nils-herrmann/clean_scirepeval huggingface dataset:
Classifying and segmenting microscopy images with deep multiple instance learning

High-content screening (HCS) technologies have enabled large scale imaging experiments for studying cell biology and for drug screening. These systems produce hundreds of thousands of microscopy images per day and their utility depends on automated image analysis. Recently, deep learning approaches that learn feature representations directly from pixel intensity values have dominated object recognition challenges. These tasks typically have a single centered object per image and existing models are not directly applicable to microscopy datasets. Here we develop an approach that combines deep convolutional neural networks (CNNs) with multiple instance learning (MIL) in order to classify and segment microscopy images using only whole image level annotations.



We introduce a new neural network architecture that uses MIL to simultaneously classify and segment microscopy images with populations of cells. We base our approach on the similarity between the aggregation function used in MIL and pooling layers used in CNNs. To facilitate aggregating across large numbers of instances in CNN feature maps we present the Noisy-AND pooling function, a new MIL operator that is robust to outliers. Combining CNNs with MIL enables training CNNs using whole microscopy images with image level labels. We show that training end-to-end MIL CNNs outperforms several previous methods on both mammalian and yeast datasets without requiring any segmentation steps.



Torch7 implementation available upon request.



oren.kraus@mail.utoronto.ca.
Spatially resolved Raman spectroscopy of single- and few-layer graphene.

We present Raman spectroscopy measurements on single- and few-layer graphene flakes. By using a scanning confocal approach, we collect spectral data with spatial resolution, which allows us to directly compare Raman images with scanning force micrographs. Single-layer graphene can be distinguished from double- and few-layer by the width of the D' line: the single peak for single-layer graphene splits into different peaks for the double-layer. These findings are explained using the double-resonant Raman model based on ab initio calculations of the electronic structure and of the phonon dispersion. We investigate the D line intensity and find no defects within the flake. A finite D line response originating from the edges can be attributed either to defects or to the breakdown of translational symmetry.
Transmission Power Control in Body Area Sensor Networks for Healthcare Monitoring

This paper investigates the opportunities and challenges in the use of dynamic radio transmit power control for prolonging the lifetime of body-wearable sensor devices used in continuous health monitoring. We first present extensive empirical evidence that the wireless link quality can change rapidly in body area networks, and a fixed transmit power results in either wasted energy (when the link is good) or low reliability (when the link is bad). We quantify the potential gains of dynamic power control in body-worn devices by benchmarking off-line the energy savings achievable for a given level of reliability.We then propose a class of schemes feasible for practical implementation that adapt transmit power in real-time based on feedback information from the receiver. We profile their performance against the offline benchmark, and provide guidelines on how the parameters can be tuned to achieve the desired trade-off between energy savings and reliability within the chosen operating environment. Finally, we implement and profile our scheme on a MicaZ mote based platform, and also report preliminary results from the ultra-low-power integrated healthcare monitoring platform we are developing at Toumaz Technology.
Manipulator grasping and pushing operations

The primary goal of this research is to develop theoretical tools for analysis, synthesis, and application of primitive manipulator operations. The primary method is to extend and apply traditional tools of classical mechanics. The results are of such a general nature that they address many different aspects of industrial robotics, including effector and sensor design, planning and programming tools, and design of auxiliary equipment. Some of the manipulator operations studied are: 1. Grasping an object. The object will usually slide and rotate during the period between first contact and prehension. 2. Placing an object. The object may slip slightly in the fingers upon contact with the table as the base aligns with the table. 3. Pushing. Often the final stage of mating two parts involves pushing one object into the other. In each of these operations the motion of the object is determined partly by the manipulator and partly by frictional forces. Hence the theoretical analysis focuses on the problem of partially constrained motion with friction. When inertial forces are dominated by frictional forces, we find that the fundamental motion of the object-whether it will rotate, and if so in what direction-may be determined by inspection. In many cases the motion may be predicted in detail, and in any case it is possible to find bounds on the motion. With these analytical tools it is sometimes possible to predict the outcome of a given manipulator operation, or, on the other hand, to plan an operation producing a given desired outcome.  Tomas Lozano-Perez and Berthold K. P. Horn Titles: Assistant Professor and Associate Professor, respectively, of Computer Science and Electrical Engineering
SNR-Aware Convolutional Neural Network Modeling for Speech Enhancement

