nils-herrmann/clean_scirepeval huggingface dataset:
Positional information and the spatial pattern of cellular differentiation.

The problem of pattern is considered in terms of how genetic information can be translated in a reliable manner to give specific and different spatial patterns of cellular differentiation. Pattern formation thus differs from molecular differentiation which is mainly concerned with the control of synthesis of specific macromolecules within cells rather than the spatial arrangement of the cells. It is suggested that there may be a universal mechanism whereby the translation of genetic information into spatial patterns of differentiation is achieved. The basis of this is a mechanism whereby the cells in a developing system may have their position specified with respect to one or more points in the system. This specification of position is positional information. Cells which have their positional information specified with respect to the same set of points constitute a field. Positional information largely determines with respect to the cells' genome and developmental history the nature of its molecular differentiation. The specification of positional information in general precedes and is independent of molecular differentiation. The concept of positional information implies a co-ordinate system and polarity is defined as the direction in which positional information is specified or measured. Rules for the specification of positional information and polarity are discussed. Pattern regulation, which is the ability of the system to form the pattern even when parts are removed, or added, and to show size invariance as in the French Flag problem, is largely dependent on the ability of the cells to change their positional information and interpret this change. These concepts are applied in some detail to early sea urchin development, hydroid regeneration, pattern formation in the insect epidermis, and the development of the chick limb. It is concluded that these concepts provide a unifying framework within which a wide variety of patterns formed from fields may be discussed, and give new meaning to classical concepts such as induction, dominance and field. The concepts direct attention towards finding mechanisms whereby position and polarity are specified, and the nature of reference points and boundaries. More specifically, it is suggested that the mechanism is required to specify the position of about
Towards Linear-Time Incremental Structure from Motion

The time complexity of incremental structure from motion (SfM) is often known as O(n^4) with respect to the number of cameras. As bundle adjustment (BA) being significantly improved recently by preconditioned conjugate gradient (PCG), it is worth revisiting how fast incremental SfM is. We introduce a novel BA strategy that provides good balance between speed and accuracy. Through algorithm analysis and extensive experiments, we show that incremental SfM requires only O(n) time on many major steps including BA. Our method maintains high accuracy by regularly re-triangulating the feature matches that initially fail to triangulate. We test our algorithm on large photo collections and long video sequences with various settings, and show that our method offers state of the art performance for large-scale reconstructions. The presented algorithm is available as part of VisualSFM at http://homes.cs.washington.edu/~ccwu/vsfm/.
A Compact Dual-Band Dual-Polarized Antenna for Base Station Application

A compact dual-band dual-polarized antenna is proposed in this paper. The two pair dipoles with strong end coupling are used for the lower frequency band, and cross-placed patch dipoles are used for the upper frequency band. The ends of the dipoles for lower frequency band are bent to increase the coupling between adjacent dipoles, which can benefit the compactness and bandwidth of the antenna. Breaches are introduced at the ends of the dipoles of the upper band, which also benefit the compactness and matching of the antenna. An antenna prototype was fabricated and measured. The measured results show that the antenna can cover from 790 MHz to 960 MHz (19.4%) for lower band and from 1710 MHz to 2170 MHz (23.7%) for upper band with VSWR < 1.5. It is expected to be a good candidate design for base station antennas.
Approaching Digitalization with Business Process Management

As digitalization sustainably alters industries and societies, small and medium-sized enterprises (SME) must initiate a digital transformation to remain competitive and to address the increasing complexity of customer needs. Although many enterprises encounter challenges in practice, research does not yet provide practicable recommendations to increase the feasibility of digitalization. Furthermore, SME frequently fail to fully realize the implications of digitalization for their organizational structures, strategies, and operations, and have difficulties to identify a suitable starting point for corresponding initiatives. In order to address these challenges, this paper uses the concept of Business Process Management (BPM) to define a set of capabilities for a management framework, which builds upon the paradigm of process orientation to cope with the various requirements of digital transformation. Our findings suggest that enterprises can use a functioning BPM as a starting point for digitalization, while establishing necessary digital capabilities subsequently.
Blindly Signed Contracts: Anonymous On-Blockchain and Off-Blockchain Bitcoin Transactions

