nils-herrmann/clean_scirepeval huggingface dataset:
A nonlinear quadrilateral layered membrane element with drilling degrees of freedom for the modeling of reinforced concrete walls

In this article, the formulation and verification of a nonlinear quadrilateral layered membrane element with drilling degrees of freedom for the nonlinear analysis of reinforced concrete (RC) walls under static and cycling loads are presented. The formulation is based on a quadrilateral element with twelve degrees of freedom (DOF) two displacements and one drilling DOF per node, which is defined by a blended field interpolation for the displacements over the element, and a layered system for the element section consisting of fully bonded, smeared steel reinforcement and smeared orthotropic concrete material with a rotating angle formulation, and a stiffness tangent approach. The drilling DOF refers to the incorporation of the in plane rotation as a DOF at each element node. The blended field interpolation has the advantage of producing a smoother strain distribution inside each element, which facilitates element convergence, and the layered section formulation allows for the properties of the concrete and steel over the thickness of the wall to be modified to properly represent the different wall components, such as the concrete cover, steel rebar and confined concrete. Additionally, the formulation introduces a rotational DOF at each node, which allows the membranes to connect directly to beam and column elements. Moreover, this formulation incorporates the coupling of axial, flexural and shear behavior observed on the different configurations of RC wall structures. To verify this formulation, the results of a set of available experimental data reported in the literature for RC wall elements, with different configurations (slender walls, squat walls, wall with irregular disposition of openings) and levels of confinement, under monotonic and reversed loads are compared with the results obtained from the corresponding analytical model. The formulation is notably consistent with the experimental data and can predict the maximum capacity, the global (force vs deformation) and local responses (strain along the wall) and incorporate the coupling of axial, flexural and shear behavior observed in the different configurations of RC wall structures.
Flexible Working Arrangement toward Organizational Commitment and Work Family Conflict

The purpose of this paper is to examine the relationship between flexible working arrangements and organizationalcommitment and work family conflict. A total of 130 questionnaires were distributed to the finance executives andthe returned yielding a response rate of 87% The results revealed that a flexible working arrangement wassignificantly and positively related to organizational commitment. It also revealed that a flexible workingarrangement was significantly and positively related to work family conflict. Theoretically, this paper hascontributed to the literature on the flexible working arrangement and organizational commitment. Practically, thepractitioners of manufacturing companies should consider implementing flexible working policies to cater to theneeds of their employees by providing flexi time, remote working or different working hours. In conclusion, thispaper has revealed the importance of flexible working arrangement policies in affecting organizational commitmentand work family conflict.
INTERCULTURAL INTERACTIONS AT MULTINATIONAL CORPORATIONS' WORKPLACE: GROUNDED THEORY

Due to globalization and the global mobility of workforce, working in multicultural environments is a new challenge for employees and managers. The aim of the paper is to analyze the social interactions in multicultural environments of multinational corporations (MNCs) as well as to propose a model of intercultural social interactions in MNCs' specific context.  The grounded theory approach was applied to create a model of intercultural interactions in MNCs. The data was obtained during the qualitative research based on comparative case studies collected in 5 MNCs' subsidiaries.  The proposed model refers to the following concepts: 1) Lazarus's transactional stress theory states that job demands may be assessed as hindrances or challenges. The specific job demands that affect intercultural interactions are multilingualism, cultural diversity, cross cultural adjustment and team work. 2) Positive psychological capital and an individual's acquired resources are in relation with intercultural interactions. 3) According to Bandura's agentic perspective, individuals take an active role in shaping social interactions and work environment. 4) Being an agent relates to Spreitzer's thriving concept that can serve as a mechanism of relations between the interactions undertaken due to MNCs' specific job demands and their outcomes.  The qualitative methodology applied to the research does not allow for a broader generalization of the results. The outcomes of intercultural interactions depend mainly on how individuals assess their job demands in MNCs.  Since the workplace in MNCs is abundant in learning opportunities, the managers of MNCs need to hire employees who exhibit needs for growth and development to boost thriving in their organizations. Additionally, these managers should search for employees with experience of multicultural settings and a signifi cant capacity of positive psychological capital, thanks to which they are likely to benefi t more from intercultural social interactions. More concern should be put on training the employees' language skills since it may build their self eff icacy in intercultural interactions.  The proposed model focuses on social interactions in MNCs' unique context. The empirical fi ndings were interpreted with regard to psychological and sociological theories and the Positive Organizational Scholarship lens.
SAR11 bacteria linked to ocean anoxia and nitrogen loss
Venue: Nature

