nils-herrmann/clean_scirepeval huggingface dataset:
Quantum machine learning for electronic structure calculations

Considering recent advancements and successes in the development of efficient quantum algorithms for electronic structure calculations-alongside impressive results using machine learning techniques for computation-hybridizing quantum computing with machine learning for the intent of performing electronic structure calculations is a natural progression. Here we report a hybrid quantum algorithm employing a restricted Boltzmann machine to obtain accurate molecular potential energy surfaces. By exploiting a quantum algorithm to help optimize the underlying objective function, we obtained an efficient procedure for the calculation of the electronic ground state energy for a small molecule system. Our approach achieves high accuracy for the ground state energy for H2, LiH, H2O at a specific location on its potential energy surface with a finite basis set. With the future availability of larger-scale quantum computers, quantum machine learning techniques are set to become powerful tools to obtain accurate values for electronic structures.
Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators

In this paper, we propose a new model that is capable of recognizing overlapping mentions. We introduce a novel notion of mention separators that can be effectively used to capture how mentions overlap with one another. On top of a novel multigraph representation that we introduce, we show that efficient and exact inference can still be performed. We present some theoretical analysis on the differences between our model and a recently proposed model for recognizing overlapping mentions, and discuss the possible implications of the differences. Through extensive empirical analysis on standard datasets, we demonstrate the effectiveness of our approach.
Improving the Transformer Translation Model with Document-Level Context

Although the Transformer translation model (Vaswani et al., 2017) has achieved state-ofthe-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge. In this work, we extend the Transformer model with a new context encoder to represent document-level context, which is then incorporated into the original encoder and decoder. As large-scale document-level parallel corpora are usually not available, we introduce a two-step training method to take full advantage of abundant sentence-level parallel corpora and limited document-level parallel corpora. Experiments on the NIST ChineseEnglish datasets and the IWSLT FrenchEnglish datasets show that our approach improves over Transformer significantly.
Who interacts on the Web?: The intersection of users' personality and social media use

In the increasingly user-generated Web, users’ personality traits may be crucial factors leading them to engage in this participatory media. The literature suggests factors such as extraversion, emotional stability and openness to experience are related to uses of social applications on the Internet. Using a national sample of US adults, this study investigated the relationship between these three dimensions of the BigFive model and social media use (defined as use of social networking sites and instant messages). It also examined whether gender and age played a role in that dynamic. Results revealed that while extraversion and openness to experiences were positively related to social media use, emotional stability was a negative predictor, controlling for socio-demographics and life satisfaction. These findings differed by gender and age. While extraverted men and women were both likely to be more frequent users of social media tools, only the men with greater degrees of emotional instability were more regular users. The relationship between extraversion and social media use was particularly important among the young adult cohort. Conversely, being open to new experiences emerged as an important personality predictor of social media use for the more mature segment of the sample.
Biosorption: a solution to pollution?

To solve the water pollution problem by toxic heavy metal contamination resulting from humans technological activities has for long presented a challenge. Biosorption can be a part of the solution. Some types of biosorbents such as seaweeds, molds, yeasts, bacteria or crab shells are examples of biomass tested for metal biosorption with very encouraging results. The uptake of heavy metals by biomass can in some cases reach up to 50% of the biomass dry weight. New biosorbents can be manipulated for better efficiency and multiple re-use to increase their economic attractiveness.
eTourism : A comparison of Online and Offline Bookings and the Importance of Hotel Attributes

Internet is an important tool for information searching and purchasing of products especially in tourism. This paper investigates the impact of demographic and travel characteristics of hotel guests on online and offline reservations. It also examines the importance of hotel attributes in selecting a hotel and the differences in priorities between guests who book their accommodation online and offline. Data was collected by surveys of guests in three hotels in Dubrovnik. The results of the study showed that the method of booking mostly depends on the nature of travel, and that the importance of hotel attributes does not differ much between different groups of guests.
Effect of manual lymph drainage in addition to guidelines and exercise therapy on arm lymphoedema related to breast cancer: randomised controlled trial

To determine the preventive effect of manual lymph drainage on the development of lymphoedema related to breast cancer.



Randomised single blinded controlled trial.



University Hospitals Leuven, Leuven, Belgium.



