A direct approach to sharp Li-Yau Estimates on closed manifolds with negative Ricci lower bound

Recently, Qi S.Zhang [26] has derived a sharp Li-Yau estimate for positive solutions of the heat equation on closed Riemannian manifolds with the Ricci curvature bounded below by a negative constant. The proof is based on an integral iteration argument which utilizes Hamilton's gradient estimate, heat kernel Gaussian bounds and parabolic Harnack inequality. In this paper, we show that the sharp Li-Yau estimate can actually be obtained directly following the classical maximum principle argument, which simplifies the proof in [26]. In addition, we apply the same idea to the heat and conjugate heat equations under the Ricci flow and prove some Li-Yau type estimates with optimal coefficients.Comment: 14 page

Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks

Spiking Neural Network (SNN), as a brain-inspired approach, is attracting attention due to its potential to produce ultra-high-energy-efficient hardware. Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train an unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to a shallow network with only one learnable layer and cannot achieve satisfactory results when compared with multi-layer SNNs. In this paper, we eased this limitation by: 1)We proposed a Spiking Inception (Sp-Inception) module, inspired by the Inception module in the Artificial Neural Network (ANN) literature. This module is trained through STDP-based competitive learning and outperforms the baseline modules on learning capability, learning efficiency, and robustness. 2)We proposed a Pooling-Reshape-Activate (PRA) layer to make the Sp-Inception module stackable. 3)We stacked multiple Sp-Inception modules to construct multi-layer SNNs. Our algorithm outperforms the baseline algorithms on the hand-written digit classification task, and reaches state-of-the-art results on the MNIST dataset among the existing unsupervised SNNs.Comment: Published at the 2020 International Joint Conference on Neural Networks (IJCNN); Extended from arXiv:2001.0168

A novel decision-making model for selecting a construction project delivery system

It is crucial for the owner of a construction project to select an appropriate project delivery system (PDS) during early decision-making stages of the project. Due to project uncertainty or a lack of project information, the parameters of a PDS are difficult to measure and quantify. Therefore, there are still major challenges to the objective selection of PDSs. This research proposes a novel systematic decision-making model to select the appropriate PDS by using the combination of case-based reasoning (CBR) and robust nonparametric production frontier method. The Bayesian-Structural Equation Modeling (SEM) supported Z-order-m method is interpreted into the case retrieves process of traditional CBR method in order to eliminate the deteriorative internal and external influence for PDS selection. The case study was based on questionnaire survey conducted in China and used to test the validation of the proposed model. The findings reveal that the systematic decision-making model can overcome some problems of the traditional methods and improve the accuracy of PDS selection. As a result, this research has both theoretical and practical implications for the construction industry

ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning

While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its algorithmic robustness against adversarial perturbations remains unexplored. The attacks and robust representation training methods that are designed for traditional RL become less effective when applied to GCRL. To address this challenge, we first propose the Semi-Contrastive Representation attack, a novel approach inspired by the adversarial contrastive attack. Unlike existing attacks in RL, it only necessitates information from the policy function and can be seamlessly implemented during deployment. Then, to mitigate the vulnerability of existing GCRL algorithms, we introduce Adversarial Representation Tactics, which combines Semi-Contrastive Adversarial Augmentation with Sensitivity-Aware Regularizer to improve the adversarial robustness of the underlying RL agent against various types of perturbations. Extensive experiments validate the superior performance of our attack and defence methods across multiple state-of-the-art GCRL algorithms. Our tool ReRoGCRL is available at https://github.com/TrustAI/ReRoGCRL.Comment: This paper has been accepted in AAAI24 (https://aaai.org/aaai-conference/

Kazakhstan’s CO2 emissions in the post-Kyoto Protocol era:Production- and consumption-based analysis

