nils-herrmann/clean_scirepeval huggingface dataset:
Data Dropout: Optimizing Training Data for Convolutional Neural Networks

Deep learning models learn to fit training data while they are highly expected to generalize well to testing data. Most works aim at finding such models by creatively designing architectures and fine-tuning parameters. To adapt to particular tasks, hand-crafted information such as image prior has also been incorporated into end-to-end learning. However, very little progress has been made on investigating how an individual training sample will influence the generalization ability of a model. In other words, to achieve high generalization accuracy, do we really need all the samples in a training dataset? In this paper, we demonstrate that deep learning models such as convolutional neural networks may not favor all training samples, and generalization accuracy can be further improved by dropping those unfavorable samples. Specifically, the influence of removing a training sample is quantifiable, and we propose a Two-Round Training approach, aiming to achieve higher generalization accuracy. We locate unfavorable samples after the first round of training, and then retrain the model from scratch with the reduced training dataset in the second round. Since our approach is essentially different from fine-tuning or further training, the computational cost should not be a concern. Our extensive experimental results indicate that, with identical settings, the proposed approach can boost performance of the well-known networks on both high-level computer vision problems such as image classification, and low-level vision problems such as image denoising.
Ranking Approaches for Microblog Search

Ranking microblogs, such as tweets, as search results for a query is challenging, among other things because of the sheer amount of microblogs that are being generated in real time, as well as the short length of each individual microblog. In this paper, we describe several new strategies for ranking microblogs in a real-time search engine. Evaluating these ranking strategies is non-trivial due to the lack of a publicly available ground truth validation dataset. We have therefore developed a framework to obtain such validation data, as well as evaluation measures to assess the accuracy of the proposed ranking strategies. Our experiments demonstrate that it is beneficial for microblog search engines to take into account social network properties of the authors of microblogs in addition to properties of the microblog itself.
Autoscaling for Hadoop Clusters

Unforeseen events such as node failures and resource contention can have a severe impact on the performance of data processing frameworks, such as Hadoop, especially in cloud environments where such incidents are common. SLA compliance in the presence of such events requires the ability to quickly and dynamically resize infrastructure resources. Unfortunately, the distributed and stateful nature of data processing frameworks makes it challenging to accurately scale the system at run-time. In this paper, we present the design and implementation of a model-driven autoscaling solution for Hadoop clusters. We first develop novel gray-box performance models for Hadoop workloads that specifically relate job execution times to resource allocation and workload parameters. We then employ these models to dynamically determine the resources required to successfully complete the Hadoop jobs as per the user-specified SLA under various scenarios including node failures and multi-job executions. Our experimental results on three different Hadoop cloud clusters and across different workloads demonstrate the efficacy of our models and highlight their autoscaling capabilities.
Using CNN To Classify Spectrograms of Seismic Events From Llaima Volcano (Chile)

The monitoring of volcanoes is mainly performed by sensors installed on their structures, aiming at recording seismic activities and reporting them to observatories to be later analyzed by specialists. However, due to the high volume of data continuously collected, the use of automatic techniques is an important requirement to support real time analyses. In this sense, a basic but challenging task is the classification of seismic activities to identify signals yielded by different sources as, for instance, the movement of magmatic fluids. Although there exists several approaches proposed to perform such task, they were mainly designed to deal with raw signals. In this paper, we present a 2D approach developed considering two main steps. Firstly, spectrograms for every collected signal are calculated by using Fourier Transform. Secondly, we set a deep neural network to discriminate seismic activities by analyzing the spectrogram shapes. As a consequence, our classifier provided outstanding results with accuracy rates greater than 95%.
Internal Fingerprint Identification With Optical Coherence Tomography

Existing biometric fingerprint devices show numerous reliability problems such as wet or fake fingers. In this letter, a secured method using the internal structures of the finger (papillary layer) for fingerprint identification is presented. With a frequency-domain optical coherence tomography (FD-OCT) system, a 3-D image of a finger is acquired and the information of the internal fingerprint extracted. The right index fingers of 51 individuals were recorded three times. Using a commercial fingerprint identification program, 95% of internal fingerprint images were successfully recognized. These results demonstrate that OCT imaging of internal fingerprints can be used for accurate and reliable fingerprint recognition.
View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network

This paper describes the details of Sighthound’s fully automated vehicle make, model and color recognition system. The backbone of our system is a deep convolutional neural network that is not only computationally inexpensive, but also provides state-of-the-art results on several competitive benchmarks. Additionally, our deep network is trained on a large dataset of several million images which are labeled through a semi-automated process. Finally we test our system on several public datasets as well as our own internal test dataset. Our results show that we outperform other methods on all benchmarks by significant margins. Our model is available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud
Inferring monopartite projections of bipartite networks: an entropy-based approach