160 consecutive patients with breast cancer and unilateral axillary lymph node dissection. The randomisation was stratified for body mass index (BMI) and axillary irradiation and treatment allocation was concealed. Randomisation was done independently from recruitment and treatment. Baseline characteristics were comparable between the groups.



For six months the intervention group (n = 79) performed a treatment programme consisting of guidelines about the prevention of lymphoedema, exercise therapy, and manual lymph drainage. The control group (n = 81) performed the same programme without manual lymph drainage.



Cumulative incidence of arm lymphoedema and time to develop arm lymphoedema, defined as an increase in arm volume of 200 mL or more in the value before surgery.



Four patients in the intervention group and two in the control group were lost to follow-up. At 12 months after surgery, the cumulative incidence rate for arm lymphoedema was comparable between the intervention group (24%) and control group (19%) (odds ratio 1.3, 95% confidence interval 0.6 to 2.9; P = 0.45). The time to develop arm lymphoedema was comparable between the two group during the first year after surgery (hazard ratio 1.3, 0.6 to 2.5; P = 0.49). The sample size calculation was based on a presumed odds ratio of 0.3, which is not included in the 95% confidence interval. This odds ratio was calculated as (presumed cumulative incidence of lymphoedema in intervention group/presumed cumulative incidence of no lymphoedema in intervention group)×(presumed cumulative incidence of no lymphoedema in control group/presumed cumulative incidence of lymphoedema in control group) or (10/90)×(70/30).



Manual lymph drainage in addition to guidelines and exercise therapy after axillary lymph node dissection for breast cancer is unlikely to have a medium to large effect in reducing the incidence of arm lymphoedema in the short term.
Meta4meaning: Automatic Metaphor Interpretation Using Corpus-Derived Word Associations

We propose a novel metaphor interpretation method, Meta4meaning. It provides interpretations for nominal metaphors by generating a list of properties that the metaphor expresses. Meta4meaning uses word associations extracted from a corpus to retrieve an approximation to properties of concepts. Interpretations are then obtained as an aggregation or difference of the saliences of the properties to the tenor and the vehicle. We evaluate Meta4meaning using a set of humanannotated interpretations of 84 metaphors and compare with two existing methods for metaphor interpretation. Meta4meaning significantly outperforms the previous methods on this task.
MOND AND COSMOLOGY

I review various ideas on MOND cosmology and structure formation beginning with non-relativistic models in analogy with Newtonian cosmology. I discuss relativistic MOND cosmology in the context of Bekenstein’s theory and propose an alternative biscalar effective theory of MOND in which the acceleration parameter, a0 is identified with the cosmic time derivative of a matter coupling scalar field and cosmic CDM appears as scalar field oscillations of the auxiliary “coupling strength” field.
UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use.
Parental eating behaviours, home food environment and adolescent intakes of fruits, vegetables and dairy foods: longitudinal findings from Project EAT.

To examine longitudinal associations of parental report of household food availability and parent intakes of fruits, vegetables and dairy foods with adolescent intakes of the same foods. This study expands upon the limited research of longitudinal studies examining the role of parents and household food availability in adolescent dietary intakes.



Longitudinal study. Project EAT-II followed an ethnically and socio-economically diverse sample of adolescents from 1999 (time 1) to 2004 (time 2). In addition to the Project EAT survey, adolescents completed the Youth Adolescent Food-Frequency Questionnaire in both time periods, and parents of adolescents completed a telephone survey at time 1. General linear modelling was used to examine the relationship between parent intake and home availability and adolescent intake, adjusting for time 1 adolescent intakes. Associations were examined separately for the high school and young adult cohorts and separately for males and females in combined cohorts.



The sample included 509 pairs of parents/guardians and adolescents.



Vegetables served at dinner significantly predicted adolescent intakes of vegetables for males (P = 0.037), females (P = 0.009), high school (P = 0.033) and young adults (P = 0.05) at 5-year follow-up. Among young adults, serving milk at dinner predicted dairy intake (P = 0.002). Time 1 parental intakes significantly predicted intakes of young adults for fruit (P = 0.044), vegetables (P = 0.041) and dairy foods (P = 0.008). Parental intake predicted intake of dairy for females (P = 0.02).



