Search

Breakthrough innovation is the focus of the society in the era of knowledge economy. Employee innovation of the enterprises is the starting point of enterprise innovation behavior, and it is the result of the combination of complex psychological capital. Meanwhile, breakthrough innovation often comes from the result of knowledge sharing brought by teamwork. At present, existing studies mainly reveal the influence of knowledge and knowledge structure on the performance of radical innovation. However, the relationship between psychological capital, knowledge sharing and the breakthrough innovation performance needs to be systematically studied. Therefore, this study adopted a research approach, that is, statistical analyses were performed by using SPSS Version 18 and AMOS version 26 (Statistical analyses performed by using SPSS Version 18 and AMOS version 26).This study collected data on employees of 345 different new high-tech enterprises to explore the mechanism by which psychological capital and knowledge sharing affects the breakthrough innovation performance. The research results respond to a positive correlation between psychological capital and knowledge sharing affects the breakthrough innovation performance. Moreover, knowledge sharing has a mediating effect on the effect of psychological capital on breakthrough innovation performance, and the effect is weakened. which is of great theoretical significance for exploring the relationship between psychological capital and knowledge sharing affects the breakthrough innovation performance.

Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdeveloped. In this paper, we collaborate with one of the world's largest OMHC to develop an agent-based simulation framework and explore the trade-offs in different matching algorithms. The simulation framework allows us to compare current mechanisms and new algorithmic matching policies on the platform, and observe their differing effects on a variety of outcome metrics. Our findings include that usage of the deferred-acceptance algorithm can significantly better the experiences of support-seekers in one-on-one chats while maintaining low waiting time. We note key design considerations that agent-based modeling reveals in the OMHC context, including the potential benefits of algorithmic matching on marginalized communities.

Entanglement entropy of typical quantum states, also known as the Page curve, plays an important role in quantum many-body systems and quantum gravity. However, little has hitherto been understood about the role of symmetry in quantum entanglement. Here, we establish the comprehensive classification of typical quantum entanglement for free fermions, or equivalently the quadratic Sachdev-Ye-Kitaev model with symmetry, on the basis of the tenfold fundamental symmetry classes of time reversal, charge conjugation, and chiral transformation. Through both analytical and numerical calculations of random matrix theory, we show that the volume-law contribution to average entanglement entropy is robust and remains unaffected by symmetry. Conversely, we uncover that the constant terms of the average and variance of entanglement entropy yield tenfold universal values unique to each symmetry class. These constant terms originate from the combination of a global scaling of the entanglement spectrum due to time-reversal symmetry and a singular peak at the center of the entanglement spectrum due to chiral or particle-hole symmetry. Our work elucidates the interplay of symmetry and entanglement in quantum physics and provides characterization of symmetry-enriched quantum chaos., Comment: 7+29 pages, 2+4 figures, 1+4 tables

Language models (LMs) are pretrained on diverse data sources, including news, discussion forums, books, and online encyclopedias. A significant portion of this data includes opinions and perspectives which, on one hand, celebrate democracy and diversity of ideas, and on the other hand are inherently socially biased. Our work develops new methods to (1) measure political biases in LMs trained on such corpora, along social and economic axes, and (2) measure the fairness of downstream NLP models trained on top of politically biased LMs. We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks. Our findings reveal that pretrained LMs do have political leanings that reinforce the polarization present in pretraining corpora, propagating social biases into hate speech predictions and misinformation detectors. We discuss the implications of our findings for NLP research and propose future directions to mitigate unfairness., Comment: ACL 2023

The multipartite entanglement structure for the ground states of two dimensional topological phases is an interesting albeit not well understood question. Utilizing the bulk-boundary correspondence, the calculation of tripartite entanglement in 2d topological phases can be reduced to that of the vertex state, defined by the boundary conditions at the interfaces between spatial regions. In this paper, we use the conformal interface technique to calculate entanglement measures in the vertex state, which include area law terms, corner contributions, and topological pieces, and a possible additional order one contribution. This explains our previous observation of the Markov gap $h = \frac{c}{3} \ln 2$ in the 3-vertex state, and generalizes this result to the $p$-vertex state, general rational conformal field theories, and more choices of subsystems. Finally, we support our prediction by numerical evidence, finding precise agreement., Comment: 22 pages, 7 figures

