Adversarial Variational Embedding for Robust Semi-supervised Learning

Semi-supervised learning is sought for leveraging the unlabelled data when labelled data is difficult or expensive to acquire. Deep generative models (e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial Networks (GANs) have recently shown promising performance in semi-supervised classification for the excellent discriminative representing ability. However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance. In particular, the learned latent representation depends on a non-exclusive component which is stochastically sampled from the prior distribution. Moreover, the semi-supervised GAN models generate data from pre-defined distribution (e.g., Gaussian noises) which is independent of the input data distribution and may obstruct the convergence and is difficult to control the distribution of the generated data. To address the aforementioned issues, we propose a novel Adversarial Variational Embedding (AVAE) framework for robust and effective semi-supervised learning to leverage both the advantage of GAN as a high quality generative model and VAE as a posterior distribution learner. The proposed approach first produces an exclusive latent code by the model which we call VAE++, and meanwhile, provides a meaningful prior distribution for the generator of GAN. The proposed approach is evaluated over four different real-world applications and we show that our method outperforms the state-of-the-art models, which confirms that the combination of VAE++ and GAN can provide significant improvements in semisupervised classification.Comment: 9 pages, Accepted by Research Track in KDD 201

Universal quantum dynamics of Bose polarons

Predicting the emergent properties of impurities immersed in a quantum bath is a fundamental challenge that can defy quasiparticle treatments. Here, we measure the spectral properties and real-time dynamics of mobile impurities injected into a homogeneous Bose--Einstein condensate, using two Feshbach resonances to tune both the impurity-bath and intrabath interactions. We map out both attractive and repulsive branches of polaron quasiparticles, resolving the repulsive polaron and the molecular state associated with the Feshbach resonance in the strongly interacting regime, and show that the latter also has a many-body character. Our measurements reveal remarkably universal behavior, controlled by the bath density and a single dimensionless interaction parameter; for near-resonant interactions the polarons are no longer well defined, but the universality still holds.Comment: Main text (6 pages, 5 figures), Supplementary Material (4 pages, 8 figures

Observation of subdiffusive dynamic scaling in a driven and disordered box-trapped Bose gas

We explore the dynamics of a tuneable box-trapped Bose gas under strong periodic forcing in the presence of weak disorder. In absence of interparticle interactions, the interplay of the drive and disorder results in an isotropic nonthermal momentum distribution that shows subdiffusive dynamic scaling, with sublinear energy growth and the universal scaling function captured well by a compressed exponential. For increasing interaction strength, the gas behavior crosses over to wave turbulence characterized by a power-law momentum distribution.Comment: Main text (4 pages, 4 figures), Supplemental Material (2 pages, 4 figures

Realizing spin squeezing with Rydberg interactions in a programmable optical clock

Neutral-atom arrays trapped in optical potentials are a powerful platform for studying quantum physics, combining precise single-particle control and detection with a range of tunable entangling interactions. For example, these capabilities have been leveraged for state-of-the-art frequency metrology as well as microscopic studies of entangled many-particle states. In this work, we combine these applications to realize spin squeezing - a widely studied operation for producing metrologically useful entanglement - in an optical atomic clock based on a programmable array of interacting optical qubits. In this first demonstration of Rydberg-mediated squeezing with a neutral-atom optical clock, we generate states that have almost 4 dB of metrological gain. Additionally, we perform a synchronous frequency comparison between independent squeezed states and observe a fractional frequency stability of $1.087(1)\times 10^{-15}$ at one-second averaging time, which is 1.94(1) dB below the standard quantum limit, and reaches a fractional precision at the $10^{-17}$ level during a half-hour measurement. We further leverage the programmable control afforded by optical tweezer arrays to apply local phase shifts in order to explore spin squeezing in measurements that operate beyond the relative coherence time with the optical local oscillator. The realization of this spin-squeezing protocol in a programmable atom-array clock opens the door to a wide range of quantum-information inspired techniques for optimal phase estimation and Heisenberg-limited optical atomic clocks.Comment: 13 pages, 4 figures; Supplementary Informatio

Interaction-driven breakdown of dynamical localization in a kicked quantum gas

Quantum interference can terminate energy growth in a continually kicked system, via a single-particle ergodicity-breaking mechanism known as dynamical localization. The effect of many-body interactions on dynamically localized states, while important to a fundamental understanding of quantum decoherence, has remained unexplored despite a quarter-century of experimental studies. We report the experimental realization of a tunably-interacting kicked quantum rotor ensemble using a Bose-Einstein condensate in a pulsed optical lattice. We observe signatures of a prethermal localized plateau, followed for interacting samples by interaction-induced anomalous diffusion with an exponent near one half. Echo-type time reversal experiments establish the role of interactions in destroying reversibility. These results quantitatively elucidate the dynamical transition to many-body quantum chaos, advance our understanding of quantum anomalous diffusion, and delimit some possibilities for protecting quantum information in interacting driven systems.Comment: 17 pages including supp inf

