Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Representation Learning Determines Drug Mechanism of Action from Cell Painting Images

Fluorescent-based microscopy screens carry a broad range of phenotypic information about how compounds affect cellular biology. From changes in cellular morphology observed in these screens, one key area of medicinal interest is determining a compound’s mechanism of action. However, much of this phenotypic information is subtle and difficult to quantify. Hence, creating quantitative embeddings that can measure cellular response to compound perturbation has been a key area of research. Here we present a deep learning enabled encoder called MOAProfiler that captures phenotypic features for determining mechanism of action from Cell Painting images. We compared our method with both a traditional computer vision means of feature encoding via CellProfiler and a deep learning encoder called DeepProfiler. The results, on two independent and biologically different datasets, indicated that MOAProfiler encoded MOA-specific features that allowed for more accurate clustering and classification of compounds over hundreds of different MOAs.
Reply to ``Comment on `Extension of the Bethe-Weizsäcker mass formula to light nuclei and some new shell closures' ''

Some properties of the modified Bethe-Weizsacker mass formula (BWM) are discussed. As BWM has no shell effect included, the extra-stability or, magicity in nuclei clearly stands out when experimental mass data are compared with BWM predictions. If the shell effect quenches, the BWM predictions come closer to the experimental data.
Magnetic hallmarks of viscous electron flow in graphene

We propose a protocol to identify spatial hallmarks of viscous electron flow in graphene and other two-dimensional viscous electron fluids. We predict that the profile of the magnetic field generated by hydrodynamic electron currents flowing in confined geometries displays unambiguous features linked to whirlpools and backflow near current injectors. We also show that the same profile sheds light on the nature of the boundary conditions describing friction exerted on the electron fluid by the edges of the sample. Our predictions are within reach of vector magnetometry based on nitrogen-vacancy centers embedded in a diamond slab mounted onto a graphene layer.
An Enormous Class of Double Half-Heusler Compounds with Low Thermal Conductivity

Since their discovery around a century ago, the structure and chemistry of the multi-functional half-Heusler semiconductors have been studied extensively as three component systems. The elemental groups constituting these ternary compounds with the nominal formula XYZ are well established. From the very same set of well-known elements we explore a phase space of quaternary double ($X'X''Y_2Z_2$, $X_2Y'Y''Z_2$, and $X_2Y_2Z'Z''$), triple ($X_2'X''Y_3Z_3$) and quadruple ($X_3'X''Y_4Z_4$) half-Heusler compositions which 10 times larger in size. Using a reliable, first-principles thermodynamics methodology on a selection of 347 novel compositions, we predict 127 new stable quaternary compounds, already more than the 89 reported almost exhaustively for ternary systems. Thermoelectric performance of the state-of-the-art ternary half-Heusler compounds are limited by their intrinsically high lattice thermal conductivity ($\kappa_{L}$). In comparison to ternary half-Heuslers, thermal transport in double half-Heuslers is dominated by low frequency phonon modes with smaller group velocities and limited by disorder scattering. The double half-Heusler composition Ti$_2$FeNiSb$_2$ was synthesized and confirmed to have a significantly lower lattice thermal conductivity (factor of 3 at room temperature) than TiCoSb, thereby providing a better starting point for thermoelectric efficiency optimization. We demonstrate a dependable strategy to assist the search for low thermal conductivity half-Heuslers and point towards a huge composition space for implementing it. Our findings can be extended for systematic discovery of other large families of multi-component intermetallic semiconductors.
Centrality dependence of strangeness and (anti)hyperon production at sNN=200 GeV

We evaluate strangeness produced in Au--Au interactions at $\sqrt{s_{\rm NN}}=200$ GeV, as function of reaction participant number $A$, and obtain the relative strange quark content at hadronization. Strange baryon and antibaryon rapidity density yields are studied, relative to, and as function of, participant number, and produced hadron yields.
Shell model study of the neutron rich nuclei around N = 28

We describe the properties of the neutron-rich nuclei around N=28 in the shell mode framework. The valence space includes the sd shell for protons and the pf shell for neutrons without any restriction. Good agreement is found with the available experimental data. The N=28 shell closure persists, even if eroded by the large neutron excess. The calculations predict that {sup 40}S and {sup 42}S are deformed with {beta}=0.29 and {beta}=0.32 respectively. {copyright} {ital 1997} {ital The American Physical Society}
A Routing Algorithm Based on Optimistic Path Analysis