Bacteria of the SAR11 clade constitute up to one half of all microbial cells in the oxygen rich surface ocean. SAR11 bacteria are also abundant in oxygen minimum zones (OMZs) where oxygen falls below detection and anaerobic microbes have vital roles in converting bioavailable nitrogen to N2 gas. Anaerobic metabolism has not yet been observed in SAR11, and it remains unknown how these bacteria contribute to OMZ biogeochemical cycling. Here, genomic analysis of single cells from the world's largest OMZ revealed previously uncharacterized SAR11 lineages with adaptations for life without oxygen, including genes for respiratory nitrate reductases (Nar) SAR11 nar genes were experimentally verified to encode proteins catalysing the nitrite producing first step of denitrification and constituted ~40% of OMZ nar transcripts, with transcription peaking in the anoxic zone of maximum nitrate reduction activity. These results link SAR11 to pathways of ocean nitrogen loss, redefining the ecological niche of Earth's most abundant organismal group.
Fabrication of an Automated Collison Avoidance System using Ultrasonic Sensor

Vehicle accidents are ubiquitous in recent years. This is because of heavy increase in population of vehicles, due to its high demand. They pose a serious threat to life and property. A system must be designed to minimize the effects of these accidents. The aim of the present study is to design a device which can successfully scan the surroundings during driving and apply brake to avoid front end collision of the vehicle, along with extension of bumper. Ultrasonic sensor provided on the front end of the vehicle detects the presence of the obstacle. The data from the sensor is an input to the microcontroller. Microcontroller 8051 decodes the logic and generates appropriate control signals for the activation of driver alert mechanism, along with automatic actuation of pneumatic braking circuit and pneumatic bumper circuit. Pneumatic braking circuit applies brake and bring the vehicle to halt, and the latter one provides the safety of vehicle during impact. Driver alert mechanism is included to alert the driver at a safe braking distance from collision. All of these are integrated in a single system to provide for maximum protection of passenger, vehicle and property.
ADAPTIVE CRUISE CONTROL USING ULTRASONIC SENSOR

This paper presents an Adaptive Cruise Control (ACC) and Collision Avoidance (CA) system for vehicle autonomous driving. The control scheme is designed to improve drivers comfort during multi vehicle driving situations and to completely avoid rear end collision using severe braking and lane change maneuver. In order to create such an application, the proposed system consists of a longitudinal control strategy for multi vehicle ACC with severe braking, a lateral control strategy for lane change, and a decision unit for integration of two strategies. The longitudinal control strategy was used to achieve comfort behavior of subject vehicle in normal driving situations and to achieve safe behavior in sever braking situations. The lateral control strategy is used to generate a trajectory for lane change and follow the generated trajectory in order to avoid collision.
Word Sense Clustering and Clusterability
Venue: CL

Word sense disambiguation and the related field of automated word sense induction traditionally assume that the occurrences of a lemma can be partitioned into senses. But this seems to be a much easier task for some lemmas than others. Our work builds on recent work that proposes describing word meaning in a graded fashion rather than through a strict partition into senses; in this article we argue that not all lemmas may need the more complex graded analysis, depending on their partitionability. Although there is plenty of evidence from previous studies and from the linguistics literature that there is a spectrum of partitionability of word meanings, this is the first attempt to measure the phenomenon and to couple the machine learning literature on clusterability with word usage data used in computational linguistics.We propose to operationalize partitionability as clusterability, a measure of how easy the occurrences of a lemma are to cluster. We test two ways of measuring clusterability: (1) existing measures from the machine learning literature that aim to measure the goodness of optimal k means clusterings, and (2) the idea that if a lemma is more clusterable, two clusterings based on two different "views" of the same data points will be more congruent. The two views that we use are two different sets of manually constructed lexical substitutes for the target lemma, on the one hand monolingual paraphrases, and on the other hand translations. We apply automatic clustering to the manual annotations. We use manual annotations because we want the representations of the instances that we cluster to be as informative and "clean" as possible. We show that when we control for polysemy, our measures of clusterability tend to correlate with partitionability, in particular some of the type (1) clusterability measures, and that these measures outperform a baseline that relies on the amount of overlap in a soft clustering.
A Large DataBase of Hypernymy Relations Extracted from the Web
Venue: LREC

Hypernymy relations (those where an hyponym term shares a "isa" relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e.g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction. In fact, such relations may provide the basis for the construction of more complex structures such as taxonomies, or be used as effective background knowledge for many word understanding applications. We present a publicly available database containing more than 400 million hypernymy relations we extracted from the CommonCrawl web corpus. We describe the infrastructure we developed to iterate over the web corpus for extracting the hypernymy relations and store them effectively into a large database. This collection of relations represents a rich source of knowledge and may be useful for many researchers. We offer the tuple dataset for public download and an Application Programming Interface (API) to help other researchers programmatically query the database.
Breaking Sticks and Ambiguities with Adaptive Skip gram
Venue: AISTATS

