Study on the transverse painting during the injection process for CSNS/RCS

For the China Spallation Neutron Source (CSNS), a combination of the H- stripping and phase space painting method is used to accumulate a high intensity beam in the Rapid Cycling Synchrotron (RCS). In this paper, firstly, the injection processes with different painting ranges and different painting methods were studied. With the codes ORBIT and MATLAB, the particle distribution and painting image were obtained. Then, the reasonable painting range which is suitable for the aperture size and magnet gap can be selected. Since the real field uniformity of BH3 and BV3 is not completely in conformity with the design requirement, the painting method and painting range also need to be selected to reduce the effects of bad field uniformity.Comment: Submitted to proceedings of IPAC2015, Richmond, VA, USA, May 3-8, 201

Dynamic Compression Ratio Selection for Edge Inference Systems with Hard Deadlines

Implementing machine learning algorithms on Internet of things (IoT) devices has become essential for emerging applications, such as autonomous driving, environment monitoring. But the limitations of computation capability and energy consumption make it difficult to run complex machine learning algorithms on IoT devices, especially when latency deadline exists. One solution is to offload the computation intensive tasks to the edge server. However, the wireless uploading of the raw data is time consuming and may lead to deadline violation. To reduce the communication cost, lossy data compression can be exploited for inference tasks, but may bring more erroneous inference results. In this paper, we propose a dynamic compression ratio selection scheme for edge inference system with hard deadlines. The key idea is to balance the tradeoff between communication cost and inference accuracy. By dynamically selecting the optimal compression ratio with the remaining deadline budgets for queued tasks, more tasks can be timely completed with correct inference under limited communication resources. Furthermore, information augmentation that retransmits less compressed data of task with erroneous inference, is proposed to enhance the accuracy performance. While it is often hard to know the correctness of inference, we use uncertainty to estimate the confidence of the inference, and based on that, jointly optimize the information augmentation and compression ratio selection. Lastly, considering the wireless transmission errors, we further design a retransmission scheme to reduce performance degradation due to packet losses. Simulation results show the performance of the proposed schemes under different deadlines and task arrival rates.Comment: 11 pages, 14 figure

Coding for Computing Irreducible Markovian Functions of Sources with Memory

One open problem in source coding is to characterize the limits of representing losslessly a non-identity discrete function of the data encoded independently by the encoders of several correlated sources with memory. This paper investigates this problem under Markovian conditions, namely either the sources or the functions considered are Markovian. We propose using linear mappings over finite rings as encoders. If the function considered admits certain polynomial structure, the linear encoders can make use of this structure to establish "implicit collaboration" and boost the performance. In fact, this approach universally applies to any scenario (arbitrary function) because any discrete function admits a polynomial presentation of required format. There are several useful discoveries in the paper. The first says that linear encoder over non-field ring can be equally optimal for compressing data generated by an irreducible Markov source. Secondly, regarding the previous function-encoding problem, there are infinitely many circumstances where linear encoder over non-field ring strictly outperforms its field counterpart. To be more precise, it is seen that the set of coding rates achieved by linear encoder over certain non-field rings is strictly larger than the one achieved by the field version, regardless which finite field is considered. Therefore, in this sense, linear coding over finite field is not optimal. In addition, for certain scenarios where the sources do not possess the ergodic property, our ring approach is still able to offer a solution

Radiation-driven Implosion in the Cepheus B Molecular Cloud

We analyze large scale mapping observations of the molecular lines in the 12CO (J=2-1), 12CO (J=3-2), 13CO (J=2-1), and 13CO (J=3-2) transition emissions toward the Cepheus B molecular cloud with the KOSMA 3m-telescope. The integrated intensity map of the 12CO (J=2-1) transition has shown a structure with a compact core and a compact ridge extended in the north-west of the core. The cloud is surrounded by an optically bright rim, where the radiation-driven implosion (RDI) may greatly change the gas properties. The intensities of the CO (J=3-2) transition are higher than those of the CO (J=2-1) transition along the rim area.We find characteristic RDI structure in positionvelocity diagrams. Non-LTE Large velocity gradient (LVG) model analysis shows that the density and temperature at the edge are higher than that in the center. Our results provide evidences that RDI is taking place in Cepheus B molecular cloud.Comment: 8 pages, 5 figure

Optimal Choice under Short Sell Limit with Sharpe Ratio as Criterion among Multiple Assets

