A note on local well-posedness of generalized KdV type equations with dissipative perturbations

In this note we report local well-posedness results for the Cauchy problems associated to generalized KdV type equations with dissipative perturbation for given data in the low regularity $L^2$-based Sobolev spaces. The method of proof is based on the {\em contraction mapping principle} employed in some appropriate time weighted spaces.Comment: 14 page

Property rights protection and access to bank loans: Evidence from private enterprises in China

Poor protection of private property has limited the access to bank loans by private enterprises in developing and transition economies. Under those circumstances, private entrepreneurs have resorted to various ways of enhancing the de facto protection of private property. Using a dataset of 3,073 private enterprises in China, this paper empirically investigates the impact of political participation and philanthropic activities - informal substitutes for the lack of formal protection of private property - on the access to bank loans. © 2006 The Authors Journal compilation © 2006 The European Bank for Reconstruction and Development.published_or_final_versio

Excludable public goods: Pricing and social welfare maximization

We compare two pricing strategies - buffet pricing and usage pricing - of excludable public goods for social welfare maximization. Buffet pricing is better than usage pricing for low consumer heterogeneity, while the opposite holds for high consumer heterogeneity. © 2009 Elsevier B.V. All rights reserved.preprin

Evaluating Summary Statistics with Mutual Information for Cosmological Inference

The ability to compress observational data and accurately estimate physical parameters relies heavily on informative summary statistics. In this paper, we introduce the use of mutual information (MI) as a means of evaluating the quality of summary statistics in inference tasks. MI can assess the sufficiency of summaries, and provide a quantitative basis for comparison. We propose to estimate MI using the Barber-Agakov lower bound and normalizing flow based variational distributions. To demonstrate the effectiveness of our method, we compare three different summary statistics (namely the power spectrum, bispectrum, and scattering transform) in the context of inferring reionization parameters from mock images of 21~cm observations with Square Kilometre Array. We find that this approach is able to correctly assess the informativeness of different summary statistics and allows us to select the optimal set of statistics for inference tasks.Comment: Accepted at the ICML 2023 Workshop on Machine Learning for Astrophysics, comments welcom

Federated Learning over a Wireless Network: Distributed User Selection through Random Access

User selection has become crucial for decreasing the communication costs of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competition mechanism in random access. Taking the carrier sensing multiple access (CSMA) mechanism as an example of random access, we manipulate the contention window (CW) size to prioritize certain users for obtaining radio resources in each round of training. Training data bias is used as a target scenario for FL with user selection. Prioritization is based on the distance between the newly trained local model and the global model of the previous round. To avoid excessive contribution by certain users, a counting mechanism is used to ensure fairness. Simulations with various datasets demonstrate that this method can rapidly achieve convergence similar to that of the centralized user selection approach

Tensorized LSSVMs for Multitask Regression

Multitask learning (MTL) can utilize the relatedness between multiple tasks for performance improvement. The advent of multimodal data allows tasks to be referenced by multiple indices. High-order tensors are capable of providing efficient representations for such tasks, while preserving structural task-relations. In this paper, a new MTL method is proposed by leveraging low-rank tensor analysis and constructing tensorized Least Squares Support Vector Machines, namely the tLSSVM-MTL, where multilinear modelling and its nonlinear extensions can be flexibly exerted. We employ a high-order tensor for all the weights with each mode relating to an index and factorize it with CP decomposition, assigning a shared factor for all tasks and retaining task-specific latent factors along each index. Then an alternating algorithm is derived for the nonconvex optimization, where each resulting subproblem is solved by a linear system. Experimental results demonstrate promising performances of our tLSSVM-MTL

Catalytic transfer hydrogenolysis of organosolv lignin using B-containing FeNi alloyed catalysts

© 2017 Elsevier B.V. In this work, FeB, NiB, and FeNiB nanomaterials were examined as catalysts for catalytic transfer hydrogenolysis (CTH) using supercritical ethanol (sc-EtOH) as the hydrogen donor and reaction solvent. The earth-abundant alloys were synthesized using simple aqueous chemical reductions and characterized using ICP-OES, XRD, and STEM-EDS. Using acetophenone to model the desired catalytic reactivity, FeNiB was identified as having superior reactivity (74% conversion) and selectivity for complete deoxygenation to ethylbenzene (84%) when compared to the monometallic materials. Given its high reactivity and selectivity for deoxygenation over ring saturation, FeNiB was screened as a lignin valorization catalyst. FeNiB mediates deoxygenation of aliphatic hydroxyl and carbonyls in organosolv lignin via CTH in sc-EtOH. A combination of gel permeation chromatography, GC/MS, and NMR spectroscopy was used to demonstrate the production of a slate of monomeric phenols with intact deoxygenated aliphatic side chains. In total, these results highlight the utility of CTH for the valorization of biorefinery-relevant lignin using an inexpensive, earth-abundant catalyst material and a green solvent system that can be directly derived from the polysaccharide fraction of lignocellulosic biomass

