nils-herrmann/clean_scirepeval huggingface dataset:
Distributed representations for fine-grained entity typing

Knowledge about entities is essential for natural language understanding. This knowledge includes several facts about entities such as their names, properties, relations and types. This data is usually stored in large scale structures called knowledge bases (KB) and therefore building and maintaining KBs is very important. Examples of such KBs are Wikipedia, Freebase and Google knowledge graph. Incompleteness is unfortunately a reality for every KB, because the world is changing – new entities are emerging, and existing entities are getting new properties. Therefore, we always need to update KBs. To do so, we propose an information extraction method that processes large raw corpora in order to gather knowledge about entities. We focus on extraction of entity types and address the task of fine-grained entity typing: given a KB and a large corpus of text with mentions of entities in the KB, find all fine-grained types of the entities. For example given a large corpus and the entity “Barack Obama” we need to find all his types including PERSON, POLITICIAN, and AUTHOR. Artificial neural networks (NNs) have shown promising results in different machine learning problems. Distributed representation (embedding) is an effective way of representing data for NNs. In this work, we introduce two models for fine-grained entity typing using NNs with distributed representations of language units: (i) A global model that predicts types of an entity based on its global representation learned from the entity’s name and contexts. (ii) A context model that predicts types of an entity based on its context-level predictions. Each of the two proposed models has some specific properties. For the global model, learning high quality entity representations is crucial because it is the only source used for the predictions. Therefore, we introduce representations using name and contexts of entities on three levels of entity, word, and character. We show each has complementary information and a multi-level representation is the best. For the context model, we need to use distant supervision since the contextlevel labels are not available for entities. Distant supervised labels are noisy and this harms the performance of models. Therefore, we introduce and apply new algorithms for noise mitigation using multi-instance learning.
Digital creativity: Research themes and framework

Digital creativity is defined as the creativity manifested in all forms that are driven by digital technologies. Due to its novelty and interdisciplinary nature, the scope, perspective, and main research themes of digital creativity study are still unclear to date. Therefore, we utilized the intellectual structure technique developed by the information scientist to help clarify the scope and themes of this research domain. We analyzed 3591 relevant literatures and tentatively identified major research themes to facilitate the comprehension and study of digital creativity. The resulting framework summarizes the progress in digital creativity research and provides future research directions.
A taxonomy of security and privacy requirements for the Internet of Things (IoT)

Capturing security and privacy requirements in the early stages of system development is essential for creating sufficient public confidence in order to facilitate the adaption of novel systems such as the Internet of Things (IoT). However, security and privacy requirements are often not handled properly due to their wide variety of facets and aspects which make them difficult to formulate. In this study, security-related requirements of IoT heterogeneous systems are decomposed into a taxonomy of quality attributes, and existing security mechanisms and policies are proposed to alleviate the identified forms of security attacks and to reduce the vulnerabilities in the future development of the IoT systems. Finally, the taxonomy is applied on an IoT smart grid scenario.
Determinants of consumer food waste behaviour: Two routes to food waste

Approximately one quarter of the food supplied for human consumption is wasted across the food supply chain. In the high income countries, the food waste generated at the household level represents about half of the total food waste, making this level one of the biggest contributors to food waste. Yet, there is still little evidence regarding the determinants of consumers' food waste behaviour. The present study examines the effect of psycho-social factors, food-related routines, household perceived capabilities and socio-demographic characteristics on self-reported food waste. Survey data gathered among 1062 Danish respondents measured consumers' intentions not to waste food, planning, shopping and reuse of leftovers routines, perceived capability to deal with household food-related activities, injunctive and moral norms, attitudes towards food waste, and perceived behavioural control. Results show that perceived behavioural control and routines related to shopping and reuse of leftovers are the main drivers of food waste, while planning routines contribute indirectly. In turn, the routines are related to consumers' perceived capabilities to deal with household related activities. With regard to intentional processes, injunctive norms and attitudes towards food waste have an impact while moral norms and perceived behavioural control make no significant contribution. Implications of the study for initiatives aimed at changing consumers' food waste behaviour are discussed.
Development of coherent neuronal activity patterns in mammalian cortical networks: Common principles and local hetereogeneity

