nils-herrmann/clean_scirepeval huggingface dataset:
Multi Agent Task Oriented Dialog Policy Learning with Role Aware Reward Decomposition
Venue: ACL
Year: 2020

Many studies have applied reinforcement learning to train a dialog policy and show great promise these years. One common approach is to employ a user simulator to obtain a large number of simulated user experiences for reinforcement learning algorithms. However, modeling a realistic user simulator is challenging. A rule based simulator requires heavy domain expertise for complex tasks, and a data driven simulator requires considerable data and it is even unclear how to evaluate a simulator. To avoid explicitly building a user simulator beforehand, we propose Multi Agent Dialog Policy Learning, which regards both the system and the user as the dialog agents. Two agents interact with each other and are jointly learned simultaneously. The method uses the actor critic framework to facilitate pretraining and improve scalability. We also propose Hybrid Value Network for the role aware reward decomposition to integrate role specific domain knowledge of each agent in the task oriented dialog. Results show that our method can successfully build a system policy and a user policy simultaneously, and two agents can achieve a high task success rate through conversational interaction.
Mizrahi perceptions of their TV portrayal in Israel
Venue: Israel Affairs
Year: 2019

This article examines how Mizrahim perceive their media representation on Israeli television. Using Karl Jaspers' psychological theory of boundary situations, the article seeks to explain the seeming disparity between the negative depiction of Mizrahim on Israeli TV and Mizrahi consumption of these representations. Having conducted in depth semi structured interviews with 25 individuals, the article found that most of the interviewees were well aware of their negative representation on Israeli TV yet used various interpretive strategies, stemming from 'media boundary situations, to deal with this prejudice.
Training End to end Single Image Generators without GANs
Venue: ArXiv
Year: 2020

We present AugurOne, a novel approach for training single image generative models. Our approach trains an upscaling neural network using non affine augmentations of the (single) input image, particularly including non rigid thin plate spline image warps. The extensive augmentations significantly increase the in sample distribution for the upsampling network enabling the upscaling of highly variable inputs. A compact latent space is jointly learned allowing for controlled image synthesis. Differently from Single Image GAN, our approach does not require GAN training and takes place in an end to end fashion allowing fast and stable training. We experimentally evaluate our method and show that it obtains compelling novel animations of single image, as well as, state of the art performance on conditional generation tasks e.g. paint to image and edges to image.
CECI: Compact Embedding Cluster Index for Scalable Subgraph Matching
Venue: SIGMOD Conference
Year: 2019

Subgraph matching finds all distinct isomorphic embeddings of a query graph on a data graph. For large graphs, current solutions face the scalability challenge due to expensive joins, excessive false candidates, and workload imbalance. In this paper, we propose a novel framework for subgraph listing based on Compact Embedding Cluster Index \idx) which divides the data graph into multiple embedding clusters for parallel processing. The \sub has three unique techniques: utilizing the BFS based filtering and reverse BFS based refinement to prune the unpromising candidates early on, replacing the edge verification with set intersection to speed up the candidate verification, and using search cardinality based cost estimation for detecting and dividing large embedding clusters in advance. The experiments performed on several real and synthetic datasets show that the \sub outperforms state of the art solutions on average by 20.4x for listing all embeddings and by 2.6x for enumerating the first 1,024 embeddings.
Improving Low Resource Neural Machine Translation With Teacher Free Knowledge Distillation
Venue: IEEE Access
Year: 2020

Knowledge Distillation (KD) aims to distill the knowledge of a cumbersome teacher model into a lightweight student model. Its success is generally attributed to the privileged information on similarities among categories provided by the teacher model, and in this sense, only strong teacher models are deployed to teach weaker students in practice. However, in low resource neural machine translation, a stronger teacher model is not available. To counteract this, We therefore propose a novel Teacher free Knowledge Distillation framework for low resource neural machine translation, where the model learns from manually designed regularization distribution as a virtual teacher model. The prior distribution of artificial design can not only obtain the similarity information between words, but also provide effective regularity for model training. Experimental results show that the proposed method has improved performance in low resource language effectively.
A Study of Evan King's Rickshaw Boy from the Perspective of Toury's Norm Theory on its Interlingual Rewriting
Year: 2012

Translation is more of a social activity than code switching.It is closely related to history,culture and society.Therefore,the success of a translation involves various social factors.Evan King's Rickshaw Boy provides a very good example.In spite of the obvious rewriting of the original,the translation gained great popularity among its English readers.This phenomenon could be better explained from the perspective of Gideon Toury's norm theory.
OC06_10 Applicability of a Non Laboratory and a Laboratory Based Cardiovascular Disease Risk Score Across Seven Regions of the World
Year: 2016

