Spin squeezing of 1011 atoms by prediction and retrodiction measurements.

The measurement sensitivity of quantum probes using N uncorrelated particles is restricted by the standard quantum limit1, which is proportional to [Formula: see text]. This limit, however, can be overcome by exploiting quantum entangled states, such as spin-squeezed states2. Here we report the measurement-based generation of a quantum state that exceeds the standard quantum limit for probing the collective spin of 1011 rubidium atoms contained in a macroscopic vapour cell. The state is prepared and verified by sequences of stroboscopic quantum non-demolition (QND) measurements. We then apply the theory of past quantum states3,4 to obtain spin state information from the outcomes of both earlier and later QND measurements. Rather than establishing a physically squeezed state in the laboratory, the past quantum state represents the combined system information from these prediction and retrodiction measurements. This information is equivalent to a noise reduction of 5.6 decibels and a metrologically relevant squeezing of 4.5 decibels relative to the coherent spin state. The past quantum state yields tighter constraints on the spin component than those obtained by conventional QND measurements. Our measurement uses 1,000 times more atoms than previous squeezing experiments5-10, with a corresponding angular variance of the squeezed collective spin of 4.6 × 10-13 radians squared. Although this work is rooted in the foundational theory of quantum measurements, it may find practical use in quantum metrology and quantum parameter estimation, as we demonstrate by applying our protocol to quantum enhanced atomic magnetometry.

Loss-tolerant and quantum-enhanced interferometer by reversed squeezing processes.

Reversed nonlinear dynamics is predicted to be capable of enhancing the quantum sensing in unprecedented ways. Here, we report the experimental demonstration of a loss-tolerant (external loss) and quantum-enhanced interferometer. Two cascaded optical parametric amplifiers are used to judiciously construct an interferometry with two orthogonal squeezing operation. As a consequence, a weak displacement introduced by a test cavity can be amplified for measurement, and the measured signal-to-noise ratio is better than that of both conventional photon shot-noise limited and squeezed-light assisted interferometers. We further confirm its superior loss-tolerant performance by varying the external losses and comparing with both conventional photon shot-noise limited and squeezed-light assisted configurations, illustrating the potential application in gravitational wave detection.

Estimation precision for a normalized response matrix in linear polarization calibration.

The purpose of polarization calibration is to obtain the response matrix of an instrument such that the subsequent observation data can be corrected. The calibration precision, however, is partially restricted by the noise of the detector. We investigate the precision of the normalized response matrix in the presence of signal-independent additive noise or signal-dependent Poisson shot noise. The influences of the source intensity, type of noise, and calibration configuration on the precision are analyzed. We compare the theoretical model and the experimental measurements of the polarization calibration to show that the relative difference between the two is less than 16%. From this result, we can use the model to determine the minimum source intensity and choose the optimal configurations that provide the required precision.

Evaluation of post-stroke spasticity from the subacute to chronic stages: A clinical and neurophysiologic study of motoneuron pool excitability.

Spasticity measured using clinical scales, such as the modified Ashworth scale (MAS), may not sufficiently evaluate the effectiveness of therapeutic interventions and predict prognosis. This study aimed to compare changes in H-reflex excitability in the spastic and unimpaired upper and lower limbs of patients with acute and chronic stroke. We also investigated the relationship between the degree of spasticity as assessed by the MAS and motor neuron pool excitability with by analyzing H-reflex excitability. Sixty adult patients with a first-ever stroke were recruited for this study. MAS scores were recorded in the post-stroke upper and lower limb muscles. H-reflexes and M-responses of the bilateral flexor carpi radialis and soleus were tested by stimulating the median and tibial nerves. The results showed that both the ratio of the maximal size of the H-reflex (Hmax) to the maximal size of the M-response (Mmax) and the ratio of the developmental slope of H-reflex (Hslp) to that of the M-responses (Mslp) were significantly higher on the spastic side than on the unimpaired side for the upper and lower limbs. In contrast, the ratio of the threshold of the H-reflex (Hth) to the threshold of the M-response (Mth) only showed significant differences between the two sides in the upper limbs. The Hslp/Mslp paretic/non-paretic ratio was increased in patients with MAS scores of 2 or 3 compared to MAS scores of 1 for both the upper and lower limbs, whereas the Hmax/Mmax paretic/non-paretic ratio showed significant differences between MAS scores of 2 or 3 and 1 only in the upper limbs. Moreover, in either the spastic or unimpaired sides, there were no significant differences in any of the three motoneuron pool excitability parameters, Hmax/Mmax, Hslp/Mslp, and Hth/Mth, between the shorter chronicity (time post-stroke ≤6 months) and longer chronicity groups (time post-stroke >6 months) for both the upper and lower limbs. These results suggest that Hslp/Mslp could be a potential neurophysiological indicator for evaluating the degree of spasticity in both the upper and lower limbs of patients with hemiplegia. The MAS and Hslp/Mslp characterize clinical and neurophysiologic spasticity, respectively, and could be used as an integrated approach to evaluate and follow up post-stroke spasticity.