BACKGROUND The use of chemical insecticides to control Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) is widespread, although it might exert a sublethal effect on its dominant parasitoid, Encarsia formosa Gahan (Hymenoptera: Aphelinidae). To investigate the sublethal effect of spirotetramat on E. formosa, we observed the ability of E. formosa to locate and handle the host, oviposit and preen after exposure to sublethal concentrations of spirotetramat. RESULTS After exposure to spirotetramat at LC50 , the response time of E. formosa to the volatile reached 223.40 s and was significantly prolonged. Only 56.44% of the wasps were attracted by the volatile and the insect crawled the slowest among all of the treatments. The averages of oviposition posture adopted and host handled by each E. formosa in 1 h decreased significantly to 1.79 and 1.27, respectively. At the sublethal concentration of LC10 , 94.59% of the wasps were attracted by the volatile and the insect crawled the fastest. The average of host handled by each E. formosa was 3.92, and the frequency of drumming while walking and drumming the host was 12.34 times per second and 12.30 times per second, respectively, demonstrating a significant acceleration in these abilities. CONCLUSION These findings demonstrate that spirotetramat induced hormesis in E. formosa on exposure to its LC10 concentration and accelerated its host locating, host handling and frequency of antennae drumming. These findings could assist in balancing the chemical and biological control of B. tabaci and enhancing the efficacy of E. formosa as a biocontrol agent.

In this work, copper (Cu2+) substituted zinc ferrite (CuxZn1-xFe2O4 (0 ≤ x ≤ 1)) hollow micro-nanospheres were synthesized using a facile solvothermal and annealing technology. We investigated the effects of Cu2+ substitution on morphology, structure and gas sensing properties of CuxZn1-xFe2O4 hollow micro-nanospheres. The results confirmed the successful substitution of Zn2+ with Cu2+ and the cations redistribution. In addition, the testing results revealed that CuxZn1-xFe2O4 (0.25 ≤ x ≤ 1) based sensors showed significantly enhanced responses to low concentration acetone vapor. Especially, Cu0.75Zn0.25Fe2O4 and CuFe2O4 displayed high responses of 2.37 and 2.43 to 0.8 ppm acetone at 125 °C respectively, while that of ZnFe2O4 was only 1.17. Moreover, CuFe2O4 demonstrated an excellent sensor response, ultra-low limit of detection and remarkable selectivity. The fast response speed and high stability of CuFe2O4 sensor further indicated that it was promising to apply for practical medical diagnosis. The enhanced sensing properties of Cu0.75Zn0.25Fe2O4 and CuFe2O4 sensors were explained by the effect of Cu2+ on lattice cation distribution, electron depletion layer thickness and adsorbing capacity.

Introduction: Lower urinary tract symptoms (LUTS) involve benign prostatic hyperplasia (BPH) and overactive bladder (OAB). Standard-of-care medical treatment includes α1-blockers and antimuscarinics for reduction of prostate and detrusor smooth muscle tone, respectively, and 5α-reductase inhibitors (5-ARI) to prevent prostate growth. Current medications are marked by high discontinuation rates due to unfavourable balance between efficacy and treatment-limiting side effects, ranging from dry mouth for antimuscarinics to cardiovascular dysregulation and a tendency to fall for α1-blockers, which results from hypotension, due to vasorelaxation. Agonist-induced smooth muscle contractions are caused by activation of receptor-coupled G-proteins. However, little is known about receptor- and organ-specific differences in coupling to G-proteins. With YM-254890, a small molecule inhibitor with presumed specificity for Gαq/11 became recently available. Here, we investigated effects of YM-254890 on prostate, bladder and vascular smooth muscle contraction, and on growth-related functions in prostate stromal cells.Methods: Contractions of human prostate and detrusor tissues, porcine renal and coronary arteries were induced in an organ bath. Proliferation (EdU assay), growth (colony formation), apoptosis and cell death (flow cytometry), viability (CCK-8) and actin organization (phalloidin staining) were studied in cultured human prostate stromal cells (WPMY-1).Results: Contractions by α1-adrenergic agonists, U46619, endothelin-1, and neurogenic contractions were nearly completely inhibited by YM-254890 (30 nM) in prostate tissues. Contractions by cholinergic agonists, U46619, endothelin-1, and neurogenic contractions were only partly inhibited in detrusor tissues. Contractions by α1-adrenergic agonists, U46619, endothelin-1, and neurogenic contractions were strongly, but not fully inhibited in renal arteries. Contractions by cholinergic agonists were completely, but by U46619 and endothelin-1 only strongly inhibited, and neurogenic contractions reduced by half in coronary arteries. YM-254890 had no effect on agonist-independent contractions induced by highmolar (80 mM) potassium chloride (KCl). Neurogenic detrusor contractions were fully sensitive to tetrodotoxin. In WPMY-1 cells, YM-254890 caused breakdown of actin polymerization and organization, and obvious, but clearly limited decreases of proliferation rate, colony formation and viability, and slightly increased apoptosis.Conclusion: Intracellular post-receptor signaling pathways are shared by Gαq-coupled contractile receptors in multiple smooth muscle-rich organs, but to different extent. While inhibition of Gαq/11 causes actin breakdown, anti-proliferative effects were detectable but clearly limited. Together this may aid in developing future pharmaceutical targets for LUTS and antihypertensive medication.