This article is the term paper of the course Investments. We mainly focus on modeling long-term investment decisions of a typical utility-maximizing individual, with features of Chinese stock market in perspective. We adopt an OR based methodology with market information as input parameters to carry out the solution. Two main features of this article are: first, we take the no short-sell constraint in Chinese stock market into consideration and use an approach otherwise identical to Markowitz to work out the optimal portfolio choice; this method has critical and practical implication to Chinese investors. Second, we incorporate the benefits of multiple assets into one single well-defined utility function and use a MIQP procedure to derive the optimal allocation of funds upon each of them along the time-line

Study on the injection beam commissioning software for CSNS/RCS

The China Spallation Neutron Source (CSNS) accelerator uses H- stripping and phase space painting method of filling large ring acceptance with the linac beam of small emittance. The beam commissioning software system is the key part of CSNS accelerator. The injection beam commissioning software for CSNS contains three parts currently: painting curve control, injection beam control and injection orbit correction. The injection beam control contains two subsections: single bunch beam calculation and LRBT beam control at the foil. The injection orbit correction also contains two subsections: injection orbit correction by the calculation and injection trim power control.Comment: Submitted to proceedings of IPAC2015, Richmond, VA, USA, May 3-8, 201

Non-damped Acoustic Plasmon and Superconductivity in Single Wall Carbon Nanotubes

We show that non-damped acoustic plasmons exist in single wall carbon nanotubes (SWCNT) and propose that the non-damped acoustic plasmons may mediate electron-electron attraction and result in superconductivity in the SWCNT. The superconducting transition temperature Tc for the SWCNT (3,3) obtained by this mechanism agrees with the recent experimental result (Z. K. Tang et al, Science 292, 2462(2001)). We also show that it is possible to get higher Tc up to 99 K by doping the SWCNT (5,5).Comment: REVTEX, 4 pages including 2 figures, Corrected typo

Surface field theories of point group symmetry protected topological phases

We identify field theories that describe the surfaces of three-dimensional bosonic point group symmetry protected topological (pgSPT) phases. The anomalous nature of the surface field theories is revealed via a dimensional reduction argument. Specifically, we study three different surface field theories. The first field theory is quantum electrodynamics in three space-time dimensions (QED3) with four flavors of fermions. We show this theory can describe the surfaces of a majority of bosonic pgSPT phases protected by a single mirror reflection, or by $C_{nv}$ point group symmetry for $n=2,3,4,6$. The second field theory is a variant of QED3 with charge-1 and charge-3 Dirac fermions. This field theory can describe the surface of a reflection symmetric pgSPT phase built by placing an $E_{8}$ state on the mirror plane. The third field theory is an ${\rm O}(4)$ non-linear sigma model with a topological theta-term at $\theta=\pi$, or, equivalently, a non-compact ${\rm CP}^1$ model. Using a coupled wire construction, we show this is a surface theory for bosonic pgSPT phases with ${\rm U}(1) \times \mathbb{Z}_{2}^{P}$ symmetry. For the latter two field theories, we discuss the connection to gapped surfaces with topological order. Moreover, we conjecture that the latter two field theories can describe surfaces of more general bosonic pgSPT phases with $C_{nv}$ point group symmetry.Comment: 16 pages, 2 figure

Dark Soliton Excitations in Single Wall Carbon Nanotubes

Dark soliton excitations are shown to exist in single wall carbon nanotubes (SWCNTs). At first, the nonlinear effective interatomic potential and the difference equation for longitudinal lattice displacement are obtained for the SWCNTs by expanding Brenner's many-body potential in a Taylor series up to fourth-order terms. Then using a multi-scale method, for short wavelength lattice excitations the equation of motion of lattice is reduced to the cubic nonlinear Schrodinger equation. Finally, the dark soliton solutions and relevant excitations in the SWCNTs with subsonic velocity are discussed.Comment: 11pages, no figure

Fast Multi-Instance Multi-Label Learning

In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multiple labels. Such tasks can be formulated as multi-instance multi-label learning (MIML) problems, and have been extensively studied during the past few years. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, in this paper we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less; particularly, on a data set with 20K bags and 180K instances, MIMLfast is more than 100 times faster than existing MIML approaches. On a larger data set where none of existing approaches can return results in 24 hours, MIMLfast takes only 12 minutes. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output label semantics