Nonreciprocity and Quantum Correlations of Light Transport in Hot Atoms via Reservoir Engineering.

The breaking of reciprocity is a topic of great interest in fundamental physics and optical information processing applications. We demonstrate nonreciprocal light transport in a quantum system of hot atoms by engineering the dissipative atomic reservoir. Our scheme is based on the phase-sensitive light transport in a multichannel photon-atom interaction configuration, where the phase of collective atomic excitations is tunable through external driving fields. Remarkably, we observe interchannel quantum correlations that originate from interactions with the judiciously engineered reservoir. The nonreciprocal transport in a quantum optical atomic system constitutes a new paradigm for atom-based nonreciprocal optics and offers opportunities for quantum simulations with coupled optical channels.

The incidence, risk factors and predictive nomograms for early death of lung cancer with synchronous brain metastasis: a retrospective study in the SEER database.

BACKGROUND: The prognosis of lung cancer with synchronous brain metastasis (LCBM) is very poor, and patients often die within a short time. However, little is known about the early mortality and related factors in patients with LCBM. METHODS: Patients diagnosed with LCBM between 2010 and 2016 were enrolled from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate logistic regression analysis were used to identify significant independent prognostic factors, which were used to construct nomograms of overall and cancer-specific early death. Then, the prediction ability of the model was verified by receiver operating characteristic (ROC) curve. At last, the clinical application value of the model was tested through decision curve analysis (DCA). RESULTS: A total of 29,902 patients with LCBM were enrolled in this study. Among them, 13,275 (44.4%) patients had early death, and 11,425 (38.2%) cases died of lung cancer. The significant independent risk factors for overall and cancer-specific early death included age, race, gender, Gleason grade, histological type, T stage, N stage, bone metastasis, liver metastasis and marital status, which were used to construct the nomogram. The ROC curve demonstrated good predictive ability and clinical application value. The areas under the curve (AUC) of the training group was 0.793 (95% CI: 0.788-0.799) and 0.794 (95% CI: 0.788-0.799), in the model of overall and cancer-specific early death respectively. And the AUC of the validation group were 0.803 (95% CI: 0.788-0.818) and 0.806 (95% CI: 0.791-0.821), respectively. The calibration plots of the model showed that the predicted early death is consistent with the actual value. The DCA analysis indicated a good clinical application value of this model. CONCLUSIONS: We established a comprehensive nomogram to predict early death in lung cancer patients with synchronous brain metastases. Nomograms may help oncologists develop better treatment strategies, such as clinical trials and hospice care.

Spontaneous phase locking of mechanical multimodes in anti-parity-time optomechanics.

We propose a system for observing the spontaneous phase locking of two frequency separate mechanical modes in an anti-parity-time symmetric optomechanical system. In our approach, a common optical cavity mode mediates the coupling between two phonon modes, leading to the phase locking of the coupled mechanical modes to a common frequency in the symmetry unbroken regime. We furthermore observe the change of quantum correlation near the exceptional point. Our results are also directly relevant to numerous other physical platforms, such as atomic ensembles in cavity quantum electrodynamics (QED) systems and spin interaction mediated by collective motional mode in trapped ions.

Magnetostrictively Induced Stationary Entanglement between Two Microwave Fields.

We present a scheme to entangle two microwave fields by using the nonlinear magnetostrictive interaction in a ferrimagnet. The magnetostrictive interaction enables the coupling between a magnon mode (spin wave) and a mechanical mode in the ferrimagnet, and the magnon mode simultaneously couples to two microwave cavity fields via the magnetic dipole interaction. The magnon-phonon coupling is enhanced by directly driving the ferrimagnet with a strong red-detuned microwave field, and the driving photons are scattered onto two sidebands induced by the mechanical motion. We show that two cavity fields can be prepared in a stationary entangled state if they are, respectively, resonant with two mechanical sidebands. The present scheme illustrates a new mechanism for creating entangled states of optical fields and enables potential applications in quantum information science and quantum tasks that require entangled microwave fields.

