nils-herrmann/clean_scirepeval huggingface dataset:
RetinaGAN: An Object aware Approach to Sim to Real Transfer
Venue: ArXiv
Year: 2020

The success of deep reinforcement learning (RL) and imitation learning (IL) in vision based robotic manipulation typically hinges on the expense of large scale data collection. With simulation, data to train a policy can be collected efficiently at scale, but the visual gap between sim and real makes deployment in the real world difficult. We introduce RetinaGAN, a generative adversarial network (GAN) approach to adapt simulated images to realistic ones with object detection consistency. RetinaGAN is trained in an unsupervised manner without task loss dependencies, and preserves general object structure and texture in adapted images. We evaluate our method on three real world tasks: grasping, pushing, and door opening. RetinaGAN improves upon the performance of prior sim to real methods for RL based object instance grasping and continues to be effective even in the limited data regime. When applied to a pushing task in a similar visual domain, RetinaGAN demonstrates transfer with no additional real data requirements. We also show our method bridges the visual gap for a novel door opening task using imitation learning in a new visual domain. Visit the project website at this https URL
Mirror Neurons Differentially Encode the Peripersonal and Extrapersonal Space of Monkeys
Venue: Science
Year: 2009

Actions performed by others may have different relevance for the observer, and thus lead to different behavioral responses, depending on the regions of space in which they are executed. We found that in rhesus monkeys, the premotor cortex neurons activated by both the execution and the observation of motor acts (mirror neurons) are differentially modulated by the location in space of the observed motor acts relative to the monkey, with about half of them preferring either the monkey's peripersonal or extrapersonal space. A portion of these spatially selective mirror neurons encode space according to a metric representation, whereas other neurons encode space in operational terms, changing their properties according to the possibility that the monkey will interact with the object. These results suggest that a set of mirror neurons encodes the observed motor acts not only for action understanding, but also to analyze such acts in terms of features that are relevant to generating appropriate behaviors.
Competing charge, spin, and superconducting orders in underdoped YBa2Cu3Oy
Year: 2014

To explore the doping dependence of the recently discovered charge density wave (CDW) order in YBa2Cu3Oy, we present a bulk sensitive high energy x ray study for several oxygen concentrations, including strongly underdoped YBa2Cu3O6.44. Combined with previous data around the so called 1/8 doping, we show that bulk CDW order exists at least for hole concentrations (p) in the CuO2 planes of 0.078 less than or similar to p less than or similar to 0.132. This implies that CDW order exists in close vicinity to the quantum critical point for spin density wave (SDW) order. In contrast to the pseudogap temperature T* the onset temperature of CDW order decreases with underdoping to T CDW similar to 90 K in YBa2Cu3O6.44. Together with a weakened order parameter this suggests a competition between CDW and SDW orders. In addition, the CDW order in YBa2Cu3O6.44 shows the same type of competition with superconductivity as a function of temperature and magnetic field as samples closer to p 1/8. At low p the CDW incommensurability continues the previously reported linear increasing trend with underdoping. In the entire doping range the in plane correlation length of the CDW order in b axis direction depends only very weakly on the hole concentration, and appears independent of the type and correlation length of the oxygen chain order. The onset temperature of the CDW order is remarkably close to a temperature T dagger that marks the maximum of 1/(T1T) in planar Cu 63 NQR/NMR experiments, potentially indicating a response of the spin dynamics to the formation of the CDW. Our discussion of these findings includes a detailed comparison to the charge stripe order in La2 xBaxCuO4.
Analysis of key microbial community during the start up of anaerobic ammonium oxidation process with paddy soil as inoculated sludge.
Venue: Journal of environmental sciences
Year: 2018

A sequencing batch reactor (SBR) anaerobic ammonium oxidation (anammox) system was started up with the paddy soil as inoculated sludge. The key microbial community structure in the system along with the enrichment time was investigated by using molecular biology methods (e.g. high throughput 16S rRNA gene sequencing and quantitative PCR) Meanwhile, the influent and effluent water quality was continuously monitored during the whole start up stage. The results showed that the microbial diversity decreased as the operation time initially and increased afterwards, and the microbial niches in the system were redistributed. The anammox bacterial community structure in the SBR anammox system shifted during the enrichment, the most dominant anammox bacteria were CandidatusJettenia. The maximum biomass of anammox bacteria achieved 1.68x109copies/g dry sludge during the enrichment period, and the highest removal rate of TN achieved around 75%
Tolerance of High Oral Doses of Nonradioactive and Radioactive Caesium Chloride in the Pale Grass Blue Butterfly Zizeeria maha
Venue: Insects
Year: 2019