The findings suggest the importance of providing parents of adolescents with knowledge and skills to enhance the home food environment and improve their own eating behaviours.
Rumor Gauge: Predicting the Veracity of Rumors on Twitter

The spread of malicious or accidental misinformation in social media, especially in time-sensitive situations, such as real-world emergencies, can have harmful effects on individuals and society. In this work, we developed models for automated verification of rumors (unverified information) that propagate through Twitter. To predict the veracity of rumors, we identified salient features of rumors by examining three aspects of information spread: linguistic style used to express rumors, characteristics of people involved in propagating information, and network propagation dynamics. The predicted veracity of a time series of these features extracted from a rumor (a collection of tweets) is generated using Hidden Markov Models. The verification algorithm was trained and tested on 209 rumors representing 938,806 tweets collected from real-world events, including the 2013 Boston Marathon bombings, the 2014 Ferguson unrest, and the 2014 Ebola epidemic, and many other rumors about various real-world events reported on popular websites that document public rumors. The algorithm was able to correctly predict the veracity of 75% of the rumors faster than any other public source, including journalists and law enforcement officials. The ability to track rumors and predict their outcomes may have practical applications for news consumers, financial markets, journalists, and emergency services, and more generally to help minimize the impact of false information on Twitter.
Scalable, High-Quality Object Detection

Current high-quality object detection approaches use the same scheme: salience-based object proposal methods followed by post-classification using deep convolutional features. This spurred recent research in improving object proposal methods [18, 32, 15, 11, 2]. However, domain agnostic proposal generation has the principal drawback that the proposals come unranked or with very weak ranking, making it hard to trade-off quality for running time. Also, it raises the more fundamental question of whether high-quality proposal generation requires careful engineering or can be derived just from data alone. We demonstrate that learning-based proposal methods can effectively match the performance of hand-engineered methods while allowing for very efficient runtime-quality trade-offs. Using our new multi-scale convolutional MultiBox (MSC-MultiBox) approach, we substantially advance the state-of-the-art on the ILSVRC 2014 detection challenge data set, with 0.5 mAP for a single model and 0.52 mAP for an ensemble of two models. MSC-Multibox significantly improves the proposal quality over its predecessor Multibox  method: AP increases from 0.42 to 0.53 for the ILSVRC detection challenge. Finally, we demonstrate improved bounding-box recall compared to Multiscale Combinatorial Grouping [18] with less proposals on the Microsoft-COCO  data set.
How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media?

Platforms such as Twitter have provided researchers with ample opportunities to analytically study social phenomena. There are however, significant computational challenges due to the enormous rate of production of new information: researchers are therefore, often forced to analyze a judiciously selected “sample” of the data. Like other social media phenomena, information diffusion is a social process–it is affected by user context, and topic, in addition to the graph topology. This paper studies the impact of different attribute and topology based sampling strategies on the discovery of an important social media phenomena–information diffusion. We examine several widely-adopted sampling methods that select nodes based on attribute (random, location, and activity) and topology (forest fire) as well as study the impact of attribute based seed selection on topology based sampling. Then we develop a series of metrics for evaluating the quality of the sample, based on user activity (e.g. volume, number of seeds), topological (e.g. reach, spread) and temporal characteristics (e.g. rate). We additionally correlate the diffusion volume metric with two external variables–search and news trends. Our experiments reveal that for small sample sizes (30%), a sample that incorporates both topology and usercontext (e.g. location, activity) can improve on naı̈ve methods by a significant margin of ∼15-20%.
Supporting Communication and Coordination in Collaborative Sensemaking

When people work together to analyze a data set, they need to organize their findings, hypotheses, and evidence, share that information with their collaborators, and coordinate activities amongst team members. Sharing externalizations (recorded information such as notes) could increase awareness and assist with team communication and coordination. However, we currently know little about how to provide tool support for this sort of sharing. We explore how linked common work (LCW) can be employed within a `collaborative thinking space', to facilitate synchronous collaborative sensemaking activities in Visual Analytics (VA). Collaborative thinking spaces provide an environment for analysts to record, organize, share and connect externalizations. Our tool, CLIP, extends earlier thinking spaces by integrating LCW features that reveal relationships between collaborators' findings. We conducted a user study comparing CLIP to a baseline version without LCW. Results demonstrated that LCW significantly improved analytic outcomes at a collaborative intelligence task. Groups using CLIP were also able to more effectively coordinate their work, and held more discussion of their findings and hypotheses. LCW enabled them to maintain awareness of each other's activities and findings and link those findings to their own work, preventing disruptive oral awareness notifications.
Behavior pattern clustering in blockchain networks

