Optimizing Quantum Programs against Decoherence: Delaying Qubits into Quantum Superposition

Quantum computing technology has reached a second renaissance in the last decade. However, in the NISQ era pointed out by John Preskill in 2018, quantum noise and decoherence, which affect the accuracy and execution effect of quantum programs, cannot be ignored and corrected by the near future NISQ computers. In order to let users more easily write quantum programs, the compiler and runtime system should consider underlying quantum hardware features such as decoherence. To address the challenges posed by decoherence, in this paper, we propose and prototype QLifeReducer to minimize the qubit lifetime in the input OpenQASM program by delaying qubits into quantum superposition. QLifeReducer includes three core modules, i.e.,the parser, parallelism analyzer and transformer. It introduces the layered bundle format to express the quantum program, where a set of parallelizable quantum operations is packaged into a bundle. We evaluate quantum programs before and after transformed by QLifeReducer on both real IBM Q 5 Tenerife and the self-developed simulator. The experimental results show that QLifeReducer reduces the error rate of a quantum program when executed on IBMQ 5 Tenerife by 11%; and can reduce the longest qubit lifetime as well as average qubit lifetime by more than 20% on most quantum workloads.Comment: To appear in TASE2019 - the 13th International Symposium on Theoretical Aspects of Software Engineering (submitted on Jan 25, 2019, and this is camera-ready version

Large-Scale Location-Aware Services in Access: Hierarchical Building/Floor Classification and Location Estimation using Wi-Fi Fingerprinting Based on Deep Neural Networks

One of key technologies for future large-scale location-aware services in access is a scalable indoor localization technique. In this paper, we report preliminary results from our investigation on the use of deep neural networks (DNNs) for hierarchical building/floor classification and floor-level location estimation based on Wi-Fi fingerprinting, which we carried out as part of a feasibility study project on Xi'an Jiaotong-Liverpool University (XJTLU) Campus Information and Visitor Service System. To take into account the hierarchical nature of the building/floor classification problem, we propose a new DNN architecture based on a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification with argmax functions to convert multi-label classification results into multi-class classification ones. We also describe the demonstration of a prototype DNN-based indoor localization system for floor-level location estimation using real received signal strength (RSS) data collected at one of the buildings on the XJTLU campus. The preliminary results for both building/floor classification and floor-level location estimation clearly show the strengths of DNN-based approaches, which can provide near state-of-the-art performance with less parameter tuning and higher scalability.Comment: 5 pages, 6 figures, FOAN 2017 (Munich, Germany, Oct. 2017

PexRAP inhibits PRDM16-mediated thermogenic gene expression

How the nuclear receptor PPARγ regulates the development of two functionally distinct types of adipose tissue, brown and white fat, as well as the browning of white fat, remains unclear. Our previous studies suggest that PexRAP, a peroxisomal lipid synthetic enzyme, regulates PPARγ signaling and white adipogenesis. Here, we show that PexRAP is an inhibitor of brown adipocyte gene expression. PexRAP inactivation promoted adipocyte browning, increased energy expenditure, and decreased adiposity. Identification of PexRAP-interacting proteins suggests that PexRAP function extends beyond its role as a lipid synthetic enzyme. Notably, PexRAP interacts with importin-β1, a nuclear import factor, and knockdown of PexRAP in adipocytes reduced the levels of nuclear phospholipids. PexRAP also interacts with PPARγ, as well as PRDM16, a critical transcriptional regulator of thermogenesis, and disrupts the PRDM16-PPARγ complex, providing a potential mechanism for PexRAP-mediated inhibition of adipocyte browning. These results identify PexRAP as an important regulator of adipose tissue remodeling

Muscle lipogenesis balances insulin sensitivity and strength through calcium signaling

Exogenous dietary fat can induce obesity and promote diabetes, but endogenous fat production is not thought to affect skeletal muscle insulin resistance, an antecedent of metabolic disease. Unexpectedly, the lipogenic enzyme fatty acid synthase (FAS) was increased in the skeletal muscle of mice with diet-induced obesity and insulin resistance. Skeletal muscle–specific inactivation of FAS protected mice from insulin resistance without altering adiposity, specific inflammatory mediators of insulin signaling, or skeletal muscle levels of diacylglycerol or ceramide. Increased insulin sensitivity despite high-fat feeding was driven by activation of AMPK without affecting AMP content or the AMP/ATP ratio in resting skeletal muscle. AMPK was induced by elevated cytosolic calcium caused by impaired sarco/endoplasmic reticulum calcium ATPase (SERCA) activity due to altered phospholipid composition of the sarcoplasmic reticulum (SR), but came at the expense of decreased muscle strength. Thus, inhibition of skeletal muscle FAS prevents obesity-associated diabetes in mice, but also causes muscle weakness, which suggests that mammals have retained the capacity for lipogenesis in muscle to preserve physical performance in the setting of disrupted metabolic homeostasis

Lysophospholipid acylation modulates plasma membrane lipid organization and insulin sensitivity in skeletal muscle