The spectacular successes of recurrent neural network models where key parameters are adjusted via backpropagation-based gradient descent have inspired much thought as to how biological neuronal networks might solve the corresponding synaptic credit assignment problem. There is so far little agreement, however, as to how biological networks could implement the necessary backpropagation through time, given widely recognized constraints of biological synaptic network signaling architectures. Here, we propose that extra-synaptic diffusion of local neuromodulators such as neuropeptides may afford an effective mode of backpropagation lying within the bounds of biological plausibility. Going beyond existing temporal truncation-based gradient approximations, our approximate gradient-based update rule, ModProp, propagates credit information through arbitrary time steps. ModProp suggests that modulatory signals can act on receiving cells by convolving their eligibility traces via causal, time-invariant and synapse-type-specific filter taps. Our mathematical analysis of ModProp learning, together with simulation results on benchmark temporal tasks, demonstrate the advantage of ModProp over existing biologically-plausible temporal credit assignment rules. These results suggest a potential neuronal mechanism for signaling credit information related to recurrent interactions over a longer time horizon. Finally, we derive an in-silico implementation of ModProp that could serve as a low-complexity and causal alternative to backpropagation through time., Comment: NeurIPS 2022 Camera Ready

We study discrete distribution estimation under user-level local differential privacy (LDP). In user-level $\varepsilon$-LDP, each user has $m\ge1$ samples and the privacy of all $m$ samples must be preserved simultaneously. We resolve the following dilemma: While on the one hand having more samples per user should provide more information about the underlying distribution, on the other hand, guaranteeing the privacy of all $m$ samples should make the estimation task more difficult. We obtain tight bounds for this problem under almost all parameter regimes. Perhaps surprisingly, we show that in suitable parameter regimes, having $m$ samples per user is equivalent to having $m$ times more users, each with only one sample. Our results demonstrate interesting phase transitions for $m$ and the privacy parameter $\varepsilon$ in the estimation risk. Finally, connecting with recent results on shuffled DP, we show that combined with random shuffling, our algorithm leads to optimal error guarantees (up to logarithmic factors) under the central model of user-level DP in certain parameter regimes. We provide several simulations to verify our theoretical findings., Comment: 26 pages, 4 figures

We study the problem of histogram estimation under user-level differential privacy, where the goal is to preserve the privacy of all entries of any single user. We consider the heterogeneous scenario where the quantity of data can be different for each user. In this scenario, the amount of noise injected into the histogram to obtain differential privacy is proportional to the maximum user contribution, which can be amplified by few outliers. One approach to circumvent this would be to bound (or limit) the contribution of each user to the histogram. However, if users are limited to small contributions, a significant amount of data will be discarded. In this work, we propose algorithms to choose the best user contribution bound for histogram estimation under both bounded and unbounded domain settings. When the size of the domain is bounded, we propose a user contribution bounding strategy that almost achieves a two-approximation with respect to the best contribution bound in hindsight. For unbounded domain histogram estimation, we propose an algorithm that is logarithmic-approximation with respect to the best contribution bound in hindsight. This result holds without any distribution assumptions on the data. Experiments on both real and synthetic datasets verify our theoretical findings and demonstrate the effectiveness of our algorithms. We also show that clipping bias introduced by bounding user contribution may be reduced under mild distribution assumptions, which can be of independent interest., Comment: 32 pages, ICML 2023

