554 results about "Logit" patented technology

The local intrinsic dimensionality (LID) for a diffusion model with respect to a particular data sample is determined by using the diffusion model's diffusion process to apply noise to a data sample and evaluate how the estimated log probability of the data sample changes at different levels of noise. Particularly, the differential of change in noise to change in log probability can be used to determine the local intrinsic dimensionality. This may be determined by evaluating the log probability at several noise levels and determining a slope of the difference. In additional examples, the differential is evaluated directly at a selected noise level. The selected noise level can be optimized by calculating the estimated LID for various data samples at a variety of noise levels and selecting the LID that corresponds to a “knee” where the estimated LID sharply changes.

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

This document discloses methods and systems for cohort identification. The methods and systems include improved calculations to perform cohort identification and practical applications of the improved calculations. Specifically, the systems and methods described herein may utilize key components that include enhancements of existing cohort clustering techniques with regard to selecting a number of cohort input dimensions, normalizing input data using a logarithm kernel-function, treatment of categorical data with mutually exclusive and not-mutually exclusive values, methods and visualization tool to determine appropriate number of cohorts, methods and visualization tool to compare cohorts extracted from different input dimensions, and methods to quantify the difference in cohorts. Beyond improvements to the cohort clustering techniques, also disclosed are ancillary tools to prepare input data by joining CRM and product usage data and facilitate subsequent automated action via an API to retrieve cohort results.

A GNSS positioning slowly-varying fault detection method based on residual-SVR regression comprises the steps that an observation information sequence is acquired based on a Kalman filter, and a covariance matrix of the observation information sequence is calculated; accumulating multi-step information through a sliding window, and constructing chi-square statistics; based on the fault-free data, constructing a training set by taking an innovation sequence as input and chi-square statistics as output, and generating an innovation-statistics mapping function; and fitting a normal slope threshold value based on an SVR predicted value, carrying out least square fitting on an observation statistic curve by sliding a window in real time, and judging whether to start a slow change fault alarm or not. According to the method, the residual error sequence is directly used as model input, and the dynamic chi-square statistical magnitude is used for replacing a traditional dichotomy label, so that the detection delay is reduced; an SVR detection model based on grid search and cross validation collaborative optimization is utilized, and an optimal parameter combination of a minimum mean square error (MSE) is screened through logarithm uniform sampling, interval linear sampling and five-fold cross validation, so that the average absolute error of slowly varying fault detection is reduced.

The invention discloses a weld defect detection method and system, and relates to the technical field of defect detection.The method comprises the steps that a visible light image sequence, an infrared image sequence and a pixel point cloud sequence are collected, windows are moved in geometric coding results of pixel point cloud of each frame and a visible light image through window attention, and the windows are extracted and sorted into visual feature maps; extracting a temperature feature map from each frame of infrared image through thermal gradient convolution and cavity convolution, and arranging the temperature feature map into a geometric feature sequence and a defect feature sequence based on cross attention and decoupling head mapping; capturing the time sequence dependence of the defect feature sequence through a Transform encoder, carrying out potential space modeling, and generating a defect probability vector by utilizing defect priori knowledge in combination with a Bayesian network; and a Gaussian regression model and a logarithmic probability equation are adopted to map the geometric feature sequence into a probability threshold of each defect type, and the probability threshold is compared with a defect probability vector to determine the defect, so that high-precision defect detection fusing geometry, temperature, time sequence and priori knowledge is realized.

The embodiment of the invention provides a multi-modal data fused postoperative plastic surgery infection early warning method and system. The method comprises the following steps: firstly, performing quality evaluation and restoration on postoperative wound images, clinical texts, wearable vital signs and baseline features; performing double-flow coding and priori segmentation on the repaired image to obtain a coarse mask; constructing a super-pixel graph, obtaining a topological consistent fine mask through a graph neural network, and quantifying the erythema / exudation area; the text and the vital signs are coded respectively and then fused with the image features and the masks through cross-attention and self-attention, and a fusion implicit vector and uncertainty are obtained through quality weight gating; an individualized dynamic threshold value is constructed by combining baseline risk, logarithmic probability calibration and a sliding window trend, a confidence band is output by using conformal prediction, and early reliable infection early warning is realized.