Many mammals are born in a very immature state and develop their rich repertoire of behavioral and cognitive functions postnatally. This development goes in parallel with changes in the anatomical and functional organization of cortical structures which are involved in most complex activities. The emerging spatiotemporal activity patterns in multi-neuronal cortical networks may indeed form a direct neuronal correlate of systemic functions like perception, sensorimotor integration, decision making or memory formation. During recent years, several studies--mostly in rodents--have shed light on the ontogenesis of such highly organized patterns of network activity. While each local network has its own peculiar properties, some general rules can be derived. We therefore review and compare data from the developing hippocampus, neocortex and--as an intermediate region--entorhinal cortex. All cortices seem to follow a characteristic sequence starting with uncorrelated activity in uncoupled single neurons where transient activity seems to have mostly trophic effects. In rodents, before and shortly after birth, cortical networks develop weakly coordinated multineuronal discharges which have been termed synchronous plateau assemblies (SPAs). While these patterns rely mostly on electrical coupling by gap junctions, the subsequent increase in number and maturation of chemical synapses leads to the generation of large-scale coherent discharges. These patterns have been termed giant depolarizing potentials (GDPs) for predominantly GABA-induced events or early network oscillations (ENOs) for mostly glutamatergic bursts, respectively. During the third to fourth postnatal week, cortical areas reach their final activity patterns with distinct network oscillations and highly specific neuronal discharge sequences which support adult behavior. While some of the mechanisms underlying maturation of network activity have been elucidated much work remains to be done in order to fully understand the rules governing transition from immature to mature patterns of network activity.
An autonomic system for intelligent truck parking

Traffic and road transport conditions are strongly influenced by the decisions of drivers. The more information they receive, the better decisions may be taken about their behavior. This is especially important in the transport of goods, since drivers are subject to traffic laws about maximum driving time and minimum time rest. In this paper a multi-agent system for negotiated management of parking spaces in road rest areas is presented. This system dynamically adapts itself to the preferences and needs of the drivers of goods about parking requests. The system is shown to be robust to incidents regarding the closure of road rest areas and an increased volume of freight traffic. The results also show that the number of illegal parkings is reduced resulting in greater road safety.
It's who you know: graph mining using recursive structural features

Given a graph, how can we extract good features for the nodes? For example, given two large graphs from the same domain, how can we use information in one to do classification in the other (i.e., perform across-network classification or transfer learning on graphs)? Also, if one of the graphs is anonymized, how can we use information in one to de-anonymize the other? The key step in all such graph mining tasks is to find effective node features. We propose ReFeX (Recursive Feature eXtraction), a novel algorithm, that recursively combines local (node-based) features with neighborhood (egonet-based) features; and outputs regional features -- capturing "behavioral" information. We demonstrate how these powerful regional features can be used in within-network and across-network classification and de-anonymization tasks -- without relying on homophily, or the availability of class labels. The contributions of our work are as follows: (a) ReFeX is scalable and (b) it is effective, capturing regional ("behavioral") information in large graphs. We report experiments on real graphs from various domains with over 1M edges, where ReFeX outperforms its competitors on typical graph mining tasks like network classification and de-anonymization.
Diagnosis of thyroid follicular neoplasm: fine-needle aspiration versus core-needle biopsy.

Although fine-needle aspiration (FNA) is a safe and accurate diagnostic procedure for assessing thyroid nodules, it has limitations in diagnosing follicular neoplasms due to its relatively high false-positive rate. The purpose of the present study was to evaluate the diagnostic role of core-needle biopsy (CNB) for thyroid nodules with follicular neoplasm (FN) in comparison with FNA.



A series of 107 patients (24 men, 83 women; mean age, 47.4 years) from 231 FNAs and 107 patients (29 men, 78 women; mean age, 46.3 years) from 186 CNBs with FN readings, all of whom underwent surgery, from October 2008 to December 2013 were retrospectively analyzed. The false-positive rate, unnecessary surgery rate, and malignancy rate for the FNA and CNB patients according to the final diagnosis following surgery were evaluated.



The CNB showed a significantly lower false-positive and unnecessary surgery rate than the FNA (4.7% versus 30.8%, 3.7% versus 26.2%, p < 0.001, respectively). In the FNA group, 33 patients (30.8%) had non-neoplasms, including nodular hyperplasia (n = 32) and chronic lymphocytic thyroiditis (n = 1). In the CNB group, 5 patients (4.7%) had non-neoplasms, all of which were nodular hyperplasia. Moreover, the CNB group showed a significantly higher malignancy rate than FNA (57.9% versus 28%, p < 0.001).