Blockchain holds promise for being the revolutionary technology, which has the potential to find applications in numerous fields such as digital money, clearing, gambling and product tracing. However, blockchain faces its own problems and challenges. One key problem is to automatically cluster the behavior patterns of all the blockchain nodes into categories. In this paper, we introduce the problem of behavior pattern clustering in blockchain networks and propose a novel algorithm termed BPC for this problem. We evaluate a long list of potential sequence similarity measures, and select a distance that is suitable for the behavior pattern clustering problem. Extensive experiments show that our proposed algorithm is much more effective than the existing methods in terms of clustering accuracy.
Resolving author name homonymy to improve resolution of structures in co-author networks

We investigate how author name homonymy distorts clustered large-scale co-author networks, and present a simple, effective, scalable and generalizable algorithm to ameliorate such distortions. We evaluate the performance of the algorithm to improve the resolution of mesoscopic network structures, that is those meso-level structures of a network resulting from groupings of nodes and their interlinking. To this end, we establish the ground truth for a sample of author names that is statistically representative of different types of nodes in the co-author network, distinguished by their role for the connectivity of the network. We finally observe that this distinction of node roles based on the mesoscopic structure of the network, in combination with a quantification of the commonality of last names, suggests a new approach to assess network distortion by homonymy and to analyze the reduction of distortion in the network after disambiguation, without requiring ground truth sampling.
An Analysis of Causality between Events and its Relation to Temporal Information

In this work we present an annotation framework to capture causality between events, inspired by TimeML, and a language resource covering both temporal and causal relations. This data set is then used to build an automatic extraction system for causal signals and causal links between given event pairs. The evaluation and analysis of the system’s performance provides an insight into explicit causality in text and the connection between temporal and causal relations.
An Empirical Performance Evaluation of Docker Container , Openstack Virtual Machine and Bare Metal Server

Server virtualization is a fundamental technological innovation that is used extensively in IT enterprises. Server virtualization enables creation of multiple virtual machines on single underlying physical machine. It is realized either in form of hypervisors or containers. Hypervisor is an extra layer of abstraction between the hardware and virtual machines that emulates underlying hardware. In contrast, the more recent container-based virtualization technology runs on host kernel without additional layer of abstraction. Thus container technology is expected to provide near native performance compared to hypervisor based technology. We have conducted a series of experiments to measure and compare the performance of workloads over hypervisor based virtual machines, Docker containers and native bare metal machine. We use a standard benchmark workload suite that stresses CPU, memory, disk IO and system. The results obtained show that Docker containers provide better or similar performance compared to traditional hypervisor based virtual machines in almost all the tests. However as expected the native system still provides the best performance as compared to either containers or hypervisors.
Psychosocial and Psychophysiological Effects of Human-Animal Interactions: The Possible Role of Oxytocin

During the last decade it has become more widely accepted that pet ownership and animal assistance in therapy and education may have a multitude of positive effects on humans. Here, we review the evidence from 69 original studies on human-animal interactions (HAI) which met our inclusion criteria with regard to sample size, peer-review, and standard scientific research design. Among the well-documented effects of HAI in humans of different ages, with and without special medical, or mental health conditions are benefits for: social attention, social behavior, interpersonal interactions, and mood; stress-related parameters such as cortisol, heart rate, and blood pressure; self-reported fear and anxiety; and mental and physical health, especially cardiovascular diseases. Limited evidence exists for positive effects of HAI on: reduction of stress-related parameters such as epinephrine and norepinephrine; improvement of immune system functioning and pain management; increased trustworthiness of and trust toward other persons; reduced aggression; enhanced empathy and improved learning. We propose that the activation of the oxytocin system plays a key role in the majority of these reported psychological and psychophysiological effects of HAI. Oxytocin and HAI effects largely overlap, as documented by research in both, humans and animals, and first studies found that HAI affects the oxytocin system. As a common underlying mechanism, the activation of the oxytocin system does not only provide an explanation, but also allows an integrative view of the different effects of HAI.