A good empathetic dialogue system should first track and understand a user's emotion and then reply with an appropriate emotion. However, current approaches to this task either focus on improving the understanding of users' emotion or on proposing better responding strategies, and very few works consider both at the same time. Our work attempts to fill this vacancy. Inspired by task-oriented dialogue systems, we propose a novel empathetic response generation model with emotion-aware dialogue management. The emotion-aware dialogue management contains two parts: (1) Emotion state tracking maintains the current emotion state of the user and (2) Empathetic dialogue policy selection predicts a target emotion and a user's intent based on the results of the emotion state tracking. The predicted information is then used to guide the generation of responses. Experimental results show that dynamically managing different information can help the model generate more empathetic responses compared with several baselines under both automatic and human evaluations.

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations., Comment: arXiv admin note: substantial text overlap with arXiv:2112.11667

To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurrent neural networks (RNNs). Yet, beyond task accuracy, it is unclear if such learning rules converge to solutions that exhibit different levels of generalization than their nonbiologically-plausible counterparts. Leveraging results from deep learning theory based on loss landscape curvature, we ask: how do biologically-plausible gradient approximations affect generalization? We first demonstrate that state-of-the-art biologically-plausible learning rules for training RNNs exhibit worse and more variable generalization performance compared to their machine learning counterparts that follow the true gradient more closely. Next, we verify that such generalization performance is correlated significantly with loss landscape curvature, and we show that biologically-plausible learning rules tend to approach high-curvature regions in synaptic weight space. Using tools from dynamical systems, we derive theoretical arguments and present a theorem explaining this phenomenon. This predicts our numerical results, and explains why biologically-plausible rules lead to worse and more variable generalization properties. Finally, we suggest potential remedies that could be used by the brain to mitigate this effect. To our knowledge, our analysis is the first to identify the reason for this generalization gap between artificial and biologically-plausible learning rules, which can help guide future investigations into how the brain learns solutions that generalize., Comment: NeurIPS 2022 Camera Ready Version

Additional file 1. List of abbreviations, the vaccination campaign in China and the supplementary Figure 1 for the total confirmed COVID-19 cases as of 7 Feb 2022 in the five major areas involved in the current study.

China has announced achieving a carbon peak before 2030 and neutralization by 2060. Cross-area UHVDC transmission provides a feasible way for the new energy base to transmit power to the load center on a large scale. Usually, the dispatching of coal power units of DC transmission line power base is divided according to the equal proportion of unit capacity, this paper takes all the units as a group and proposes a unit commitment solution to realize lower carbon emission. As the capacity of new energy in the power base increases, this paper also considers the generation margin for new energy to do the optimization simulation. The results show that by operating coal-fired units as a group, it operates in a more economic and environmentally friendly way.

With the increasing of building forms and the improving of technology, the curved surfaces as a type of free morphologies are adopted by many architects. A new multi-objective optimized workflow which inserted the Genetic algorithm and the Pareto Optimality is introduced to assist architects improving their design of curved surfaces in the early scheme phase. The workflow can optimize energy utilization and stress distribution by adjusting the parameters of the curved surface, after the initial design about aesthetics and function. To realize the purpose, two searching processes must be operate in the workflow. One process is to find the fittest form to collect the sunlight by laying thin film photovoltaic, the other process is to construct the low strain energy structure at the same time. A standardized case will be executed throughout the whole process to prove its feasibility. The integration of radiance analysis and structure analysis for the buildings in the initial design process can help architects meeting the requirements of energy saving and structure stabilizing.

Crystalline rubrene films were fabricated by polymer-assisted layer in a sequential solution deposition process. Controllable crystal growth was achieved through varying the solution concentration of the polystyrene layers of the bilayer thin films. And the transitions from dendrites to spherulites were observed. The formation mechanism of rubrene crystals was proposed. Phototransistors based on rubrene thin film modified by PS-assisted layer showed a mobility of 1.67 × 10−3 cm2/Vs, a photoresponsivity of 4.7 μA/W, and a photosensitivity of 6900 under 200 mW/cm2 light power conditions. This process provides a valuable platform for studying the organic crystal growth and its photoelectric properties.