Aberrant lipid metabolism promotes the development of skeletal muscle insulin resistance, but the exact identity of lipid-mediated mechanisms relevant to human obesity remains unclear. A comprehensive lipidomic analysis of primary myocytes from individuals who were insulin-sensitive and lean (LN) or insulin-resistant with obesity (OB) revealed several species of lysophospholipids (lyso-PLs) that were differentially abundant. These changes coincided with greater expression of lysophosphatidylcholine acyltransferase 3 (LPCAT3), an enzyme involved in phospholipid transacylation (Lands cycle). Strikingly, mice with skeletal muscle-specific knockout of LPCAT3 (LPCAT3-MKO) exhibited greater muscle lysophosphatidylcholine/phosphatidylcholine, concomitant with improved skeletal muscle insulin sensitivity. Conversely, skeletal muscle-specific overexpression of LPCAT3 (LPCAT3-MKI) promoted glucose intolerance. The absence of LPCAT3 reduced phospholipid packing of cellular membranes and increased plasma membrane lipid clustering, suggesting that LPCAT3 affects insulin receptor phosphorylation by modulating plasma membrane lipid organization. In conclusion, obesity accelerates the skeletal muscle Lands cycle, whose consequence might induce the disruption of plasma membrane organization that suppresses muscle insulin action

Metabolic effects of selective deletion of group VIA phospholipase A2 from macrophages or pancreatic islet beta-cells

To examine the role of group VIA phospholipase

Modeling Occupant Window Behavior in Hospitals—A Case Study in a Maternity Hospital in Beijing, China

Nowadays, relevant data collected from hospital buildings remain insufficient because hospital buildings often have stricter environmental requirements resulting in more limited data access than other building types. Additionally, existing window-opening behavior models were mostly developed and validated using data measured from the experimental building itself. Hence, their accuracy is only assessed by the algorithm’s evaluation index, which limits the model’s applicability, given that it is not tested by the actual cases nor cross-verified with other buildings. Based on the aforementioned issues, this study analyzes the window-opening behavior of doctors and patients in spring in a maternity hospital in Beijing and develops behavioral models using logistic regression. The results show that the room often has opened windows in spring when the outdoor temperature exceeds 20 °C. Moreover, the ward windows’ use frequency is more than 10 times higher than those of doctors’ office. The window-opening behavior in wards is more susceptible to the influence of outdoor temperature, while in the doctors’ office, more attention is paid to indoor air quality. Finally, by embedding the logistic regression model of each room into the EnergyPlus software to simulate the CO2 concentration of the room, it was found that the model has better applicability than the fixed schedule model. However, by performing cross-validation with different building types, it was found that, due to the particularity of doctors’ offices, the models developed for other building types cannot accurately reproduce the window-opening behavior of doctors. Therefore, more data are still needed to better understand window usage in hospital buildings and support the future building performance simulations of hospital buildings

Reinforcement learning-guided long-timescale simulation of hydrogen transport in metals

Atomic diffusion in solids is an important process in various phenomena. However, atomistic simulations of diffusion processes are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interests. In this work, we developed a long-timescale method using reinforcement learning that simulates diffusion processes. As a testbed, we simulate hydrogen diffusion in pure metals and a medium entropy alloy, CrCoNi, getting hydrogen diffusivity reasonably consistent with previous experiments. We also demonstrate that our method can accelerate the sampling of low-energy configurations compared to the Metropolis-Hastings algorithm using hydrogen migration to copper (111) surface sites as an example

Spectrum resource allocation for high-throughput satellite communications based on behavior cloning

In high-throughput multi-beam satellite systems, the dimensionality of the spectrum resource allocation problem increased drastically with the number of satellite beams and service users, which caused an exponential rise in the complexity of the solution. To address the challenge, a two-stage algorithm that combined behavior cloning (BC) with deep reinforcement learning (DRL) was proposed. In the first stage, the strategy network was pretrained using existing decision data from satellite operation through behavior cloning, which mimicked expert behavior to reduce blind exploration and accelerate algorithm convergence. In the second stage, the strategy network was further optimized using the proximal policy optimization (PPO), and a convolutional block attention module (CBAM) was employed to better extract the user traffic features, thereby enhancing overall algorithm performance. Simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithms in terms of convergence speed and algorithm stability, and also delivers superior performance in system delay, average system satisfaction, and spectrum efficiency

P53 codon 72 polymorphism, human papillomavirus infection, and their interaction to oral carcinoma susceptibility

BACKGROUND: Tumor suppressor gene p53 plays an important role in the maintenance of the genomic integrity, and mutation in the gene may alter an individual’s susceptibility to various carcinomas. P53 Arg72Pro or codon 72 polymorphism has been indicated to increase the risk of developing certain cancers such as bladder cancer and cervical cancer. Human papillomavirus (HPV) infection has been shown as a risk factor for certain cancers such as cervical cancer and oral cancer as well, and the HPV oncoprotein E6 may induce the degradation of p53 function. However, the association between p53 Arg72Pro polymorphism and the risk of oral cancer with HPV infection remains inconclusive. Therefore, this meta-analysis involving 5,614 participants was performed to investigate the relations among the p53 Arg72Pro polymorphism, HPV infection, and the risk of developing oral cancer. RESULTS: A search of the literature by PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases was conducted to identify studies based on the inclusion and exclusion criteria. Odds ratios with 95 % confidence intervals were combined using a random-effect model or a fixed-effect model. The current study was conducted with 13 studies consisting of 2,413 cases and 3,201 controls. Neither overall analysis nor stratified analyses detected any obvious evidence of association between p53 Arg72Pro polymorphism and oral cancer susceptibility in all genetic models. However, a significant association between p53 Arg72Pro polymorphism and the risk of oral cancer with HPV infection was detected in the Arg/Arg vs. Arg/Pro + Pro/Pro model. CONCLUSION: In the current meta-analysis which used the quantitative data synthesis for the first time, our study demonstrated that p53 Arg72Pro polymorphism together with HPV infection might jointly alter an individual’s susceptibility to the risk of oral cancer. Our results suggested that p53 Arg72Pro polymorphism may partly contribute to the pathogenesis of oral cancer development