Rare Decays

Studies of rare decays play an important role in the search of physics beyond the standard model. New particles may participate in the loop processes and can be probed by seeing any deviations from the standard model predictions. The very rare decay $B_s\to\mu^+\mu^-$ has been observed with the data collected by CMS and LHCb experiments. The signal seen by the ATLAS experiment is less significant but is compatible with the predictions. The measurement itself provides stringent constraints to new physics models. The first effective lifetime measurement with $B_s\to\mu^+\mu^-$ candidates has been carried out by the LHCb experiment. More data are still required to observe the $B^0\to\mu^+\mu^-$ decays. The $B\to K^*\mu^+\mu^-$ decay also proceeds through a flavour changing neutral current process, and is sensitive to the new physics. Extended measurements are carried out for $B\to K^*\mu^+\mu^-$ decays. Most of the classical physics parameters are found to be consistent with the predictions, but tensions do emerge in some of the observables. More data will help to clarify these potential deviations.Comment: 7 pages, for LHCP 2017 conferenc

Jet Discrimination with Quantum Complete Graph Neural Network

Machine learning, particularly deep neural networks, has been widely utilized in high energy physics and has shown remarkable results in various applications. Moreover, the concept of machine learning has been extended to quantum computers, giving rise to a new research area known as quantum machine learning. In this paper, we propose a novel variational quantum circuit model, Quantum Complete Graph Neural Network (QCGNN), designed for learning complete graphs. We argue that QCGNN has a polynomial speedup against its classical counterpart, due to the property of quantum parallelism. In this paper, we study the application of QCGNN through the challenging jet discrimination, where the jets are represented with complete graphs. Subsequently, we conduct a comparative analysis with classical graph neural networks to establish a benchmark

Performance Evaluation and Modeling of HPC I/O on Non-Volatile Memory

HPC applications pose high demands on I/O performance and storage capability. The emerging non-volatile memory (NVM) techniques offer low-latency, high bandwidth, and persistence for HPC applications. However, the existing I/O stack are designed and optimized based on an assumption of disk-based storage. To effectively use NVM, we must re-examine the existing high performance computing (HPC) I/O sub-system to properly integrate NVM into it. Using NVM as a fast storage, the previous assumption on the inferior performance of storage (e.g., hard drive) is not valid any more. The performance problem caused by slow storage may be mitigated; the existing mechanisms to narrow the performance gap between storage and CPU may be unnecessary and result in large overhead. Thus fully understanding the impact of introducing NVM into the HPC software stack demands a thorough performance study. In this paper, we analyze and model the performance of I/O intensive HPC applications with NVM as a block device. We study the performance from three perspectives: (1) the impact of NVM on the performance of traditional page cache; (2) a performance comparison between MPI individual I/O and POSIX I/O; and (3) the impact of NVM on the performance of collective I/O. We reveal the diminishing effects of page cache, minor performance difference between MPI individual I/O and POSIX I/O, and performance disadvantage of collective I/O on NVM due to unnecessary data shuffling. We also model the performance of MPI collective I/O and study the complex interaction between data shuffling, storage performance, and I/O access patterns.Comment: 10 page

Treatment of juxtafoveal central serous chorioretinopathy by compound anisodine injection

AIM: To investigate the efficiency and security of compound anisodine injection in the treatment of juxtafoveal central serous chorioretinopathy(CSC). <p>METHODS: Sixty patients(60 eyes)who were diagnosed of juxtafoveal CSC were assigned randomly into 2 groups: 32 cases(32 eyes, therapeutic group)were injected subcutaneously compound anisodine injection for 2mL q.d around superficial temporal arteries in the affected eyes; 28 cases(28 eyes, control group)received only traditional oral medication. Both groups received therapy for 2 to 4 courses of treatment. The main observations were the best corrected visual acuity(BCVA), subjective symptom, visual field, average light sensitivity and optical coherent topography(OCT).<p>RESULTS: There was no significant difference between the therapeutic group and the control group before treatment(<i>P</i>>0.05), but all the outcome measures at 1, 3mo in the treatment group were significantly improved as compared with control group(<i>P</i><0.05). After 6mo, there were no significant difference between the two groups in all measures(<i>P</i>>0.05). No severe adverse reaction was noted except mild ones such as temporary dry mouth, dizziness and palpitation in a few cases.<p>CONCLUSION: Compound anisodine injection has remarkable effects in the treatment of juxtafoveal CSC. It can shorten the course, improved the visual function and decreased the recurrence rate of CSC

Towards Fair Disentangled Online Learning for Changing Environments

In the problem of online learning for changing environments, data are sequentially received one after another over time, and their distribution assumptions may vary frequently. Although existing methods demonstrate the effectiveness of their learning algorithms by providing a tight bound on either dynamic regret or adaptive regret, most of them completely ignore learning with model fairness, defined as the statistical parity across different sub-population (e.g., race and gender). Another drawback is that when adapting to a new environment, an online learner needs to update model parameters with a global change, which is costly and inefficient. Inspired by the sparse mechanism shift hypothesis, we claim that changing environments in online learning can be attributed to partial changes in learned parameters that are specific to environments and the rest remain invariant to changing environments. To this end, in this paper, we propose a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor. The semantic factor is further used for fair prediction under a group fairness constraint. To evaluate the sequence of model parameters generated by the learner, a novel regret is proposed in which it takes a mixed form of dynamic and static regret metrics followed by a fairness-aware long-term constraint. The detailed analysis provides theoretical guarantees for loss regret and violation of cumulative fairness constraints. Empirical evaluations on real-world datasets demonstrate our proposed method sequentially outperforms baseline methods in model accuracy and fairness.Comment: Accepted by KDD 202

BTS: Bifold Teacher-Student in Semi-Supervised Learning for Indoor Two-Room Presence Detection Under Time-Varying CSI

In recent years, indoor human presence detection based on supervised learning (SL) and channel state information (CSI) has attracted much attention. However, the existing studies that rely on spatial information of CSI are susceptible to environmental changes, such as object movement, atmospheric factors, and machine rebooting, which degrade prediction accuracy. Moreover, SL-based methods require time-consuming labeling for retraining models. Therefore, it is imperative to design a continuously monitored model life-cycle using a semi-supervised learning (SSL) based scheme. In this paper, we conceive a bifold teacher-student (BTS) learning approach for presence detection systems that combines SSL by utilizing partially labeled and unlabeled datasets. The proposed primal-dual teacher-student network intelligently learns spatial and temporal features from labeled and unlabeled CSI. Additionally, the enhanced penalized loss function leverages entropy and distance measures to distinguish drifted data, i.e., features of new datasets affected by time-varying effects and altered from the original distribution. The experimental results demonstrate that the proposed BTS system sustains asymptotic accuracy after retraining the model with unlabeled data. Furthermore, the label-free BTS outperforms existing SSL-based models in terms of the highest detection accuracy while achieving the asymptotic performance of SL-based methods