Coherent two-dimensional multiphoton photoelectron spectroscopy of metal surfaces

Light interacting with metals elicits an ultrafast coherent many-body screening response on sub- to few-femtosecond time-scales, which makes its experimental observation challenging. Here, we describe the coherent two-dimensional (2D) multi-photon photoemission study of the Shockley surface state (SS) of Ag(111) as a benchmark for spectroscopy of the coherent nonlinear response of metals to an optical field in the perturbative regime. Employing interferometrically time-resolved multi-photon photoemission spectroscopy (ITR-mPP), we correlate the coherent polarizations and populations excited in the sample with final photoelectron distributions where the interaction terminates. By measuring the non-resonant 3- and 4-photon photoemission of the SS state, as well as its replica structures in the above-threshold photoemission (ATP), we record the coherent response of the Ag(111) surface by 2D photoemission spectroscopy and relate it to its band structure. We interpret the mPP process by an optical Bloch equation (OBE) model, which reproduces the main features of the surface state coherent polarization dynamics recorded in ITR-mPP experiments: The spectroscopic components of the 2D photoelectron spectra are shown to depend on the nonlinear orders of the coherent photoemission process m as well as on the induced coherence n.Comment: 34 pages, 8 figures in main paper, pages 33 and 34: supplemental material, 1 figur

Distinct fingerprints of charge density waves and electronic standing waves in ZrTe3_3

Experimental signatures of charge density waves (CDW) in high-temperature superconductors have evoked much recent interest, yet an alternative interpretation has been theoretically raised based on electronic standing waves resulting from quasiparticles scattering off impurities or defects, also known as Friedel oscillations (FO). Indeed the two phenomena are similar and related, posing a challenge to their experimental differentiation. Here we report a resonant X-ray diffraction study of ZrTe$_3$, a model CDW material. Near the CDW transition, we observe two independent diffraction signatures that arise concomitantly, only to become clearly separated in momentum while developing very different correlation lengths in the well-ordered state. Anomalously slow dynamics of mesoscopic ordered nanoregions are further found near the transition temperature, in spite of the expected strong thermal fluctuations. These observations reveal that a spatially-modulated CDW phase emerges out of a uniform electronic fluid via a process that is promoted by self-amplifying FO, and identify a viable experimental route to distinguish CDW and FO.Comment: 6 pages, 4 figures; supplementary information available upon reques

SR-OOD: Out-of-Distribution Detection via Sample Repairing

It is widely reported that deep generative models can classify out-of-distribution (OOD) samples as in-distribution with high confidence. In this work, we propose a hypothesis that this phenomenon is due to the reconstruction task, which can cause the generative model to focus too much on low-level features and not enough on semantic information. To address this issue, we introduce SR-OOD, an OOD detection framework that utilizes sample repairing to encourage the generative model to learn more than just an identity map. By focusing on semantics, our framework improves OOD detection performance without external data and label information. Our experimental results demonstrate the competitiveness of our approach in detecting OOD samples

Maximum likelihood estimation-assisted ASVSF through state covariance-based 2D SLAM algorithm

The smooth variable structure filter (ASVSF) has been relatively considered as a new robust predictor-corrector method for estimating the state. In order to effectively utilize it, an SVSF requires the accurate system model, and exact prior knowledge includes both the process and measurement noise statistic. Unfortunately, the system model is always inaccurate because of some considerations avoided at the beginning. Moreover, the small addictive noises are partially known or even unknown. Of course, this limitation can degrade the performance of SVSF or also lead to divergence condition. For this reason, it is proposed through this paper an adaptive smooth variable structure filter (ASVSF) by conditioning the probability density function of a measurementto the unknown parameters at one iteration. This proposed method is assumed to accomplish the localization and direct point-based observation task of a wheeled mobile robot, TurtleBot2. Finally, by realistically simulating it and comparing to a conventional method, the proposed method has been showing a better accuracy and stability in term of root mean square error (RMSE) of the estimated map coordinate (EMC) and estimated path coordinate (EPC)

