Energy average formula of photon gas rederived by using the generalized Hermann-Feynman theorem

By virtue of the generalized Hermann-Feynmam theorem and the method of characteristics we rederive energy average formula of photon gas, this is another useful application of the theorem.Comment: 2 page

Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems

Calibration is defined as the ratio of the average predicted click rate to the true click rate. The optimization of calibration is essential to many online advertising recommendation systems because it directly affects the downstream bids in ads auctions and the amount of money charged to advertisers. Despite its importance, calibration optimization often suffers from a problem called "maximization bias". Maximization bias refers to the phenomenon that the maximum of predicted values overestimates the true maximum. The problem is introduced because the calibration is computed on the set selected by the prediction model itself. It persists even if unbiased predictions can be achieved on every datapoint and worsens when covariate shifts exist between the training and test sets. To mitigate this problem, we theorize the quantification of maximization bias and propose a variance-adjusting debiasing (VAD) meta-algorithm in this paper. The algorithm is efficient, robust, and practical as it is able to mitigate maximization bias problems under covariate shifts, neither incurring additional online serving costs nor compromising the ranking performance. We demonstrate the effectiveness of the proposed algorithm using a state-of-the-art recommendation neural network model on a large-scale real-world dataset

Photon Number-Phase Uncertainty Relation in the Evolution of the Field in a Kerr-Like Medium

A model of a single-mode field, initially prepared in a coherent state, coupled to a two-level atom surrounded by a nonlinear Kerr-like medium contained inside a very good quality cavity is considered. We derive the photon number-phase uncertainty relation in the evolution of the field for a weak and strong nonlinear coupling respectively, within the Hermitian phase operator formalism of Pegg and Barnett, and discuss the effects of nonlinear coupling of the Kerr-like medium on photon number-phase uncertainty relation of the field

Distributional Robust Batch Contextual Bandits

Policy learning using historical observational data is an important problem that has found widespread applications. Examples include selecting offers, prices, advertisements to send to customers, as well as selecting which medication to prescribe to a patient. However, existing literature rests on the crucial assumption that the future environment where the learned policy will be deployed is the same as the past environment that has generated the data--an assumption that is often false or too coarse an approximation. In this paper, we lift this assumption and aim to learn a distributional robust policy with incomplete (bandit) observational data. We propose a novel learning algorithm that is able to learn a robust policy to adversarial perturbations and unknown covariate shifts. We first present a policy evaluation procedure in the ambiguous environment and then give a performance guarantee based on the theory of uniform convergence. Additionally, we also give a heuristic algorithm to solve the distributional robust policy learning problems efficiently.Comment: The short version has been accepted in ICML 202

The Quantum Phase-Dynamical Properties of the Squeezed Vacuum State Intensity-Couple Interacting with the Atom

The Phase-dynamical properties of the squeezed vacuum state intensity-couple interacting with the two-level atom in an ideal cavity are studied using the Hermitian phase operator formalism. Exact general expressions for the phase distribution and the associated expectation value and variance of the phase operator have been derived. we have also obtained the analytic results of the phase variance for two special cases-weakly and strongly squeezed vacuum. The results calculated numerically show that squeezing has a significant effect on the phase properties of squeezed vacuum

SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation

Accurate medical image segmentation especially for echocardiographic images with unmissable noise requires elaborate network design. Compared with manual design, Neural Architecture Search (NAS) realizes better segmentation results due to larger search space and automatic optimization, but most of the existing methods are weak in layer-wise feature aggregation and adopt a ``strong encoder, weak decoder" structure, insufficient to handle global relationships and local details. To resolve these issues, we propose a novel semi-supervised hybrid NAS network for accurate medical image segmentation termed SSHNN. In SSHNN, we creatively use convolution operation in layer-wise feature fusion instead of normalized scalars to avoid losing details, making NAS a stronger encoder. Moreover, Transformers are introduced for the compensation of global context and U-shaped decoder is designed to efficiently connect global context with local features. Specifically, we implement a semi-supervised algorithm Mean-Teacher to overcome the limited volume problem of labeled medical image dataset. Extensive experiments on CAMUS echocardiography dataset demonstrate that SSHNN outperforms state-of-the-art approaches and realizes accurate segmentation. Code will be made publicly available.Comment: Submitted to ICASSP202

