The influence of resistance exercise training prescription variables on skeletal muscle mass,strength,and physical function in healthy adults:An umbrella review

Purpose:The aim of this umbrella review was to determine the impact of resistance training(RT)and individual RT prescription variables on muscle mass,strength,and physical function in healthy adults.Methods:Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,we systematically searched and screened eligible systematic reviews reporting the effects of differing RT prescription variables on muscle mass(or its proxies),strength,and/or physical function in healthy adults aged>18 years.Results:We identified 44 systematic reviews that met our inclusion criteria.The methodological quality of these reviews was assessed using A Measurement Tool to Assess Systematic Reviews;standardized effectiveness statements were generated.We found that RT was consistently a potent stimulus for increasing skeletal muscle mass(4/4 reviews provide some or sufficient evidence),strength(4/6 reviews provided some or sufficient evidence),and physical function(1/1 review provided some evidence).RT load(6/8 reviews provided some or sufficient evidence),weekly frequency(2/4 reviews provided some or sufficient evidence),volume(3/7 reviews provided some or sufficient evidence),and exercise order(1/1 review provided some evidence)impacted RT-induced increases in muscular strength.We discovered that 2/3 reviews provided some or sufficient evidence that RT volume and contraction velocity influenced skeletal muscle mass,while 4/7 reviews provided insufficient evidence in favor of RT load impacting skeletal muscle mass.There was insufficient evidence to conclude that time of day,periodization,inter-set rest,set configuration,set end point,contraction velocity/time under tension,or exercise order(only pertaining to hypertrophy)influenced skeletal muscle adaptations.A paucity of data limited insights into the impact of RT prescription variables on physical function.Conclusion:Overall,RT increased muscle mass,strength,and physical function compared to no exercise.RT intensity(load)and weekly frequency impacted RT-induced increases in muscular strength but not muscle hypertrophy.RT volume(number of sets)influenced muscular strength and hypertrophy.

Surgical intervention combined with weight-bearing walking training promotes recovery in patients with chronic spinal cord injury:a randomized controlled study

For patients with chronic spinal cord injury,the co nventional treatment is rehabilitation and treatment of spinal cord injury complications such as urinary tract infection,pressure sores,osteoporosis,and deep vein thrombosis.Surgery is rarely perfo rmed on spinal co rd injury in the chronic phase,and few treatments have been proven effective in chronic spinal cord injury patients.Development of effective therapies fo r chronic spinal co rd injury patients is needed.We conducted a randomized controlled clinical trial in patients with chronic complete thoracic spinal co rd injury to compare intensive rehabilitation(weight-bearing walking training)alone with surgical intervention plus intensive rehabilitation.This clinical trial was registered at ClinicalTrials.gov(NCT02663310).The goal of surgical intervention was spinal cord detethering,restoration of cerebrospinal fluid flow,and elimination of residual spinal cord compression.We found that surgical intervention plus weight-bearing walking training was associated with a higher incidence of American Spinal Injury Association Impairment Scale improvement,reduced spasticity,and more rapid bowel and bladder functional recovery than weight-bearing walking training alone.Overall,the surgical procedures and intensive rehabilitation were safe.American Spinal Injury Association Impairment Scale improvement was more common in T7-T11 injuries than in T2-T6 injuries.Surgery combined with rehabilitation appears to have a role in treatment of chronic spinal cord injury patients.

Application value research of swallowing treatment device combined with swallowing rehabilitation training in the treatment of swallowing disorders after stroke