Bipartite networks are currently regarded as providing amajor insight into the organization ofmany real-world systems, unveiling themechanisms driving the interactions occurring between distinct groups of nodes. One of themost important issues encounteredwhenmodeling bipartite networks is devising away to obtain a (monopartite) projection on the layer of interest, which preserves asmuch as possible the information encoded into the original bipartite structure. In the present paper we propose an algorithm to obtain statistically-validated projections of bipartite networks, according to which any twonodes sharing a statistically-significant number of neighbors are linked. Since assessing the statistical significance of nodes similarity requires a proper statistical benchmark, herewe consider a set of four nullmodels, definedwithin the exponential randomgraph framework. Our algorithm outputs amatrix of link-specific p-values, fromwhich a validated projection is straightforwardly obtainable, upon running amultiple hypothesis testing procedure. Finally, we test ourmethod on an economic network (i.e. the countries-productsWorld TradeWeb representation) and a social network (i.e.MovieLens, collecting the users’ ratings of a list ofmovies). In both cases non-trivial communities are detected: while projecting theWorld TradeWeb on the countries layer reveals modules of similarly-industrialized nations, projecting it on the products layer allows communities characterized by an increasing level of complexity to be detected; in the second case, projecting MovieLens on thefilms layer allows clusters ofmovies whose affinity cannot be fully accounted for by genre similarity to be individuated.
Rotated QAM Constellations to Improve BICM Performance for DVB-T2

A technique intended to increase the diversity order of bit-interleaved coded modulations (BICM) over non Gaussian channels is presented. It introduces simple modifications to the mapper and to the corresponding demapper. They consist of a constellation rotation coupled with signal space component interleaving. Iterative processing at the receiver side can provide additional improvement to the BICM performance. This method has been shown to perform well over fading channels with or without erasures. It has been adopted for the 4-, 16-, 64- and 256-QAM constellations considered in the DVB-T2 standard. Resulting gains can vary from 0.2 dB to several dBs depending on the order of the constellation, the coding rate and the channel model.
The heritability of bipolar affective disorder and the genetic relationship to unipolar depression.

Twin studies of bipolar affective disorder (BPD) have either been small or have not used explicit diagnostic criteria. There has been little use of genetic model fitting and no analyses to explore the etiological overlap with unipolar depression (UPD).



Sixty-seven twin pairs, 30 monozygotic and 37 dizygotic, in which the proband had BPD were ascertained, and lifetime diagnoses were made using DSM-IV criteria. Univariate models were applied to estimate the contribution of additive genetic and environmental effects. Bipolar data were then combined with those from 68 monozygotic and 109 dizygotic pairs in which the proband had UPD. Two models were explored: a classic 2-threshold approach, in which BPD and UPD occupy the same continuum of liability but differ in severity, and a correlated liability model of mania and depression.



Heritability of BPD was estimated at 85% (95% confidence interval [CI], 0.73-0.93) using narrow concordance and 89% (95% CI, 0.61-1.0) using broad concordance, with no shared environmental effects detected. A 2-threshold model was an unsatisfactory fit. Fitting a correlated liability model revealed a genetic correlation of 0.65 (95% CI, 0.58-0.75) between mania and depression and a correlation of 0.59 (95% CI, 0.15-0.84) for nonfamilial environment. Approximately 71% of the genetic variance for mania was not shared with depression.



As defined by the DSM-IV, BPD is highly heritable. There are substantial genetic and nonshared environmental correlations between mania and depression, but most of the genetic variance in liability to mania is specific to the manic syndrome.
The ethics of human-chicken relationships in video games: the origins of the digital chicken

In this paper, we look at the historical place that chickens have held in media depictions and as entertainment, analyse several types of representations of chickens in video games, and draw out reflections on society in the light of these representations. We also look at real-life, modern historical, and archaeological evidence of chicken treatment and the evolution of social attitudes with regard to animal rights, and deconstruct the depiction of chickens in video games in this light.
The Architecture of Human-Like General Intelligence