The invention provides an unsupervised industrial anomaly detection method based on an improved full-convolution cross-scale flow network. The method comprises the steps of performing data preprocessing on an industrial image data set; industrial image data is used as input, a pre-trained visual backbone network is used for extracting multi-scale features, a resolution-perceived channel attention module is used for enhancing expression of the multi-scale features, and an enhanced multi-scale feature map is obtained; performing multilayer reversible transformation by taking the enhanced multi-scale feature map as input and taking an ICSF-Net model based on hierarchical attention and expansion convolution as a cross-scale normalized flow network, and modeling multi-scale feature distribution; in the training stage, optimizing and updating ICSF-Net model parameters by maximizing the log likelihood of a normal sample under potential Gaussian distribution based on multi-scale feature distribution and combining an LSGR mechanism; in the reasoning stage, probability density estimation is carried out based on multi-scale feature distribution, an abnormal score graph is generated, and defect detection and positioning are achieved. According to the invention, the accuracy, robustness and pixel-level positioning precision of industrial product defect detection are improved.

The invention discloses a distribution line early-stage fault time sequence hidden Markov modeling and identification method and system based on multistage evolution characteristics, and belongs to the field of distribution line early-stage fault identification. Comprising the steps of obtaining a current waveform sample sequence of an early fault in a distribution line; for each current waveform sample in the sequence, extracting a multi-dimensional time-frequency feature, and constructing a feature vector of each sample; performing fault stage identification on each sample by using the first-level hidden Markov model group, and outputting a fault stage tag sequence corresponding to each sample; combining the fault stage label sequences of the current sample and a plurality of previous historical samples to form a stage label sequence window; and respectively inputting the stage label sequence window into a second-level tree line fault hidden Markov model and a second-level non-tree line fault hidden Markov model, calculating a corresponding first average log-likelihood value and a corresponding second average log-likelihood value, comparing the two average log-likelihood values, and judging whether a current sample belongs to a tree line early fault or not.

The invention relates to the technical field of power system operation and control, and particularly discloses a time-sharing electric quantity prediction method based on a logarithmic load density growth curve, which comprises the following steps of: firstly, performing causal detection and dynamic time-delay optimization on historical load and multivariate external data through convergence cross mapping and mutual information technologies, and constructing a causal time-delay feature set; and the problems of multi-element coupling and time-delay effect quantization are solved. Secondly, fitting a load trend by using time-frequency decomposition in cooperation with a segmented logistic model, extracting dynamic parameters representing a growth rate and a saturation capacity, and endowing the model with a sensing ability for a load evolution stage; then, causal features, growth parameters and load components are deeply fused through cross-domain modulation and a gating mechanism, the nonlinear modulation effect of an external environment on a load mode is explicitly modeled, and finally, a probability interval is generated in combination with quantile regression and residual error correction. According to the scheme, accurate and probabilistic prediction of the time-sharing electric quantity in a complex scene is realized, and the scientificity of an agent electricity purchase decision is improved.

The invention provides a civil structure deformation anomaly detection method based on time series data, and relates to the field of civil structure deformation anomaly detection. A disturbance intensity response value, a disturbance curvature and a local disturbance folding feature are constructed, a range adjustment enhancement value is formed by combining a symbol jump mark and a neighborhood disturbance difference value, a latent guide feature is generated based on the range adjustment enhancement value, a disturbance reconstruction feature is constructed through superposition of a diffusion residual error and an asymmetric difference item, normalization mapping is completed to obtain a normalization feature, and the normalization feature is obtained. A disturbance spinor modulation factor is constructed in combination with a nonlinear suppression correlation coefficient, a disturbance spinor tensor is formed through a logarithmic compression channel and a square amplification channel and by applying quadrature phase coding, a tensor potential mapping map is generated under a path coupling and self-coupling mechanism, disturbance energy offset and extreme value deflection are constructed based on the tensor potential mapping map, and disturbance energy offset and extreme value deflection are obtained. And a probability potential index is formed, and civil structure deformation anomaly detection model training is completed based on the probability potential index, so that civil structure deformation anomaly detection is realized.