The first commitment period of the Kyoto Protocol came to an end in 2012 and more developing countries began to participate in the new phase of world carbon emission reduction. Kazakhstan is an important energy export country and a pivot of the “Belt and Road Initiative” (BRI). Despite its emissions are relatively small compared with huge emitters such as China and the US, Kazakhstan also faces great pressure in terms of CO2 emission reduction and green development. Accurately accounting CO2 emissions in Kazakhstan from both production and consumption perspectives is the first step for further emissions control actions. This paper constructs production-based CO2 emission inventories for Kazakhstan from 2012 to 2016, and then further analyses the demand-driven emissions within the domestic market and international trade (exports and imports) using environmentally extended input-output analysis. The production-based inventory includes 43 energy products and 30 sectors to provide detailed data for CO2 emissions in Kazakhstan. The consumption-based accounting results showed that certain sectors like construction drive more emissions and that the fuel consumption in different sectors varies. Furthermore, Russia and China are major consumers of Kazakhstan's energy and associated emissions, with the construction sector playing the most important role in it. The results suggested that both technology and policy actions should be taken into account to reduce CO2 emissions and that the BRI is also a good chance for Kazakhstan to develop a “Green Economy”

Unlocking Hardware Security Assurance: The Potential of LLMs

System-on-Chips (SoCs) form the crux of modern computing systems. SoCs enable high-level integration through the utilization of multiple Intellectual Property (IP) cores. However, the integration of multiple IP cores also presents unique challenges owing to their inherent vulnerabilities, thereby compromising the security of the entire system. Hence, it is imperative to perform hardware security validation to address these concerns. The efficiency of this validation procedure is contingent on the quality of the SoC security properties provided. However, generating security properties with traditional approaches often requires expert intervention and is limited to a few IPs, thereby resulting in a time-consuming and non-robust process. To address this issue, we, for the first time, propose a novel and automated Natural Language Processing (NLP)-based Security Property Generator (NSPG). Specifically, our approach utilizes hardware documentation in order to propose the first hardware security-specific language model, HS-BERT, for extracting security properties dedicated to hardware design. To evaluate our proposed technique, we trained the HS-BERT model using sentences from RISC-V, OpenRISC, MIPS, OpenSPARC, and OpenTitan SoC documentation. When assessedb on five untrained OpenTitan hardware IP documents, NSPG was able to extract 326 security properties from 1723 sentences. This, in turn, aided in identifying eight security bugs in the OpenTitan SoC design presented in the hardware hacking competition, Hack@DAC 2022

The Early Events That Initiate β-Amyloid Aggregation in Alzheimer’s Disease

Alzheimer’s disease (AD) is characterized by the development of amyloid plaques and neurofibrillary tangles (NFTs) consisting of aggregated β-amyloid (Aβ) and tau, respectively. The amyloid hypothesis has been the predominant framework for research in AD for over two decades. According to this hypothesis, the accumulation of Aβ in the brain is the primary factor initiating the pathogenesis of AD. However, it remains elusive what factors initiate Aβ aggregation. Studies demonstrate that AD has multiple causes, including genetic and environmental factors. Furthermore, genetic factors, many age-related events and pathological conditions such as diabetes, traumatic brain injury (TBI) and aberrant microbiota also affect the aggregation of Aβ. Here we provide an overview of the age-related early events and other pathological processes that precede Aβ aggregation

SoCCAR: Detecting System-on-Chip Security Violations Under Asynchronous Resets

Modern SoC designs include several reset domains that enable asynchronous partial resets while obviating complete system boot. Unfortunately, asynchronous resets can introduce security vulnerabilities that are difficult to detect through traditional validation. In this paper, we address this problem through a new security validation framework, SoCCCAR, that accounts for asynchronous resets. The framework involves (1) efficient extraction of reset-controlled events while avoiding combinatorial explosion, and (2) concolic testing for systematic exploration of the extracted design space. Our experiments demonstrate that SoCCAR can achieve almost perfect detection accuracy and verification time of a few seconds on realistic SoC designs