Data complexity-based batch sanitization method against poison in distributed learning

The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate attacks.Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model training.We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection process.Our model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new attacks.Being neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.

Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms.In this paper,we experimentally validate the HAR process and its various algorithms independently.On the base of which,it is further proposed that,in addition to the necessary eigenvalues and intelligent algorithms,correct prior knowledge is even more critical.The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object,the sampling process,the sampling data,the HAR algorithm,etc.Thus,a solution is presented under the guidance of right prior knowledge,using Back-Propagation neural networks(BP networks)and simple Convolutional Neural Networks(CNN).The results show that HAR can be achieved with 90%–100%accuracy.Further analysis shows that intelligent algorithms for pattern recognition and classification problems,typically represented by HAR,require correct prior knowledge to work effectively.

A Localized-Statistic-Based Approach for Biomarker Identification of Omics Data

Omics data provides an essential means for molecular biology and systems biology to capture the systematic properties of inner activities of cells. And one of the strongest challenge problems biological researchers have faced is to find the methods for discovering biomarkers for tracking the process of disease such as cancer. So some feature selection methods have been widely used to cope with discovering biomarkers problem. However omics data usually contains a large number of features, but a small number of samples and some omics data have a large range distribution, which make feature selection methods remains difficult to deal with omics data. In order to overcome the problems, wepresent a computing method called localized statistic of abundance distribution based on Gaussian window(LSADBGW) to test the significance of the feature. The experiments on three datasets including gene and protein datasets showed the accuracy and efficiency of LSADBGW for feature selection.

High-Intensity Focused Ultrasound-Induced Disulfide Mechanophore Activation in Polymeric Nanostructures for Molecule Release

Ultrasound(US)activation of mechanophores in polymers that initiates cascade chemical reactions is a promising strategy for on-demand molecule release.However,the typical US frequency used for mechanochemistry is around 20 kHz,producing inertial cavitation that exceeds the tolerance threshold of biological systems.Here,high-intensity focused US(HIFU)as a mechanical stimulus is introduced to drive the activation of disulfide mechanophores in hyperbranched star polymers(HBSPs)and microgels(MGLs).The mechanism of molecular release is attributed to the thiol-disulfide exchange reaction and subsequent intramolecular cyclization.We reveal that HBSPs and MGLs effectively transduce HIFU as mechanical input to chemical output,demonstrated by the quantification of the release of fluorescent umbelliferone(UMB).Moreover,an in vitro study of drug release is carried out using camptothecin as the model drug,which is covalently loaded in MGLs,demonstrating the potential of our system for controlled drug delivery to cancer cells.

Effect of Martensite Fine Structure on Mechanical Properties of an 1100 MPa Grade Ultra-high Strength Steel

An 1100 MPa grade ultra-high strength steel with different martensite fine structures, characterized by prior austenite grain size, martensite packet size, block width and lath width, was studied by various heat treatment processes. The result shows that with decreasing prior austenite grain size, both the packet size and block width decrease, while the lath width has virtually no change. Accordingly, both strength and toughness increase, while total elongation decreases. The yield strength has a Hall Petch type relationship with the prior austenite grain size, packet size and block width, and the block width may be regarded as a key factor influencing strength. On the other hand, the ductile to brittle transition temperature （DBTT） is found to be more related lo the packet size, which may be considered as a dominant factor influencing toughness.