To solve the problem of low delivery rate, high network load and high average forwarding delay caused by blind forwarding of messages in mobile social networks considering the selfish attribute of nodes, the optimistic path analysis (OPA) algorithm is proposed. OPA puts forward the concept of encounter intensity based on the historical encounter records and gives its calculation formula. Encounter intensity uses time as an important basis, which can more accurately reflect the possibility of two nodes meeting next time. At the same time, based on the intensity of encounter, the “small world principle” is used to limit the number of hops of routing paths, and finally a set of preferred paths for the message from the source node to the destination node is constructed, which is used as a basis to determine whether the message is forwarded or not. Simulation results show that compared with classic routing algorithms, OPA can effectively increase message delivery rate, reduce network load and forwarding delay.
NWP Feature Selection and GCN-based Ultra-short-term Wind Farm Cluster Power Forecasting Method

Cluster-level wind power forecasting is of great significance for the centralized integration of wind power into the grid. Studies have shown that adjacent wind farms have high spatial correlation, from whose power and numerical weather prediction (NWP) data the graph convolutional neural network (GCN) can well extract spatio-temporal features. However, existing GCN-based methods for wind power forecasting have not considered the redundant information and noisy data contained in NWPs which may also be extracted by GCN, thus leading to many problems such as high model complexity and computational cost, suboptimal model training results and decrease in prediction accuracy. Focusing on this problem, this paper selects the optimal feature subsets from the available NWPs of wind farm cluster using maximum relevance minimum redundancy (MRMR) algorithm based on mutual information (MI) theory. Cross-validation is applied to ensure that the selected features maximize the valuable information of the NWPs while minimizing the redundant information and noisy data contained. The simulation results show that selecting fewer features can make errors smaller than the state-of-the-art deep learning models and reduce the computational cost under the premise of ensuring the prediction accuracy.
Research on Efficient Information Mining Method for Defect Record of Traction Power Supply Equipment

The defect record of traction power supply equipment not only describes the defect situation of the equipment, but also provides historical experience for the defect treatment of similar equipment. The recognition of named entity information of the defect record is the premise of constructing the knowledge graph. In order to mine the entity information of defect records more efficiently and accurately, this paper proposes a named entity recognition algorithm to solve the current situation of lack of utilization and analysis of defect records of traction power supply equipment and low efficiency of manual information processing modes. The bidirectional encoder representation from transformers (BERT) pre-training language model is used as the encoding layer of the word vector, the bidirectional long short term memory (BiLSTM) is used as the character label prediction layer, and the conditional random field (CRF) is used to output the global optimal label. The defect records of the traction power supply equipment of a maintenance department from 2016 to 2019 are used as the data set for case study, and, the comprehensive evaluation index of entity recognition of the model has reached 94.66%, saving 98.50% time compared with manual recognition. The results show that the entity information recognition algorithm proposed in this paper can effectively and accurately mine the defect record information of traction power supply equipment.
Are Male Candidates Better than Females? Debiasing BERT Resume Retrieval System*

Advanced language models like BERT have great performances in various Natural Language Processing tasks. However, recent researches have shown that language models can learn gender biases from corpus, and lead to discriminatory decisions and unfair allocation of resources. We proposed a measure of gender bias in BERT resume retrieval system, by performing job searches on a group of resumes with different genders but consistent abilities. We proposed to calculate the average ranking and Discounted Cumulative Gain (DCG) scores of male and female resumes, and found that men outperformed women even though the two resumes were identical except for gender. This shows BERT has gender stereotypes, and its resume retrieval systems prefer male candidates. Therefore, we also proposed a regularized debiasing method to promote gender equity. Referring to Densifier method, we can get the subspace vector encoding bias semantics through matrix transformation of word vector difference between occupational and gender words. By defining the correlation between BERT word vector and gender bias subspace as the loss term, we can remove the bias semantics in BERT, and also avoid it learning stereotypes even trained on an unfair data set. After regularized debiased, the gender ranking gap of BERT was reduced by an average of 61.8%, while DCG scores were reduced by 53.9%.
Multi-Element Fully Decoupled Inverted-F Antennas for Mobile Terminals

