nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning for Imaging Flow Cytometry : 1 Cell Cycle Analysis of Jurkat Cells

18 Imaging flow cytometry combines the fluorescence sensitivity and high-throughput 19 capabilities of flow cytometry with single-cell imaging, and hence provides high-volume 20 data well-matched to the strengths of deep learning. We present DeepFlow, a data 21 analysis workflow for imaging flow cytometry that combines deep convolutional neural 22 networks with non-linear dimension reduction. DeepFlow uses learned features of the 23 neural network to visualize, organize and biologically interpret single-cell data. 24 Dissecting the cell cycle as a source of cell-to-cell variability is crucial for quantitative 25 single-cell biology. We demonstrate DeepFlow for a large dataset of cell-cycling Jurkat 26 cells. First, we reconstruct the cells’ continuous progression through cell cycle from raw 27 image data. This shows that DeepFlow can learn a continuous distance measure between 28 categorical phenotypes. Second, we are able to detect and separate a subpopulation of 29 dead cells, although the data set had been cleaned using established approaches. 30 DeepFlow detects this morphologically abnormal subpopulation in an unsupervised 31 manner. Third, in label-free classification of cell cycle phases, we reach a 6-fold 32 reduction in error rate as compared to a recent approach based on boosting on a series 33 of image features. In contrast to previous methods, DeepFlow’s predictions are fast 34 enough to consider integration with the imaging flow cytometry measurement process. 35
Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment

Aggregation structures with explicit information, such as image attributes and scene semantics, are effective and popular for intelligent systems for assessing aesthetics of visual data. However, useful information may not be available due to the high cost of manual annotation and expert design. In this paper, we present a novel multi-patch (MP) aggregation method for image aesthetic assessment. Different from state-of-the-art methods, which augment an MP aggregation network with various visual attributes, we train the model in an end-to-end manner with aesthetic labels only (i.e., aesthetically positive or negative). We achieve the goal by resorting to an attention-based mechanism that adaptively adjusts the weight of each patch during the training process to improve learning efficiency. In addition, we propose a set of objectives with three typical attention mechanisms (i.e., average, minimum, and adaptive) and evaluate their effectiveness on the Aesthetic Visual Analysis (AVA) benchmark. Numerical results show that our approach outperforms existing methods by a large margin. We further verify the effectiveness of the proposed attention-based objectives via ablation studies and shed light on the design of aesthetic assessment systems.
Compression therapy for venous leg ulcers: risk factors for adverse events and complications, contraindications - a review of present guidelines.

The adequate use of compression in venous leg ulcer treatment is equally important to patients as well as clinicians. Currently, there is a lack of clarity on contraindications, risk factors, adverse events and complications, when applying compression therapy for venous leg ulcer patients.



The project aimed to optimize prevention, treatment and maintenance approaches by recognizing contraindications, risk factors, adverse events and complications, when applying compression therapy for venous leg ulcer patients. A literature review was conducted of current guidelines on venous leg ulcer prevention, management and maintenance.



Searches took place from 29th February 2016 to 30th April 2016 and were prospectively limited to publications in the English and German languages and publication dates were between January 2009 and April 2016. Twenty Guidelines, clinical pathways and consensus papers on compression therapy for venous leg ulcer treatment and for venous disease, were included. Guidelines agreed on the following absolute contraindications: Arterial occlusive disease, heart failure and ankle brachial pressure index (ABPI) <0.5, but gave conflicting recommendations on relative contraindications, risks and adverse events. Moreover definitions were unclear and not consistent.



Evidence-based guidance is needed to inform clinicians on risk factor, adverse effects, complications and contraindications. ABPI values need to be specified and details should be given on the type of compression that is safe to use. Ongoing research challenges the present recommendations, shifting some contraindications into a list of potential indications. Complications of compression can be prevented when adequate assessment is performed and clinicians are skilled in applying compression.
High Density Through Wafer Via Technology