During the COVID-19 pandemic, people started to discuss about pandemic-related topics on social media. On subreddit \textit{r/COVID19positive}, a number of topics are discussed or being shared, including experience of those who got a positive test result, stories of those who presumably got infected, and questions asked regarding the pandemic and the disease. In this study, we try to understand, from a linguistic perspective, the nature of discussions on the subreddit. We found differences in linguistic characteristics (e.g. psychological, emotional and reasoning) across three different categories of topics. We also classified posts into the different categories using SOTA pre-trained language models. Such classification model can be used for pandemic-related research on social media., Comment: 5 pages, 4 figures, coursework for DS-GA 1011

Deep Neural Networks (DNNs) are witnessing increased adoption in multiple domains owing to their high accuracy in solving real-world problems. However, this high accuracy has been achieved by building deeper networks, posing a fundamental challenge to the low latency inference desired by user-facing applications. Current low latency solutions trade-off on accuracy or fail to exploit the inherent temporal locality in prediction serving workloads. We observe that caching hidden layer outputs of the DNN can introduce a form of late-binding where inference requests only consume the amount of computation needed. This enables a mechanism for achieving low latencies, coupled with an ability to exploit temporal locality. However, traditional caching approaches incur high memory overheads and lookup latencies, leading us to design learned caches - caches that consist of simple ML models that are continuously updated. We present the design of GATI, an end-to-end prediction serving system that incorporates learned caches for low-latency DNN inference. Results show that GATI can reduce inference latency by up to 7.69X on realistic workloads.

In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model. We conduct an in-depth analysis of key model performance statistics, with a focus on quantities such as predicted distributions of defect shapes, defect sizes, and defect areal densities relevant to informing modeling and understanding of irradiated Fe-based materials properties. To better understand the performance and present limitations of the model, we provide examples of useful evaluation tests which include a suite of random splits, and dataset size-dependent and domain-targeted cross validation tests. Overall, we find that the current model is a fast, effective tool for automatically characterizing and quantifying multiple defect types in microscopy images, with a level of accuracy on par with human domain expert labelers. More specifically, the model can achieve average defect identification F1 scores as high as 0.8, and, based on random cross validation, have low overall average (+/- standard deviation) defect size and density percentage errors of 7.3 (+/- 3.8)% and 12.7 (+/- 5.3)%, respectively. Further, our model predicts the expected material hardening to within 10-20 MPa (about 10% of total hardening), which is about the same error level as experiments. Our targeted evaluation tests also suggest the best path toward improving future models is not expanding existing databases with more labeled images but instead data additions that target weak points of the model domain, such as images from different microscopes, imaging conditions, irradiation environments, and alloy types. Finally, we discuss the first phase of an effort to provide an easy-to-use, open-source object detection tool to the broader community for identifying defects in new images.

Much of the literature on differential privacy focuses on item-level privacy, where loosely speaking, the goal is to provide privacy per item or training example. However, recently many practical applications such as federated learning require preserving privacy for all items of a single user, which is much harder to achieve. Therefore understanding the theoretical limit of user-level privacy becomes crucial. We study the fundamental problem of learning discrete distributions over $k$ symbols with user-level differential privacy. If each user has $m$ samples, we show that straightforward applications of Laplace or Gaussian mechanisms require the number of users to be $\mathcal{O}(k/(m\alpha^2) + k/\epsilon\alpha)$ to achieve an $\ell_1$ distance of $\alpha$ between the true and estimated distributions, with the privacy-induced penalty $k/\epsilon\alpha$ independent of the number of samples per user $m$. Moreover, we show that any mechanism that only operates on the final aggregate counts should require a user complexity of the same order. We then propose a mechanism such that the number of users scales as $\tilde{\mathcal{O}}(k/(m\alpha^2) + k/\sqrt{m}\epsilon\alpha)$ and hence the privacy penalty is $\tilde{\Theta}(\sqrt{m})$ times smaller compared to the standard mechanisms in certain settings of interest. We further show that the proposed mechanism is nearly-optimal under certain regimes. We also propose general techniques for obtaining lower bounds on restricted differentially private estimators and a lower bound on the total variation between binomial distributions, both of which might be of independent interest., Comment: NeurIPS 2020, 38 pages

We consider the problem of estimating sparse discrete distributions under local differential privacy (LDP) and communication constraints. We characterize the sample complexity for sparse estimation under LDP constraints up to a constant factor and the sample complexity under communication constraints up to a logarithmic factor. Our upper bounds under LDP are based on the Hadamard Response, a private coin scheme that requires only one bit of communication per user. Under communication constraints, we propose public coin schemes based on random hashing functions. Our tight lower bounds are based on the recently proposed method of chi squared contractions.