The invention relates to the technical field of oil and gas field development, in particular to a fracture parameter inversion method based on a Bayesian neural network, which comprises the following steps: establishing a bottom hole net pressure conversion model based on an actual construction curve, and drawing a bottom hole net pressure curve; calculating a bottom hole net pressure index sequence and a corresponding time sequence; establishing a shaft bottom crack extension mode judgment criterion on the basis of a classic double logarithmic curve analysis method; taking the net pressure index sequence and the time sequence obtained in the previous step as input data, combining actual physical parameter constraints, and establishing an inversion fracture parameter model based on a Bayesian neural network; and inputting the pressure index sequence to be inverted into the Bayesian neural network model to obtain a specific fracture parameter inversion result. According to the technical scheme, the confidence interval of the prediction result can be given, the prediction result and uncertainty quantification capability can be synchronously provided, and the reliability and decision support value of the inversion result are greatly improved.

The invention discloses a model reasoning method, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. In the model reasoning process, various information is integrated, and the logarithm probability of candidate lexical elements to be generated is corrected, so that thinking switching of a model in the later stage of reasoning is avoided, and the reasoning efficiency is improved. In this way, the situation that the reasoning process is too long due to frequent switching of reasoning ideas is avoided, and waste of model reasoning resources is avoided while the accuracy of the model reasoning result is guaranteed.

The present application relates to the technical field of data processing, and particularly relates to a power monitoring method and system for remote electric energy meter, the method comprising: constructing a time series factor graph model with voltage and current phasor as hidden variables and original electric parameter data as observation nodes; performing synchronous compression wavelet transform on current phasors in the electric energy state sequence to generate a time-frequency energy distribution graph and obtain a monitoring feature vector sequence; inputting the monitoring feature vector sequence into a deep auto-encoder pre-trained on normal power consumption working condition data to calculate a reconstruction error, simultaneously calculating a Lyapunov index of the sequence within a preset time window, and obtaining a negative log-likelihood probability according to a pre-established Gaussian mixture model describing the distribution of the index under normal working condition; and weighting and fusing the reconstruction error and the negative log-likelihood probability to generate a comprehensive abnormality index for judging power consumption events. The present application can realize high-precision and low-false-alarm-rate detection of power consumption events.

The invention discloses a finite geometric code decoding method and system based on a generalized check matrix, and relates to the communication technology, and the method comprises the following steps: constructing a corresponding generalized check matrix according to an algebraic structure of a multi-step large number logic decodable finite geometric code; sending a finite geometric code sequence, transmitting the sequence after coding and BPSK modulation, and receiving a posterior probability log-likelihood ratio sequence by a receiving end; and carrying out iterative decoding on the finite geometric code received by the receiving end based on the constructed generalized check matrix. According to the invention, a new generalized check matrix is constructed according to the algebraic structure of the finite geometric code, and multi-step large-number logic iterative decoding is carried out on the finite geometric code based on the matrix, so that the iterative decoding performance of the multi-step large-number logic decodable finite geometric code is improved.

The invention provides an AI text recognition method and device based on ensemble learning and advanced semantic statistical feature analysis, and the method comprises the steps: 1, respectively sending a to-be-recognized text into a Bert detector and a high-order natural language statistical feature detector for recognition, the high-order natural language statistical feature detector comprises a word logarithm probability detector, a word ranking logarithm detector, an Entropy detector and a confusion degree detector; and 2, performing election on detection results output by the Bert detector and the high-order natural language statistical feature detector by using an election module to obtain an AI text recognition result. According to the method, an integrated learning strategy is adopted, and a pre-training language model subjected to fine tuning is combined with high-order natural language statistical characteristics, so that when the model detects a large language model to generate a text, the strong expression ability of the pre-training language model can be fully utilized, and a deep rule of the text can be captured through the high-order statistical characteristics; and the detection accuracy is improved.