CNB showed a significantly lower false-positive rate and a higher malignancy rate than FNA in diagnosing FN. Therefore, CNB could minimize unnecessary surgery and provide diagnostic confidence when managing patients with FN to perform surgery.
Conformal RFID antenna design suitable for human monitoring and metallic platforms

This paper presents the design of a conformal, low profile RFID (Radio Frequency Identification) tag for applications like human monitoring and metallic cylinder tracking. The design of the tag is based on simple microstrip-type structure. For human monitoring study, firstly realized tag gain vs read range curves are presented. Input impedance and radiation pattern measurements are performed for the tag in a realistic setup and compared to simulation human model. For metallic cylinder tracking study, the input impedance and radiation patterns for the tag are evaluated through simulations. Finally, exhaustive read range measurements are performed for the tag on different surfaces (human, metal).
CROSS-GRADIENT TRAINING

We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training. In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains. We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances. CROSSGRAD parallelly trains a label and a domain classifier on examples perturbed by loss gradients of each other’s objectives. This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions. Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and that (2) data augmentation is a more stable and accurate method than domain adversarial training.
New Cu paste with high bonding strength—Nano composite alloy particles synthesized by nanomized method

In the metallization technology for crystalline Si solar cell, it has been required to develop new material with low cost for high-temperature and highly-reliable interconnection. We have developed new method to fabricate fine metal alloy particles with narrow distribution of particle size from 0.5 to 10μm. We called it as Nanomized method. The fine particles are composed of uniform structure dispersed metal alloy in nano-scale level and do not include void in the particle. The metal alloy particles fabricated by conventional atomized method include void inside particle and non-uniformly dispersed alloy components. We confirmed reliability and mechanical strength. We produced bonding material from the mixture of Cu particles and Sn-based alloy particles. The bonding strength of die chip on Al electrode reached 80 MPa as a top data, 50 MPa in average. The bonding strength of Sn-based alloy particle fabricated from conventional atomized method deteriorated less than 8 MPa after 500 hours later at 300 degree C.
Real-Time Monitoring System for a Utility-Scale Photovoltaic Power Plant

There is, at present, considerable interest in the storage and dispatchability of photovoltaic (PV) energy, together with the need to manage power flows in real-time. This paper presents a new system, PV-on time, which has been developed to supervise the operating mode of a Grid-Connected Utility-Scale PV Power Plant in order to ensure the reliability and continuity of its supply. This system presents an architecture of acquisition devices, including wireless sensors distributed around the plant, which measure the required information. It is also equipped with a high-precision protocol for synchronizing all data acquisition equipment, something that is necessary for correctly establishing relationships among events in the plant. Moreover, a system for monitoring and supervising all of the distributed devices, as well as for the real-time treatment of all the registered information, is presented. Performances were analyzed in a 400 kW transformation center belonging to a 6.1 MW Utility-Scale PV Power Plant. In addition to monitoring the performance of all of the PV plant's components and detecting any failures or deviations in production, this system enables users to control the power quality of the signal injected and the influence of the installation on the distribution grid.
Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria

The discovery of ammonia oxidation by mesophilic and thermophilic Crenarchaeota and the widespread distribution of these organisms in marine and terrestrial environments indicated an important role for them in the global nitrogen cycle. However, very little is known about their physiology or their contribution to nitrification. Here we report oligotrophic ammonia oxidation kinetics and cellular characteristics of the mesophilic crenarchaeon ‘Candidatus Nitrosopumilus maritimus’ strain SCM1. Unlike characterized ammonia-oxidizing bacteria, SCM1 is adapted to life under extreme nutrient limitation, sustaining high specific oxidation rates at ammonium concentrations found in open oceans. Its half-saturation constant (Km = 133 nM total ammonium) and substrate threshold (≤10 nM) closely resemble kinetics of in situ nitrification in marine systems and directly link ammonia-oxidizing Archaea to oligotrophic nitrification. The remarkably high specific affinity for reduced nitrogen (68,700 l per g cells per h) of SCM1 suggests that Nitrosopumilus-like ammonia-oxidizing Archaea could successfully compete with heterotrophic bacterioplankton and phytoplankton. Together these findings support the hypothesis that nitrification is more prevalent in the marine nitrogen cycle than accounted for in current biogeochemical models.
Research on Permanent Magnet Linear Synchronous Motors With Ring Windings for Electromagnetic Launch System

Since permanent magnet linear synchronous motor (PMLSM) has a bright future in electromagnetic launch (EML), moving-magnet PMLSM with multisegment primary is a potential choice. To overcome the end effect in the junctions of armature units, three different ring windings are proposed for the multisegment primary of PMLSM: slotted ring windings, slotless ring windings, and quasi-sinusoidal ring windings. They are designed for various demands of EML, regarding the load levels and force fluctuations. Auxiliary iron yokes are designed to reduce the mover weights, and also help restrain the end effect. PMLSM with slotted ring windings has a higher thrust for heavy load EML. PMLSM with slotless ring windings eliminates the cogging effect, while PMLSM with quasi-sinusoidal ring windings has very low thrust ripple; they aim to launch the light aircraft and run smooth. Structure designs of these motors are introduced; motor models and parameter optimizations are accomplished by finite-element method (FEM). Then, performance advantages of the proposed motors are investigated by comparisons of common PMLSMs. At last, the prototypes are manufactured and tested to validate the feasibilities of ring winding motors with auxiliary iron yokes. The results prove that the proposed motors can effectively satisfy the requirements of EML.
Sugars and flowering in the grapevine (Vitis vinifera L.).