This paper proposes a signal-to-noise-ratio (SNR) aware convolutional neural network (CNN) model for speech enhancement (SE). Because the CNN model can deal with local temporal-spectral structures of speech signals, it can effectively disentangle the speech and noise signals given the noisy speech signals. In order to enhance the generalization capability and accuracy, we propose two SNR-aware algorithms for CNN modeling. The first algorithm employs a multi-task learning (MTL) framework, in which restoring clean speech and estimating SNR level are formulated as the main and the secondary tasks, respectively, given the noisy speech input. The second algorithm is an SNR adaptive denoising, in which the SNR level is explicitly predicted in the first step, and then an SNR-dependent CNN model is selected for denoising. Experiments were carried out to test the two SNR-aware algorithms for CNN modeling. Results demonstrate that CNN with the two proposed SNR-aware algorithms outperform the deep neural network counterpart in terms of standardized objective evaluations when using the same number of layers and nodes. Moreover, the SNR-aware algorithms can improve the denoising performance with unseen SNR levels, suggesting their promising generalization capability for real-world applications.
Axial Flux PM Machines With Concentrated Armature Windings: Design Analysis and Test Validation of Wind Energy Generators

Some design and operation aspects of axial flux permanent magnet synchronous machines, wound with concentrated coils, are presented. Due to their high number of poles, compactness, and excellent waveform quality and efficiency, these machines show satisfactory operation at low speeds, both as direct drive generators and as motors. In this paper, after a general analysis of the model and design features of this kind of machine, the attention is focused on wind power generation: The main sizing equations are defined, and the most relevant figures of merit are examined by means of a suitable parametric analysis. Some experimental results obtained by testing a three-phase, 50-kW, and 70-rpm prototype are presented and discussed, validating the modeling theory and the design procedure.
Urdu text classification

This paper compares statistical techniques for text classification using Naïve Bayes and Support Vector Machines, in context of Urdu language. A large corpus is used for training and testing purpose of the classifiers. However, those classifiers cannot directly interpret the raw dataset, so language specific preprocessing techniques are applied on it to generate a standardized and reduced-feature lexicon. Urdu language is morphological rich language which makes those tasks complex. Statistical characteristics of corpus and lexicon are measured which show satisfactory results of text preprocessing module. The empirical results show that Support Vector Machines outperform Naïve Bayes classifier in terms of classification accuracy.
Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accounts

So-called ‘social bots’ have garnered a lot of attention lately. Previous research showed that they attempted to influence political events such as the Brexit referendum and the US presidential elections. It remains, however, somewhat unclear what exactly can be understood by the term ‘social bot’. This paper addresses the need to better understand the intentions of bots on social media and to develop a shared understanding of how ‘social’ bots differ from other types of bots. We thus describe a systematic review of publications that researched bot accounts on social media. Based on the results of this literature review, we propose a scheme for categorising bot accounts on social media sites. Our scheme groups bot accounts by two dimensions – Imitation of human behaviour and Intent.
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages

We present BPEmb, a collection of pre-trained subword unit embeddings in 275 languages, based on Byte-Pair Encoding (BPE). In an evaluation using fine-grained entity typing as testbed, BPEmb performs competitively, and for some languages better than alternative subword approaches, while requiring vastly fewer resources and no tokenization. BPEmb is available at https://github.com/bheinzerling/bpemb.
RFID security and privacy: a research survey

This paper surveys recent technical research on the problems of privacy and security for radio frequency identification (RFID). RFID tags are small, wireless devices that help identify objects and people. Thanks to dropping cost, they are likely to proliferate into the billions in the next several years-and eventually into the trillions. RFID tags track objects in supply chains, and are working their way into the pockets, belongings, and even the bodies of consumers. This survey examines approaches proposed by scientists for privacy protection and integrity assurance in RFID systems, and treats the social and technical context of their work. While geared toward the nonspecialist, the survey may also serve as a reference for specialist readers.
AN IMAGE PROCESSING AND NEURAL NETWORK BASED APPROACH FOR DETECTION AND CLASSIFICATION OF PLANT LEAF DISEASES