Reservoir-Mediated Quantum Correlations in Non-Hermitian Optical System.

Recent advances in non-Hermitian physical systems have led to numerous novel optical phenomena and applications. Such systems typically involve gain and loss associated with dissipative coupling to the environment, hence interesting quantum phenomena are often washed out, rendering most realizations classical. Here, in contrast, we propose to employ dissipative coupling to enable quantum correlations. In particular, two distant optical channels are judiciously designed to couple to and exchange information with a common reservoir environment, under an anti-parity-time-symmetric setting of hot but coherent atoms. We realize a non-Hermitian nonlinear phase sensitive parametric process, where atomic motion leads to quantum correlations between two distant light beams in the symmetry-unbroken phase. This Letter starts a new route to exploring the non-Hermitian quantum phenomena by bridging the fields of atomic physics, non-Hermitian optics, quantum information, and reservoir engineering. Potential applications include novel quantum light sources, quantum information processing and sensing, and generalization to correlated many-body systems.

Dysregulated immunological and metabolic functions discovered by a polygenic integrative analysis for PCOS.

RESEARCH QUESTION: Polycystic ovary syndrome (PCOS) is a complex disease and its pathophysiology is still unclear. This polygenic study may provide some clues. DESIGN: A polygenic, functionome-based study with the ovarian gene expression profiles downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, including 48 PCOS and 181 normal control samples. These profiles were converted to the gene set regularity (GSR) indices, which were computed by the modified differential rank conversion algorithm and were defined by the gene ontology terms. RESULTS: Machine learning could accurately recognize the patterns of functional regularities between PCOS and normal controls. The significantly aberrant functions in PCOS included transporter activity, catalytic activity, the receptor signalling pathway via signal transducer and activator of transcription (STAT), the cellular metabolic process, and immune response. CONCLUSION: This study provided a comprehensive view of the dysregulated functions and information for further studies on the management of PCOS.

Spatial Multiplexing of Squeezed Light by Coherence Diffusion.

Spatially splitting nonclassical light beams is in principle prohibited due to noise contamination during beam splitting. We propose a platform based on thermal motion of atoms to realize spatial multiplexing of squeezed light. Light channels of separate spatial modes in an antirelaxation coated vapor cell share the same long-lived atomic coherence jointly created by all channels through the coherent diffusion of atoms, which in turn enhances the individual channel's nonlinear process responsible for light squeezing. Consequently, it behaves as squeezed light in one optical channel transferring to other distant channels even with laser powers below the threshold for squeezed light generation. An array of squeezed light beams is created with low laser power ∼ milliwatt. This approach holds great promise for applications in a multinode quantum network and quantum enhanced technologies such as quantum imaging and sensing.

Clinicopathological features, diagnosis, and treatment of IgA nephropathy with minimal change disease related to exposure to mercury-containing cosmetics: a case report
.

AIM: Membranous nephropathy and minimal change disease (MCD) have been involved in mercury-induced nephrotic syndrome. IgA nephropathy is not known to be a common pathological type. In the present article, we report a case of IgA nephropathy with MCD following exposure to mercury-containing skin lightening cream. MATERIAL AND METHODS: The patient was a 39-year-old woman who presented with nephrotic syndrome. She had a 6-month history of using as many as 8 kinds of skin-lightening creams, and urinary mercury excretion was high. Renal biopsy revealed IgA nephropathy with MCD. The use of cosmetics was stopped and chelation therapy was given. After 4 courses (1 month) of chelation therapy, there was a complete remission of proteinuria and hematuria, and urine tests remained normal during the 5-year follow-up period. RESULTS AND CONCLUSIONS: The unique clinical and pathological features of IgA nephropathy with MCD had raised the controversial question of whether MCD and IgA deposition are separate entities or a common pathophysiology. Repeated renal biopsy and similar cases were helpful and should be carried out as far as possible.
.

Antimicrobials from Mushrooms for Assuring Food Safety.

The interest in discovering and developing natural antimicrobials has significantly increased due to consumer preferences for foods that are free of chemical preservatives while still microbiologically safe. One of the best sources of natural antimicrobials is certain mushrooms (fungi) as many of them not only have nutraceutical functions but also possess antimicrobial properties. This article reviews the available information on mushroom antimicrobials for food safety control. It includes available resources, extraction procedures, antimicrobial activities, and the status of their applications to food safety. The review indicates that there are great potential benefits to be gained from mushroom antimicrobials in food production, processing, and preservation as a biosolution to meet the increasing demands for food quality and safety.