The Through Silicon Via (TSV) process developed by Silex offers sub 50 μm pitch for through wafer connections in up to 600 μm thick substrates. Silex via process enables MEMS designs with significantly reduced die size and true "Wafer Level Packaging" features that are particularly important in consumer market applications. The TSV technology also enables integration of advanced interconnect functions in optical MEMS, sensors and microfluidic devices. With several foundry customers using the process today and an extraordinary line-up of potential users, Silex aims at making the process a standard in the MEMS industry. The swift propagation of the technology will be facilitated by reasonable licensing agreements and technology transfer programs with customers and partners who may favor implementation in their existing manufacturing lines. This paper gives a brief introduction to the via formation process and focuses in more detail on the novel solutions made available by this enabling technology.
Giving Doppler More Bounce: A 5.8 GHz Microwave High-Sensitivity Doppler Radar System

This article has described the development of a 5.8-GHz microwave Doppler radar. The radar system's detection capability has been demonstrated under the testing environment diagrammed in the paper. To receive the maximum scattered field from the moving metallic target, the Tx and Rx ports of the radar module share a single antenna array using a PCB-based isolation device to realize a monostatic radar architecture. Due to the radar's operating frequency of 5.8 GHz, the minimum detectable motion range of the developed radar is limited to 100 nm. In the future, a higher operating frequency, such as 24 GHz, could be chosen to obtain a smaller detectable motion range.
Prediction of Heart Disease using Data Mining Techniques

Data mining is process to analyses number of data sets and then extracts the meaning of data. It helps to predict the patterns and future trends, allowing business in decision making. Data mining applications are able to give the answer of business questions which can take much time to resolve traditionally. High amount of data that can be generated for the prediction of disease is analyzed traditionally and is too complicated along with voluminous to be processed. Data mining provides methods and techniques for transformation of the data into useful information for decision making. These techniques can make process fast and take less time to predict the heart disease with more accuracy. The healthcare sector assembles enormous quantity of healthcare data which cannot be mined to uncover hidden information for effectual decision making. However, there is a plenty of hidden information in this data which is untapped and not being used appropriately for predictions. It becomes more influential in case of heart disease that is considered as the predominant reason behind death all over the world. In medical field, Data Mining provides several methods which are widely used in the medical and clinical decision support systems which should be helpful for diagnosis and predicting of various diseases. These data mining techniques can be used in heart diseases takes less time and make the process much faster for the prediction system to predict diseases with good accuracy to improve their health. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. By Applying data mining techniques to heart disease data which requires to be processed, we can get effective results and achieve reliable performance which will help in decision making in healthcare industry. It will help the medical practitioners to diagnose the disease in less time and predict probable complications well in advance. Identify the major risk factors of Heart Disease categorizing the risk factors in an order which causes damages to the heart such as diabetes, high blood cholesterol, obesity, hyper tension, smoking, poor diet, stress, etc. Data mining techniques and functions are used to identify the level of risk factors which helps the patients to take precautions in advance to save their life. Data mining, disease prediction, KNN,decision tree, SVM
Compliance of IEEE 802.22 WRAN for field area network in smart grid

Distributed power system network is going to be complex, and it will require high-speed, reliable and secure communication systems for managing intermittent generation with coordination of centralised power generation, including load control. Cognitive Radio (CR) is highly favourable for providing communications in Smart Grid by using spectrum resources opportunistically. The IEEE 802.22 Wireless Regional Area Network (WRAN) having the capabilities of CR use vacant channels opportunistically in the frequency range of 54 MHz to 862 MHz occupied by TV band. A comprehensive review of using IEEE 802.22 for Field Area Network in power system network using spectrum sensing (CR based communication) is provided in this paper. The spectrum sensing technique(s) at Base Station (BS) and Customer Premises Equipment (CPE) for detecting the presence of incumbent in order to mitigate interferences is also studied. The availability of backup and candidate channels are updated during “Quite Period” for further use (spectrum switching and management) with geolocation capabilities. The use of IEEE 802.22 for (a) radio-scene analysis, (b) channel identification, and (c) dynamic spectrum management are examined for applications in power management.
Adaptation and Re-identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-identification

Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras. To address this task, one typically requires a large amount labeled data for training an effective Re-ID model, which might not be practical for real-world applications. To alleviate this limitation, we choose to exploit a sufficient amount of pre-existing labeled data from a different (auxiliary) dataset. By jointly considering such an auxiliary dataset and the dataset of interest (but without label information), our proposed adaptation and re-identification network (ARN) performs unsupervised domain adaptation, which leverages information across datasets and derives domain-invariant features for Re-ID purposes. In our experiments, we verify that our network performs favorably against state-of-the-art unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID methods which require fully supervised data for training.
Psychological and behavioral correlates of family violence in child witnesses and victims.

