Efficient quantum cryptography network without entanglement and quantum memory

An efficient quantum cryptography network protocol is proposed with d-dimension polarized photons, without resorting to entanglement and quantum memory. A server on the network, say Alice, provides the service for preparing and measuring single photons whose initial state are |0>. The users code the information on the single photons with some unitary operations. For preventing the untrustworthy server Alice from eavesdropping the quantum lines, a nonorthogonal-coding technique (decoy-photon technique) is used in the process that the quantum signal is transmitted between the users. This protocol does not require the servers and the users to store the quantum state and almost all of the single photons can be used for carrying the information, which makes it more convenient for application than others with present technology. We also discuss the case with a faint laser pulse.Comment: 4 pages, 1 figures. It also presented a way for preparing decoy photons without a sinigle-photon sourc

NTAP: for NimbleGen tiling array ChIP-chip data analysis

Summary:NTAP is designed to analyze ChIP-chip data generated by the NimbleGen tiling array platform and to accomplish various pattern recognition tasks that are useful especially for epigenetic studies. The modular design of NTAP makes the data processing highly customizable. Users can either use NTAP to perform the full process of NimbleGen tiling array data analysis, or choose post-processing modules in NTAP to analyze pre-processed epigenetic data generated by other platforms. The output of NTAP can be saved in standard GFF format files and visualized in GBrowse

Multiagent Q-learning with Sub-Team Coordination

In many real-world cooperative multiagent reinforcement learning (MARL) tasks, teams of agents can rehearse together before deployment, but then communication constraints may force individual agents to execute independently when deployed. Centralized training and decentralized execution (CTDE) is increasingly popular in recent years, focusing mainly on this setting. In the value-based MARL branch, credit assignment mechanism is typically used to factorize the team reward into each individual's reward - individual-global-max (IGM) is a condition on the factorization ensuring that agents' action choices coincide with team's optimal joint action. However, current architectures fail to consider local coordination within sub-teams that should be exploited for more effective factorization, leading to faster learning. We propose a novel value factorization framework, called multiagent Q-learning with sub-team coordination (QSCAN), to flexibly represent sub-team coordination while honoring the IGM condition. QSCAN encompasses the full spectrum of sub-team coordination according to sub-team size, ranging from the monotonic value function class to the entire IGM function class, with familiar methods such as QMIX and QPLEX located at the respective extremes of the spectrum. Experimental results show that QSCAN's performance dominates state-of-the-art methods in matrix games, predator-prey tasks, the Switch challenge in MA-Gym. Additionally, QSCAN achieves comparable performances to those methods in a selection of StarCraft II micro-management tasks

Accurate Diagnosis of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence

Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, \u3e 14,680 WSIs, from \u3e 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results: Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions: This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition

Constraints on Spin-Independent Nucleus Scattering with sub-GeV Weakly Interacting Massive Particle Dark Matter from the CDEX-1B Experiment at the China Jin-Ping Laboratory

We report results on the searches of weakly interacting massive particles (WIMPs) with sub-GeV masses ($m_{\chi}$) via WIMP-nucleus spin-independent scattering with Migdal effect incorporated. Analysis on time-integrated (TI) and annual modulation (AM) effects on CDEX-1B data are performed, with 737.1 kg$\cdot$day exposure and 160 eVee threshold for TI analysis, and 1107.5 kg$\cdot$day exposure and 250 eVee threshold for AM analysis. The sensitive windows in $m_{\chi}$ are expanded by an order of magnitude to lower DM masses with Migdal effect incorporated. New limits on $\sigma_{\chi N}^{\rm SI}$ at 90\% confidence level are derived as $2\times$10$^{-32}\sim7\times$10$^{-35}$ $\rm cm^2$ for TI analysis at $m_{\chi}\sim$ 50$-$180 MeV/$c^2$, and $3\times$10$^{-32}\sim9\times$10$^{-38}$ $\rm cm^2$ for AM analysis at $m_{\chi}\sim$75 MeV/$c^2-$3.0 GeV/$c^2$.Comment: 5 pages, 4 figure

Search for Light Weakly-Interacting-Massive-Particle Dark Matter by Annual Modulation Analysis with a Point-Contact Germanium Detector at the China Jinping Underground Laboratory

We present results on light weakly interacting massive particle (WIMP) searches with annual modulation (AM) analysis on data from a 1-kg mass $p$-type point-contact germanium detector of the CDEX-1B experiment at the China Jinping Underground Laboratory. Datasets with a total live time of 3.2 yr within a 4.2 yr span are analyzed with analysis threshold of 250 eVee. Limits on WIMP-nucleus (${\chi}$-$N$) spin-independent cross sections as function of WIMP mass ($m_{\chi}$) at 90\% confidence level (C.L.) are derived using the dark matter halo model. Within the context of the standard halo model, the 90\% C.L. allowed regions implied by the DAMA/LIBRA and CoGeNT AM-based analysis are excluded at $>$99.99\% and 98\% C.L., respectively. These results correspond to the best sensitivity at $m_{\chi}$$<$6$~{\rm GeV}/c^2$ among WIMP AM measurements to date.Comment: 5 pages, 4 figure