recently, contrastive learning (CL) has been successfully used to pull correlated patches together and push uncorrelated ones apart. In this work, we exploit semantic and structural consistency between synthetic and refined images and adopt CL to reduce the semantic distortion. Besides, we incorporate hard negative mining to improve the performance furthermore. We compare the performance of our method with several other benchmarking methods using qualitative and quantitative measures and show that our method offers the state-of-the-art performance

LGSQE: Lightweight Generated Sample Quality Evaluatoin

Despite prolific work on evaluating generative models, little research has been done on the quality evaluation of an individual generated sample. To address this problem, a lightweight generated sample quality evaluation (LGSQE) method is proposed in this work. In the training stage of LGSQE, a binary classifier is trained on real and synthetic samples, where real and synthetic data are labeled by 0 and 1, respectively. In the inference stage, the classifier assigns soft labels (ranging from 0 to 1) to each generated sample. The value of soft label indicates the quality level; namely, the quality is better if its soft label is closer to 0. LGSQE can serve as a post-processing module for quality control. Furthermore, LGSQE can be used to evaluate the performance of generative models, such as accuracy, AUC, precision and recall, by aggregating sample-level quality. Experiments are conducted on CIFAR-10 and MNIST to demonstrate that LGSQE can preserve the same performance rank order as that predicted by the Frechet Inception Distance (FID) but with significantly lower complexity

Cerenkov Line Emission as a Possible Mechanism of X-ray Lines in Gamma-ray Bursts

The recent discoveries of X-ray lines in the afterglows of gamma-ray bursts (GRBs) provide significant clues to the nature of GRB progenitors and central environments. However, the iron line interpretation by fluorescence or recombination mechanism requires a large amount of iron material. We argue that the very strong iron line could be attributed to an alternative mechanism: Cerenkov line emission since relativistic electrons and dense medium exist near GRB sites. Therefore, the broad iron lines are expected, and line intensity will be nearly independent of the iron abundance, the medium with the anomalously high Fe abundance is not required.Comment: 4 pages, revised version accepted for the publication in ApJ

Non-Markovian Quantum Trajectories of Many-Body Quantum Open Systems

A long-standing open problem in non-Markovian quantum state diffusion (QSD) approach to open quantum systems is to establish the non-Markovian QSD equations for multiple qubit systems. In this paper, we settle this important question by explicitly constructing a set of exact time-local QSD equations for $N$-qubit systems. Our exact time-local (convolutionless) QSD equations have paved the way towards simulating quantum dynamics of many-body open systems interacting with a common bosonic environment. The applicability of this multiple-qubit stochastic equation is exemplified by numerically solving several quantum open many-body systems concerning quantum coherence dynamics and dynamical control.Comment: 8 pages, 2 figures. manuscript revised and reference update

Beam Pattern and Reflection Pattern Design for Channel Estimation in RIS-assisted mmWave MIMO Systems

Reconfigurable intelligent surface (RIS) is a revolutionary technology that can be applied in millimeter wave (mmWave) communications to reduce the high power consumption and propagation loss. However, channel estimation (CE) is challenging due to the large number of passive RIS elements without signal processing abilities. In this paper, the uplink CE for RIS-assisted mmWave multi-input multi-output (MIMO) systems is formulated as a sparse signal recovery problem in a novel way. Then, the beam pattern and reflection pattern design based on the compressed sensing (CS) theory are proposed to guarantee the efficient CE. Simulation results demonstrate that, for various CS-based CE algorithms, the proposed patterns can reduce more than 50% pilot overhead at 0 dB signal-to-noise ratio (SNR) while maintaining the same accuracy of CE compared with the existing patterns

Investigation into circular economy of plastics: The case of the UK fast moving consumer goods industry

Despite the recognised importance of the issue of plastic waste and an emerging circular economy (CE) in recent years, there is a lack of comprehensive and relevant studies regarding CE and the role of plastics. This study addresses a significant gap in the literature by revealing current initiatives implemented in the UK fast moving consumer goods (FMCG) industry through an in-depth exploration of four case organisations that have committed to the UK Plastic Pact, a pioneering collective initiative on plastic recycling. The study discloses a variety of present initiatives within the industry including the removal of unrecyclable plastics, packaging innovation, in-store retailer schemes, and label modifications. Collaboration was concluded as an essential enabler, internally and across the industry. Fundamental barriers were identified as inadequate infrastructure to support plastics in the CE and technical implications of packaging

PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression

Existing methods on visual emotion analysis mainly focus on coarse-grained emotion classification, i.e. assigning an image with a dominant discrete emotion category. However, these methods cannot well reflect the complexity and subtlety of emotions. In this paper, we study the fine-grained regression problem of visual emotions based on convolutional neural networks (CNNs). Specifically, we develop a Polarity-consistent Deep Attention Network (PDANet), a novel network architecture that integrates attention into a CNN with an emotion polarity constraint. First, we propose to incorporate both spatial and channel-wise attentions into a CNN for visual emotion regression, which jointly considers the local spatial connectivity patterns along each channel and the interdependency between different channels. Second, we design a novel regression loss, i.e. polarity-consistent regression (PCR) loss, based on the weakly supervised emotion polarity to guide the attention generation. By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the emotion regression performance. Extensive experiments are conducted on the IAPS, NAPS, and EMOTIC datasets, and the results demonstrate that the proposed PDANet outperforms the state-of-the-art approaches by a large margin for fine-grained visual emotion regression. Our source code is released at: https://github.com/ZizhouJia/PDANet.Comment: Accepted by ACM Multimedia 201

Drinfeld Twists and Symmetric Bethe Vectors of Supersymmetric Fermion Models

We construct the Drinfeld twists (factorizing $F$-matrices) of the $gl(m|n)$-invariant fermion model. Completely symmetric representation of the pseudo-particle creation operators of the model are obtained in the basis provided by the $F$-matrix (the $F$-basis). We resolve the hierarchy of the nested Bethe vectors in the $F$-basis for the $gl(m|n)$ supersymmetric model.Comment: Latex File, 24 pages, no figure, some misprints are correcte

ΔFosB Regulates Gene Expression and Cognitive Dysfunction in a Mouse Model of Alzheimer\u27s Disease.

Alzheimer\u27s disease (AD) is characterized by cognitive decline and 5- to 10-fold increased seizure incidence. How seizures contribute to cognitive decline in AD or other disorders is unclear. We show that spontaneous seizures increase expression of ΔFosB, a highly stable Fos-family transcription factor, in the hippocampus of an AD mouse model. ΔFosB suppressed expression of the immediate early gene c-Fos, which is critical for plasticity and cognition, by binding its promoter and triggering histone deacetylation. Acute histone deacetylase (HDAC) inhibition or inhibition of ΔFosB activity restored c-Fos induction and improved cognition in AD mice. Administration of seizure-inducing agents to nontransgenic mice also resulted in ΔFosB-mediated suppression of c-Fos, suggesting that this mechanism is not confined to AD mice. These results explain observations that c-Fos expression increases after acute neuronal activity but decreases with chronic activity. Moreover, these results indicate a general mechanism by which seizures contribute to persistent cognitive deficits, even during seizure-free periods