In this article, a multielement fully decoupled linearly placed inverted-F antenna (IFA) array is proposed for mobile terminals. A hybrid decoupling method is used to mitigate the mixed types of mutual couplings among the multiple IFAs: introducing multiple decoupling capacitors for the couplings of two back-to-back IFA pairs and T-shaped mode transformers for the remaining couplings. The decoupling capacitors not only decouple the back-to-back IFA pairs but also broaden the impedance bandwidth of each IFA. The T-shaped mode transformer transforms the concerned antenna from an IFA mode into a balanced loop mode, preventing the induced current on the victim IFAs of the face-to-face and back-to-face pairs. One four-element example and one eight-element example are designed and measured to justify the proposed decoupling concept. The measured results of the four-element IFAs show that after decoupling, all the six mutual couplings among the four IFAs are reduced by about 10 dB from 3.3 to 3.8 GHz, with average total efficiencies throughout the band improving from 57% to 72% for IFA 1 and from 47% to 55% for IFA 2. An eight-element IFA array is built utilizing the pattern diversity of two proposed four-element IFA blocks, achieving inter-block isolation of better than 20 dB. And a six-element linear IFA array is also designed to prove the generality of the proposed method.
Deep Reinforcement Learning-based UAV-assisted Mobile Edge Computing Offloading and Resource Allocation Decision

Nowadays, the surge of user data traffic has brought great challenges to the computing and energy capacity of mobile terminals (MTs). Mobile edge computing (MEC) technology is reckoned to be an efficient method to alleviate this problem. It can transfer tasks to MEC server and improve quality of service (QoS). In case of network failure, unmanned aerial vehicle (UAV) is deployed as a data transmission hub connecting MEC server to restore the network. In this article, we consider a UAV transmission hub (UTH) to communicate with the macro base station (MBS). MTs can offload tasks to MBS for processing through UTH, and the MEC server in MBS allocates computing resources to MTs. We raise a computing offloading and resource allocation decision scheme based on deep deterministic policy gradient (DDPG). The scheme considers the continuous generation of dynamic tasks, and the optimization objectives is to minimize the long-term average system cost. The simulation experiment datas verify the performance of DDPG-Based offloading and resource allocation decision scheme. It can validly optimize the average system cost in a random dynamic environment.
Performant Binary Fuzzing without Source Code using Static Instrumentation

Advancements in fuzz testing have achieved the ability to quickly and comprehensively find security-critical faults in software systems. Yet, some of these techniques rely on access to source code, which is often unavailable in practice. In this paper, we explore techniques to replicate the depth and efficiency of source-code available fuzzers via static binary instrumentation. Developing such instrumentation is difficult because compilation is a lossy process, and much of the source-level semantics leveraged by these techniques are not available in binaries. We recover much of this information via heuristic control flow reconstruction, a shadow stack for function identification, and a novel technique for instrumenting comparison instructions. We evaluate RWFUZZ on the LAVA-M dataset, achieving the same effectiveness as a best-in-class source-available fuzzer with a $3.4\times$ execution time overhead (lower than existing dynamic fuzzing approaches). In this way, we show that techniques for binary fuzzing may approach the functional ability of source-available fuzzing.
Design and Development of We-CDSS Using Django Framework: Conducing Predictive and Prescriptive Analytics for Coronary Artery Disease

Clinical Decision Support System (CDSS) has become the ultimate point of care for healthcare sectors to assist the public in terms of Medical care. Through this research work, we propose the design and development of a Web-based Clinical Decision Support System (We-CDSS) using the Django framework with an aim to make CDSS accessible for both common people and clinicians on computers and mobile devices that integrates Predictive Analytics involving the LWGMK-NN algorithm for predicting Coronary Artery Disease, and Prescriptive Analytics involving prescription rules. The proposed research work consists of three phases. The first phase presents the design and development of a web-based CDSS outlook using the Django framework. The second and final phase embeds Predictive Analytics and Prescriptive Analytics into the We-CDSS to manage the functionalities of predictive decisions, and the functionalities of prescriptive processes that evaluate the predictive results and recommends personalized lifestyle suggestions to reduce the risk of coronary artery disease.
Balanced Power, Speed and Area Transformation Procedure for Finite State Machines

A balanced method for the merging and splitting transformation procedures for incompletely specified finite state machines implemented on field programmable logic arrays is proposed. In this method, such optimization criteria as the power consumption, speed of operation and cost of implementation are considered already in the early phase of FSM synthesis. The method also takes into account the technological features of programmable logic and the state assignment method. The transformation quality ratio is calculated on the base of estimations of power, speed and area parameters and the user can select the direction of the optimization by setting weights for each criterion. The approach to the estimation of optimization criteria values is presented and experimental results are also discussed.
Multi-Step Occurrence Prediction of Urban Crimes with Enhanced GRU-ODE-Bayes Model