In the present paper we propose and evaluate a framework for detection and classification of plant leaf/stem diseases using image processing and neural network technique. The images of plant leaves affected by four types of diseases namely early blight, late blight, powdery-mildew and septoria has been considered for study and evaluation of feasibility of the proposed method. The color transformation structures were obtained by converting images from RGB to HSI color space. The Kmeans clustering algorithm was used to divide images into clusters for demarcation of infected area of the leaves. After clustering, the set of color and texture features viz. moment, mean, variance, contrast, correlation and entropy were extracted based on Color Co-occurrence Method (CCM). A feed forward back propagation neural network was configured and trained using extracted set of features and subsequently utilized for detection of leaf diseases.  Color Co-Occurrence Method, K-Means, Feed Forward Neural Network
Student Performance Prediction via Online Learning Behavior Analytics

With the continuous development of online learning platforms, educational data analytics and prediction have become a promising research field, which are helpful for the development of personalized learning system. However, the indicator's selection process does not combine with the whole learning process, which may affect the accuracy of prediction results. In this paper, we induce 19 behavior indicators in the online learning platform, proposing a student performance prediction model which combines with the whole learning process. The model consists of four parts: data collection and pre-processing, learning behavior analytics, algorithm model building and prediction. Moreover, we apply an optimized Logistic Regression algorithm, taking a case to analyze students' behavior and to predict their performance. Experimental results demonstrate that these eigenvalues can effectively predict whether a student was probably to have an excellent grade.
Accurate Keyphrase Extraction from Scientific Papers by Mining Linguistic Information

In this paper we investigate the impact of candidate terms filtering using linguistic information on the accuracy of automatic keyphrase extraction from scientific papers. According to linguistic knowledge, the noun phrases are most likely to be keyphrases. However the definition of a noun phrase can vary from a system to another. We have identified five POS tag sequence definitions of a noun phrase in keyphrase extraction literature and proposed a new definition. We estimated experimentally the accuracy of a keyphrase extraction system using different noun phrase filters in order to determine which noun phrase definition yields to the best results. Conference Topic Text mining and information extraction
Simple Real-Time Digital PWM Implementation for Class-D Amplifiers With Distortion-Free Baseband

A real-time, digital algorithm for pulse width modulation (PWM) with distortion-free baseband is developed in this paper. The algorithm not only eliminates the intrinsic baseband distortion of digital PWM but also avoids the appearance of side-band components of the carrier in the baseband even for low switching frequencies. Previous attempts to implement digital PWM with these spectral properties required several processors due to their complexity; the proposed algorithm uses only several FIR filters and a few multiplications and additions and therefore is implemented in real time on a standard DSP. The performance of the algorithm is compared with that of uniform, double-edge PWM modulator via experimental measurements for several bandlimited modulating signals.
Multi-sensor fusion for robust autonomous flight in indoor and outdoor environments with a rotorcraft MAV

We present a modular and extensible approach to integrate noisy measurements from multiple heterogeneous sensors that yield either absolute or relative observations at different and varying time intervals, and to provide smooth and globally consistent estimates of position in real time for autonomous flight. We describe the development of algorithms and software architecture for a new 1.9kg MAV platform equipped with an IMU, laser scanner, stereo cameras, pressure altimeter, magnetometer, and a GPS receiver, in which the state estimation and control are performed onboard on an Intel NUC 3rd generation i3 processor. We illustrate the robustness of our framework in large-scale, indoor-outdoor autonomous aerial navigation experiments involving traversals of over 440 meters at average speeds of 1.5 m/s with winds around 10 mph while entering and exiting buildings.
Modellingof a special class of spherical parallel manipulators with Euler parameters

A method of workspace modelling for spherical parallel manipulators (SPMs) of symmetrical architecture is developed by virtue of Euler parameters in the paper. The adoption of Euler parameters in the expression of spatial rotations of SPMs helps not only to eliminate the possible singularity in the rotation matrix, but also to formulate all equations in polynomials, which are more easily manipulated. Moreover, a homogeneous workspace can be obtained with Euler parameters for the SPMs, which facilitates the evaluation of dexterity. In this work, the problem of workspace modelling and analysis is formulated in terms of Euler parameters. An equation dealing with boundary surfaces is derived and branches of boundary surface are identified. Evaluation of dexterity is explored to quantitatively describe the capability of a manipulator to attain orientations. The singularity identification is also addressed. Examples are included to demonstrate the application of the proposed method.
Epidemic processes in complex networks