The biological effects of the Fukushima nuclear accident have been examined in the pale grass blue butterfly, Zizeeria maha (Lepidoptera: Lycaenidae) In previous internal exposure experiments, larvae were given field collected contaminated host plant leaves that contained up to 43.5 kBq/kg (leaf) of radioactive caesium. Larvae ingested up to 480 kBq/kg (larva) resulting in high mortality and abnormality rates. However, these results need to be compared with the toxicological data of caesium. Here, we examined the toxicity of both nonradioactive and radioactive caesium chloride on the pale grass blue butterfly. Larvae were fed a caesium containing artificial diet, ingesting up to 149 MBq/kg (larva) of radioactive caesium (137Cs) or a much higher amount of nonradioactive caesium. We examined the pupation rate, eclosion rate, survival rate up to the adult stage, and the forewing size. In contrast to previous internal exposure experiments using field collected contaminated leaves, we could not detect any effect. We conclude that the butterfly is tolerant to ionising radiation from 137Cs in the range tested but is vulnerable to radioactive contamination in the field. These results suggest that the biological effects in the field may be mediated through ecological systems and cannot be estimated solely based on radiation doses.
The Widening Gender Gap in Opposition to Pornography, 1975 2012
Year: 2015

In the last several decades, pornography in the United States has become more mainstream, more accessible, more phallocentric, and more degrading to women. Furthermore, consumption of pornography remains a major difference in the sexual experiences of men and women. Yet research has not addressed how opposition to pornography has changed over the period, despite shifts in the accessibility and visibility of pornography as well as new cultural and legal issues presented by the advent of Internet pornography. We examine gender differences in opposition to pornography from 1975 to 2012, measured by support for legal censorship of pornography in the General Social Survey. Results show that both men's and women's opposition to pornography have decreased significantly over the past 40 years, suggesting a cultural shift toward "pornographication" affecting attitudes. However, women remain more opposed to pornography than men, and men's opposition has declined faster, so the gender gap in opposition to pornography has widened, indicating further divergence of men's and women's sexual attitudes over time. This is consistent with the increasingly normative nature of pornography consumption for men, increases over time in men's actual consumption of pornography, and its increasingly degrading depiction of women.
Orientations to Happiness and Subjective Well Being in Chinese Adolescents: The Roles of Prosocial Behavior and Internet Addictive Behavior
Year: 2017

Researchers have studied individual's pursuit of well being through two perspectives: the eudaimonic perspective and the hedonic perspective. Peterson and his colleagues (2005) introduced their Orientations to Happiness scale, a self report measure assessing individual's pursuit of well being that corresponds to these two perspectives. Specifically, the Life of Meaning subscale is the index of the eudaimonic pursuit; the Life of Pleasure subscale is the index of the hedonic pursuit. Previous research has demonstrated that orientations to happiness are positively associated with individual's subjective well being, whereas little research has addressed the mechanisms underlying the associations. Based on goal theory of happiness, the present study investigated how orientations to happiness were associated with subjective well being by examining the indirect effects of the prosocial behavior and Internet addictive behavior in a sample of Chinese adolescents aged between 13 and 18 (N 2082) The results showed that: (1) both life of meaning and life of pleasure were positively associated with adolescents' subjective well being; (2) prosocial behavior partially mediated the positive association between life of meaning and subjective well being; and (3) prosocial behavior also partially mediated the positive association between life of pleasure and subjective well being, whereas Internet addictive behavior undermined the positive association here. The findings shed light on the underlying mechanisms between orientations to happiness and subjective well being.
Start ups in the logistics industry: Advancing a framework for the disruptive potential of digital freight forwarder platforms (DFFs)
Venue: Proceedings of The 8th International Conference on New Ideas in Management, Economics and Accounting
Year: 2021

Digital platforms play an increasing role in the logistics industry and start ups are challenging traditional logistics providers with new business models. It is, however, not clear whether these platforms have the potential to disrupt current industries. As a response, this paper contributes a more nuanced view on the notion of disruption and disruptive innovation and is concerned with digital freight forwarders (DFFs) in particular. To distinguish between disruptive and sustained innovations, we develop a framework from literature that allows characterizing digital platforms and their disruptive potential along the initial target market, the value contribution, the ecosystem network and the industry supervision. We apply this framework to a comparative dual case study of two DFFs and reveal important differences. Beyond its very contribution, the paper invites researchers to expand on the given approach, in particular to advance the predictive ability of the proposed framework.
Topography of Fear and Violence in Ahmad Al Sadawi's Frankenstein in Baghdad
Venue: Journal of Education College Wasit University
Year: 2019