Abused and nonabused child witnesses to parental violence temporarily residing in a battered women's shelter were compared to children from a similar economic background on measures of self-esteem, anxiety, depression, and behavior problems, using mothers' and self-reports. Results indicated significantly more distress in the abused-witness children than in the comparison group, with nonabused witness children's scores falling between the two. Age of child and types of violence were mediating factors. Implications of the findings are discussed.
Local Learning Regularized Nonnegative Matrix Factorization

Sequence alignment is an important problem in computational biology. We compare two different approaches to the problem of optimally aligning two or more character strings: bounded dynamic programming (BDP), and divide-and-conquer frontier search (DCFS). The approaches are compared in terms of time and space requirements in 2 through 5 dimensions with sequences of varying similarity and length. While BDP performs better in two and three dimensions, it consumes more time and memory than DCFS for higher-dimensional problems.
Examining the Stability of Logging Statements

Logging statements produce logs that assist in understanding system behavior, monitoring choke-points and debugging. Prior research demonstrated the importance of logging statements in operating, understanding and improving software systems. The importance of logs has lead to a new market of log management and processing tools. However, logs are often unstable, i.e., the logging statements that generate logs are often changed without the consideration of other stakeholders, causing misleading results and failures of log processing tools. In order to proactively mitigate such issues that are caused by unstable logging statements, in this paper we empirically study the stability of logging statements in four open source applications namely:Liferay, ActiveMQ, Camel and Cloud Stack. We find that 20-45% of the logging statements in our studied applications change throughout their lifetime. The median number of days between the introduction of a logging statement and the first change to that statement is between 1 and 17 in our studied applications. These numbers show that in order to reduce maintenance effort, developers of log processing tools must be careful when selecting the logging statements on which they will let their tools depend. In this paper, we make an important first step towards assisting developers of log processing tools in determining whether a logging statement is likely to remain unchanged in the future. Using random forest classifiers, we examine which metrics are important for understanding whether a logging statement will change. We show that our classifiers achieve 83%-91% precision and 65%-85% recall in the four studied applications. We find that file ownership, developer experience, log density and SLOC are important metrics for determining whether a logging statement will change in the future. Developers can use this knowledge to build more robust log processing tools, by making those tools depend on logs that are generated by logging statements that are likely to remain unchanged.
A top-down approach to teaching programming

Programming is traditionally taught using a bottom-up approach, where details of syntax and implementation of data structures are the predominant concepts. The top-down approach proposed focuses instead on understanding the abstractions represented by the classical data structures without regard to their physical implementation. Only after the students are comfortable with the behavior and applications of the major data structures do they learn about their implementations or the basic data types like arrays and pointers that are used. This paper discusses the benefits of such an approach and how it is being used in a Computer Science curriculum.
Three-phase grid-connected photovoltaic system with active and reactive power control using dq0 transformation

This paper presents a three-phase grid-connected photovoltaic generation system with unity power factor for any situation of solar radiation. The model of the PWM inverter and a control strategy using dq0 transformation are proposed the system operates as an active filter capable of compensate harmonic components and reactive power, generated by the other loads connected to the system. A input voltage clamping technique is proposed to control the power between the grid and photovoltaic system, where it is intended to achieve the maximum power point operation. Simulation results and analyses are presented to validate the proposed methodology for grid connected photovoltaic generation system.
Fruit and vegetables classification system using image saliency and convolutional neural network

Fruit and vegetables classification and recognition are still challenging in daily production and life. In this paper, we propose an efficient fruit and vegetables classification system using image saliency to draw the object regions and convolutional neural network (CNN) model to extract image features and implement classification. Image saliency is utilized to select main saliency regions according to saliency map. A VGG model is chosen to train for fruit and vegetables classification. Another contribution in this paper is that we establish a fruit and vegetables images database spanning 26 categories, which covers the major types in real life. Experiments are conducted on our own database, and the results show that our classification system achieves an excellent accuracy rate of 95.6%.
Higher Order Compensation for Inductive-Power-Transfer Converters With Constant-Voltage or Constant-Current Output Combating Transformer Parameter Constraints