Although Bitcoin is often perceived to be an anonymous currency, research has shown that a user’s Bitcoin transactions can be linked to compromise the user’s anonymity. We present solutions to the anonymity problem for both transactions on Bitcoin’s blockchain and off the blockchain (in so called micropayment channel networks). We use an untrusted third party to issue anonymous vouchers which users redeem for Bitcoin. Blind signatures and Bitcoin transaction contracts (aka smart contracts) ensure the anonymity and fairness during the bitcoin ↔ voucher exchange. Our schemes are practical, secure and anonymous.
IZA DP No . 3939 Reference Points and Effort Provision

Reference Points and Effort Provision A key open question for theories of reference-dependent preferences is what determines the reference point. One candidate is expectations: what people expect could affect how they feel about what actually occurs. In a real-effort experiment, we manipulate the rational expectations of subjects and check whether this manipulation influences their effort provision. We find that effort provision is significantly different between treatments in the way predicted by models of expectation-based reference-dependent preferences: if expectations are high, subjects work longer and earn more money than if expectations are low.
Wireless transmission using universal software radio peripheral

Software-Defined Radio (SDR) is a technique using software to make the radio functions hardware independent. SDR is starting to be the basis of advanced wireless communication systems such as Joint Tactical Radio System (JTRS). More interestingly, the adoption of SDR technology by JTRS program is followed by military satellite communications programs. In the development of the SDR implementation, GNU Radio emerged as an open source tool that provides functions to support SDR. Later, Universal Software Radio Peripheral (USRP) was developed as a low cost, high-speed SDR platform. USRP in conjunction with GNU Radio is a very powerful tool to develop SDR based wireless communication system. This paper discusses the employment of GNU Radio and USRP for developing software based wireless transmission system. Furthermore, retransmission scheme, buffering and Leaky Bucket Algorithm are implemented to solve the transmission error and environment interference problems found during the implementation.
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition

In this report, our approach to tackling the task of ActivityNet 2018 Kinetics-600 challenge is described in detail. Though spatial-temporal modelling methods, which adopt either such end-to-end framework as I3D  or two-stage frameworks (i.e., CNN+RNN), have been proposed in existing state-of-the-arts for this task, video modelling is far from being well solved. In this challenge, we propose spatial-temporal network (StNet) for better joint spatial-temporal modelling and comprehensively video understanding. Besides, given that multimodal information is contained in video source, we manage to integrate both early-fusion and later-fusion strategy of multi-modal information via our proposed improved temporal Xception network (iTXN) for video understanding. Our StNet RGB single model achieves 78.99% top-1 precision in the Kinetics-600 validation set and that of our improved temporal Xception network which integrates RGB, flow and audio modalities is up to 82.35%. After model ensemble, we achieve top-1 precision as high as 85.0% on the validation set and rank No.1 among all submissions.
Image segmentation using K-mean clustering for finding tumor in medical application

Clustering algorithms have successfully been applied as a digital image segmentation technique in various fields and applications. However, those clustering algorithms are only applicable for specific images such as medical images, microscopic images etc. In this paper, we present a new clustering algorithm called Image segmentation using K-mean clustering for finding tumor in medical application which could be applied on general images and/or specific images (i.e., medical and microscopic images), captured using MRI, CT scan, etc. The algorithm employs the concepts of fuzziness and belongingness to provide a better and more adaptive clustering process as compared to several conventional clustering algorithms.
Understanding cloud-native applications after 10 years of cloud computing - A systematic mapping study

It is common sense that cloud-native applications (CNA) are intentionally designed for the cloud. Although this understanding can be broadly used it does not guide and explain what a cloud-native application exactly is. The term ”cloud-native” was used quite frequently in birthday times of cloud computing (2006) which seems somehow obvious nowadays. But the term disappeared almost completely. Suddenly and in the last years the term is used again more and more frequently and shows increasing momentum. This paper summarizes the outcomes of a systematic mapping study analyzing research papers covering ”cloud-native” topics, research questions and engineering methodologies. We summarize research focuses and trends dealing with cloud-native application engineering approaches. Furthermore, we provide a definition for the term ”cloud-native application” which takes all findings, insights of analyzed publications and already existing and well-defined terminology into account.
UMAN-IN-THEL OOP