In most regions of the world, cardiovascular disease (CVD) risk scores have undergone limited evaluation in prospective cohort studies to determine their accuracy for estimating CVD risk.  We evaluated the performance of the Non laboratory INTERHEART Risk Score (NL IHRS) and fasting cholesterol based IHRS (FC IHRS) across seven distinct geographic regions in the Prospective Urban Rural Epidemiology (PURE) study.  We tested the performance of the NL IHRS (N1/498,775) and FC IHRS (N1/4108,516) for predicting incident CVD in a community based, prospective study across 7 geographic regions: South Asia, China, Southeast Asia, Middle East, Europe/ North America, South America and Africa. CVD was defined as the composite outcome of cardiovascular death, myocardial infarction, stroke, heart failure or coronary revascularization. The performance of the score was evaluated using measures of discrimination (e.g. concordance statistic [C statistic] and calibration (e.g. intercept and calibration slope)  The NL IHRS had satisfactory discrimination for incident CVD, although recalibration was necessary in all regions, which improved its overall performance (C statistic (0.67 to 0.71, p <0.001) Regional discrimination was highest in South America and the Middle East (C statistic 1/4 0.72 in both regions) and lowest in South Asia (C statistic 1/4 0.61) Regional recalibration was also necessary for the FC IHRS, which also improved its overall discrimination (C statistic 0.70 to 0.73, p<0.001) In 83,880 participants with complete data for both scores, the FC IHRS performed better than the NL IHRS although absolute differences were small (C statistic 0.71 vs. 0.72, p<0.001)  External validation of the NL IHRS and FC IHRS in the PURE study suggests that regionally recalibrated versions of both are useful for estimating CVD risk across a diverse range of community dwelling populations from diverse geographic regions. The NL IHRS may be used as an alternative to a cholesterol based risk prediction tool with only a modest reduction in accuracy. Disclosure of Interest: None Declared
Learning to Learn Kernels with Variational Random Features
Venue: ICML
Year: 2020

In this work, we introduce kernels with random Fourier features in the meta learning framework to leverage their strong few shot learning ability. We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base learner, which is developed in a latent variable model by treating the random feature basis as the latent variable. We formulate the optimization of MetaVRF as a variational inference problem by deriving an evidence lower bound under the meta learning framework. To incorporate shared knowledge from related tasks, we propose a context inference of the posterior, which is established by an LSTM architecture. The LSTM based inference network can effectively integrate the context information of previous tasks with task specific information, generating informative and adaptive features. The learned MetaVRF can produce kernels of high representational power with a relatively low spectral sampling rate and also enables fast adaptation to new tasks. Experimental results on a variety of few shot regression and classification tasks demonstrate that MetaVRF delivers much better, or at least competitive, performance compared to existing meta learning alternatives.
Rituals alleviate grieving for loved ones, lovers, and lotteries.
Venue: Journal of experimental psychology. General
Year: 2014

Three experiments explored the impact of mourning rituals after losses of loved ones, lovers, and lotteries on mitigating grief. Participants who were directed to reflect on past rituals or who were assigned to complete novel rituals after experiencing losses reported lower levels of grief. Increased feelings of control after rituals mediated the link between use of rituals and reduced grief after losses, and the benefits of rituals accrued not only to individuals who professed a belief in rituals' effectiveness but also to those who did not. Although the specific rituals in which people engage after losses vary widely by culture and religion and among our participants our results suggest a common psychological mechanism underlying their effectiveness: regained feelings of control.
Remotely sensed indicators and open access biodiversity data to assess bird diversity patterns in Mediterranean rural landscapes
Year: 2018

Changes in ecosystem area are often used to assess human impacts on habitats and estimate biodiversity change. However, because species respond to structural changes at fine spatial scales the use of area alone may not capture all relevant changes. Operational costs limit the assessment of biodiversity change at a simultaneously fine spatial resolution and large scales. The development of cost effective and expedite methods to monitor biodiversity change is therefore required. We use open access satellite imagery and biodiversity data to investigate the importance of variables of habitat extent and structure in explaining species richness and community dissimilarity of forest and open land birds at the regional scale. Moreover, because Mediterranean landscapes are subject to seasonal dynamics, we explore the indicator value of remotely sensed variables measured in spring and summer. A large scale dataset of bird occurrence data, including 8042 observations and 78 species, distributed by 40 landscape sized cells, was assembled from GBIF after controlling for data quality. We found that summer satellite imagery, when the green perennial vegetation is more apparent, is particularly suited to model the diversity patterns of forest species, because distribution of tree cover in the landscape is well captured. Summer data is also useful to monitor the perennial elements that shape landscape structure and the habitat of open land species. Specifically, mean NDVI and a second order NDVI texture variable, were found to be good indicators of forest and open land habitats, respectively. The use of spring imagery appears to be useful to monitor habitat structure within open land habitat patches. Overall, NDVI texture measures were found to be good predictors of bird diversity patterns at large scales. Also, we were able to successfully conduct a regional scale analysis using open access data, which illustrates their potential to inform large scale biodiversity monitoring.
Values of Single Cell RNA Sequencing in Development of Cerebral Cortex.
Venue: Advances in experimental medicine and biology
Year: 2020