Assessing the value of omalizumab for pediatric asthma in China : a multicriteria decision analysis

Background/Objectives: This study aimed to apply a multicriteria decision analysis to assess the comprehensive value of omalizumab for moderate to severe pediatric asthma in China. Methods: A multidisciplinary panel of 17 experts assessed the value of omalizumab plus the standard of care (SOC) using SOC alone as a comparator. We developed a hierarchical criteria system with six main domains and 15 specific criteria. To establish a comprehensive evidence matrix, we integrated findings from a systematic literature review (SLR) and a real-world pharmacovigilance study based on the FAERS database. The overall estimated value of each strategy was obtained by combining the criterion weights with the score of each strategy in each criterion. A sensitivity analysis was conducted to validate the robustness of the results. Results: According to the AHP methods, the following weights were assigned to the criteria: safety (38.55%), effectiveness (28.85%), economics (9.65%), innovation (8.24%), accessibility (7.84%), and applicability (6.88%). Based on the evidence matrix, omalizumab plus SOC scored higher than the SOC in effectiveness (2.53 vs. 1.94) and innovation (0.70 vs. 0.15). When the weight and score of each strategy in each criterion were combined, the overall estimated values were 7.40 points for omalizumab plus SOC and 7.19 points for SOC. Conclusions: Adding omalizumab was assessed as a conditionally recommended strategy for treating moderate to severe asthma in Chinese children

mTOR signaling in VIP neurons regulates circadian clock synchrony and olfaction

Mammalian/mechanistic target of rapamycin (mTOR) signaling controls cell growth, proliferation, and metabolism in dividing cells. Less is known regarding its function in postmitotic neurons in the adult brain. Here we created a conditional mTOR knockout mouse model to address this question. Using the Cre-LoxP system, the mTOR gene was specifically knocked out in cells expressing Vip (vasoactive intestinal peptide), which represent a major population of interneurons widely distributed in the neocortex, suprachiasmatic nucleus (SCN), olfactory bulb (OB), and other brain regions. Using a combination of biochemical, behavioral, and imaging approaches, we found that mice lacking mTOR in VIP neurons displayed erratic circadian behavior and weakened synchronization among cells in the SCN, the master circadian pacemaker in mammals. Furthermore, we have discovered a critical role for mTOR signaling in mediating olfaction. Odor stimulated mTOR activation in the OB, anterior olfactory nucleus, as well as piriform cortex. Odor-evoked c-Fos responses along the olfactory pathway were abolished in mice lacking mTOR in VIP neurons, which is consistent with reduced olfactory sensitivity in these animals. Together, these results demonstrate that mTOR is a key regulator of SCN circadian clock synchrony and olfaction

Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors

Abstract. The Internet of Things is a multi-sensor technology with the unique advantage of supporting non-intrusive and non-device occupancy detection, while also allowing us to explore new forecasting occupancy models. However, forecasting occupancy presence is not a trivial task, since it is still unknown the main criteria in selecting a forecasting modelling approach according to a non-intrusive sensing strategy. Towards this challenge, this paper proposes an analytical workflow developed to support the Prophet model for forecasting occupancy presence in indoor spaces throughout the tasks of sensing, processing, and analysing event triggered data generated from ten non-intrusive sensors, including motion, temperature, luminosity, CO2, TVOC, sound, pressure, accelerometer, gyroscope, and humidity sensors. The usefulness of this analytical workflow is demonstrated with the implementation of an IoT platform for an experiment operating non-intrusive sensing in a classroom. The assessment is made at different time intervals and the results confirm that there is a relationship between the event-count and occupancy presence in such a way that the larger the number of events triggered in an indoor space, the higher the probability of an indoor space being occupied. </jats:p

Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors

The Internet of Things is a multi-sensor technology with the unique advantage of supporting non-intrusive and non-device occupancy detection, while also allowing us to explore new forecasting occupancy models. However, forecasting occupancy presence is not a trivial task, since it is still unknown the main criteria in selecting a forecasting modelling approach according to a non-intrusive sensing strategy. Towards this challenge, this paper proposes an analytical workflow developed to support the Prophet model for forecasting occupancy presence in indoor spaces throughout the tasks of sensing, processing, and analysing event triggered data generated from ten non-intrusive sensors, including motion, temperature, luminosity, CO2, TVOC, sound, pressure, accelerometer, gyroscope, and humidity sensors. The usefulness of this analytical workflow is demonstrated with the implementation of an IoT platform for an experiment operating non-intrusive sensing in a classroom. The assessment is made at different time intervals and the results confirm that there is a relationship between the event-count and occupancy presence in such a way that the larger the number of events triggered in an indoor space, the higher the probability of an indoor space being occupied