nils-herrmann/clean_scirepeval huggingface dataset:
DNA extraction and quantitation of forensic samples using the phenol-chloroform method and real-time PCR.

Forensic laboratories are increasingly confronted with problematic samples from the scene of crime, containing only minute amounts of deoxyribonucleic acid (DNA), which may include polymerase chain reaction (PCR)-inhibiting substances. Efficient DNA extraction procedures, as well as accurate DNA quantification methods, are critical steps involved in the process of successful DNA analysis of such samples. The phenol-chloroform method is a sensitive method for the extraction of DNA from a wide variety of forensic samples, although it is known to be laborious compared with single-tube extraction methods. The relatively high DNA recovery and the quality of the extracted DNA speak for itself. For reliable and sensitive DNA quantitation, the application of real-time PCR is described. We modified a published real-time PCR assay, which allows for the combined analysis of nuclear and mitochondrial DNA, by introducing 1) improved hybridization probes with the use of minor groove binders; 2) an internal positive control (for both nuclear and mitochondrial DNA) for the detection of PCR inhibitors; and 3) different amplicon lengths for the determination of the degradation state of the DNA. The internal positive controls were constructed by site directed mutagenesis by overlap extension of the wild-type mitochondrial and nuclear DNA target with the advantage that no additional probes, which are cost-intensive, are required. The quantitation system is accomplished as a modular concept, which allows for the combined determination of the above-mentioned features (quantity/inhibition or quantity/degradation) depending on the situation.
The TGFbeta superfamily signaling pathway.

The transforming growth factor (TGF)beta superfamily of secreted factors is comprised of over 30 members including Activins, Nodals, Bone Morphogenetic Proteins (BMPs), and Growth and Differentiation Factors (GDFs). Members of the family, which are found in both vertebrates and invertebrates, are ubiquitously expressed in diverse tissues and function during the earliest stages of development and throughout the lifetime of animals. Indeed, key roles in embryonic stem cell self-renewal, gastrulation, differentiation, organ morphogenesis, and adult tissue homeostasis have been delineated. Consistent with this ubiquitous activity, aberrant TGFbeta superfamily signaling is associated with a wide range of human pathologies including autoimmune, cardiovascular and fibrotic diseases, as well as cancer. TGFbeta superfamily ligands signal through cell-surface serine/threonine kinase receptors to the intracellular Smad proteins, which in turn accumulate in the nucleus to regulate gene expression. In addition to this universal cascade, Smad-independent pathways are also employed in a cell-specific manner to transduce TGFbeta signals. Ligand access to the signaling receptors is regulated by numerous secreted agonists and antagonists and by membrane-associated coreceptors that act in a context-dependent manner. Given the fundamental role of the TGFbeta superfamily in metazoans and the diversity of biological responses, it is not surprising that the signaling pathway is subject to tight and complex regulation at levels both outside and inside the cell.
Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling

Most existing sequence labelling models rely on a fixed decomposition of a target sequence into a sequence of basic units. These methods suffer from two major drawbacks: 1) the set of basic units is fixed, such as the set of words, characters or phonemes in speech recognition, and 2) the decomposition of target sequences is fixed. These drawbacks usually result in sub-optimal performance of modeling sequences. In this paper, we extend the popular CTC loss criterion to alleviate these limitations, and propose a new loss function called Gram-CTC. While preserving the advantages of CTC, Gram-CTC automatically learns the best set of basic units (grams), as well as the most suitable decomposition of target sequences. Unlike CTC, Gram-CTC allows the model to output variable number of characters at each time step, which enables the model to capture longer term dependency and improves the computational efficiency. We demonstrate that the proposed Gram-CTC improves CTC in terms of both performance and efficiency on the large vocabulary speech recognition task at multiple scales of data, and that with Gram-CTC we can outperform the state-of-the-art on a standard speech benchmark.
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging scientific studies to address the problem, a major limitation of existing work is the lack of comparative evaluations, which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and gated recurrent networks. We conduct an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available Twitter datasets to date, and show that compared to previously reported results on these datasets, our proposed method is able to capture both word sequence and order information in short texts, and it sets new benchmark by outperforming on 6 out of 7 datasets by between 1 and 13 percents in F1. We also extend the existing dataset collection on this task by creating a new dataset covering different topics.
Macrophage Polarization Contributes to Glioblastoma Eradication by Combination Immunovirotherapy and Immune Checkpoint Blockade.