Assessment of the reliability of a novel self-sampling device for performing cervical sampling in Malaysia

Background: The participation of women in cervical cancer screening in Malaysia is low. Self-sampling might be able to overcome this problem. The aim of this study was to assess the reliability of self-sampling for cervical smear in our country. Materials and methods: This cross-sectional study was conducted on 258 community dwelling women from urban and rural settings who participated in health campaigns. In order to reduce the sampling bias, half of the study population performed the self-sampling prior to the physician sampling while the other half performed the self-sampling after the physician sampling, randomly. Acquired samples were assessed for cytological changes as well as HPV DNA detection. Results: The mean age of the subjects was 40.4±11.3 years. The prevalence of abnormal cervical changes was 2.7%. High risk and low risk HPV genotypes were found in 4.0% and 2.7% of the subjects, respectively. A substantial agreement was observed between self-sampling and the physician obtained sampling in cytological diagnosis (k=0.62, 95%CI=0.50, 0.74), micro-organism detection (k=0.77, 95%CI=0.66, 0.88) and detection of hormonal status (k=0.75, 95%CI=0.65, 0.85) as well as detection of high risk (k=0.77, 95%CI=0.4, 0.98) and low risk (K=0.77, 95%CI=0.50, 0.92) HPV. Menopausal state was found to be related with 8.39 times more adequate cell specimens for cytology but 0.13 times less adequate cell specimens for virological assessment. Conclusions: This study revealed that self-sampling has a good agreement with physician sampling in detecting HPV genotypes. Self-sampling can serve as a tool in HPV screening while it may be useful in detecting cytological abnormalities in Malaysia

Learning with Language-Guided State Abstractions

We describe a framework for using natural language to design state abstractions for imitation learning. Generalizable policy learning in high-dimensional observation spaces is facilitated by well-designed state representations, which can surface important features of an environment and hide irrelevant ones. These state representations are typically manually specified, or derived from other labor-intensive labeling procedures. Our method, LGA (language-guided abstraction), uses a combination of natural language supervision and background knowledge from language models (LMs) to automatically build state representations tailored to unseen tasks. In LGA, a user first provides a (possibly incomplete) description of a target task in natural language; next, a pre-trained LM translates this task description into a state abstraction function that masks out irrelevant features; finally, an imitation policy is trained using a small number of demonstrations and LGA-generated abstract states. Experiments on simulated robotic tasks show that LGA yields state abstractions similar to those designed by humans, but in a fraction of the time, and that these abstractions improve generalization and robustness in the presence of spurious correlations and ambiguous specifications. We illustrate the utility of the learned abstractions on mobile manipulation tasks with a Spot robot.Comment: ICLR 202

Ba6RE2Ti4O17 (RE= Nd, Sm,Gd, Dy-Yb): A family of quasi-two-dimensional triangular lattice magnets

Rare-earth-based triangular-lattice magnets provide the fertile ground to explore the exotic quantum magnetic state. Herein, we report a new family of RE-based triangular-lattice magnets Ba6RE2Ti4O17(RE= rare earth ions) crystallized into the hexagonal structure with space group of P63 mmc, where magnetic rare earth ions form an ideal triangular lattice within the ab-plane and stack in an AA -type fashion along the c-axis. The low-temperature magnetic susceptibility results reveal all the serial compounds have the dominant antiferromagnetic interactions and an absence of magnetic ordering down to 1.8 K. The magnetization and electron spin resonance results indicate distinct magnetic anisotropy for the compounds with different RE ions. Moreover, Ba6Nd2Ti4O17 single crystal is successfully grown and it exhibits strong Ising like anisotropy with magnetic easy-axis perpendicular to the triangle-lattice plane, being a candidate to explore quantum spin liquid state with dominant Ising-type interaction.Comment: 18 pages, 8 figure