BACKGROUND Stroke is a common disabling disease,whether it is ischemic stroke or hemorrhagic stroke,both can result in neuronal damage,leading to various manifestations of neurological dysfunction.AIM To explore of the application value of swallowing treatment device combined with swallowing rehabilitation training in the treatment of swallowing disorders after stroke.METHODS This study selected 86 patients with swallowing disorders after stroke admitted to our rehabilitation department from February 2022 to December 2023 as research subjects.They were divided into a control group(n=43)and an observation group(n=43)according to the treatment.The control group received swallowing rehabilitation training,while the observation group received swallowing treatment device in addition to the training.Both groups underwent continuous intervention for two courses of treatment.RESULTS The total effective rate in the observation group(93.02%)was higher than that in the control group(76.74%)(P=0.035).After intervention,the oral transit time,swallowing response time,pharyngeal transit time,and laryngeal closure time decreased in both groups compared to before intervention.In the observation group,the oral transit time,swallowing response time,and pharyngeal transit time were shorter than those in the control group after intervention.However,the laryngeal closure time after intervention in the observation group was compared with that in the control group(P=0.142).After intervention,average amplitude value and duration of the genioglossus muscle group during empty swallowing and swallowing 5 mL of water are reduced compared to before intervention in both groups.After intervention,the scores of the chin-tuck swallowing exercise and the Standardized Swallowing Assessment are both reduced compared to pre-intervention levels in both groups.However,the observation group scores lower than the control group after intervention.Additionally,the Functional Oral Intake Scale scores of both groups are increased after intervention compared to pre-intervention levels,with the observation group scoring higher than the control group after intervention(P<0.001).The cumulative incidence of complications in the observation group is 9.30%,which is lower than the 27.91%in the control group(P=0.027).CONCLUSION The combination of swallowing therapy equipment with swallowing rehabilitation training can improve the muscle movement level of the genioglossus muscle group,enhance swallowing function,and prevent the occurrence of swallowing-related complications after stroke.

Research and Practice of Cardiopulmonary Rehabilitation Training Programs

In today’s society, the incidence of cardiopulmonary diseases is increasing annually, seriously affecting patients’ quality of life. Therefore, developing a scientific and effective rehabilitation training program is of great significance. This study first analyzes the theoretical basis of cardiopulmonary rehabilitation training, including the effects of aerobic exercise, interval training, and strength training on cardiopulmonary function. Based on this, a comprehensive rehabilitation training program is designed, which includes personalized training plans, comprehensive interventions, multidisciplinary collaboration, patient education, and regular follow-up visits. The cardiopulmonary rehabilitation training plan developed in this study has certain scientific practicability, which provides a theoretical basis for cardiopulmonary rehabilitation training, and also provides a reference for medical institutions, rehabilitation centers and communities, which is helpful for promotion and application to a wider range of patients with cardiopulmonary diseases.

GeoNER:Geological Named Entity Recognition with Enriched Domain Pre-Training Model and Adversarial Training

As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge.While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition accuracy.This study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological entities.The model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer algorithm.The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

Impact of task-oriented training based on acupuncture rehabilitation on upper extremity function and quality of life of patients with early stroke

BACKGROUND Eighty percent of stroke patients develop upper limb dysfunction,especially hand dysfunction,which has a very slow recovery,resulting in economic burden to families and society.AIM To investigate the impact of task-oriented training based on acupuncture therapy on upper extremity function in patients with early stroke.METHODS Patients with early stroke hemiplegia who visited our hospital between January 2021 and October 2022 were divided into a control group and an observation group,each with 50 cases.The control group underwent head acupuncture plus routine upper limb rehabilitation training(acupuncture therapy).In addition to acupuncture and rehabilitation,the observation group underwent upper limb task-oriented training(30 min).Each group underwent treatment 5 d/wk for 4 wk.Upper extremity function was assessed in both groups using the Fugl-Meyer Assessment-Upper Extremity(FMA-UE),Wolf Motor Function Rating Scale(WMFT),modified Barthel Index(MBI),and Canadian Occupational Performance Measure(COPM).Quality of life was evaluated using the Short-Form 36-Item Health Survey(SF-36).Clinical efficacy of the interventions was also evaluated.RESULTS Before intervention,no significant differences were observed in the FMA-UE,MBI,and WMFT scores between the two groups(P>0.05).After intervention,the FMA-UE,WMFT,MBI,COPM-Functional Mobility and Satisfaction,and SF-36 scores increased in both groups(P<0.05),with even higher scores in the observation group(P<0.05).The observation group also obtained a higher total effective rate than the control group(P<0.05).CONCLUSION Task-oriented training based on acupuncture rehabilitation significantly enhanced upper extremity mobility,quality of life,and clinical efficacy in patients with early stroke.