A one-step salt melt assisted thermal polymerization for the synthesis of nitrogen-rich water soluble graphitic carbon nitride (WS-GCN) is presented. The as-prepared WS-GCN shows strong tunable excitation-emission pathways that have multi-colored photoluminescence from blue to green with extremely high absolute quantum yields (AQY) of 22%, which is almost 6 fold of pristine graphitic carbon nitride (GCN) nanosheets. It also shows significant red-shift of response edge benefiting from the narrowed band energy, due to the feature of N-defected surface and N-rich inner of WS-GCN. After being decorated onto TiO2, the WS-GCN endows a great enhancement of NO-photodegradation under visible light irradiation, 5.5-fold increase to comparison with what the pristine GCN does. Further, the starting edge of photocatalysis is enlarged to about 650 nm for TiO2/WS-GCN from about 450 nm for TiO2/GCN. This is ascribed to both enhanced light-response range and efficiently restraining the radiative transition probability of WS-GCN owing to the migration of photoinduced electrons from the CB of WS-GCN to TiO2. This multi-functional material may provide a way for the development of bio-imaging, file secrecy and broad absorption photocatalysts.

With the rapid development of new energy vehicles in China, the trend of decommissioning of power batteries has arrived, and the stagger utilization of decommissioned power batteries has become the focus of the industry. Once a power battery is decommissioned, the retired power battery needs to undergo relevant tests and evaluations before it can be carried out the cascade development and utilization. In this paper, we creatively propose the AHP-CRITIC method. Firstly, according to the health characteristics of the decommissioned power batteries, the improved K-means cluster analysis is used to analyze the curve characteristics of the decommissioned power batteries. Secondly, for the economic indicators of decommissioned batteries, we take advantage of the AHP-CRITIC method to evaluate the decommissioned power batteries. Then, a comprehensive evaluation model that synthetically considers the health characteristics and economics of decommissioned power batteries is established. Finally, an example is given to verify the effectiveness of the proposed evaluation model.

Knots are intricate structures that cannot be unambiguously distinguished with any single topological invariant. Momentum space knots, in particular, have been elusive due to their requisite finely tuned long-ranged hoppings. Even if constructed, probing their intricate linkages and topological "drumhead” surface states will be challenging due to the high precision needed. In this work, we overcome these practical and technical challenges with RLC circuits, transcending existing theoretical constructions which necessarily break reciprocity, by pairing nodal knots with their mirror image partners in a fully reciprocal setting. Our nodal knot circuits can be characterized with impedance measurements that resolve their drumhead states and image their 3D nodal structure. Doing so allows for reconstruction of the Seifert surface and hence knot topological invariants like the Alexander polynomial. We illustrate our approach with large-scale simulations of various nodal knots and an experiment which maps out the topological drumhead region of a Hopf-link., Topological phases with knotted configurations in momentum space have been challenging to realize. Here, Lee et al. provide a systematic design and measurement of a three-dimensional knotted nodal structure, and resolve its momentum space drumhead states via a topolectrical RLC-type circuit.

It is a fundamental, but still elusive question whether the schemes based on quantum mechanics, in particular on quantum entanglement, can be used for classical information processing and machine learning. Even partial answer to this question would bring important insights to both fields of machine learning and quantum mechanics. In this work, we implement simple numerical experiments, related to pattern/images classification, in which we represent the classifiers by many-qubit quantum states written in the matrix product states (MPS). Classical machine learning algorithm is applied to these quantum states to learn the classical data. We explicitly show how quantum entanglement (i.e., single-site and bipartite entanglement) can emerge in such represented images. Entanglement characterizes here the importance of data, and such information are practically used to guide the architecture of MPS, and improve the efficiency. The number of needed qubits can be reduced to less than 1/10 of the original number, which is within the access of the state-of-the-art quantum computers. We expect such numerical experiments could open new paths in charactering classical machine learning algorithms, and at the same time shed lights on the generic quantum simulations/computations of machine learning tasks., Comment: 10 pages, 5 figures