By exploring the relationships between different AGI architectures, one can work toward a holistic cognitive model of human-level intelligence. In this vein, here an integrative architecture diagram for human-like general intelligence is proposed, via merging of lightly modified version of prior diagrams including Aaron Sloman’s high-level cognitive model, Stan Franklin and the LIDA group’s model of working memory and the cognitive cycle, Joscha Bach and Dietrich Dorner’s Psi model of motivated action and cognition, James Albus’s three-hierarchy intelligent robotics model, and the author’s prior work on cognitive synergy in deliberative thought and metacognition, along with ideas from deep learning and computational linguistics. The purpose is not to propose an actual merger of the various AGI systems considered, but rather to highlight the points of compatibility between the different approaches, as well as the differences of both focus and substance. The result is perhaps the most comprehensive architecture diagram of human-cognition yet produced, tying together all key aspects of human intelligence in a coherent way that is not tightly bound to any particular cognitive or AGI theory. Finally, the question of the dynamics associated with the architecture is considered, including the potential that human-level intelligence requires cognitive synergy between these various components is considered; and the possibility of a “trickiness” property causing the intelligence of the overall system to be badly suboptimal if any of the components are missing or insufficiently cooperative. One idea emerging from these dynamic consideration is that implementing the whole integrative architecture diagram may be necessary for achieving anywhere near human-level, human-like general intelligence.
Security Enhancements in EMV Protocol for NFC Mobile Payment

Today, by integrating Near Field Communication (NFC) technology in smartphones, bank cards and payment terminals, a purchase transaction can be executed immediately without any physical contact, without entering a PIN code or a signature. Europay Mastercard Visa (EMV) is the standard dedicated for securing contactless-NFC payment transactions. However, it does not ensure two main security proprieties: (1) the authentication of the payment terminal to the client's payment device, (2) the confidentiality of personal banking data. In this paper, we first of all detail EMV standard and its security vulnerabilities. Then, we propose a solution that enhances the EMV protocol by adding a new security layer aiming to solve EMV weaknesses. We formally check the correctness of the proposal using a security verification tool called Scyther.
Cryptanalyzing a RGB image encryption algorithm based on DNA encoding and chaos map

Recently, a RGB image encryption algorithm based on DNA encoding and chaos map has been proposed. It was reported that the encryption algorithm can be broken with four pairs of chosen plain-images and the corresponding cipher-images. This paper re-evaluates the security of the encryption algorithm, and finds that the encryption algorithm can be broken efficiently with only one known plain-image. The effectiveness of the proposed known-plaintext attack is supported by both rigorous theoretical analysis and experimental results. In addition, two other security defects are also reported.
Osteoarthritis as an inflammatory disease (osteoarthritis is not osteoarthrosis!).

Osteoarthritis (OA) has long been considered a "wear and tear" disease leading to loss of cartilage. OA used to be considered the sole consequence of any process leading to increased pressure on one particular joint or fragility of cartilage matrix. Progress in molecular biology in the 1990s has profoundly modified this paradigm. The discovery that many soluble mediators such as cytokines or prostaglandins can increase the production of matrix metalloproteinases by chondrocytes led to the first steps of an "inflammatory" theory. However, it took a decade before synovitis was accepted as a critical feature of OA, and some studies are now opening the way to consider the condition a driver of the OA process. Recent experimental data have shown that subchondral bone may have a substantial role in the OA process, as a mechanical damper, as well as a source of inflammatory mediators implicated in the OA pain process and in the degradation of the deep layer of cartilage. Thus, initially considered cartilage driven, OA is a much more complex disease with inflammatory mediators released by cartilage, bone and synovium. Low-grade inflammation induced by the metabolic syndrome, innate immunity and inflammaging are some of the more recent arguments in favor of the inflammatory theory of OA and highlighted in this review.
Primary cutaneous follicle center lymphoma mimicking folliculitis.

Primary cutaneous follicle center lymphoma (PCFCL) is the most common type of cutaneous B-cell lymphoma. The cutaneous manifestations of PCFCL typically include solitary erythematous or violaceous plaques, nodules, or tumors of varying sizes. Grouped lesions also may be observed, but multifocal disease is rare. We report the case of a 54-year-old woman diagnosed with PCFCL with an unusual clinical presentation resembling folliculitis or Grover disease. Histologic studies demonstrated extensive lymphoma cells in a nodular and diffuse pattern. Immunohistochemical studies demonstrated that the neoplastic cells were positive for CD20, CD79a, BCL-2, and BCL-6; CD3, CD5, and cyclin D1 were negative. These findings were consistent with PCFCL. Further evaluation for systemic disease via positron emission tomography-computed tomography and bone marrow biopsy was unremarkable. Increased awareness of this presentation of PCFCL can facilitate earlier diagnosis and intervention, which may result in improved patient outcomes.
On dynamic data-driven selection of sensor streams

Sensor nodes have limited local storage, computational power, and battery life, as a result of which it is desirable to minimize the storage, processing and communication from these nodes during data collection. The problem is further magnified by the large volumes of data collected. In real applications, sensor streams are often highly correlated with one another or may have other kinds of functional dependencies. For example, a group of sound sensors in a given geographical proximity may pick almost the same set of signals. Clearly, since there are considerable functional dependencies between different sensors, there are huge  redundancies in the data collected by sensors. These redundancies may also change as the data evolve over time. In this paper, we discuss real time algorithms for reducing the volume of the data collected in sensor networks. The broad idea is to determine the functional dependencies between sensor streams efficiently in real time, and actively collect the data only from a minimal set of sensors. The remaining sensors collect the data passively at low sampling rates in order to detect any changing trends in the underlying data. We present real time algorithms in order to minimize the power consumption in reducing the data collected and show that the resulting data retains almost the same amount of information at a much lower cost.
Analyzing College Students' Social Media Communication Apprehension.