Semantic Consistency and Correctness Verification of Digital Traffic Rules

The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).

Transurethral resection of bladder tumor: A systematic review of simulator-based training courses and curricula

Objective:Transurethral resection of bladder tumor is one of the most common everyday urological procedures.This kind of surgery demands a set of skills that need training and experience.In this review,we aimed to investigate the current literature to find out if simulators,phantoms,and other training models could be used as a tool for teaching urologists.Methods:A systematic review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement and the recommendations of the European Association of Urology guidelines for conducting systematic reviews.Fifteen out of 932 studies met our inclusion criteria and are presented in the current review.Results:The UroTrainer(Karl Storz GmbH,Tuttlingen,Germany),a virtual reality training simulator,achieved positive feedback and an excellent face and construct validity by the participants.The inspection of bladder mucosa,blood loss,tumor resection,and procedural time was improved after the training,especially for inexperienced urologists and medical students.The construct validity of UroSim®(VirtaMed,Zurich,Switzerland)was established.SIMBLA simulator(Samed GmbH,Dresden,Germany)was found to be a realistic and useful tool by experts and urologists with intermediate experience.The test objective competency model based on SIMBLA simulator could be used for evaluating urologists.The porcine model of the Asian Urological Surgery Training and Education Group also received positive feedback by the participants that tried it.The Simulation and Technology Enhanced Learning Initiative Project had an extraordinary face and content validity,and 60%of participants would like to use the simulators in the future.The 5-day multimodal training curriculum“Boot Camp”in the United Kingdom achieved an increase of the level of confidence of the participants that lasted months after the project.Conclusion:Simulators and courses or curricula based on a simulator training could be a valuable learning tool for any surgeon,and there is no doubt that they should be a part of every urologist's technical education.

Study on the Medication Rules of Traditional Chinese Medicine Prescriptions for the Treatment of New Crown Pneumonia Based on Data Mining Technology

[Objectives]This study was conducted to analyze the medication rules of clinical prescriptions of traditional Chinese medicine decoction pieces for the treatment of novel coronavirus pneumonia(COVID-19)during the epidemic in multiple regions based on data mining technology,so as to provide a reference for the treatment of COVID-19 with traditional Chinese medicine.[Methods]The traditional Chinese medicine prescriptions used since the outbreak of COVID-19 in Hubei Province during the fight against the epidemic from February 25,2020 to February 14,2022,the traditional Chinese medicine prescriptions used by Guizhou traditional Chinese medicine expert team aiding Hubei Province,the traditional Chinese medicine prescriptions for rehabilitation and conditioning of patients in Ezhou of Hubei Province after discharge,the traditional Chinese medicine prescriptions for the prevention and treatment of COVID-19 in Guizhou Province,and the traditional Chinese medicine prescriptions for the treatment of COVID-19 collected from the end of 2019 to the present from the Chinese database of CNKI were collected as the data of this study.Excel was used to establish a database and enter it into the TCM inheritance calculation platform V3.5,and the association rules and k-means clustering algorithm were used to analyze the frequency of herbal medicines in prescriptions during the treatment of COVID-19,the frequency of four natures,five flavors,meridian distribution,and drug combinations.[Results]A total of 1859 COVID-19 patients treated with traditional Chinese medicine were included,and the proportion of males was higher than that of females,and middle-aged and elderly people were the most common group.A total of 2170 prescriptions of traditional Chinese medicine were included,involving a total of 383 traditional Chinese medicines.High-frequency medicines included poria,Radix Bupleuri,Radix Scutellariae,Herba Pogostemonis,Fructus Forsythiae,Flos Loniceraeetc.The four natures were mainly concentrated in cold,warm and neutral,and the five flavors were mainly concentrated in bitter,pungent and sweet.The herbal medicines were mainly attributed to the lungs and stomach meridians,and were mainly of heat-clearing,exterior syndrome-relieving and diuresis-promoting and damp-clearing types.A total of 24 high-frequency herbal combinations and 35 association rule were excavated,and 3 types of formulas were obtained by cluster analysis.[Conclusions]The analysis results and medicine combinations obtained in the formulas are consistent with the traditional Chinese medicine treatment theory of COVID-19 caused by wind-heat filth accompanied with damp and toxin.