In-line polarization rotator based on the quantum-optical analogy.

An in-line polarization rotator (PR) is proposed based on the quantum-optical analogy (QOA). The proposed PR possesses an auxiliary E7 liquid crystal (LC) waveguide in the vicinity of the single-mode fiber (SMF) core. Because of the matched core size, the PR demonstrates good compatibility with the established backbone networks which are composed of conventional SMFs. With optimized parameters for the auxiliary waveguide, the PR offers a near 100% polarization conversion efficiency at the 1550 nm band with a bandwidth of ∼30 nm, a length of ∼4625.9 µm with a large tolerance of ∼550 µm, and a tolerance of the input light polarization angle and rotation angle of the E7 LC of ∼π/30 and ∼π/36 rad, respectively. The performance was verified by the full-vector finite-element method. The proposed PR can be easily fabricated based on the existing photonics crystal fiber manufacturing process, making it a potentially inexpensive device for applications in modern communication systems. Moreover, the QOA, compared with the previous supermode-theory design method, allows a designer to consider several waveguides separately. Therefore, various unique characteristics can be met simultaneously which is consistent with the trend of modern fiber design.

Feature Noise Boosts DNN Generalization Under Label Noise.

The presence of label noise in the training data has a profound impact on the generalization of deep neural networks (DNNs). In this study, we introduce and theoretically demonstrate a simple feature noise (FN) method, which directly adds noise to the features of training data and can enhance the generalization of DNNs under label noise. Specifically, we conduct theoretical analyses to reveal that label noise leads to weakened DNN generalization by loosening the generalization bound, and FN results in better DNN generalization by imposing an upper bound on the mutual information between the model weights and the features, which constrains the generalization bound. Furthermore, we conduct a qualitative analysis to discuss the ideal type of FN that obtains good label noise generalization. Finally, extensive experimental results on several popular datasets demonstrate that the FN method can significantly enhance the label noise generalization of state-of-the-art methods. The source codes of the FN method are available on https://github.com/zlzenglu/FN.

Visually Source-Free Domain Adaptation via Adversarial Style Matching.

The majority of existing works explore Unsupervised Domain Adaptation (UDA) with an ideal assumption that samples in both domains are available and complete. In real-world applications, however, this assumption does not always hold. For instance, data-privacy is becoming a growing concern, the source domain samples may be not publicly available for training, leading to a typical Source-Free Domain Adaptation (SFDA) problem. Traditional UDA methods would fail to handle SFDA since there are two challenges in the way: the data incompleteness issue and the domain gaps issue. In this paper, we propose a visually SFDA method named Adversarial Style Matching (ASM) to address both issues. Specifically, we first train a style generator to generate source-style samples given the target images to solve the data incompleteness issue. We use the auxiliary information stored in the pre-trained source model to ensure that the generated samples are statistically aligned with the source samples, and use the pseudo labels to keep semantic consistency. Then, we feed the target domain samples and the corresponding source-style samples into a feature generator network to reduce the domain gaps with a self-supervised loss. An adversarial scheme is employed to further expand the distributional coverage of the generated source-style samples. The experimental results verify that our method can achieve comparative performance even compared with the traditional UDA methods with source samples for training.

A Comprehensive Survey on Source-Free Domain Adaptation.

Over the past decade, domain adaptation has become a widely studied branch of transfer learning which aims to improve performance on target domains by leveraging knowledge from the source domain. Conventional domain adaptation methods often assume access to both source and target domain data simultaneously, which may not be feasible in real-world scenarios due to privacy and confidentiality concerns. As a result, the research of Source-Free Domain Adaptation (SFDA) has drawn growing attention in recent years, which only utilizes the source-trained model and unlabeled target data to adapt to the target domain. Despite the rapid explosion of SFDA work, there has been no timely and comprehensive survey in the field. To fill this gap, we provide a comprehensive survey of recent advances in SFDA and organize them into a unified categorization scheme based on the framework of transfer learning. Instead of presenting each approach independently, we modularize several components of each method to more clearly illustrate their relationships and mechanisms in light of the composite properties of each method. Furthermore, we compare the results of more than 30 representative SFDA methods on three popular classification benchmarks, namely Office-31, Office-home, and VisDA, to explore the effectiveness of various technical routes and the combination effects among them. Additionally, we briefly introduce the applications of SFDA and related fields. Drawing on our analysis of the challenges confronting SFDA, we offer some insights into future research directions and potential settings.

MSFlow: Multiscale Flow-Based Framework for Unsupervised Anomaly Detection.

Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications, where only anomaly-free samples are available for training. Some UAD applications intend to locate the anomalous regions further even without any anomaly information. Although the absence of anomalous samples and annotations deteriorates the UAD performance, an inconspicuous, yet powerful statistics model, the normalizing flows, is appropriate for anomaly detection (AD) and localization in an unsupervised fashion. The flow-based probabilistic models, only trained on anomaly-free data, can efficiently distinguish unpredictable anomalies by assigning them much lower likelihoods than normal data. Nevertheless, the size variation of unpredictable anomalies introduces another inconvenience to the flow-based methods for high-precision AD and localization. To generalize the anomaly size variation, we propose a novel multiscale flow-based framework (MSFlow) composed of asymmetrical parallel flows followed by a fusion flow to exchange multiscale perceptions. Moreover, different multiscale aggregation strategies are adopted for image-wise AD and pixel-wise anomaly localization according to the discrepancy between them. The proposed MSFlow is evaluated on three AD datasets, significantly outperforming existing methods. Notably, on the challenging MVTec AD benchmark, our MSFlow achieves a new state-of-the-art (SOTA) with a detection AUORC score of up to 99.7%, localization AUCROC score of 98.8% and PRO score of 97.1%.

Intrinsic Consistency Preservation With Adaptively Reliable Samples for Source-Free Domain Adaptation.

Unsupervised domain adaptation (UDA) aims to alleviate the domain shift by transferring knowledge learned from a labeled source dataset to an unlabeled target domain. Although UDA has seen promising progress recently, it requires access to data from both domains, making it problematic in source data-absent scenarios. In this article, we investigate a practical task source-free domain adaptation (SFDA) that alleviates the limitations of the widely studied UDA in simultaneously acquiring source and target data. In addition, we further study the imbalanced SFDA (ISFDA) problem, which addresses the intra-domain class imbalance and inter-domain label shift in SFDA. We observe two key issues in SFDA that: 1) target data form clusters in the representation space regardless of whether the target data points are aligned with the source classifier and 2) target samples with higher classification confidence are more reliable and have less variation in their classification confidence during adaptation. Motivated by these observations, we propose a unified method, named intrinsic consistency preservation with adaptively reliable samples (ICPR), to jointly cope with SFDA and ISFDA. Specifically, ICPR first encourages the intrinsic consistency in the predictions of neighbors for unlabeled samples with weak augmentation (standard flip-and-shift), regardless of their reliability. ICPR then generates strongly augmented views specifically for adaptively selected reliable samples and is trained to fix the intrinsic consistency between weakly and strongly augmented views of the same image concerning predictions of neighbors and their own. Additionally, we propose to use a prototype-like classifier to avoid the classification confusion caused by severe intra-domain class imbalance and inter-domain label shift. We demonstrate the effectiveness and general applicability of ICPR on six benchmarks of both SFDA and ISFDA tasks. The reproducible code of our proposed ICPR method is available at https://github.com/CFM-MSG/Code_ICPR.

Semantics Disentangling for Cross-Modal Retrieval.

Cross-modal retrieval (e.g., query a given image to obtain a semantically similar sentence, and vice versa) is an important but challenging task, as the heterogeneous gap and inconsistent distributions exist between different modalities. The dominant approaches struggle to bridge the heterogeneity by capturing the common representations among heterogeneous data in a constructed subspace which can reflect the semantic closeness. However, insufficient consideration is taken into the fact that learned latent representations are actually heavily entangled with those semantic-unrelated features, which obviously further compounds the challenges of cross-modal retrieval. To alleviate the difficulty, this work makes an assumption that the data are jointly characterized by two independent features: semantic-shared and semantic-unrelated representations. The former presents characteristics of consistent semantics shared by different modalities, while the latter reflects the characteristics with respect to the modality yet unrelated to semantics, such as background, illumination, and other low-level information. Therefore, this paper aims to disentangle the shared semantics from the entangled features, andthus the purer semantic representation can promote the closeness of paired data. Specifically, this paper designs a novel Semantics Disentangling approach for Cross-Modal Retrieval (termed as SDCMR) to explicitly decouple the two different features based on variational auto-encoder. Next, the reconstruction is performed by exchanging shared semantics to ensure the learning of semantic consistency. Moreover, a dual adversarial mechanism is designed to disentangle the two independent features via a pushing-and-pulling strategy. Comprehensive experiments on four widely used datasets demonstrate the effectiveness and superiority of the proposed SDCMR method by achieving a new bar on performance when compared against 15 state-of-the-art methods.