Gelatin-based composite hydrogels with biomimetic lubrication and sustained drug release

The occurrence of osteoarthritis is closely related to progressive and irreversible destruction of the articular cartilage,which increases the friction significantly and causes further inflammation of the joint.Thus,a scaffold for articular cartilage defects should be developed via lubrication restoration and drug intervention.In this study,we successfully synthesized gelatin-based composite hydrogels,namely GelMA–PAM–PMPC,with the properties of biomimetic lubrication and sustained drug release by photopolymerization of methacrylic anhydride modified gelatin(GelMA),acrylamide(AM),and 2-methacryloyloxyethyl phosphorylcholine(MPC).Tribological test showed that the composite hydrogels remarkably enhanced lubrication due to the hydration lubrication mechanism,where a tenacious hydration shell was formed around the zwitterionic phosphocholine headgroups.In addition,drug release test indicated that the composite hydrogels efficiently encapsulated an anti-inflammatory drug(diclofenac sodium)and achieved sustained release.Furthermore,the in vitro test revealed that the composite hydrogels were biocompatible,and the mRNA expression of both anabolic and catabolic genes of the articular cartilage was suitably regulated.This indicated that the composite hydrogels could effectively protect chondrocytes from inflammatory cytokine-induced degeneration.In summary,the composite hydrogels that provide biomimetic hydration lubrication and sustained local drug release represent a promising scaffold for cartilage defects in the treatment of osteoarthritis.

MATRIEX imaging:multiarea two-photon real-time in vivo explorer

Two-photon laser scanning microscopy has been extensively applied to study in vivo neuronal activity at cellular and subcellular resolutions in mammalian brains.However,the extent of such studies is typically confined to a single functional region of the brain.Here,we demonstrate a novel technique,termed the multiarea two-photon real-time in vivo explorer(MATRIEX),that allows the user to target multiple functional brain regions distributed within a zone of up to 12mm in diameter,each with a field of view(FOV)of ~200μm in diameter,thus performing two-photon Ca2+imaging with single-cell resolution in all of the regions simultaneously.For example,we demonstrate real-time functional imaging of single-neuron activities in the primary visual cortex,primary motor cortex and hippocampal CA1 region of mice in both anesthetized and awake states.A unique advantage of the MATRIEX technique is the configuration of multiple microscopic FOVs that are distributed in three-dimensional space over macroscopic distances(>1 mm)both laterally and axially but that are imaged by a single conventional laser scanning device.In particular,the MATRIEX technique can be effectively implemented as an add-on optical module for an existing conventional single-beam-scanning two-photon microscope without requiring any modification to the microscope itself.Thus,the MATRIEX technique can be readily applied to substantially facilitate the exploration of multiarea neuronal activity in vivo for studies of brain-wide neural circuit function with single-cell resolution.

A multi-band atomic candle with microwave-dressed Rydberg atoms

Stabilizing important physical quantities to atom-based standards lies at the heart of modern atomic,molecular and optical physics,and is widely applied to the field of precision metrology.Of particular importance is the atom-based microwave field amplitude stabilizer,the so-called atomic candle.Previous atomic candles are realized with atoms in their ground state,and hence suffer from the lack of frequency band tunability and small stabilization bandwidth,severely limiting their development and potential applications.To tackle these limitations,we employ microwave-dressed Rydberg atoms to realize a novel atomic candle that features multi-band frequency tunability and large stabilization bandwidth.We demonstrate amplitude stabilization of microwave field from C-band to Ka-band,which could be extended to quasi-DC and terahertz fields by exploring abundant Rydberg levels.Our atomic candle achieves stabilization bandwidth of 100 Hz,outperforming previous ones by more than two orders of magnitude.Our simulation indicates the stabilization bandwidth can be further increased up to 100 kHz.Our work paves a route to develop novel electric field control and applications with a noise-resilient,miniaturized,sensitive and broadband atomic candle.

Operational effects of the UNOT gate on classical and quantum correlations

The NOT gate that flips a classical bit is ubiquitous in classical information processing.However its quantum analogue,the universal NOT(UNOT) gate that flips a quantum spin in any alignment into its antipodal counterpart is strictly forbidden.Here we explore the connection between this discrepancy and how UNOT gates affect classical and quantum correlations.We show that while a UNOT gate always preserves classical correlations between two spins,it can non-locally increase or decrease their shared discord in ways that allow violation of the data processing inequality.We experimentally illustrate this using a multi-level trapped ^(171)Yb^+ ion that allows simulation of anti-unitary operations.