Recently proposed Skip gram model is a powerful method for learning high dimensional word representations that capture rich semantic relationships between words. However, Skip gram as well as most prior work on learning word representations does not take into account word ambiguity and maintain only single representation per word. Although a number of Skip gram modifications were proposed to overcome this limitation and learn multi prototype word representations, they either require a known number of word meanings or learn them using greedy heuristic approaches. In this paper we propose the Adaptive Skip gram model which is a nonparametric Bayesian extension of Skip gram capable to automatically learn the required number of representations for all words at desired semantic resolution. We derive efficient online variational learning algorithm for the model and empirically demonstrate its efficiency on word sense induction task.
Multichannel Analysis and Suppression of Sea Clutter for Airborne Microwave Radar Systems
Venue: IEEE Transactions on Geoscience and Remote Sensing

One efficient method to detect slow targets with low signal to clutter plus noise ratios is space time adaptive processing (STAP) To apply STAP to maritime radar systems, the multichannel properties of sea clutter have to be known. This paper investigates these characteristics for airborne microwave radar systems to detect vessels in the open sea. It is shown that the motion of sea echoes and different sea scattering types cause different multichannel properties of sea clutter compared with land clutter. This paper provides a mathematical basis to describe multichannel sea clutter and predict STAP performance over water. To confirm the derived properties, several real multichannel sea data sets for different swell directions and sea states were acquired with the airborne radar system Phased Array Multifunctional Imaging Radar (PAMIR) and simulations were conducted. This paper demonstrates the reproducibility of real sea clutter multichannel characteristics for different sea conditions with the help of calculations and simulations, which are proposed in this analysis.
Coherent radar processing in sea clutter environments, part 2: adaptive normalised matched filter versus adaptive matched filter performance
Venue: IEEE Transactions on Aerospace and Electronic Systems

The maritime surveillance performance of the adaptive normalised matched filter (ANMF) detector structure against real multichannel, medium grazing angle, radar sea clutter data processed via space time adaptive processing (STAP) is assessed and shown to exhibit constant false alarm rate (CFAR) performance characteristics which diverge from predictions over a large segment of the endo clutter spectrum. The non CFAR behaviour is linked to the existence of a two component clutter model composed of contributions from Bragg and fast scattering mechanisms.
Interrogating Race

We address long standing debates on the utility of racial categories and color scales for understanding inequality in the United States and Latin America, using novel data that enable comparisons of these measures across both broad regions. In particular, we attend to the degree to which color and racial category inequality operate independently of parental socioeconomic status. We find a variety of patterns of racial category and color inequality, but that in most countries accounting for maternal education changes our coefficients by 5% or less. Overall, we argue that several posited divergences in ethnoracial stratification processes in the United States, compared with Latin America, might be overstated. We conclude that the comparison of the effects of multiple ethnoracial markers, such as color and racial categories, for the analysis of social stratification holds substantial promise for untangling the complexities of "race" across the Americas.
Anomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio Temporal Features
Venue: Intelligent Information Processing

Anomalous behavior detection in crowded and unanticipated scenarios is an important problem in real life applications. Detection of anomalous behaviors such as people standing statically and loitering around a place are the focus of this paper. In order to detect anomalous events and objects, ViBe was used for background modeling and object detection at first. Then, a Kalman filter and Hungarian cost algorithm were implemented for tracking and generating trajectories of people. Next, spatio temporal features were extracted and represented. Finally, hyperspherical clustering was used for anomaly detection in an unsupervised manner. We investigate three different approaches to extracting and representing spatio temporal features, and we demonstrate the effectiveness of our proposed feature representation on a standard benchmark dataset and a real life video surveillance environment.
Network Anomaly Detection Using Unsupervised Feature Selection and Density Peak Clustering
Venue: ACNS

Intrusion detection systems (IDSs) play a significant role to effectively defend our crucial computer systems or networks against attackers on the Internet. Anomaly detection is an effective way to detect intrusion, which can discover patterns that do not conform to expected behavior. The mainstream approaches of ADS (anomaly detection system) are using data mining technology to automatically extract normal pattern and abnormal ones from a large set of network data and distinguish them from each other. However, supervised or semi supervised approaches in data mining rely on data label information. This is not practical when the network data is large scale. In this paper, we propose a two stage approach, unsupervised feature selection and density peak clustering to tackle label lacking situations. First, the density peak based clustering approach is introduced for network anomaly detection, which considers both distance and density nature of data. Second, to achieve better performance of clustering process, we use maximal information coefficient and feature clustering to remove redundant and irrelevant features. Experimental results show that our method can get rid of useless features of high dimensional data and achieves high detection accuracy and efficiency in the meanwhile.
Classification of small molecule protein kinase inhibitors based upon the structures of their drug enzyme complexes.
Venue: Pharmacological research