The single cell RNA sequencing (scRNA seq) is a powerful tool for exploring the complexity, clusters, and specific functions of the brain cells. Using scRNA seq, the heterogeneity and changes in transcriptomic profiles of a single neuron were defined during dynamic development and differentiation of cells in cerebral cortex regions, and in the pathogenesis of neurological diseases. One of the great challenges is that the brain sample is susceptible to interference and confounding. More advanced methodologies of computational systems biology need to be developed to overcome the inherent interference and technical differences in the detection of single cell signals. It is expected that scRNA seq will be extended to metabolic profiles of the single neuron cell on basis of transcriptional profiles and regulatory networks. It is also expected if the transcriptional profiles can be integrated with molecular and functional phenomes in a single neuron and with disease specific phenomes to understand molecular mechanisms of brain development and disease occurrence. scRNA seq will provide the new emerging neurological disciple of the artificial intelligent single neuron for deep understanding of brain diseases.
THE POWER OF POLITICAL ECONOMY
Venue: The Review of Politics
Year: 2012

countries" (1) In such situations, people in developed nations treat people in developing nations with inadequate regard for the equal moral importance of their interests, taking advantage of their bargaining weaknesses due to desperate neediness. One of Miller's core examples is a case involving Chinese leather workers. Miller rightly claims that the fact that the workers are made better off than they would be without the work, voluntarily accept employment, and report satisfaction with their lives does not mitigate the moral wrong of the exploitative arrangement between manufacturers and laborers. Miller's focus on exploitation has great intuitive appeal. But Miller's discussion leaves some unanswered questions. It seems plausible that once the basic needs of the leather workers are met they are not being exploited in a morally objectionable way just by being made to do something they would not do if they were not in a (relatively) weak bargaining position. This view is at least suggested by Miller's characterization of the leather workers' situation as "stultifying" (64) and his claim that improving wages and working conditions would avoid the wrongdoing of exploitation. If Miller has a basicneeds threshold beyond which exploitation as he defines it is not morally objectionable, then clearly much hinges on how robust a conception of "basic needs" Miller has in mind. Miller's Globalizing Justice is an important contribution to thought about transnational justice. The book's combination of fresh, compelling theoretical argument and deep engagement in the realities of global political and economic life are a paradigm for philosophical work on this subject, and indeed for applied ethics in general.
Unity in Variety in website aesthetics: A systematic inquiry
Venue: Int. J. Hum. Comput. Stud.
Year: 2017

This research experimentally investigates whether Unity in Variety can account for the aesthetic appreciation of websites. In a first study we designed two sets of web pages, differing in layout style and content, to systematically and independently vary on both unity and variety via the design factors contrast and symmetry (for unity) and dissimilarity of elements and colourfulness (for variety) It was demonstrated that only the sets based on symmetry and colourfulness resulted in independent manipulations of unity and variety, respectively. These two sets of web pages were tested in Study 2 showing that, as predicted, both unity and variety independently and positively influence aesthetic appreciation. Following the principle of Unity in Variety, simultaneously maximizing unity and variety leads to an optimal balance where aesthetic appreciation is highest. Our research lends further support to the principle of Unity in Variety, extends it to the domain of HCI, and provides directions on how to purposefully design for an optimal balance between unity and variety.
Biometric From Surface Electromyogram (sEMG) Feasibility of User Verification and Identification Based on Gesture Recognition
Venue: Frontiers in Bioengineering and Biotechnology
Year: 2020

Electrical biosignals are favored as biometric traits due to their hidden nature and allowing for liveness detection. This study explored the feasibility of surface electromyogram (sEMG) the electrical manifestation of muscle activities, as a biometric trait. The accurate gesture recognition from sEMG provided a unique advantage over two traditional electrical biosignal traits, electrocardiogram (ECG) and electroencephalogram (EEG) enabling users to customize their own gesture codes. The performance of 16 static wrist and hand gestures was systematically investigated in two identity management modes: verification and identification. The results showed that for a single fixed gesture, using only 0.8 second data, the averaged equal error rate (EER) for verification was 3.5% and the averaged rank 1 for identification was 90.3% both comparable to the reported performance of ECG and EEG. The function of customizing gesture code could further improve the verification performance to 1.1% EER. This work demonstrated the potential and effectiveness of sEMG as a biometric trait in user verification and identification, beneficial for the design of future biometric systems.
Evolution of Magnetic Double Helix and Quantum Criticality near a Dome of Superconductivity in CrAs
Year: 2018