Romualdo Pastor-Satorras, Claudio Castellano, 3 Piet Van Mieghem, and Alessandro Vespignani 6 Departament de F́ısica i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Campus Nord B4, 08034 Barcelona, Spain Istituto dei Sistemi Complessi (ISC-CNR), via dei Taurini 19, I-00185 Roma, Italy Dipartimento di Fisica, “Sapienza” Università di Roma, P.le A. Moro 2, I-00185 Roma, Italy Delft University of Technology, Delft, The Netherlands Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston MA 02115 USA Institute for Scientific Interchange Foundation, Turin 10133,
Permanent-Magnet Temperature Estimation in PMSMs Using Pulsating High-Frequency Current Injection

The injection of a high-frequency signal in the stator via inverter has been shown to be a viable option to estimate the magnet temperature in permanent-magnet synchronous machines (PMSMs). The variation of the magnet resistance with temperature is reflected in the stator high-frequency resistance, which can be measured from the resulting current when a high-frequency voltage is injected. However, this method is sensitive to d- and q-axis inductance (Ld and Lq) variations, as well as to the machine speed. In addition, it is only suitable for surface PMSMs (SPMSMs) and inadequate for interior PMSMs (IPMSMs). In this paper, the use of a pulsating high-frequency current injection in the d-axis of the machine for temperature estimation purposes is proposed. The proposed method will be shown to be insensitive to the speed, Lq, and Ld variations. Furthermore, it can be used with both SPMSMs and IPMSMs.
An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition

Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed. Compared with previous systems for scene text recognition, the proposed architecture possesses four distinctive properties: (1) It is end-to-end trainable, in contrast to most of the existing algorithms whose components are separately trained and tuned. (2) It naturally handles sequences in arbitrary lengths, involving no character segmentation or horizontal scale normalization. (3) It is not confined to any predefined lexicon and achieves remarkable performances in both lexicon-free and lexicon-based scene text recognition tasks. (4) It generates an effective yet much smaller model, which is more practical for real-world application scenarios. The experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets, demonstrate the superiority of the proposed algorithm over the prior arts. Moreover, the proposed algorithm performs well in the task of image-based music score recognition, which evidently verifies the generality of it.
Limited Knowledge Shilling Attacks in Collaborative Filtering Systems

Recent research in recommender systems has shown that collaborative filtering algorithms are highly susceptible to attacks that insert biased profile data. Theoretical analyses and empirical experiments have shown that certain attacks can have a significant impact on the recommendations a system provides. These analyses have generally not taken into account the cost of mounting an attack or the degree of prerequisite knowledge for doing so. For example, effective attacks often require knowledge about the distribution of user ratings: the more such knowledge is required, the more expensive the attack to be mounted. In our research, we are examining a variety of attack models, aiming to establish the likely practical risks to collaborative systems. In this paper, we examine user-based collaborative filtering and some attack models that are successful against it, including a limited knowledge "bandwagon" attack that requires only that the attacker identify a small number of very popular items and a user-focused "favorite item" attack that is also effective against item-based algorithms.
The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator

The purpose of this paper is to investige whether online environment cues (web site quality and web site brand) affect customer purchase intention towards an online retailer and whether this impact is mediated by customer trust and perceived risk. The study also aimed to assess the degree of reciprocity between consumers’ trust and perceived risk in the context of an online shopping environment.  The study proposed a research framework for testing the relationships among the constructs based on the stimulus-organism-response framework. In addition, this study developed a non-recursive model. After the validation of measurement scales, empirical analyses were performed using structural equation modelling.  The findings confirm that web site quality and web site brand affect consumers’ trust and perceived risk, and in turn, consumer purchase intention. Notably, this study finds that the web site brand is a more important cue than web site quality in influencing customers’ purchase intention. Furthermore, the study reveals that the relationship between trust and perceived risk is reciprocal.  This study adopted four dimensions – technical adequacy, content quality, specific content and appearance – to measure web site quality. However, there are still many competing concepts regarding the measurement of web site quality. Further studies using other dimensional measures may be needed to verify the research model.  Online retailers should focus their marketing strategies more on establishing the brand of the web site rather than improving the functionality of the web site.  This study proposed a non-recursive model for empirically analysing the link between web site quality, web site brand, trust, perceived risk and purchase intention towards the online retailer.
Stan : A probabilistic programming language for Bayesian inference and optimization

Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia, and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users’ and developers’ perspective and illustrate with a simple but nontrivial nonlinear regression example.