Sleep condition is closely related to an individual's health. Poor sleep conditions such as sleep disorder and sleep deprivation affect one's daily performance, and may also cause many chronic diseases. Many efforts have been devoted to monitoring people's sleep conditions. However, traditional methodologies require sophisticated equipment and consume a significant amount of time. In this paper, we attempt to develop a novel way to predict individual's sleep condition via scrutinizing facial cues as doctors would. Rather than measuring the sleep condition directly, we measure the sleep-deprived fatigue which indirectly reflects the sleep condition. Our method can predict a sleep-deprived fatigue rate based on a selfie provided by a subject. This rate is used to indicate the sleep condition. To gain deeper insights of human sleep conditions, we collected around 100,000 faces from selfies posted on Twitter and Instagram, and identified their age, gender, and race using automatic algorithms. Next, we investigated the sleep condition distributions with respect to age, gender, and race. Our study suggests among the age groups, fatigue percentage of the 0-20 youth and adolescent group is the highest, implying that poor sleep condition is more prevalent in this age group. For gender, the fatigue percentage of females is higher than that of males, implying that more females are suffering from sleep issues than males. Among ethnic groups, the fatigue percentage in Caucasian is the highest followed by Asian and African American., Comment: 2017 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS'17)

The majority of deep-sea oil and gas exploration areas are located in seismic active zone, such as the South China Sea and Suez basin in Egypt. As casing string vibration caused by earthquake and low deep shallow soil cementation strength may result in the instability of deep water flow string, moreover, the pipe string failure may bring about economic loss and environmental damage. In this paper, lateral stability analysis of pipe in deep water shallow is taken into consideration for seismic analysis, and the string is considered as a Winkler model. First, it carries out stress analysis of flow string, and lateral stress includes wave load in wellhead, transverse moment imposed by bottom casing, ground resisting force and seismic force. Dynamic differential equation of string in deep water shallow is established on the basis of soil dynamics and pipe-string mechanics. Displacement attenuates and bending moment distribution of the string are calculated in three different seismic data, therefore the conclusion can be obtained as below: The string displacement amplitude increases as the intensity of earthquakes increases, and the biggest bending moment lies in the casing cross section, which is the point that may be easily damaged if the earthquake happens. In this paper, analysis of the transverse vibration of the shallow casing string wil help us better understand the casing string vibration in deepwater oil and gas exploitation and transportation so as to reduce the occurrence of the accident. Finally, some suggestions are given below, such as RMR \ MPD drilling technology, Open Hole Interval and structure of wellhead support device to enhance the stability of production string, ensure well integrity and reduce accident loss.

In this paper, a backtepping-nonsingular fast terminal sliding mode (NFTSM) controller is proposed for active vibration isolation problem of cubic configuration Stewart platform, so that to ensure the system error can converge to zero in finite time. Compared with traditional linear sliding mode, the proposed control scheme can improve the robustness and the anti-disturbance capacity of the system. With various disturbance and parameter uncertainties taken into consideration, the numerical simulation results reveal the validity of the proposed control approach.

Objective To investigate the clinical efficacy of postoperative estrogen therapy, intrauterine device (IUD) and intrauterine balloon in preventing intrauterine adhesions after transcervical resection of septum (TCRS). Study design 238 patients who underwent TCRS in our hospital from March 2012 to December 2013 were allocated into one of four groups. In Group 1 (50 patients), women received postoperative estrogen therapy. In Group 2 (59 patients), an intrauterine contraceptive device (IUD) was placed into the uterine cavity at the end of the operation. In Group 3 (75 patients), a Foley catheter with the balloon inflated with 4 ml of normal saline solution was placed into the uterine cavity at the end of the operation for five days. In Group 4 (54 patients), women did not receive any of the above treatment (comparison group). All subjects underwent two further hysteroscopy, one and three months after the initial surgery. Results The intrauterine adhesion rates among the four groups at one month were 22.0%, 28.81, 26.7% and 24.1% (p > 0.05); and at the third month were 0%, 1.7%, 1.3% and 3.4%, respectively (p > 0.05). Conclusions The use of postoperative estrogen therapy, IUD or intrauterine balloon did not appear to have any benefit in reducing the incidence of postoperative intrauterine adhesion formation.