The invention relates to a seismic liquefaction assessment method based on conditional random field simulation, which comprises the following steps: firstly, obtaining a logarithmic normal distribution random field of a target area under a corresponding SPT-N value, then resampling through a Bootstrap method, constructing a weighted prior probability density function of the target area in combination with a likelihood function, and finally calculating the seismic liquefaction of the target area according to a Bayesian theory. A Markov chain Monte Carlo sampling method is combined, through posterior probability density distribution, an optimal horizontal direction correlation distance is determined, a covariance matrix is constructed to generate a conditional random field, and then through multiple times of simulation, the conditional random field is converged; and finally, aiming at the target area, through calculation of a cyclic stress ratio and a cyclic resistance ratio, constructing a liquefaction probability distribution diagram corresponding to the target area. According to the method, a conditional random field simulation method is inferred and improved by combining Bootstrap and Bayesian theories, the precision and reliability of geological parameter simulation are remarkably improved, and reliable data support is provided for seismic liquefaction assessment of deep and uneven site engineering.

The invention discloses a multi-modal fusion method based on evidence dual uncertainty, and the method comprises the following steps: S1, constructing a dual uncertainty evidence network according to each modal feature, and modeling randomness and cognitive uncertainty at the same time; s2, predicting uncertainty distribution of a target modal based on a source modal, and designing a cross-modal uncertainty reconstruction mechanism to repair degradation modal information; s3, combining the prediction confidence coefficient and the balance uncertainty of each modal, proposing an adaptive fusion method, and dynamically allocating modal weights by adopting logarithm normalization; and S4, constructing unified uncertainty vector and modal feature joint representation, introducing a layered uncertainty perception gating module, and performing selection according to modal reliability. The method has robustness under noise input, modal missing and distribution offset, and can significantly improve the accuracy and generalization ability of multi-modal classification and prediction.

The invention relates to the technical field of dam safety monitoring, and discloses a multi-source remote sensing data deep learning dam slope monitoring method, which comprises the following steps: resampling each mode to a common ground grid, and calculating robust statistics and a time stability agent on grid and block scales; determining a reference image according to the block-level robust score, and adaptively setting a local displacement search range with a high-intensity centroid difference; evaluating the discrete candidate displacement in a grid neighborhood by using a median absolute difference to obtain a local displacement field and a residual error; three types of subitems are constructed based on robust noise, time stability and registration residual errors, and pixel-level reliability weights are adaptively synthesized through logarithmic variance proportions among modes; analyzing and solving local linear mapping in a neighborhood by using a weight-weighted observation matrix, and calculating a weighted residual error according to the local linear mapping; a binary and probability anomaly graph is generated with a robust threshold.

The invention discloses a deep reinforcement learning-driven intelligent real-time optimization method for drilling parameters, which belongs to the technical field of drilling optimization, and is technically characterized by comprising the following steps of: 1, constructing a drilling speed prediction model; step 2, modeling in a decision-making process: selecting a group of parameter combinations # imgabs0 # to obtain the optimal drilling speed, only selecting one parameter # imgabs2 # at each moment # imgabs1 # needing decision-making, and representing the MDP as a quintuple # imgabs3 #; each drilling parameter is decided one by one, and the previous decision parameter and drilling data are input; and step 3, obtaining a state # imgabs5 # from the environment at each time step # imgabs4 #, inputting the state # imgabs5 # into an Agent, obtaining an action # imgabs6 # and the logarithmic probability # imgabs7 # of the current strategy for updating the subsequent strategy of the algorithm, and achieving the optimization of the bit pressure, the torque and the rotating speed parameters.