This paper examines the postcolonial Iraqi novel which gives a voice to the Iraqi writers to describe for the first time the political repression and violence in Iraq before and after the US invasion. They address the bad conditions in Iraq after the 2003 and discuss the reshaping of the Iraqi identity due to the sectarian conflicts that have endangers the minority population in this pluralistic society. We intend to stress the contemporary fears of Iraqi people through the use of the monster. In this paper, the researchers attempt to present a new voice in the Iraqi novel that was silenced for a long time. The Iraqi writers tend to describe aesthetically the terrible national condition in Iraq post 2003.
Web Based Acceptance and Commitment Therapy for Mental Health Problems in College Students
Venue: Behavior modification
Year: 2017

There are significant challenges in addressing the mental health needs of college students. The current study tested an acceptance and commitment therapy (ACT) web based self help program to treat a broad range of psychological problems students struggle with. A sample of 79 college students was randomized to web based ACT or a waitlist condition, with assessments at baseline and posttreatment. Results indicated adequate acceptability and program engagement for the ACT website. Relative to waitlist, participants receiving ACT improved on overall distress, general anxiety, social anxiety, depression, academic concerns, and positive mental health. There were no between group effects on eating concerns, alcohol use, or hostility, or on some key ACT process of change measures. ACT participants improved more on mindful acceptance and obstruction to valued living, both of which mediated treatment outcomes. Results are discussed in the context of lessons learned with the website prototype, and areas for further research are presented.
Computer aided proofs in cryptography: an overview
Year: 2014

The goal of modern cryptography is to design efficient constructions that simultaneously achieve some desired functionality and provable security against resource bounded adversaries. Over the years, the realm of cryptography has expanded from basic functionalities such as encryption, decryption and key agreement, to elaborate functionalities such as zero knowledge protocols, secure multiparty computation, and more recently verifiable computation. In many cases, these elaborate functionalities can only be achieved through cryptographic systems, in which several elementary constructions interact. As a consequence of the evolution towards more complex functionalities, cryptographic proofs have become significantly more involved, and more difficult to check. Several cryptographers have therefore advocated the use of tool supported frameworks for building and verifying proofs; the most vivid recommendation for using computer support is elaborated in a farseeing article  in which Shai Halevi describes a potential approach for realizing this vision.
From Intrusion Detection to Intrusion Detection and Diagnosis: An Ontology Based Approach
Venue: SEUS
Year: 2009

Currently available products only provide some support in terms of Intrusion Prevention and Intrusion Detection, but they very much lack Intrusion Diagnosis features. We discuss the limitations of current Intrusion Detection System (IDS) technology, and propose a novel approach which we call Intrusion Detection Diagnosis System (ID2S) technology to overcome such limitations. The basic idea is to collect information at several architectural levels, using multiple security probes, which are deployed as a distributed architecture, to perform sophisticated correlation analysis of intrusion symptoms. This makes it possible to escalate from intrusion symptoms to the adjudged cause of the intrusion, and to assess the damage in individual system components. The process is driven by ontologies. We also present preliminary experimental results, providing evidence that our approach is effective against stealthy and non vulnerability attacks.
Order Batch Formations for Less Picker Blocking in a Narrow Aisle Picking System
Year: 2015

This paper analyses the best batch formations for order picking throughput in narrow aisle order picking systems. Our analytical comparison finds that a high pick density variation leads to a heavy picker blocking. Simulation experiments show that a distance based batching algorithm reduces picker blocking by decreasing the number of aisles visited and stabilizing the variation in number of picks per aisle by packing orders tightly, and that the solution quality and mechanism for determining the batch size dictated by the sorting strategy causes varying amounts of blocking. We conclude that combining a distance based batching method with an appropriate batch sizing strategy will reduce picker blocking and shorten travel in narrow aisle picking systems.
Exploration and Practice of Inquiry based Teaching in the Course Mechanical Graphics
Year: 2019

As one of the research based teaching methods inquiry based teaching can stimulate students' independent learning, which is an effective teaching concept and method for cultivating innovative talents in modern higher engineering education. Mechanical Graphics is a professional basic course that lays the basic quality of mechanical engineering. It has the characteristics of training whole brain thinking, and the weakness of traditional teaching is obvious. Through the introduction of problem driven, analytical comparison, case teaching, combination of theory and practice in the inquiry based small class teaching to different stages of the mechanical graphics course, the classroom is more lively and enthusiastic, and the teaching becomes more targeted, and it is able to mobilize the subjective initiative of students with different levels of knowledge and inspire innovative thinking. The inquiry based teaching is worthy of promotion in higher engineering education.
Automatic Grading of Knee Osteoarthritis from Plain Radiographs Using Densely Connected Convolutional Networks
Venue: AIST
Year: 2020