An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes. Most research has focused on learning from fixed training sets of labeled data rather than interacting with a dialogue partner in an online fashion. In this paper we explore this direction in a reinforcement learning setting where the bot improves its question-answering ability from feedback a teacher gives following its generated responses. We build a simulator that tests various aspects of such learning in a synthetic environment, and introduce models that work in this regime. Finally, real experiments with Mechanical Turk validate the approach.
Driving me around the bend: Learning to drive from visual gist

This article proposes an approach to learning steering and road following behaviour from a human driver using holistic visual features. We use a random forest (RF) to regress a mapping between these features and the driver's actions, and propose an alternative to classical random forest regression based on the Medoid (RF-Medoid), that reduces the underestimation of extreme control values. We compare prediction performance using different holistic visual descriptors: GIST, Channel-GIST (C-GIST) and Pyramidal-HOG (P-HOG). The proposed methods are evaluated on two different datasets: predicting human behaviour on countryside roads and also for autonomous control of a robot on an indoor track. We show that 1) C-GIST leads to the best predictions on both sequences, and 2) RF-Medoid leads to a better estimation of extreme values, where a classical RF tends to under-steer. We use around 10% of the data for training and show excellent generalization over a dataset of thousands of images. Importantly, we do not engineer the solution but instead use machine learning to automatically identify the relationship between visual features and behaviour, providing an efficient, generic solution to autonomous control.
End-to-End Instance Segmentation with Recurrent Attention

While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. Techniques that combine large graphical models with low-level vision have been proposed to address this problem, however, we propose an end-to-end recurrent neural network (RNN) architecture with an attention mechanism to model a human-like counting process, and produce detailed instance segmentations. The network is jointly trained to sequentially produce regions of interest as well as a dominant object segmentation within each region. The proposed model achieves competitive results on the CVPPP [27], KITTI , and Cityscapes  datasets.
Botivist: Calling Volunteers to Action using Online Bots

To help activists call new volunteers to action, we present Botivist: a platform that uses Twitter bots to find potential volunteers and request contributions. By leveraging different Twitter accounts, Botivist employs different strategies to encourage participation. We explore how people respond to bots calling them to action using a test case about corruption in Latin America. Our results show that the majority of volunteers (80\%) who responded to Botivist's calls to action contributed relevant proposals to address the assigned social problem. Different strategies produced differences in the quantity and relevance of contributions. Some strategies that work well offline and face-to-face appeared to hinder people's participation when used by an online bot. We analyze user behavior in response to being approached by bots with an activist purpose. We also provide strong evidence for the value of this type of civic media, and derive design implications.
Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks

We propose a 3D convolutional neural network to simultaneously segment and detect cell nuclei in confocal microscopy images. Mirroring the co-dependency of these tasks, our proposed model consists of two serial components: the first part computes a segmentation of cell bodies, while the second module identifies the centers of these cells. Our model is trained end-to-end from scratch on a mouse parotid salivary gland stem cell nuclei dataset comprising 107 image stacks from three independent cell preparations, each containing several hundred individual cell nuclei in 3D. In our experiments, we conduct a thorough evaluation of both detection accuracy and segmentation quality, on two different datasets. The results show that the proposed method provides significantly improved detection and segmentation accuracy compared to state-of-the-art and benchmark algorithms. Finally, we use a previously described test-time drop-out strategy to obtain uncertainty estimates on our predictions and validate these estimates by demonstrating that they are strongly correlated with accuracy.
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimates. MAPO is applicable to deterministic environments with discrete actions, such as structured prediction and combinatorial optimization. Our key idea is to express the expected return objective as a weighted sum of two terms: an expectation over the high-reward trajectories inside a memory buffer, and a separate expectation over trajectories outside of the buffer. To design an efficient algorithm based on this idea, we propose: (1) memory weight clipping to accelerate and stabilize training; (2) systematic exploration to discover high-reward trajectories; (3) distributed sampling from inside and outside of the memory buffer to speed up training. MAPO improves the sample efficiency and robustness of policy gradient, especially on tasks with sparse rewards. We evaluate MAPO on weakly supervised program synthesis from natural language (semantic parsing). On the WIKITABLEQUESTIONS benchmark, we improve the state-of-the-art by 2.6%, achieving an accuracy of 46.3%. On the WIKISQL benchmark, MAPO achieves an accuracy of 74.9% with only weak supervision, outperforming several strong baselines with full supervision.
Impacts of biodiversity loss on ocean ecosystem services.