The invention discloses a context poisoning attack defense method oriented to an RAG application, and relates to the technical field of RAG. the method comprises the following steps: inputting a target query statement, and retrieving the target query statement to obtain multiple pieces of context information; taking representative sentences in the retrieved context information, and identifying and filtering potential malicious template clusters; the big language model gives all candidate answers according to existing context information, the logarithmic probability of all contexts to different candidate answers is calculated, and after the influence of parameter knowledge of the big language model is removed from the logarithmic probability, the support degree of all contexts to different candidate answers is obtained; the whole logarithmic probability vector is used as a support degree distribution condition of the context to the candidate answers; identifying a single piece of harmful information from the support degree distribution condition of the context to the candidate answers through a logistic regression model so as to filter wrong answers; according to the attack defense method provided by the invention, centralized injection of multiple malicious texts and sparse injection of a small number of malicious texts can be defended.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

Provided are techniques for the calibration of distillation learning from a teacher model to a student model. Specifically, the present disclosure proposes systems and methods that provide convergence with both high quality and speed. That is, example proposed systems both enable the distillation loss to be minimized at the probability mean value in the probability domain of the teacher's predictions distributions while also providing a loss that is nicely (e.g., symmetrically and / or strongly) convex around an optimum in the logit and / or probability domains (e.g., including far from the minimum) to encourage fast convergence of gradient based methods (e.g., irrespective of distance from the minimum).

The invention relates to the technical field of data processing, in particular to an electric energy monitoring method and system for a remote electric energy meter, and the method comprises the steps: constructing a time sequence factor graph model with voltage and current phasors as hidden variables and original electric parameter data as observation nodes; performing synchronous compression wavelet transform on current phasors in the electric energy state sequence, generating a time-frequency energy distribution diagram, and obtaining a monitoring feature vector sequence; inputting the monitoring feature vector sequence into a depth auto-encoder pre-trained on normal power utilization condition data to calculate a reconstruction error, calculating a Lyapunov index of the sequence in a preset time window, and calculating the reconstruction error according to a pre-established Gaussian mixture model for describing index distribution under a normal condition. Obtaining a negative logarithm likelihood probability; and carrying out weighted fusion on the reconstruction error and the negative logarithm likelihood probability to generate a comprehensive abnormal index which is used for judging a power utilization event. According to the invention, high-precision and low-false-alarm-rate detection of power consumption events can be realized.

The invention discloses an anti-interference underwater acoustic channel estimation method and device based on model data dual drive, and the method comprises the steps: obtaining an interference data set, and training a neural network; preprocessing the underwater acoustic signal to obtain a pilot frequency part signal, and initializing a channel, an interference sample, a sampling moment and a channel precedence variance gamma; respectively obtaining interference and channel prior scores at the t'moment by using a neural network and an analytical model; the logarithmic likelihood function obtains a gradient about interference and channel conjugation to obtain interference and channel condition scores, and the prior and condition scores are weighted and summed to obtain interference and channel posterior scores; the channel samples and the interference samples are corrected through annealing sampling, gamma is updated, and the posterior score is calculated again; a channel sample and an interference sample at the t'moment are obtained through the inverse process of the diffusion model; and if t'reaches tmin, outputting a final estimated value, otherwise, updating gamma according to the channel sample at the t 'moment, and circulating. According to the invention, accurate estimation of the single carrier communication channel in the structured interference environment is realized.

The invention discloses a temperature normalization knowledge distillation method and system based on multi-scale decoupling, and the method comprises the following steps: S1, inputting an image into a teacher model and a student model at the same time, respectively outputting logit feature maps by the teacher model and the student model, decomposing the global logit output of the teacher model into a plurality of local logit outputs, each local logit output corresponding to a specific region of the input image, extracting local logit information of the region from the feature map of the teacher model; the student model takes the local logit output by the teacher model as a learning result, and the learning result corresponds to the local logit of the teacher model on the same scale and the same area; s2, inputting the image into a pre-trained teacher model and a pre-trained student model respectively, calculating a standard deviation, performing local logit calculation on the teacher model and the student model according to an adaptive temperature mechanism to obtain probability distribution of the teacher model and probability distribution of the student model respectively, introducing a temperature scaling factor, and controlling the softening degree; and S3, combining the teacher and student model probability distribution with the normalized distillation loss, the semantic decoupling distillation loss and the cross entropy loss to calculate the total loss.