TSS: Transformation-Specific Smoothing for Robustness Certification

As machine learning (ML) systems become pervasive, safeguarding their security is critical. However, recently it has been demonstrated that motivated adversaries are able to mislead ML systems by perturbing test data using semantic transformations. While there exists a rich body of research providing provable robustness guarantees for ML models against $\ell_p$ norm bounded adversarial perturbations, guarantees against semantic perturbations remain largely underexplored. In this paper, we provide TSS -- a unified framework for certifying ML robustness against general adversarial semantic transformations. First, depending on the properties of each transformation, we divide common transformations into two categories, namely resolvable (e.g., Gaussian blur) and differentially resolvable (e.g., rotation) transformations. For the former, we propose transformation-specific randomized smoothing strategies and obtain strong robustness certification. The latter category covers transformations that involve interpolation errors, and we propose a novel approach based on stratified sampling to certify the robustness. Our framework TSS leverages these certification strategies and combines with consistency-enhanced training to provide rigorous certification of robustness. We conduct extensive experiments on over ten types of challenging semantic transformations and show that TSS significantly outperforms the state of the art. Moreover, to the best of our knowledge, TSS is the first approach that achieves nontrivial certified robustness on the large-scale ImageNet dataset. For instance, our framework achieves 30.4% certified robust accuracy against rotation attack (within $\pm 30^\circ$) on ImageNet. Moreover, to consider a broader range of transformations, we show TSS is also robust against adaptive attacks and unforeseen image corruptions such as CIFAR-10-C and ImageNet-C.Comment: 2021 ACM SIGSAC Conference on Computer and Communications Security (CCS '21

The Glauber model and flow analysis with Pb-Pb collisions at sNN \sqrt{s_{\rm NN}}=2.76 TeV

This work presents data analysis on Pb-Pb collisions at $\sqrt{s_{\rm NN}}$=2.76 TeV with centrality $40\%-50\%$. We present introduction and Monte-Carlo simulation results of the Glauber model, which shed light on the initial geometric configuration of heavy ion collisions. Three-dimensional correlation function is plotted, and Fourier decomposition is carried out in order to obtain elliptic flow. Based on the assumption that non-flow effect is less prominent in long-range area, we separate it from the second Fourier decomposition of two-particle correlation function by making polynomial curve fitting.Comment: 10 pages,8 figures, revisions are made, accepted by ICAPM 2022 Conference Proceeding

HDAC6 inhibition alleviates acute pulmonary embolism: a possible future therapeutic option

Introduction. Acute pulmonary embolism (APE) is a clinical syndrome of pulmonary circulation disorder caused by obstruction of the pulmonary artery or its branches. Histone deacetylase 6 (HDAC6) has been reported to play an important role in lung-related diseases. However, the functional role of HDAC6 in APE remains unclear. Material and methods. Male Sprague Dawley rats were used. The APE model was constructed by inserting an intravenous cannula into the right femoral vein and injecting Sephadex G-50 microspheres (12 mg/kg; 300 μm in diameter). After 1 h, the control and APE rats were intraperitoneally injected with tubastatin A (TubA) (40 mg/kg, an inhibitor of HDAC6) and sampled at 24 h after modeling. H&E staining, arterial blood gas analysis, and wet/dry (W/D) weight ratio were used to evaluate the histopathological changes and pulmonary function in APE rats. ELISA, Western blot, and immunohistochemistry were used to explore the potential mechanism of HDAC6-mediated inflammation in APE. Results. The results indicated that HDAC6 expression was significantly increased in lungs of APE rats. TubA treatment in vivo decreased HDAC6 expression in lung tissues. HDAC6 inhibition alleviated histopathological damage and pulmonary dysfunction, as evidenced by decreased PaO2/FiO2 ratio and W/D weight ratio in APE rats. Furthermore, HDAC6 inhibition alleviated APE-induced inflammatory response. Specifically, APE rats exhibited increased production of pro-inflammatory cytokines, including tumor necrosis factor-alpha (TNF-α), interleukin (IL)-1β, IL-6, and IL-18, however, this increase was reversed by HDAC6 inhibition. Meanwhile, the activation of the NLRP3 inflammasome was also observed in lungs of APE rats, while HDAC6 inhibition blocked this activation. Mechanically, we demonstrated that HDAC6 inhibition blocked the activation of the protein kinase B (AKT)/extracellular signal-regulated protein kinase (ERK) signaling pathway, a classic pathway promoting inflammation. Conclusions. These findings demonstrate that the inhibition of HDAC6 may alleviate lung dysfunction and pathological injury resulting from APE by blocking the AKT/ERK signaling pathway, providing new theoretical fundamentals for APE therapy