The System That Cried Wolf: Sensor Security Analysis of Wide-area Smoke Detectors for Critical Infrastructure

Fire alarm and signaling systems are a networked system of fire detectors, fire control units, automated fire extinguishers, and fire notification appliances. Malfunction of these safety-critical cyber-physical systems may lead to chaotic evacuations, property damages, and even loss of human life. Therefore, reliability is one of the most crucial factors for fire detectors. Indeed, even a single report of a fire cannot be ignored considering the importance of early fire detection and suppression. In this paper, we show that wide-area smoke detectors, which are globally installed in critical infrastructures such as airports, sports facilities, and auditoriums, have significant vulnerabilities in terms of reliability; one can remotely and stealthily induce false fire alarms and suppress real fire alarms with a minimal attacker capability using simple equipment. The practicality and generalizability of these vulnerabilities has been assessed based on the demonstration of two types of sensor attacks on two commercial-off-the-shelf optical beam smoke detectors from different manufacturers. Further, the practical considerations of building stealthy attack equipment has been analyzed, and an extensive survey of almost all optical beam smoke detectors on the market has been conducted. In addition, we show that the current standards of the fire alarm network connecting the detector and a control unit exacerbate the problem, making it impossible or very difficult to mitigate the threats we found. Finally, we discuss hardware and software-based possible countermeasures for both wide-area smoke detectors and the fire alarm network; the effectiveness of one of the countermeasures is experimentally evaluated

Inverse Design of Solid-State Materials via a Continuous Representation

The non-serendipitous discovery of materials with targeted properties is the ultimate goal of materials research, but to date, materials design lacks the incorporation of all available knowledge to plan the synthesis of the next material. This work presents a framework for learning a continuous representation of materials and building a model for new discovery using latent space representation. The ability of autoencoders to generate experimental materials is demonstrated with vanadium oxides via rediscovery of experimentally known structures when the model was trained without them. Approximately 20,000 hypothetical materials are generated, leading to several completely new metastable V_xO_y materials that may be synthesizable. Comparison with genetic algorithms suggests computational efficiency of generative models that can explore chemical compositional space effectively by learning the distributions of known materials for crystal structure prediction. These results are an important step toward machine-learned inverse design of inorganic functional materials using generative models

Unveiling new stable manganese based photoanode materials via theoretical high-throughput screening and experiments

With the increasing energy demand, developing renewable fuel production strategies such as photoelectrocatalytic hydrogen production is critical to mitigating the global climate change. In this work, we experimentally validate a new stable and photoactive material, Mg₂MnO₄, from the exhaustive theoretical exploration of the chemical space of X (=Mg and Ca), Mn and O

Inverse Design of Solid-State Materials via a Continuous Representation

The non-serendipitous discovery of materials with targeted properties is the ultimate goal of materials research, but to date, materials design lacks the incorporation of all available knowledge to plan the synthesis of the next material. This work presents a framework for learning a continuous representation of materials and building a model for new discovery using latent space representation. The ability of autoencoders to generate experimental materials is demonstrated with vanadium oxides via rediscovery of experimentally known structures when the model was trained without them. Approximately 20,000 hypothetical materials are generated, leading to several completely new metastable V_xO_y materials that may be synthesizable. Comparison with genetic algorithms suggests computational efficiency of generative models that can explore chemical compositional space effectively by learning the distributions of known materials for crystal structure prediction. These results are an important step toward machine-learned inverse design of inorganic functional materials using generative models

ConfigurationStabilityPredictionML

* Trained model parameters - RandomSeed_[random_seed]/[model_name]/Model_[num_dat]_ [id,ood_ads,ood_cat,ood_both].pth.tar  - [random_seed]: 1111,2222,3333,4444,5555 (five independent trials for random extraction of training set) - [model_name]: CGCNN,SchNet,MPNN,MPNN_A,Matforer - [num_dat]: 10000(2%),46032(10%),115082(25%),230164(50%),345246(75%) * Trained model parameters with whole training dataset (#: 460328) can be found in RandomSeed_1111/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar </p

A structure translation model for crystal compounds

Abstract High-throughput virtual screening for crystals aims to discover new materials by evaluating the property of every virtual candidate in the database exhaustively. During this process, the major computational bottleneck is the costly structural relaxation of each hypothetical material on the large-scale dataset using density functional theory (DFT) calculations. Here, we present a generative domain translation framework that maps the unrelaxed structural domains to the relaxed domains, enabling data-driven structural translations. The model predicts the materials formation energy with a small mean absolute error without DFT relaxations, and furthermore can produce the atomic coordinates consistent with the DFT relaxed structures. The utility of the proposed concept is not restricted to the structural domains, and we expect that it can be extended to translate the domain of easy-to-compute properties into the domain of more difficult properties

Association of Education and Smoking Status on Risk of Diabetes Mellitus: A Population-Based Nationwide Cross-Sectional Study

Background: Exposure to smoke, including environmental tobacco smoke (ETS), is a well-known risk factor for diabetes. Low socioeconomic status, especially lack of education, is also a risk factor for diabetes. Therefore, we assessed the association of demographic, socioeconomic, clinical, and behavior risk factor-related variables and smoking status, including ETS exposure, with the prevalence of diabetes. Methods: Data were from the 2007–2013 Korea National Health and Nutritional Evaluation Survey (KNHANES). Multivariable logistic regression examined associations between various lifestyle and health factors and the prevalence of diabetes while controlling for potential confounding variables. Subgroup analysis was performed according to smoking status to determine factors associated with diabetes. Results: Of 19,303 individuals analyzed, 1325 (11.4%) had diabetes. Greater average age, male sex, lower educational level, unemployment, and coexisting health problems were significantly associated with diabetes. Individuals with only elementary, middle, or high school level education had significantly greater odds ratios (p &lt; 0.05) compared to college graduates; smokers and nonsmokers exposed to ETS had significantly greater OR (p &lt; 0.05) than nonsmokers unexposed to ETS. Subgroup analysis of diabetics according to smoking status revealed significant associations (p &lt; 0.05) for diabetic nonsmokers exposed to ETS with female sex, single status, elementary level education, urban residence, National Health Insurance (NHI), hypertension, a lack of alcohol intake, and a lack of moderate physical activity. For diabetic smokers, there were significant associations (p &lt; 0.05) with elementary education, urban residence, a lack of moderate physical activity, a lack of alcohol intake, and NHI. Conclusions: The results suggested that smoking status, as well as ETS exposure, was associated with a higher prevalence of diabetes, especially in populations with less education. Thus, we should direct efforts for controlling diabetes toward individuals with lower levels of education and those who are smokers and nonsmokers exposed to ETS