Salient Object Detection via Integrity Learning

Albeit current salient object detection (SOD) works have achieved fantastic progress, they are cast into the shade when it comes to the integrity of the predicted salient regions. We define the concept of integrity at both the micro and macro level. Specifically, at the micro level, the model should highlight all parts that belong to a certain salient object, while at the macro level, the model needs to discover all salient objects from the given image scene. To facilitate integrity learning for salient object detection, we design a novel Integrity Cognition Network (ICON), which explores three important components to learn strong integrity features. 1) Unlike the existing models that focus more on feature discriminability, we introduce a diverse feature aggregation (DFA) component to aggregate features with various receptive fields (i.e.,, kernel shape and context) and increase the feature diversity. Such diversity is the foundation for mining the integral salient objects. 2) Based on the DFA features, we introduce the integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects at the macro level, while suppressing the other distracting ones. 3) After extracting the enhanced features, the part-whole verification (PWV) method is employed to determine whether the part and whole object features have strong agreement. Such part-whole agreements can further improve the micro-level integrity for each salient object. To demonstrate the effectiveness of ICON, comprehensive experiments are conducted on seven challenging benchmarks, where promising results are achieved

Ahmed glaucoma valve implantation treatment of traumatic angle recession glaucoma

AIM: To study the influence of Ahmed glaucoma valve implantation on eyesight, intraocular pressure and corneal endothelial cell density of patients with traumatic angle recession glaucoma. METHODS: Totally 33 cases(35 eyes)of patients with traumatic angle recession glaucoma admitted to our hospital since June 2014 to June 2016 were selected and treated with Ahmed glaucoma valve implantation. The clinical data of all patients were retrospectively analyzed, so as to evaluated to success rate of surgery. Non-contact tonometer was applied to surveying intraocular pressure before treatment and at 1wk, 1,3,6mo and 1a post treatment. Specular microscope was adopted to examine and calculate the corneal endothelial cell density before treatment and at 1wk, 1,3,6mo and 1a post treatment. All affected eyes were compared for visual acuity before surgery and in 1a after surgery, moreover, patients were followed-up, received the further consultations and the complications were recorded. RESULTS: As for 35 affected eyes, the absolute success rate of surgery was 54%, while the relative success rate was 40%, and the total successful rate and failure rate were 94% and 6% respectively. In terms of the number of people who had no light sensation before surgery, or who had light sensation, ≤0.01, 0.01-0.10 or >0.10-0.20, there was no significant difference(Z=-0.132, P=0.362). The intraocular pressure before treatment was 43.43±3.65mmHg, at 1wk after surgery was 13.50±2.54mmHg, at 1mo was 15.93±2.61mmHg,at 6mo was 16.00±2.18mmHg and at 1a was 16.45±2.21mmHg, and the difference among different time points had statistical significance(F=887.82, PPP>0.05). Before treatment, the corneal endothelial cell density was 2443.35±343.12 pieces/mm2, in 1wk after the surgery was 2231.67±334.45 pieces /mm2, in 1mo after the surgery was 2065.47±336.45 pieces /mm2, in 3mo after surgery was 2031.47±345.76 pieces/mm2, in 6mo was 2001.72±337.18 pieces /mm2 and in 1a after the surgery was 1979.65±301.32 pieces /mm2, and the difference among different time points had statistical significance(F=13.49, PPP>0.05). After surgery, there were 4 cases(4 eyes)of ocular hypotension, 3 cases(3 eyes)of hyphema, 2 cases(2 eyes)of drainage tube plugging and 2 cases(2 eyes)of intraocular hypertension, which were all quickly relieved after basic intervention treatment. CONCLUSION: Treating traumatic angle recession glaucoma with Ahmed glaucoma valve implantation can dramatically optimize the state of intraocular hypertension and protect the retaining visual acuity, and visual acuity can be optimized in some cases. It causes little complication that can be relieved with basic prognosis, but postoperative corneal endothelial cell loss exists in some cases