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

Beam Training and Tracking in mmWave Communication:A Survey

Communicating on millimeter wave(mmWave)bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission.However,mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles.To overcome these challenges,highly directional beams are exploited to achieve robust links by hybrid beamforming.Accurately aligning the transmitter and receiver beams,i.e.beam training,is vitally important to high data rate transmission.However,it may cause huge overhead which has negative effects on initial access,handover,and tracking.Besides,the mobility patterns of users are complicated and dynamic,which may cause tracking error and large tracking latency.An efficient beam tracking method has a positive effect on sustaining robust links.This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation(6G)mobile network.Especially,some open research problems are proposed to realize fast,accurate,and robust beam training and tracking.We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Enhancing clinical proficiency through laparoscopic training using Japanese origami

Objective This study aimed to investigate the clinical efficacy of laparoscopic training using origami,a traditional Japanese papercraft,using laparoscopic forceps to create origami cranes.Methods In this retrospective study,4 surgeons were randomly divided into 2 groups:The training group,consisting of surgeons 1 and 2,and the non-training group,consisting of surgeons 3 and 4.Over the course of a one-year study period,the training group regularly underwent laparoscopic surgery training with a dry box,wherein they folded a total of 1000 origami cranes using laparoscopic instruments.The non-training group periodically underwent common laparoscopic surgery training of techniques such as suturing and ligation.Each surgeon regularly performed the transabdominal preperitoneal approach for inguinal hernias.Each training was conducted concurrently with the surgeries.The procedure time(peritoneum detachment,mesh placement,and closure of the peritoneum),total operation time(time from peritoneum detachment to closure of the peritoneum),and surgical outcomes were examined.Results The training group showed greater improvement in the total operation time and more stable performance than the non-training group.Additionally,the time taken for peritoneum detachment was significantly shorter in the training group.Conclusion Laparoscopic training using origami has the potential to enhance laparoscopic surgical skills and improve surgical outcomes.

Effect of resistance training volume on body adiposity,metabolic risk,and inflammation in postmenopausal and older females:Systematic review and meta-analysis of randomized controlled trials

Purpose:This meta-analytical study aimed to explore the effects of resistance training(RT) volume on body adiposity,metabolic risk,and inflammation in postmenopausal and older females.Methods:A systematic search was performed for randomized controlled trials in PubMed,Scopus,Web of Science,and SciELO.Randomized controlled trials with postmenopausal and older females that compared RT effects on body adiposity,metabolic risk,and inflammation with a control group(CG) were included.Independent reviewers selected the studies,extracted the data,and performed the risk of bias and certainty of the evidence(Grading of Recommendations,Assessment,Development,and Evaluation(GRADE)) evaluations.Total body and abdominal adiposity,blood lipids,glucose,and C-reactive protein were included for meta-analysis.A random-effects model,standardized mean difference(Hedges’ g),and 95% confidence interval(95%CI) were used for meta-analysis.Results:Twenty randomized controlled trials(overall risk of bias:some concerns;GRADE:low to very low) with overweight/obese postmenopausal and older females were included.RT groups were divided into low-volume RT(LVRT,~44 sets/week) and high-volume RT(HVRT,~77 sets/week).Both RT groups presented improved body adiposity,metabolic risk,and inflammation when compared to CG.However,HVRT demonstrated higher effect sizes than LVRT for glucose(HVRT=-1.19;95%CI:-1.63 to-0.74;LVRT=-0.78;95%CI:-1.15 to-0.41) and C-reactive protein(HVRT=-1.00;95%CI:-1.32 to-0.67;LVRT=-0.34;95%CI,-0.63 to-0.04)) when compared to CG.Conclusion:Compared to CG,HVRT protocols elicit greater improvements in metabolic risk and inflammation outcomes than LVRT in overweight/obese postmenopausal and older females.

Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.

Boosting Adversarial Training with Learnable Distribution

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural networks.Adversarial training is one of the most potent methods to defend against adversarial attacks.However,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training.This paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture model.The distribution centroid is built to classify samples and constrain the distribution of the sample features.The natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the model.The proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial training.This algorithm gradually increases the accuracy and robustness of the model by scaling perturbation.Finally,the proposed method outputs the predicted labels and the distance between the sample and the distribution centroid.The distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model defense.The effectiveness of the proposed method is demonstrated through comprehensive experiments.

Utility of real-time 3D visualization system in the early stage of phacoemulsification training

●AIM:To determine the teaching effects of a real-time three dimensional(3D)visualization system in the operating room for early-stage phacoemulsification training.●METHODS:A total of 10 ophthalmology residents of the first-year postgraduate were included.All the residents were novices to cataract surgery.Real-time cataract surgical observations were performed using a custom-built 3D visualization system.The training lasted 4wk(32h)in all.A modified International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubric(ICO-OSCAR)containing 4 specific steps of cataract surgery was applied.The self-assessment(self)and expert-assessment(expert)were performed through the microsurgical attempts in the wet lab for each participant.●RESULTS:Compared with pre-training assessments(self 3.2±0.8,expert 2.5±0.6),the overall mean scores of posttraining(self 5.2±0.4,expert 4.7±0.6)were significantly improved after real-time observation training of 3D visualization system(P<0.05).Scores of 4 surgical items were significantly improved both self and expert assessment after training(P<0.05).●CONCLUSION:The 3D observation training provides novice ophthalmic residents with a better understanding of intraocular microsurgical techniques.It is a useful tool to improve teaching efficiency of surgical education.

Learning Vector Quantization-Based Fuzzy Rules Oversampling Method

Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.

Cambricon-QR:a sparse and bitwise reproducible quantized training accelerator

Quantized training has been proven to be a prominent method to achieve deep neural network training under limited computational resources.It uses low bit-width arithmetics with a proper scaling factor to achieve negligible accuracy loss.Cambricon-Q is the ASIC design proposed to efficiently support quantized training,and achieves significant performance improvement.However,there are still two caveats in the design.First,Cambricon-Q with different hardware specifications may lead to different numerical errors,resulting in non-reproducible behaviors which may become a major concern in critical applications.Second,Cambricon-Q cannot leverage data sparsity,where considerable cycles could still be squeezed out.To address the caveats,the acceleration core of Cambricon-Q is redesigned to support fine-grained irregular data processing.The new design not only enables acceleration on sparse data,but also enables performing local dynamic quantization by contiguous value ranges(which is hardware independent),instead of contiguous addresses(which is dependent on hardware factors).Experimental results show that the accuracy loss of the method still keeps negligible,and the accelerator achieves 1.61×performance improvement over Cambricon-Q,with about 10%energy increase.

Effect of cognitive training on brain dynamics

The human brain is highly plastic.Cognitive training is usually used to modify functional connectivity of brain networks.Moreover,the structures of brain networks may determine its dynamic behavior which is related to human cognitive abilities.To study the effect of functional connectivity on the brain dynamics,the dynamic model based on functional connections of the brain and the Hindmarsh–Rose model is utilized in this work.The resting-state fMRI data from the experimental group undergoing abacus-based mental calculation(AMC)training and from the control group are used to construct the functional brain networks.The dynamic behavior of brain at the resting and task states for the AMC group and the control group are simulated with the above-mentioned dynamic model.In the resting state,there are the differences of brain activation between the AMC group and the control group,and more brain regions are inspired in the AMC group.A stimulus with sinusoidal signals to brain networks is introduced to simulate the brain dynamics in the task states.The dynamic characteristics are extracted by the excitation rates,the response intensities and the state distributions.The change in the functional connectivity of brain networks with the AMC training would in turn improve the brain response to external stimulus,and make the brain more efficient in processing tasks.