Precision Higgs physics at the CEPC

The discovery of the Higgs boson with its mass around 125 GeV by the ATLAS and CMS Collaborations marked the beginning of a new era in high energy physics. The Higgs boson will be the subject of extensive studies of the ongoing LHC program. At the same time, lepton collider based Higgs factories have been proposed as a possible next step beyond the LHC, with its main goal to precisely measure the properties of the Higgs boson and probe potential new physics associated with the Higgs boson. The Circular Electron Positron Collider~(CEPC) is one of such proposed Higgs factories. The CEPC is an $e^+e^-$ circular collider proposed by and to be hosted in China. Located in a tunnel of approximately 100~km in circumference, it will operate at a center-of-mass energy of 240~GeV as the Higgs factory. In this paper, we present the first estimates on the precision of the Higgs boson property measurements achievable at the CEPC and discuss implications of these measurements.Comment: 46 pages, 37 figure

A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks

In distributed training of deep neural networks or Federated Learning (FL), people usually run Stochastic Gradient Descent (SGD) or its variants on each machine and communicate with other machines periodically. However, SGD might converge slowly in training some deep neural networks (e.g., RNN, LSTM) because of the exploding gradient issue. Gradient clipping is usually employed to address this issue in the single machine setting, but exploring this technique in the FL setting is still in its infancy: it remains mysterious whether the gradient clipping scheme can take advantage of multiple machines to enjoy parallel speedup. The main technical difficulty lies in dealing with nonconvex loss function, non-Lipschitz continuous gradient, and skipping communication rounds simultaneously. In this paper, we explore a relaxed-smoothness assumption of the loss landscape which LSTM was shown to satisfy in previous works and design a communication-efficient gradient clipping algorithm. This algorithm can be run on multiple machines, where each machine employs a gradient clipping scheme and communicate with other machines after multiple steps of gradient-based updates. Our algorithm is proved to have $O\left(\frac{1}{N\epsilon^4}\right)$ iteration complexity for finding an $\epsilon$-stationary point, where $N$ is the number of machines. This indicates that our algorithm enjoys linear speedup. We prove this result by introducing novel analysis techniques of estimating truncated random variables, which we believe are of independent interest. Our experiments on several benchmark datasets and various scenarios demonstrate that our algorithm indeed exhibits fast convergence speed in practice and thus validates our theory

FSVM: A Few-Shot Threat Detection Method for X-ray Security Images

In recent years, automatic detection of threats in X-ray baggage has become important in security inspection. However, the training of threat detectors often requires extensive, well-annotated images, which are hard to procure, especially for rare contraband items. In this paper, a few-shot SVM-constraint threat detection model, named FSVM is proposed, which aims at detecting unseen contraband items with only a small number of labeled samples. Rather than simply finetuning the original model, FSVM embeds a derivable SVM layer to back-propagate the supervised decision information into the former layers. A combined loss function utilizing SVM loss is also created as the additional constraint. We have evaluated FSVM on the public security baggage dataset SIXray, performing experiments on 10-shot and 30-shot samples under three class divisions. Experimental results show that compared with four common few-shot detection models, FSVM has the highest performance and is more suitable for complex distributed datasets (e.g., X-ray parcels)

Identifying Topological Motif Patterns of Human Brain Functional Networks

Recent imaging connectome studies demonstrated that the human functional brain network follows an efficient small-world topology with cohesive functional modules and highly connected hubs. However, the functional motif patterns that represent the underlying information flow remain largely unknown. Here, we investigated motif patterns within directed human functional brain networks, which were derived from resting-state functional magnetic resonance imaging data with controlled confounding hemodynamic latencies. We found several significantly recurring motifs within the network, including the two-node reciprocal motif and five classes of three-node motifs. These recurring motifs were distributed in distinct patterns to support intra-and inter-module functional connectivity, which also promoted integration and segregation in network organization. Moreover, the significant participation of several functional hubs in the recurring motifs exhibited their critical role in global integration. Collectively, our findings highlight the basic architecture governing brain network organization and provide insight into the information flow mechanism underlying intrinsic brain activities. (C) 2017 Wiley Periodicals, Inc.</p

Underwater Superoleophobicity of Pseudozwitterionic SAMs: Effects of Chain Length and Ionic Strength

Surfaces with controlled oil wettability in water have great potential for numerous underwater applications. In this work, we proposed two schemes, alkyl chain length dependent and ionic strength dependent, to achieve controllable oelophobic surfaces. The underwater oil-resistant property of the obtained self-assembled monolayers (SAMs) was evaluated by using an oil droplet (1,2-dichloroethane) as a detecting probe. The oleophobicity of SAM surfaces could be modulated from superoleophilic (contact angle of ca. 0°) to superoleophobic (contact angle over 170°) by controlling the chain length difference between negatively charged HS­(CH₂)_<i>n</i>COO^–-SAM (<i>n</i> = 17, 16, 14, 12, 10, 8, 6, 4) and positively charged HS­(CH₂)₅N­(CH₃)₃⁺-SAM. The observed phenomena could be explained by interchain interactions between charged −N­(CH₃)₃⁺ and −COO^–, in addition with the bending effect of the long chain in mixed-charged (pseudozwitterionic) SAMs. Furthermore, the effect of ionic strength on mixed-charged SAMs (negatively charged HS­(CH₂)<i>_m</i>COO^–-SAM and positively charged HS­(CH₂)₈N­(CH₃)₃⁺-SAM, <i>m</i> = 8, 7, 6, 5, 4, 3) is also studied. Higher ionic strength could promote underwater superoleophobicity to an ideal oil contact angle of 180°. The additional ions markedly neutralized the effect of interchain interaction among charged head groups, which contributed to the formation of a more robust hydration network. This work provides two stratagies for preparation of hydrophilic mixed-charged surfaces with tunable underwater oleophobicity, which could not only help the fabrication of tunable underwater oil wetting surfaces, but also be potentially useful in numerous important applications, such as microfluidic devices, bioadhesion, chemical microreactors, and antifouling materials

Molecular Understanding on the Underwater Oleophobicity of Self-Assembled Monolayers: Zwitterionic versus Nonionic

Molecular dynamics simulations are conducted to investigate the underwater oleophobicity of self-assembled monolayers (SAMs) with different head groups. Simulation results show that the order of underwater oleophobicity of SAMs is methyl < amide < oligo­(ethylene glycol) (OEG) < ethanolamine (ETA) < hydroxyl < mixed-charged zwitterionic. The underwater–oil contact angles (OCAs) are <133° for all nonionic hydrophilic SAMs, while the mixed-charged zwitterionic SAMs are underwater superoleophobic (OCA can reach 180°). It appears that surfaces with stronger underwater oleophobicity have better antifouling performance. Further study on the effect of different alkyl ammonium ions on mixed-charged SAMs reveals that the underwater OCAs are >143.6° for all SAMs; mixed-charged SAMs containing primary alkyl ammonium ion are likely to possess the best underwater oleophobicity for its strong hydration capacity. It seems that alkyl sulfonate anion (SO₃^–) is more hydrophilic than alkyl trimethylammonium ion (NC₃⁺) for the hydrophobic methyl groups on nitrogen atoms and that the hydration of SO₃^– in mixed-charged SAMs can be seriously blocked by NC₃⁺. The monomer of SO₃^– should be slightly longer than that of NC₃⁺ to obtain better underwater oleophobicity in NC₃⁺–/SO₃^––SAMs. In addition, the underwater oleophobicity of SAMs might become worse at low grafting densities. This work systematically proves that a zwitterionic surface is more underwater oleophobic than a nonionic surface. These results will help for the design and development of superoleophobic surfaces