Because dysregulation and mutations of protein kinases play causal roles in human disease, this family of enzymes has become one of the most important drug targets over the past two decades. The X ray crystal structures of 21 of the 27 FDA approved small molecule inhibitors bound to their target protein kinases are depicted in this paper. The structure of the enzyme bound antagonist complex is used in the classification of these inhibitors. Type I inhibitors bind to the active protein kinase conformation (DFG Asp in, aC helix in) Type I 1/2 inhibitors bind to a DFG Asp in inactive conformation while Type II inhibitors bind to a DFG Asp out inactive conformation. Type I, I 1/2 and type II inhibitors occupy part of the adenine binding pocket and form hydrogen bonds with the hinge region connecting the small and large lobes of the enzyme. Type III inhibitors bind next to the ATP binding pocket and type IV inhibitors do not bind to the ATP or peptide substrate binding sites. Type III and IV inhibitors are allosteric in nature. Type V inhibitors bind to two different regions of the protein kinase domain and are therefore bivalent inhibitors. The type I V inhibitors are reversible. In contrast, type VI inhibitors bind covalently to their target enzyme. Type I, I 1/2 and II inhibitors are divided into A and B subtypes. The type A inhibitors bind in the front cleft, the back cleft, and near the gatekeeper residue, all of which occur within the region separating the small and large lobes of the protein kinase. The type B inhibitors bind in the front cleft and gate area but do not extend into the back cleft. An analysis of the limited available data indicates that type A inhibitors have a long residence time (minutes to hours) while the type B inhibitors have a short residence time (seconds to minutes) The catalytic spine includes residues from the small and large lobes and interacts with the adenine ring of ATP. Nearly all of the approved protein kinase inhibitors occupy the adenine binding pocket; thus it is not surprising that these inhibitors interact with nearby catalytic spine (CS) residues. Moreover, a significant number of approved drugs also interact with regulatory spine (RS) residues.
Cyclin dependent protein kinase inhibitors including palbociclib as anticancer drugs.
Venue: Pharmacological research

Cyclins and cyclin dependent protein kinases (CDKs) are important regulatory components that are required for cell cycle progression. The levels of the cell cycle CDKs are generally constant and their activities are controlled by cyclins, proteins whose levels oscillate during each cell cycle. Additional CDK family members were subsequently discovered that play significant roles in a wide range of activities including the control of gene transcription, metabolism, and neuronal function. In response to mitogenic stimuli, cells in the G1 phase of the cell cycle produce cyclins of the D type that activate CDK4/6. These activated enzymes catalyze the monophosphorylation of the retinoblastoma protein. Then CDK2 cyclin E catalyzes the hyperphosphorylation of Rb that promotes the release and activation of the E2F transcription factors, which in turn lead to the generation of several proteins required for cell cycle progression. As a result, cells pass through the G1 restriction point and are committed to complete cell division. CDK2 cyclin A, CDK1 cyclin A, and CDK1 cyclin B are required for S, G2, and M phase progression. Increased cyclin or CDK expression or decreased levels of endogenous CDK inhibitors such as INK4 or CIP/KIP have been observed in various cancers. In contrast to the mutational activation of EGFR, Kit, or B Raf in the pathogenesis of malignancies, mutations in the CDKs that cause cancers are rare. Owing to their role in cell proliferation, CDKs represent natural targets for anticancer therapies. Abemaciclib (LY2835219) ribociclib (Lee011) and palbociclib (Ibrance((r) or PD0332991) target CDK4/6 with IC50 values in the low nanomolar range. Palbociclib and other CDK inhibitors bind in the cleft between the small and large lobes of the CDKs and inhibit the binding of ATP. Like ATP, palbociclib forms hydrogen bonds with residues in the hinge segment of the cleft. Like the adenine base of ATP, palbociclib interacts with catalytic spine residues CS6 and CS7. CDK antagonists are in clinical trials for the treatment of a variety of malignancies. Significantly, palbociclib has been approved by the FDA for the treatment of hormone receptor positive/human epidermal growth factor receptor 2 negative breast cancer in conjunction with letrozole as a first line therapy and with fulvestrant as a second line treatment. As inhibitors of the cell cycle, it is not surprising that one of their most common toxicities is myelosuppression with decreased neutrophil production.