Crime occurrence prediction is an important task for public security and urban governance. In real world, some crimes are quite harmful, thus it’s necessary to predict these crimes in the next few days (weeks). However, these harmful crime events may have relatively lower frequency, and their records are discontinuous and sporadic, making the multistep occurrence prediction difficult. Due to the sparsity and irregularity of the crime sequences, most of the existing crime prediction techniques do not distinguish crime types and have difficulties to predict low-frequency crimes’ occurrences. In addition, they cannot accurately predict the occurrence of crimes over multiple recent time slots due to sporadic crime records. In this paper, we propose Multi-Time-Slot Crime Occurrence Prediction (MCOP) model, which aims to (1) predict crime types separately and (2) predict crime occurrences in the next multiple time slots. Specifically, MCOP uses a GRU-ODE-Bayes model to handle the discontinuous and sporadic crime events sequences. MCOP is enhanced by a data augmentation to address the data sparsity problem, and exploits the near repeat phenomenon to enable continuous crime predictions. We evaluated our model using real-world urban data, and the results showed our model outperforms the baseline techniques.
A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services

The purpose of this paper is to explore the performance of graph neural network (GNN) based short-term traffic supply-demand gap prediction for online car-hailing. In recent years, with the rapid development of smart cities, the technology as well as the scale of online car-hailing has grown rapidly, and it is necessary to analyze the travel characteristics of online car-hailing, and secondly, there are quite few studies on the supply-demand gap prediction of online car-hailing. Based on the dataset of online car-hailing operation, we analyzed the travel characteristics of online car-hailing from several dimensions, such as traffic congestion, weather, air quality, and temperature. In response to the current road traffic congestion and the reasonable allocation of online car-hailing, this paper proposes an online car-hailing supply-demand gap prediction model based on graph convolutional neural network and long and short-term memory neural network (GCN-LSTM), with mean absolute error (MAE) and root mean squared error (RMSE) as the evaluation index, and analyzes the performance of the model through simulation. The results show that the MAE and RMSE of the proposed method is only 12.3 and 26.4, respectively, which performs better than LightGBM, LSTM and other models on this dataset. Therefore, the constructed model for predicting the supply-demand gap of online car-hailing has a high-quality prediction performance.
COVID-19 Self-Test Guidance System For Swab Collection Using Deep Learning

The COVID-19 rapid antigen self-test kits are widely administered in several countries to increase the testing frequency and reduce the load on clinics for in-person tests. Yet, the telehealth worker supervision is mandatory to ensure proper sampling procedure is followed and high-quality swab samples are taken. To reduce the load on the health workers in telehealth, we propose a system that eliminates the need for any human supervision by guiding the testers throughout the self-test to ensure the collection of high-quality swab samples. The proposed system takes a live video stream of the frontal face of a user as input and provides real-time instructions to do the self-test correctly with corrective actions when detecting wrong steps. This is mainly done using a collection of deep learning (DL) models. The system uses a novel swab position classification model, Small-MobileNetV2 with Depth-Wise Attention (S-MBNV2-DWAtt), to detect whether a swab is in one of the nostrils or not, which is an optimized version of MobileNetV2 in terms of parameter count and inference speed. The depth-wise attention block allows it to focus on specific parts of the images where the swabs would possibly lie. Lastly, a large-scale synthetic dataset is created to increase the generalization to a variety of swabs and users and a small real dataset is collected to finetune the model on scenes that are similar to the deployment scenarios. The proposed swab position classification model is found to have outstanding performance in terms of both accuracy and speed; it outperforms the ResNet and VGG architectures by 22.83% and 35.11% respectively on a real-world test set while operating at 25 FPS on CPU.
Investigation of Periodically Unsteady Flow in a Radial Pump by CFD Simulations and LDV Measurements

The periodically unsteady flow fields in a low specific speed radial diffuser pump have been investigated both numerically and experimentally for the design condition (Q des ) and also one part-load condition (0.5Q des ). Three-dimensional, unsteady Reynolds-averaged Navier―Stokes equations are solved on high-quality structured grids with the shear stress transport turbulence model by using the CFD (computational fluid dynamics) code CFX-10. Furthermore, two-dimensional laser Doppler velocimetry (LDV) measurements are successfully conducted in the interaction region between the impeller and the vaned diffuser, in order to capture the complex flow with abundant measurement data and to validate the CFD results. The analysis of the obtained results has been focused on the behavior of the periodic velocity field and the turbulence field, as well as the associated unsteady phenomena due to the unsteady interaction. In addition, the comparison between CFD and LDV results has also been addressed. The blade orientation effects caused by the impeller rotation are quantitatively examined and detailedly compared with the turbulence effect. This work offers a good data set to develop the comprehension of the impeller-diffuser interaction and how the flow varies with relative impeller position to the diffuser in radial diffuser pumps.