Sugars play an important role in grapevine flowering. This complex process from inflorescence initiation to fruit maturity takes two growing seasons. Currently, most of the available data concern the involvement of sugars as energy sources during the formation of reproductive structures from initiation of inflorescences during the summer of the first year, until flower opening during the following spring. Sugars devoted to the development of reproductive structures are supplied either by wood reserves or by photosynthesis in leaves or inflorescences, depending on the stage of development. Female meiosis appears to be a key point in the success of flower formation because (i) flowers are vulnerable at this stage and (ii) it corresponds in the whole plant to the transition between reserve mobilization from perennial organs (roots, trunk, and canes) towards efficient leaf photosynthesis. The perturbation of reserve replenishment during the previous year provokes perturbation in the development of inflorescences, whereas altering the photosynthetic sources affects the formation of flowers during the same year. In particular, a lack of sugar availability in flowers at female meiosis caused by various environmental or physiological fluctuations may lead to drastic flower abortion. Apart from energy, sugars also play roles as regulators of gene expression and as signal molecules that may be involved in stress responses. In the future, these two topics should be further investigated in the grapevine considering the sensitivity of flowers to environmental stresses at meiosis.
Stray current control in DC mass transit systems

Stray current control is essential in direct current (DC) mass transit systems where the rail insulation is not of sufficient quality to prevent a corrosion risk to the rails, supporting and third-party infrastructure. This paper details the principles behind the need for stray current control and examines the relationship between the stray current collection system design and its efficiency. The use of floating return rails is shown to provide a reduction in stray current level in comparison to a grounded system, significantly reducing the corrosion level of the traction system running rails. An increase in conductivity of the stray current collection system or a reduction in the soil resistivity surrounding the traction system is shown to decrease the corrosion risk to the supporting and third party infrastructure.
Learning Robust Hash Codes for Multiple Instance Image Retrieval

In this paper, for the first time, we introduce a multiple instance (MI) deep hashing technique for learning discriminative hash codes with weak bag-level supervision suited for large-scale retrieval. We learn such hash codes by aggregating deeply learnt hierarchical representations across bag members through a dedicated MI pool layer. For better trainability and retrieval quality, we propose a two-pronged approach that includes robust optimization and training with an auxiliary single instance hashing arm which is down-regulated gradually. We pose retrieval for tumor assessment as an MI problem because tumors often coexist with benign masses and could exhibit complementary signatures when scanned from different anatomical views. Experimental validations on benchmark mammography and histology datasets demonstrate improved retrieval performance over the state-of-the-art methods.
CARDIAC COUNTERCLOCKWISE ROTATION IS A RISK FACTOR FOR HIGH-DOSE IRRADIATION TO THE LEFT ANTERIOR DESCENDING CORONARY ARTERY IN PATIENTS WITH LEFT-SIDED BREAST CANCER WHO RECEIVING ADJUVANT RADIOTHERAPY AFTER BREAST-CONSERVING SURGERY

Patients irradiated for left-sided breast cancer have higher incidence of cardiovascular disease than those receiving irradiation for right-sided breast cancer. Most abnormalities were in the left anterior descending (LAD) coronary artery territory. We analyzed the relationships between preoperative examination results and irradiation dose to the LAD artery in patients with left-sided breast cancer. Seventy-one patients receiving breast radiotherapy were analyzed. The heart may rotate around longitudinal axis, showing either clockwise or counterclockwise rotation (CCWR). On electrocardiography, the transition zone (TZ) was judged in precordial leads. CCWR was considered to be present if TZ was at or to the right of V3. The prescribed dose was 50 Gy in 25 fractions. The maximum (Dmax) and mean (Dmean) doses to the LAD artery and the volumes of the LAD artery receiving at least 20 Gy, 30 Gy and 40 Gy (V20Gy, V30Gy and V40Gy, respectively) were significantly higher in CCWR than in the non-CCWR patients. On multivariate analysis, TZ was significantly associated with Dmax, Dmean, V20Gy, V30Gy, and V40Gy. CCWR is a risk factor for high-dose irradiation to the LAD artery. Electrocardiography is useful for evaluating the cardiovascular risk of high-dose irradiation to the LAD artery.