Human-dominated marine ecosystems are experiencing accelerating loss of populations and species, with largely unknown consequences. We analyzed local experiments, long-term regional time series, and global fisheries data to test how biodiversity loss affects marine ecosystem services across temporal and spatial scales. Overall, rates of resource collapse increased and recovery potential, stability, and water quality decreased exponentially with declining diversity. Restoration of biodiversity, in contrast, increased productivity fourfold and decreased variability by 21%, on average. We conclude that marine biodiversity loss is increasingly impairing the ocean's capacity to provide food, maintain water quality, and recover from perturbations. Yet available data suggest that at this point, these trends are still reversible.
Deep learning for neuroimaging: a validation study

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
ULTIMATE AXIAL LOAD CAPACITY OF V-BAND CLAMP JOINTS

Band clamps with a flat bottomed V-section are used to connect a pair of circular flanges to provide a joint with significant axial strength. Despite the wide application of V-band clamps, their behaviour is not fully understood and the ultimate axial strength is currently only available from physical testing. This physical testing has indicated that the ultimate strength is determined by two different types of structural deformation, an elastic deformation mode and a plastic deformation mode. Initial finite element analysis work has demonstrated that analysis of this class of problem is not straightforward. This paper discusses the difficulties encountered when simulating this type of component interaction where contact is highly localised and contact pressures are high and therefore presents a finite element model to predict the ultimate axial load capacity of V-band clamps.
A clinical case involving severe erosion of the maxillary anterior teeth restored with direct composite resin restorations.

THE INTERNATIONAL JOURNAL OF ESTHETIC DENTISTRY The management of individual cases presents each clinician with a variety of attractive options and sophisticated evidence-based solutions. Financial constraints can often restrict these options and limit the choice pathways that can be offered. The case presented here demonstrates the management of severe erosion on the maxillary anterior teeth via a minimally invasive, practical, and economic route. When tooth surface loss occurs,1 it can be clinically challenging to isolate a single etiological factor since it is usually multifactorial in origin. The patient presented with the classic signs of erosion (Fig 1a). A major causative factor of this erosion was a large consumption of carbonated beverages on a daily basis over a number of years. Chronic exposure of dental hard tissues to acidic substrates led to extensive enamel and dentin loss from both intrinsic and extrinsic sources (Fig 1b and c). The ACE classification guides the clinician on the management options of treatment modalities, which are dependent on the severity of the erosion.2 A clinical case involving severe erosion of the maxillary anterior teeth restored with direct composite resin restorations
Neural Machine Translation of Text from Non-Native Speakers

Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data, especially when the system is trained only on clean data. In this paper, we show that augmenting training data with sentences containing artificially-introduced grammatical errors can make the system more robust to such errors. In combination with an automatic grammar error correction system, we can recover 1.9 BLEU out of 3.1 BLEU lost due to grammatical errors. We also present a set of Spanish translations of the JFLEG grammar error correction corpus, which allows for testing NMT robustness to real grammatical errors.
Principles of Two-Photon Excitation Microscopy and Its Applications to Neuroscience

The brain is complex and dynamic. The spatial scales of interest to the neurobiologist range from individual synapses (approximately 1 microm) to neural circuits (centimeters); the timescales range from the flickering of channels (less than a millisecond) to long-term memory (years). Remarkably, fluorescence microscopy has the potential to revolutionize research on all of these spatial and temporal scales. Two-photon excitation (2PE) laser scanning microscopy allows high-resolution and high-sensitivity fluorescence microscopy in intact neural tissue, which is hostile to traditional forms of microscopy. Over the last 10 years, applications of 2PE, including microscopy and photostimulation, have contributed to our understanding of a broad array of neurobiological phenomena, including the dynamics of single channels in individual synapses and the functional organization of cortical maps. Here we review the principles of 2PE microscopy, highlight recent applications, discuss its limitations, and point to areas for future research and development.