In this paper, we consider densely connected convolutional networks and their applicability to the problem of assessment of knee osteoarthritis (OA) severity in the five point Kellgren Lawrence scale. First, we use trained from scratch Single Shot Detector (SSD) to localize knee joint areas in radiographs. Then, we apply DenseNets to quantify OA stages in the images of detected knee joints. We consider networks of different depths, trained both from scratch and pre trained on the ImageNet dataset and fine tuned in the images from Osteoarthritis Initiative dataset (OAI) Also, different loss functions are examined to understand which one gives the best training results. In the knee joint localization task, we obtain an accuracy of 94.03% under the Jaccard index threshold of 0.75. Also, our classifier outperforms the current state of the art with accuracy of 71% in the classification task.
A Comprehensive Study of Deep Neural Networks for Unsupervised Deep Learning
Year: 2020

Deep learning methods aims at learning meaningful representations in the field of machine learning (ML) Unsupervised deep learning architectures has grown at a fast pace owing to their ability to learn intricate problems. Availability of large amount of labelled and unlabeled data with highly efficient computational resources makes deep learning models more practicable for different applications. Recently, deep neural networks (DNNs) have become an extremely effective and widespread research area in the field of machine learning. The significant aim of deep learning is to learn the primary structure of input data and also to investigate the nonlinear mapping between the inputs and outputs. The main emphasis of this chapter is on unsupervised deep learning. We first study difficulties with neural networks while training with backpropagation algorithms. Later, different structures, namely, restricted Boltzmann machines (RBMs) Deep Belief Networks (DBNs) nonlinear autoencoders, deep Boltzmann machines are covered. Lastly, sustainable real applications in agricultural domain with deep learning are described.
An Analysis of University Students' Perspective On Online Learning in The Midst of Covid 19 Pandemic
Year: 2020

In this study we address and investigate university students' perspective on utilizing online learning in the midst of COVID19 pandemic. This research used qualitative descriptive method. The subject of this research were the learners of Elementary School Teacher Education, Physical Education, Sociology Education and English Education Department of Universitas Megarezky in 2019/2020 academic year. The number of respondents was 40 respondents. The research instruments were questionnaire that was obtained by google form through analyzing participants' response to know their perspective in using online learning and in depth interview. The study showed that previously, students did not use online learning before the pandemic because learning was conducted in the classroom. In the midst of covid 19 pandemic, the students are interested in using online learning in which can be accessed flexible, anywhere and everywhere. The best application used by lecturers and students was WhatsApp application. It can be concluded that the students' perspective on online learning had a positive impact in the midst of the covid 19 pandemic.
Relapsing polychondritis as an unusual cause of multiple cranial nerve palsies A Case report
Venue: Muscle nerve
Year: 2017

along with the other symptoms after immunotherapy, suggests involvement of peripheral myelin in anti LGI1 syndromes. Similar conclusions have been obtained experimentally by Silva et al. in mice. We are aware that a single case is not sufficient to demonstrate a cause effect relationship, and that the involvement of peripheral nerves was not the predominant manifestation. We are also aware that our patient was affected by diabetes; however, the improvement of symptoms and NCS parameters after immunotherapy suggests the autoimmune process rather than diabetes as the most probable cause of the polyneuropathy. Nevertheless, our findings should not be overlooked, and NCS studies could be included in the diagnostic protocol of suspected limbic encephalitis both to improve differential diagnosis and to better define the possible role of antiLGI1 and other antibodies as causative agents of peripheral nerve injury.
End To End Multi Speaker Speech Recognition With Transformer
Venue: ICASSP 2020 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020

Recently, fully recurrent neural network (RNN) based end to end models have been proven to be effective for multi speaker speech recognition in both the single channel and multi channel scenarios. In this work, we explore the use of Transformer models for these tasks by focusing on two aspects. First, we replace the RNN based encoder decoder in the speech recognition model with a Transformer architecture. Second, in order to use the Transformer in the masking network of the neural beamformer in the multi channel case, we modify the self attention component to be restricted to a segment rather than the whole sequence in order to reduce computation. Besides the model architecture improvements, we also incorporate an external dereverberation preprocessing, the weighted prediction error (WPE) enabling our model to handle reverberated signals. Experiments on the spatialized wsj1 2mix corpus show that the Transformer based models achieve 40.9% and 25.6% relative WER reduction, down to 12.1% and 6.4% WER, under the anechoic condition in single channel and multi channel tasks, respectively, while in the reverberant case, our methods achieve 41.5% and 13.8% relative WER reduction, down to 16.5% and 15.2% WER.