Glioblastoma is an immunosuppressive, fatal brain cancer that contains glioblastoma stem-like cells (GSCs). Oncolytic herpes simplex virus (oHSV) selectively replicates in cancer cells while inducing anti-tumor immunity. oHSV G47Δ expressing murine IL-12 (G47Δ-mIL12), antibodies to immune checkpoints (CTLA-4, PD-1, PD-L1), or dual combinations modestly extended survival of a mouse glioma model. However, the triple combination of anti-CTLA-4, anti-PD-1, and G47Δ-mIL12 cured most mice in two glioma models. This treatment was associated with macrophage influx and M1-like polarization, along with increased T effector to T regulatory cell ratios. Immune cell depletion studies demonstrated that CD4+ and CD8+ T cells as well as macrophages are required for synergistic curative activity. This combination should be translatable to the clinic and other immunosuppressive cancers.
gMission: A General Spatial Crowdsourcing Platform

As one of the successful forms of using Wisdom of Crowd, crowdsourcing, has been widely used for many human intrinsic tasks, such as image labeling, natural language understanding, market predication and opinion mining. Meanwhile, with advances in pervasive technology, mobile devices, such as mobile phones and tablets, have become extremely popular. These mobile devices can work as sensors to collect multimedia data(audios, images and videos) and location information. This power makes it possible to implement the new crowdsourcing mode: spatial crowdsourcing. In spatial crowdsourcing, a requester can ask for resources related a specific location, the mobile users who would like to take the task will travel to that place and get the data. Due to the rapid growth of mobile device uses, spatial crowdsourcing is likely to become more popular than general crowdsourcing, such as Amazon Turk and Crowdflower. However, to implement such a platform, effective and efficient solutions for worker incentives, task assignment, result aggregation and data quality control must be developed. In this demo, we will introduce gMission, a general spatial crowdsourcing platform, which features with a collection of novel techniques, including geographic sensing, worker detection, and task recommendation. We introduce the sketch of system architecture and illustrate scenarios via several case analysis.
Scaling a Natural Language Generation System

A key goal in natural language generation (NLG) is to enable fast generation even with large vocabularies, grammars and worlds. In this work, we build upon a recently proposed NLG system, Sentence Tree Realization with UCT (STRUCT). We describe four enhancements to this system: (i) pruning the grammar based on the world and the communicative goal, (ii) intelligently caching and pruning the combinatorial space of semantic bindings, (iii) reusing the lookahead search tree at different search depths, and (iv) learning and using a search control heuristic. We evaluate the resulting system on three datasets of increasing size and complexity, the largest of which has a vocabulary of about 10K words, a grammar of about 32K lexicalized trees and a world with about 11K entities and 23K relations between them. Our results show that the system has a median generation time of 8.5s and finds the best sentence on average within 25s. These results are based on a sequential, interpreted implementation and are significantly better than the state of the art for planningbased NLG systems.
Disability Insurance and the Dynamics of the Incentive Insurance Trade-Off.

We provide a life-cycle framework for comparing insurance and disincentive effects of disability benefits. The risks that individuals face and the parameters of the Disability Insurance (DI ) program are estimated from consumption, health, disability insurance, and wage data. We characterize the effects of disability insurance and study how policy reforms impact behavior and welfare. DI features high rejection rates of disabled applicants and some acceptance of healthy applicants. Despite worse incentives, welfare increases as programs become less strict or generosity increases. Disability insurance interacts with welfare programs: making unconditional means-tested programs more generous improves disability insurance targeting and increases welfare.
Microencapsulation: its application in nutrition.

The development of new functional foods requires technologies for incorporating health-promoting ingredients into food without reducing their bioavailability or functionality. In many cases, microencapsulation can provide the necessary protection for these compounds, but in all cases bioavailability should be carefully studied. The present paper gives an overview of the application of various microencapsulation technologies to nutritionally-important compounds, i.e. vitamins, n-3 polyunsaturated fatty acids, Ca, Fe and antioxidants. It also gives a view on future technologies and trends in microencapsulation technology for nutritional applications.
Somatic, Endurance Performance and Heart Rate Variability Profiles of Professional Soccer Players Grouped According to Age