Compensation is crucial for improving performance of inductive-power-transfer (IPT) converters. With proper compensation at some specific frequencies, an IPT converter can achieve load-independent constant output voltage or current, near zero reactive power, and soft switching of power switches simultaneously, resulting in simplified control circuitry, reduced component ratings, and improved power conversion efficiency. However, constant output voltage or current depends significantly on parameters of the transformer, which is often space constrained, making the converter design hard to optimize. To free the design from the constraints imposed by the transformer parameters, this paper proposes a family of higher order compensation circuits for IPT converters that achieves any desired constant-voltage or constant-current (CC) output with near zero reactive power and soft switching. Detailed derivation of the compensation method is given for the desired transfer function not constrained by transformer parameters. Prototypes of CC IPT configurations based on a single transformer are constructed to verify the analysis with three different output specifications.
Mass spectrometry based metabolomic approaches in urinary biomarker study of women's cancers.

The metabolomic approaches for mining biomarkers of women's cancers based on gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry combined with partial least squares-discriminant analysis are described.



To identify urinary potential biomarkers, the qualitative and quantitative analyses were introduced with 10 breast, 9 ovarian and 12 cervical cancer patients as well as 22 normal controls, which were considered with their ages and menopausal state.



For comprehensive metabolomic approaches, the non-targeted qualitative profiling was first achieved to get metabolic patterns of collected samples and the targeted quantitative analysis focused on hormonal metabolism was also conducted. Two known biomarkers, i.e., 5-hydroxymethyl-2-deoxyuridine and 8-hydroxy-2-deoxyguanosine, in breast cancer were also confirmed using the present methods. In addition, 3 potential biomarkers for ovarian cancer i.e. 1-methyladenosine, 3-methyluridine, and 4-androstene-3,17-dione, which were categorized in significantly increased level using one way of variance analysis (p<0.05), were identified as quantitatively targeted metabolites with pattern analysis. The cancer markers identified in this study are highly related to metabolites which are responsible for oxidative DNA damage and DNA methylation process.



The present metabolomic approaches are not only useful for diagnostic tools and patient stratification, but may be mapped on metabolic network to reflect disease states.
Impedance Matching of Tag Antenna to Maximize RFID Read Ranges & Design Optimization

Radiofrequency Identification (RFID) tags are ever increasing in use, from the monitoring of components to the tracking of produce or livestock during processing & production. They are also widely used in the touch-less technologies seen today in store and payment cards and banking services. With this there has been the ever increasing need to reduce the power required to activate the RFID tag, while maximizing the read range. In addition there is a need to reduce the size of the RFID tags, which are typically embedded in labels and/or cards, in order to make them discreet. In order to maximize read ranges, one needs to ideally match the impedance of the RFID tag antenna to the chip utilized in the tag & ensure that for a particular reader that a minimum threshold power is achieved to activate the tag chip at the required operating frequencies. In this work, we look at the modelling of a physical RFID tag used in a store card and its read ranges obtained from literature, and make comparisons of the model simulations to physical test data. In addition, we take the validated model and find an optimal tag antenna design for a particular application with both size and manufacturing constraints.
Deep Reinforcement Learning Based Dynamic Channel Allocation Algorithm in Multibeam Satellite Systems

Dynamic channel allocation (DCA) is the key technology to efficiently utilize the spectrum resources and decrease the co-channel interference for multibeam satellite systems. Most works allocate the channel on the basis of the beam traffic load or the user terminal distribution of the current moment. These greedy-like algorithms neglect the intrinsic temporal correlation among the sequential channel allocation decisions, resulting in the spectrum resources underutilization. To solve this problem, a novel deep reinforcement learning (DRL)-based DCA (DRL-DCA) algorithm is proposed. Specifically, the DCA optimization problem, which aims at minimizing the service blocking probability, is formulated in the multibeam satellite systems. Due to the temporal correlation property, the DCA optimization problem is modeled as the Markov decision process (MDP) which is the dominant analytical approach in DRL. In modeled MDP, the system state is reformulated into an image-like fashion, and then, convolutional neural network is used to extract useful features. Simulation results show that the DRL-DCA algorithm can decrease the blocking probability and improve the carried traffic and spectrum efficiency compared with other channel allocation algorithms.