Gait phase detection by using a portable system and artificial neural network

Gait phases are important to evaluate the walking function and to identify the characteristics of pathological gaits.However,it is difficult to differentiate gait phases outside gait laboratories,thus,this study aimed to develop a method to detect 8 gait sub-phases using a wearable multiple sensor system and artificial neural network(ANN).Motion sensors were used to acquire the acceleration of lower limbs,and force sensitive resistors were used to detect contact state and force between the foot and the ground.Walking was recorded using a high-speed camera.Two feed forward back-propagation(BP)neural networks were developed.The resilient BP algorithm was used to train ANN.A total of 66 volunteers participated in this study.For the stance and swing phase detection,simulation of the training data showed an accuracy of 98.0%.The data from the test set showed a recognition accuracy of 97.75%.Because the ending point of the last phase‘Terminal Swing’is always 100%GC,we only listed seven phases.The prediction accuracy of seven phases were:35.9%,63.8%,93.6%,94.9%,94.8%,97.9%and 98%using the limb acceleration data only.The average accuracy for seven phases were 68%,91.3%,97.8%,98.9%,98.8%,99.1%,and 99.5%using the limb acceleration and foot pressure data for fast,normal,and slow gait speeds.This study provides a new method for eight gait sub-phases detection with high accuracy combining a wearable system and ANN,which may make gait phase analysis possible under free-living conditions.

Modeling and Utilization of Biomass-to-syngas for Industrial Multi-energy Systems

Excessive consumption of fossil fuels in the industry sector has caused hij»h operating costs and severe environmental pollution,advocating a cost-effective and sustainable substitute for fossil fuels.I'his paper proposes an enhanced utilization mechanism of biomass-to-syngas(B2S)to provide various types of steam flows in industrial multi-energy systems(MESs).In this mechanism,the available generations from renewable energy sources(RESs)can be harvested to assist in the biomass gasification in a B2S gasifier for enhancing the syngas yield and its calorific value.A thermodynamic interaction model for B2S is formulated to capture gasification temperature dynamics under high-temperature steam injections and optimally control the thermochemical behaviors of biomass drying,pyrolysis,and gasification.A B2S based energy hub framework with its multienergy coupling matrix is formulated for mapping the input hiomass-wind-solar energy into electricity,syngas,and various types of’steam carriers to satisfy industrial energy cieniands.A hierarchical multi-timeframe dispatch scheme is developed for the energy-efficient conversion and utilization of multi-energy carriers to minimize the system operation costs.Comparative studies are implemented to demonstrate the superior performance of the proposed methodology on system operational economy and sustainability.

High-efficiency inspecting method for mobile robots based on task planning for heat transfer tubes in a steam generator

Many heat transfer tubes are distributed on the tube plates of a steam generator that requires periodic inspection by robots.Existing inspection robots are usually involved in issues:Robots with manipulators need complicated installation due to their fixed base;tube mobile robots suffer from low running efficiency because of their structural restricts.Since there are thousands of tubes to be checked,task planning is essential to guarantee the precise,orderly,and efficient inspection process.Most in-service robots check the task tubes using row-by-row and column-bycolumn planning.This leads to unnecessary inspections,resulting in a long shutdown and affecting the regular operation of a nuclear power plant.Therefore,this paper introduces the structure and control system of a dexterous robot and proposes a task planning method.This method proceeds into three steps:task allocation,base position search,and sequence planning.To allocate the task regions,this method calculates the tool work matrix and proposes a criterion to evaluate a sub-region.And then all tasks contained in the sub-region are considered globally to search the base positions.Lastly,we apply an improved ant colony algorithm for base sequence planning and determine the inspection orders according to the planned path.We validated the optimized algorithm by conducting task planning experiments using our robot on a tube sheet.The results show that the proposed method can accomplish full task coverage with few repetitive or redundant inspections and it increases the efficiency by 33.31% compared to the traditional planning algorithms.