This cross-sectional study compared somatic, endurance performance determinants and heart rate variability (HRV) profiles of professional soccer players divided into different age groups: GI (17-19.9 years; n = 23), GII (20-24.9 years; n = 45), GIII (25-29.9 years; n = 30), and GIV (30-39 years; n = 26). Players underwent somatic and HRV assessment and maximal exercise testing. HRV was analyzed by spectral analysis of HRV, and high (HF) and low (LF) frequency power was transformed by a natural logarithm (Ln). Players in GIV (83 ± 7 kg) were heavier (p < 0.05) compared to both GI (73 ± 6 kg), and GII (78 ± 6 kg). Significantly lower maximal oxygen uptake (VO2max, ml•kg-1•min-1) was observed for GIV (56.6 ± 3.8) compared to GI (59.6 ± 3.9), GII (59.4 ± 4.2) and GIV (59.7 ± 4.1). All agegroups, except for GII, demonstrated comparable relative maximal power output (Pmax). For supine HRV, significantly lower Ln HF (ms2) was identified in both GIII (7.1 ± 0.8) and GIV (6.9 ± 1.0) compared to GI (7.9 ± 0.6) and GII (7.7 ± 0.9). In conclusion, soccer players aged >25 years showed negligible differences in Pmax unlike the age group differences demonstrated in VO2max. A shift towards relative sympathetic dominance, particularly due to reduced vagal activity, was apparent after approximately 8 years of competing at the professional level.
Toward Automatic Bootstrapping of Online Communities Using Decision-theoretic Optimization

Successful online communities (e.g., Wikipedia, Yelp, and StackOverflow) can produce valuable content. However, many communities fail in their initial stages. Starting an online community is challenging because there is not enough content to attract a critical mass of active members. This paper examines methods for addressing this cold-start problem in datamining-bootstrappable communities by attracting non-members to contribute to the community. We make four contributions: 1) we characterize a set of communities that are “datamining-bootstrappable” and define the bootstrapping problem in terms of decision-theoretic optimization, 2) we estimate the model parameters in a case study involving the Open AI Resources website, 3) we demonstrate that non-members' predicted interest levels and request design are important features that can significantly affect the contribution rate, and 4) we ran a simulation experiment using data generated with the learned parameters and show that our decision-theoretic optimization algorithm can generate as much community utility when bootstrapping the community as our strongest baseline while issuing only 55% as many contribution requests.
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey

Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial neural networks. Researchers in these disciplines, sometimes working on quite different problems, identified similar issues and heuristics for decision tree construction. This paper surveys existing work on decision tree construction, attempting to identify the important issues involved, directions the work has taken and the current state of the art.
Semantic embedding space for zero-shot action recognition

The number of categories for action recognition is growing rapidly. It is thus becoming increasingly hard to collect sufficient training data to learn conventional models for each category. This issue may be ameliorated by the increasingly popular “zero-shot learning” (ZSL) paradigm. In this framework a mapping is constructed between visual features and a human interpretable semantic description of each category, allowing categories to be recognised in the absence of any training data. Existing ZSL studies focus primarily on image data, and attribute-based semantic representations. In this paper, we address zero-shot recognition in contemporary video action recognition tasks, using semantic word vector space as the common space to embed videos and category labels. This is more challenging because the mapping between the semantic space and space-time features of videos containing complex actions is more complex and harder to learn. We demonstrate that a simple self-training and data augmentation strategy can significantly improve the efficacy of this mapping. Experiments on human action datasets including HMDB51 and UCF101 demonstrate that our approach achieves the state-of-the-art zero-shot action recognition performance.
Intrusion and intrusion detection

Assurance technologies for computer security have failed to have significant impacts in the marketplace, with the result that most of the computers connected to the internet are vulnerable to attack. This paper looks at the problem of malicious users from both a historical and practical standpoint. It traces the history of intrusion and intrusion detection from the early 1970s to the present day, beginning with a historical overview. The paper describes the two primary intrusion detection techniques, anomaly detection and signature-based misuse detection, in some detail and describes a number of contemporary research and commercial intrusion detection systems. It ends with a brief discussion of the problems associated with evaluating intrusion detection systems and a discussion of the difficulties associated with making further progress in the field. With respect to the latter, it notes that, like many fields, intrusion detection has been based on a combination of intuition and brute-force techniques. We suspect that these have carried the field as far as they can and that further significant progress will depend on the development of an underlying theoretical basis for the field.
The Rise of the Sharing Economy : Estimating the Impact of Airbnb on the Hotel Industry

A number of decentralized peer-to-peer markets, now colloquially known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. A central question surrounding the sharing economy regards its long-term impact: will peer-to-peer platforms materialize as viable mainstream alternatives to traditional providers, or will they languish as niche markets? In this paper, we study Airbnb, a sharing economy pioneer offering shortterm accommodation. Combining data from Airbnb and the Texas hotel industry, we estimate the impact of Airbnb’s entry into the Texas market on hotel room revenue, and study the market response of hotels. To identify Airbnb’s causal impact on hotel room revenue, we use a difference-in-differences empirical strategy that exploits the significant spatiotemporal variation in the patterns of Airbnb adoption across city-level markets. We estimate that in Austin, where Airbnb supply is highest, the impact on hotel revenue is roughly 8-10%. We find that Airbnb’s impact is non-uniformly distributed, with lower-priced hotels, and hotels not catering to business travel being the most affected segments. Finally, we find that affected hotels have responded by reducing prices, an impact that benefits all consumers, not just participants in the sharing economy. Our work provides empirical evidence that the sharing economy is making inroads by successfully competing with, and acquiring market share from, incumbent firms.
Chunk-based Decoder for Neural Machine Translation

Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk rather than a word as the basic translation unit, local (intrachunk) and global (inter-chunk) word orders and dependencies can be easily modeled. The chunk structure, despite its importance, has not been considered in the decoders used for neural machine translation (NMT). In this paper, we propose chunk-based decoders for NMT, each of which consists of a chunk-level decoder and a word-level decoder. The chunklevel decoder models global dependencies while the word-level decoder decides the local word order in a chunk. To output a target sentence, the chunk-level decoder generates a chunk representation containing global information, which the wordlevel decoder then uses as a basis to predict the words inside the chunk. Experimental results show that our proposed decoders can significantly improve translation performance in a WAT ’16 Englishto-Japanese translation task.
Geometric Camera Calibration Using Circular Control Points

Modern CCD cameras are usually capable of a spatial accuracy greater than 1/50 of the pixel size. However, such accuracy is not easily attained due to various error sources that can affect the image formation process. Current calibration methods typically assume that the observations are unbiased, the only error is the zero-mean independent and identically distributed random noise in the observed image coordinates, and the camera model completely explains the mapping between the 3-D coordinates and the image coordinates. In general, these conditions are not met, causing the calibration results to be less accurate than expected. In this paper, a calibration procedure for precise 3-D computer vision applications is described. It introduces bias correction for circular control points and a non-recursive method for reversing the distortion model. The accuracy analysis is presented and the error sources that can reduce the theoretical accuracy are discussed. The tests with synthetic images indicate improvements in the calibration results in limited error conditions. In real images, the suppression of external error sources becomes a prerequisite for successful calibration.
TwinsCoin: A Cryptocurrency via Proof-of-Work and Proof-of-Stake

We design and implement TwinsCoin, the first cryptocurrency based on a provably secure and scalable public blockchain design using both proof-of-work and proof-of-stake mechanisms. Different from the proof-of-work based Bitcoin, our construction uses two types of resources, computing power and coins (i.e., stake). The blockchain in our system is more robust than that in a pure proof-of-work based system; even if the adversary controls the majority of mining power, we can still have the chance to secure the system by relying on honest stake. In contrast, Bitcoin blockchain will be insecure if the adversary controls more than 50% of mining power.
 Our design follows a recent provably secure proof-of-work/proof-of-stake hybrid blockchain[11]. In order to make our construction practical, we considerably enhance its design. In particular, we introduce a new strategy for difficulty adjustment in the hybrid blockchain and provide a theoretical analysis of it. We also show how to construct a light client for proof-of-stake cryptocurrencies and evaluate the proposal practically.
 We implement our new design. Our implementation uses a recent modular development framework for blockchains, called Scorex. It allows us to change only certain parts of an application leaving other codebase intact. In addition to the blockchain implementation, a testnet is deployed. Source code is publicly available.
Security and Privacy in Fog Computing: Challenges

Fog computing paradigm extends the storage, networking, and computing facilities of the cloud computing toward the edge of the networks while offloading the cloud data centers and reducing service latency to the end users. However, the characteristics of fog computing arise new security and privacy challenges. The existing security and privacy measurements for cloud computing cannot be directly applied to the fog computing due to its features, such as mobility, heterogeneity, and large-scale geo-distribution. This paper provides an overview of existing security and privacy concerns, particularly for the fog computing. Afterward, this survey highlights ongoing research effort, open challenges, and research trends in privacy and security issues for fog computing.
Inside-outside duality for planar billiards: A numerical study.

This paper reports the results of extensive numerical studies related to spectral properties of the Laplacian and the scattering matrix for planar domains (called billiards). There is a close connection between eigenvalues of the billiard Laplacian and the scattering phases, basically that every energy at which a scattering phase is 2π corresponds to an eigenenergy of the Laplacian. Interesting phenomena appear when the shape of the domain does not allow an extension of the eigenfunction to the exterior. In this paper these phenomena are studied and illustrated from several points of view. DIETZ, Barbara, et al. Inside-outside duality for planar billiards: A numerical study.