Systems and methods for detecting Large Language Model (LLM) generated text are provided. The systems and methods include sampling a text passage to generate alternative samples conditioned on the text passage based on a next token prediction in a surrogate LLM model and scoring a likelihood that the test passage sample is generated by an LLM model. The scoring includes a conditional probability which quantifies a distribution gap of a log of logits from the surrogate LLM model. The systems and methods further include comparing the scored text passage with a sample text generated in the surrogate LLM model trained to imitate a target LLM model. The comparison includes transforming the scores into a scaled representation and normalizing the scores.

The invention relates to a time-frequency difference passive positioning method based on an adaptive step cuckoo search algorithm, and belongs to the technical field of passive positioning, and the method comprises the following steps: S1, constructing a time-frequency difference positioning mathematical model; s2, proposing a time-frequency difference positioning algorithm based on an adaptive step cuckoo search algorithm ASCS, and solving the time-frequency difference positioning mathematical model; according to the time-frequency difference positioning algorithm based on the adaptive step cuckoo search algorithm ASCS, an adaptive step strategy and the discovery probability of nonlinear logarithmic decline are introduced on the basis of the cuckoo search algorithm; according to the adaptive step length strategy, the position is adaptively updated by introducing a global historical optimal solution in the optimization process.

The invention discloses a bridge temperature-induced strain prediction method under the influence of non-constant noise, and the method comprises the steps: building a training set containing main beam temperature and temperature-induced strain according to historical monitoring data, and assuming a test set; under a heterovariance Gaussian process framework, constructing conditional distribution of temperature-induced strain of a main beam of the test set as an undetermined heterovariance Gaussian regression model; a variational Bayesian inference method is adopted to construct a variational free energy boundary based on variational posteriori distribution, and approximation is performed on the variational free energy boundary to obtain an edge variational boundary; assuming that the variational posteriori distribution of the noise logarithmic variance function obeys multivariate normal distribution, simplifying an edge variational boundary into a new edge variational boundary, and solving to obtain a hyper-parameter and a variational parameter; and substituting the obtained result into the to-be-determined heterovariance Gaussian regression model, obtaining new main beam temperature data, and predicting the temperature-induced strain of the main beam in the test set. The bridge temperature-induced strain prediction method under the influence of the non-constant noise is established, the prediction precision can be ensured, and the calculation efficiency can be improved.

The invention provides a text classification method and device, electronic equipment and a storage medium, and relates to the technical field of natural language process.The method comprises the steps that a training sample set is obtained, and the training sample set comprises sample texts and sample classification labels and sample reasoning reasons corresponding to the sample texts; performing fine tuning on a first pre-trained large language model through the training sample set to obtain a text classification model; in response to a text classification request, through the text classification model, based on a preset reasoning constraint parameter, only performing classification processing on a to-be-classified text to obtain a prediction classification label; wherein the preset reasoning constraint parameters comprise an output length limiting parameter and a logit probability intervention parameter. According to the method, the text classification efficiency can be improved while the accuracy and the reliability of a text classification result are improved.

The invention provides a speculative prefix multiplexing method for verifiable award reinforcement learning training, which comprises the following steps: acquiring a prompt set of a current training batch and an old response generated in a previous training iteration, the old response comprising a plurality of old lexical element sequences, each old lexical element sequence carries a first logarithmic probability and length information corresponding to each lexical element under the old strategy; calculating a second logarithmic probability corresponding to each lexical element under the current strategy according to each prompt in the prompt set and the corresponding old lexical element sequence; according to the first logarithmic probability and the second logarithmic probability, judging each lexical element according to an old lexical element sequence; if the judgment result of each lexical element is accepted, directly multiplexing the old lexical element sequence; if rejected lexical elements appear in the judgment process, the judgment is stopped to obtain reusable prefixes, and the model is called to generate suffixes corresponding to the reusable prefixes to obtain new responses, so that the expenditure in the sampling stage is reduced, and the reply efficiency is improved.