Research has shown that college students are heavy users of social media. Yet, very little has looked at the connection between college students' social media use and communication apprehension (CA). Due to the shortage of research concerning CA and social media, this study aims to test the relationship between social media CA and introversion in relationship to social media use and social media addiction. To test these relationships, 396 undergraduate students were surveyed. The survey consisted of instruments used to measure the individuals' levels of social media use, social media addiction, introversion, and CA. After conducting multiple linear regressions, it was determined that there was a negative relationship between social media CA and introversion with (1) social media use and (2) social media addiction. Results indicated that social media CA was significantly related to social media addiction. These findings suggest that college students might gravitate toward social media to communicate rather than face-to-face communication.
Current roles of microRNAs in infectious diseases – advancing into healthcare

Among all new emerging RNA species, microRNAs (miRNAs) have attracted the interest of the scientific community due to their implications as biomarkers of prognostic value, disease progression, or diagnosis, because of defining features as robust association with the disease, or stable presence in easily accessible human biofluids. This field of research has been established twenty years ago, and the development has been considerable. The regulatory nature of miRNAs makes them great candidates for the treatment of infectious diseases, and a successful example in the field is currently being translated to clinical practice. This review will present a general outline of miRNAmolecules, as well as successful stories of translational significance which are getting us closer from the basic bench studies into clinical practice.
The affective shift model of work engagement.

On the basis of self-regulation theories, the authors develop an affective shift model of work engagement according to which work engagement emerges from the dynamic interplay of positive and negative affect. The affective shift model posits that negative affect is positively related to work engagement if negative affect is followed by positive affect. The authors applied experience sampling methodology to test the model. Data on affective events, mood, and work engagement was collected twice a day over 9 working days among 55 software developers. In support of the affective shift model, negative mood and negative events experienced in the morning of a working day were positively related to work engagement in the afternoon if positive mood in the time interval between morning and afternoon was high. Individual differences in positive affectivity moderated within-person relationships. The authors discuss how work engagement can be fostered through affect regulation.
Investigation of the chick embryo as a potential alternative to the mouse for evaluation of radiopharmaceuticals.

The chick embryo is an emerging in vivo model in several areas of pre-clinical research including radiopharmaceutical sciences. Herein, it was evaluated as a potential test system for assessing the biodistribution and in vivo stability of radiopharmaceuticals. For this purpose, a number of radiopharmaceuticals labeled with (18)F, (125)I, (99m)Tc, and (177)Lu were investigated in the chick embryo and compared with the data obtained in mice.



Chick embryos were cultivated ex ovo for 17-19 days before application of the radiopharmaceutical directly into the peritoneum or intravenously using a vein of the chorioallantoic membrane (CAM). At a defined time point after application of radioactivity, the embryos were euthanized by shock-freezing using liquid nitrogen. Afterwards they were separated from residual egg components for post mortem imaging purposes using positron emission tomography (PET) or single photon emission computed tomography (SPECT).



SPECT images revealed uptake of [(99m)Tc]pertechnetate and [(125)I]iodide in the thyroid of chick embryos and mice, whereas [(177)Lu]lutetium, [(18)F]fluoride and [(99m)Tc]-methylene diphosphonate ([(99m)Tc]-MDP) were accumulated in the bones.  and the somatostatin analog [(177)Lu]-DOTATOC, as well as the folic acid derivative [(177)Lu]-DOTA-folate showed accumulation in the renal tissue whereas [(99m)Tc]-mebrofenin accumulated in the gall bladder and intestine of both species. In vivo dehalogenation of [(18)F]fallypride and of the folic acid derivative [(125)I]iodo-tyrosine-folate was observed in both species. In contrast, the 3'-aza-2'-[(18)F]fluorofolic acid ([(18)F]-AzaFol) was stable in the chick embryo as well as in the mouse.



Our results revealed the same tissue distribution profile and in vivo stability of radiopharmaceuticals in the chick embryo and the mouse. This observation is promising with regard to a potential use of the chick embryo as an inexpensive and simple test model for preclinical screening of novel radiopharmaceuticals.