The market value of sustainable practices in the luxury industry:An identity mismatch and institutional theoretical perspective

Shareholders of luxury firms uphold a view of identity mismatch between being luxury and sustainable. We examine the associated market value with sustainable practice adoption of luxury firms from an institutional theoretical lens and an identity mismatch perspective. Based on 289 announcements made by public luxury firms, results from event study show that the stock market reacts negatively to the announcements of sustainable practices. Nevertheless, the negative effect attenuates in more recent year announcements, and more profitable and smaller luxury firms. Our results alert managers to better align their sustainability goals with luxury firms' identity and the ever-changing environment

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

A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids

We propose a neural Lyapunov approach to assessing transient stability in power electronic-interfaced microgrid interconnections. The problem of transient stability assessment is cast as one of learning a neural network-structured Lyapunov function in the state space. Based on the function learned, a security region is estimated for monitoring the security of interconnected microgrids in real-time operation. The efficacy of the approach is tested and validated in a grid-connected microgrid and a three-microgrid interconnection. A comparison study suggests that the proposed method can achieve a less conservative characterization of the security region, as compared with a conventional approach

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping

While deep reinforcement learning has proven to be successful in solving control tasks, the "black-box" nature of an agent has received increasing concerns. We propose a prototype-based post-hoc policy explainer, ProtoX, that explains a blackbox agent by prototyping the agent's behaviors into scenarios, each represented by a prototypical state. When learning prototypes, ProtoX considers both visual similarity and scenario similarity. The latter is unique to the reinforcement learning context, since it explains why the same action is taken in visually different states. To teach ProtoX about visual similarity, we pre-train an encoder using contrastive learning via self-supervised learning to recognize states as similar if they occur close together in time and receive the same action from the black-box agent. We then add an isometry layer to allow ProtoX to adapt scenario similarity to the downstream task. ProtoX is trained via imitation learning using behavior cloning, and thus requires no access to the environment or agent. In addition to explanation fidelity, we design different prototype shaping terms in the objective function to encourage better interpretability. We conduct various experiments to test ProtoX. Results show that ProtoX achieved high fidelity to the original black-box agent while providing meaningful and understandable explanations

New interpretation of matter-antimatter asymmetry based on branes and possible observational consequences

Motivated by the AMS project, we assume that after the Big Bang or inflation epoch, antimatter was repelled onto one brane which is separated from our brane where all the observational matter resides. It is suggested that CP may be spontaneously broken, the two branes would correspond to ground states for matter and antimatter respectively. Generally a complex scalar field which is responsible for the spontaneous CP violation, exists in the space between the branes and causes a repulsive force against the gravitation. A possible potential barrier prevents the mater(antimatter) particles to enter the space between two branes. However, by the quantum tunnelling, a sizable anti-matter flux may come to our brane. In this work by considering two possible models, i.e. the naive flat space-time and Randall-Sundrum models and using the observational data on the visible matter in our universe as inputs, we derive the antimatter flux which would be observed by the AMS detector.Comment: 10 pages, 4 figures and 2 tables. Replaced by new versio

Network motif comparison rationalizes Sec1/Munc18-SNARE regulation mechanism in exocytosis

BackgroundNetwork motifs, recurring subnetwork patterns, provide significant insight into the biological networks which are believed to govern cellular processes. MethodsWe present a comparative network motif experimental approach, which helps to explain complex biological phenomena and increases the understanding of biological functions at the molecular level by exploring evolutionary design principles of network motifs. ResultsUsing this framework to analyze the SM (Sec1/Munc18)-SNARE (N-ethylmaleimide-sensitive factor activating protein receptor) system in exocytic membrane fusion in yeast and neurons, we find that the SM-SNARE network motifs of yeast and neurons show distinct dynamical behaviors. We identify the closed binding mode of neuronal SM (Munc18-1) and SNARE (syntaxin-1) as the key factor leading to mechanistic divergence of membrane fusion systems in yeast and neurons. We also predict that it underlies the conflicting observations in SM overexpression experiments. Furthermore, hypothesis-driven lipid mixing assays validated the prediction. ConclusionTherefore this study provides a new method to solve the discrepancies and to generalize the functional role of SM proteins

Supply chain security certification and operational performance:The role of upstream complexity

Supply chain security (SCS) incidents increasingly cause financial losses to manufacturing facilities and logistics service providers. Thus, supply chain security certification can have implications for production economics, particularly for importing firms who rely on a smooth logistics flow across country borders. However, it largely remains unknown regarding how such certification could influence a firm's operational performance. To this end, we empirically examine whether and how the adoption of Customs-Trade Partnership Against Terrorism (C-TPAT) certification, initiated by the U.S. Customs and Border Protection (CBP), could improve operational performance in adopter firms. This study draws upon signaling theory to empirically investigate the value of C-TPAT certification on U.S. publicly-traded importer firms' operational performance by analyzing the longitudinal data of properly-matched sample-control groups. The data come from multiple sources: public announcements of C-TPAT certification from the News Retrieval Service database, import data from lading records, and financial data from Standard & Poor's COMPUSTAT database. Employing a coarsened exact matching (CEM) method and a difference-in-difference (DID) analysis, we find that C-TPAT certified importers have better operational performance than that of non-certified importers. We also find that the level of upstream supply chain complexity (detail, dynamic, and spatial complexity) enhances the operational performance derived from C-TPAT certification. This study sheds light on the performance value of a management standard that is attributed to the non-process mechanism (not due to process improvements) enabled by the signaling effectiveness incorporating the upstream supply chain complexities. Our findings have important theoretical and practical implications for production economics and supply chain management studies

The Nematic Energy Scale and the Missing Electron Pocket in FeSe

Superconductivity emerges in proximity to a nematic phase in most iron-based superconductors. It is therefore important to understand the impact of nematicity on the electronic structure. Orbital assignment and tracking across the nematic phase transition prove to be challenging due to the multiband nature of iron-based superconductors and twinning effects. Here, we report a detailed study of the electronic structure of fully detwinned FeSe across the nematic phase transition using angle-resolved photoemission spectroscopy. We clearly observe a nematicity-driven band reconstruction involving dxz, dyz, and dxy orbitals. The nematic energy scale between dxz and dyz bands reaches a maximum of 50 meV at the Brillouin zone corner. We are also able to track the dxz electron pocket across the nematic transition and explain its absence in the nematic state. Our comprehensive data of the electronic structure provide an accurate basis for theoretical models of the superconducting pairing in FeSe