Cyclic Locking and Memristor-based Obfuscation Against CycSAT and Inside Foundry Attacks

The high cost of IC design has made chip protection one of the first priorities of the semiconductor industry. Although there is a common impression that combinational circuits must be designed without any cycles, circuits with cycles can be combinational as well. Such cyclic circuits can be used to reliably lock ICs. Moreover, since memristor is compatible with CMOS structure, it is possible to efficiently obfuscate cyclic circuits using polymorphic memristor-CMOS gates. In this case, the layouts of the circuits with different functionalities look exactly identical, making it impossible even for an inside foundry attacker to distinguish the defined functionality of an IC by looking at its layout. In this paper, we propose a comprehensive chip protection method based on cyclic locking and polymorphic memristor-CMOS obfuscation. The robustness against state-of-the-art key-pruning attacks is demonstrated and the overhead of the polymorphic gates is investigated

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Background: Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes. Methods: We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment. Results: Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference − 0.40 [95% CI − 0.71 to − 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference − 1.6% [95% CI − 4.3% to 1.2%]; P = 0.42) between groups. Conclusions: In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792. Registered November 20th, 2017

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial.

BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial (vol 26, 46, 2022)

BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

Gaseous Pollutent Source Term Estimation Based on Adjoint Probability and Regularization Method

Fast and accurate identification of source locations and release rates is particularly important for improving indoor air quality and ensuring the safety and health of people. Existing methods based on adjoint probability are difficult to distinguish the release rate of dynamic sources, and optimization algorithms based on regularization are limited to analysing only a small amount of potential pollutant source information. Therefore, this study proposed an algorithm combining adjoint equations and regularization models to identify the location and release intensity of pollutant sources in the entire computational domain of a room. Based on the validated indoor CFD computational model, we first obtained a series of response matrices corresponding to the sensor position by solving the adjoint equation, and then used the regularization method and Bayesian inference to extrapolate the release rate and location of dynamic pollutant source in the room. The results shown that the proposed algorithm is convenient and feasible to identify the location and intensity of the indoor pollutant source. Compared with the real source intensity, the identification of constant source intensity is lower than the error threshold (10%) in 97.4% of the time nodes, and the identification of periodic source is lower than the error threshold (10%) in 95.4% of the time nodes. This research provides a new method and perspective for the estimation of indoor pollutant source information

The rational side of decision bias based on verbal probabilities

Verbal probabilistic expressions (verbal probabilities) contain a communicative function called directionality and can becategorized as positive (e.g., likely or probable) or negative (e.g., unlikely or doubtful) on the ba-sis of their directionality.Previous studies have demonstrated that the directionality of phrases affects decisions. In particular, people tend to be morerisk seeking when presented with positive phrases and risk averse when presented with negative phrases. The rationality(i.e., maximizing utility) of such seemingly biased decisions is examined in this study. We hypothesize that because aspeaker tends to choose a positive or negative expression on the basis of context, the selected phrase works as an adaptivecue for understanding the situational change, and that decision biases based on differences in expressions will lead tomore rational decision making. Computer simulations were conducted regarding decisions with uncertainty based onverbal probabilities. We found that despite speaker biases in probability judgments, miscommunication generated by thevagueness of verbal expressions, and individual differences in subjective values, biased deci-sion makers who changedtheir risk attitude on the basis of the directionality of verbal probabilities could make more decisions that were rationalthan could those who did not show such decision biases

Black-box Models’ Explainability: A Theoretical and Practical Perspective

The lack of explainability remains a critical challenge to the widespread adoption of artificial intelligence (AI) in many fields. “Understanding brings in trust”, while machine-learning models offer superior prediction accuracy, understanding the underlying logic is equally important to foster trust in these models. In this paper, we present eXplainable AI (XAI) as a solution to this challenge. Our research focuses on three key aspects of XAI: mathematics, humanities and social sciences, and practical applications. We demonstrated the feasibility of XAI through the use of artificially-constructed and model-derived ground truth, and verified performances of different XAIs. We also explored three dimensions of explainable consistency and emphasized the significance of human-machine consistency. Finally, we applied our research to a real-world scenario by cooperating with a national bank in China. Our findings highlight that XAI is both mathematically and practically meaningful, but more efforts need to be dedicated to this human-machine communication field

Associations between State-Level Obesity Rates, Engagement with Food Brands on Social Media, and Hashtag Usage

Food advertisement exposure is associated with increased caloric intake, but little is known about food/beverage placements in the digital media environment. We aimed to examine the correlation between the number of people who follow food and beverage brand social media accounts (i.e., user engagement) and state-level obesity rates; quantify social media followers’ use of “healthy” vs. “unhealthy” hashtags; and analyze the relationship between user engagement and hashtag usage. We identified the 26 fast-food and beverage brands with the highest advertising expenditures and used Demographics Pro to determine the characteristics of social media users amongst the 26 brands. A series of regression analyses were conducted that related the mean percentage of brand followers and state-level obesity rates. We then identified 733 hashtags on Instagram and 703 hashtags on Twitter, coding them as “healthy”, “unhealthy”, “neutral”, or “unrelated to health”. Intercoder reliability was established using ReCal2, which indicated a 90% agreement between coders. Finally, we conducted ANCOVA to examine the relationship between the mean percentage of brand followers and their hashtag usage. There was a significant, positive correlation between the state-level obesity rate and the mean percentage of followers of sugary drink or fast-food brands on Instagram and Twitter, but such a correlation between obesity and low-calorie drink brand followers was only found on Twitter. Our findings illustrate the relationship between the social media food environment and obesity rates in the United States. Given the high rates of engagement with food brands on social media, policies should limit digital advertisements featuring fast-food, sugary drink, and low-calorie drink brands

All-Optical Non-Inverted Parity Generator and Checker Based on Semiconductor Optical Amplifiers

An all-optical non-inverted parity generator and checker based on semiconductor optical amplifiers (SOAs) are proposed with four-wave mixing (FWM) and cross-gain modulation (XGM) non-linear effects. A 2-bit parity generator and checker using by exclusive NOR (XNOR) and exclusive OR (XOR) gates are implemented by first SOA and second SOA with 10 Gb/s return-to-zero (RZ) code, respectively. The parity and check bits are provided by adjusting the center wavelength of the tunable optical bandpass filter (TOBPF). A saturable absorber (SA) is used to reduce the negative effect of small signal clock (Clk) probe light to improve extinction ratio (ER) and optical signal-to-noise ratio (OSNR). For Pe and Ce (even parity bit and even check bit) without Clk probe light, ER and OSNR still maintain good performance because of the amplified effect of SOA. For Po (odd parity bit), ER and OSNR are improved to 1 dB difference for the original value. For Co (odd check bit), ER is deteriorated by 4 dB without SA, while OSNR is deteriorated by 12 dB. ER and OSNR are improved by about 2 dB for the original value with the SA. This design has the advantages of simple structure and great integration capability and low cost