At ambient pressure CrAs undergoes a first order transition into a double helical magnetic state at TN 265 K, which is accompanied by a structural transition. The recent discovery of pressure induced superconductivity in CrAs makes it important to clarify the nature of quantum phase transitions out of its coupled structural/helimagnetic order. Here we show, via neutron diffraction on the single crystal CrAs under hydrostatic pressure (P) that the combined order is suppressed at Pc 10 kbar, near which bulk superconductivity develops with a maximal transition temperature Tc 2 K. We further show that the coupled order is also completely suppressed by phosphorus doping in CrAs1 xPx at a critical xc 0.05, above which inelastic neutron scattering evidenced persistent antiferromagnetic correlations, providing a possible link between magnetism and superconductivity. In line with the presence of antiferromagnetic fluctuations near Pc (xc) the A coefficient of the quadratic temperature dependence of resistivity exhibits a dramatic enhancement as P (x) approaches Pc (xc) around which Res(T) has a non Fermi liquid form. Accordingly, the electronic specific heat coefficient of CrAs1 xPx peaks out around xc. These properties provide clear evidences for quantum criticality, which we interpret as originating from a nearly second order helimagnetic quantum phase transition that is concomitant with a first order structural transition. Our findings in CrAs highlight the distinct characteristics of quantum criticality in bad metals, thereby bringing out new insights into the physics of unconventional superconductivity such as occurring in the high Tc iron pnictides.
Demystifying cloud benchmarking
Venue: 2016 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
Year: 2016

The popularity of online services has grown exponentially, spurring great interest in improving server hardware and software. However, conducting research on servers has traditionally been challenging due to the complexity of setting up representative server configurations and measuring their performance. Recent work has eased the effort of benchmarking servers by making benchmarking software and benchmarking instructions readily available to the research community. Unfortunately, the existing benchmarks are a black box; their users are expected to trust the design decisions made in the construction of these benchmarks with little justification and few cited sources. In this work, we have attempted to overcome this problem by building new server benchmarks for three popular network intensive workloads: video streaming, web serving, and object caching. This paper documents the benchmark construction process, describes the software, and provides the resources we used to justify the design decisions that make our benchmarks representative for system level studies.
Checkmate 040: Nivolumab (NIVO) in patients (pts) with advanced hepatocellular carcinoma (aHCC) and Child Pugh B (CPB) status.
Venue: Journal of Clinical Oncology
Year: 2019

Pts with aHCC and CPB liver status are often excluded from clinical trials of novel therapies due to their poor prognosis (Greten British J Cancer 2005) Historical overall survival (OS) for these pts when treated with sorafenib (SOR) has ranged 3 5 mo in retrospective or descriptive studies (Abou Alfa Gastrointest Cancer Res 2011; Da Fonseca Mol Clin Oncol 2015;  Chiu Cancer 2012) thus, novel treatment options are needed for these pts. The PD 1 inhibitor NIVO is approved in the US, Canada, and elsewhere, most recently Australia, for SOR treated pts with aHCC based on results from CheckMate 040 (NCT01658878) (El Khoueiry Lancet 2017) Here we report data from the CPB cohort of CheckMate 040, the first prospective study of immunotherapy in this pt group.  Pts with CPB (B7 B8) aHCC who were SOR naive (n 25) or experienced (n 24) received NIVO 240 mg IV for 30 min Q2W until unacceptable toxicity or disease progression. Primary endpoints were objective response rate (ORR) (investigator assessed [INV] RECIST v1.1) and duration of response (DOR) Safety was assessed in all treated pts using NCI CTCAE v4.0.  Of 49 analyzed pts, 28 (57.1% had vascular invasion or extrahepatic spread. During a follow up range of 6 18 mo, INV ORR was 10.2% with 5 pts responding; disease control rate (DCR) was 55.1% Median (m) time to response was 2.7 mo and mDOR was 9.9 mo; 2 pts had ongoing responses at data cutoff. The mOS was 7.6 mo (mOS follow up was 7.4 mo) mOS in SOR naive and treated pts was 9.8 and 7.3 mo, respectively. Treatment related adverse events (TRAEs) were reported in 25 (51% pts; 4 (8.2% pts had select hepatic TRAEs. TRAEs led to discontinuation in 2 pts (4.1% NIVO safety profile in these pts appeared comparable to cohorts of pts with CPA aHCC. Comparison data for pts with CPA aHCC and extended follow up for pts with CPB aHCC will be presented.  Encouraging DCR and durable responses were observed in pts with CPB aHCC treated with NIVO. AEs were manageable and did not lead to higher discontinuation compared with pts with CPA aHCC. NIVO showed promising efficacy and tolerability compared with historical data, supporting further investigation.