nils-herrmann/clean_scirepeval huggingface dataset:
The costly pursuit of self-esteem.

Researchers have recently questioned the benefits associated with having high self-esteem. The authors propose that the importance of self-esteem lies more in how people strive for it rather than whether it is high or low. They argue that in domains in which their self-worth is invested, people adopt the goal to validate their abilities and qualities, and hence their self-worth. When people have self-validation goals, they react to threats in these domains in ways that undermine learning; relatedness; autonomy and self-regulation; and over time, mental and physical health. The short-term emotional benefits of pursuing self-esteem are often outweighed by long-term costs. Previous research on self-esteem is reinterpreted in terms of self-esteem striving. Cultural roots of the pursuit of self-esteem are considered. Finally, the alternatives to pursuing self-esteem, and ways of avoiding its costs, are discussed.
An Intriguing Influence of Visual Data in Learning a Representation

We present an intriguing property of visual data that we observe in our attempt to isolate the influence of data for learning a visual representation. We observe that we can get better performance than existing model by just conditioning the existing representation on a million unlabeled images without any extra knowledge. As a by-product of this study, we achieve results better than prior state-of-theart for surface normal estimation on NYU-v2 depth dataset, and improved results for semantic segmentation using a selfsupervised representation on PASCAL-VOC 2012 dataset.
A review on recent Mppt techniques for photovoltaic system

Solar energy is an abundant renewable energy source (RES) which is available without any price from the Sun to the earth. It can be a good alternative of energy source in place of non-renewable sources (NRES) of energy like as fossil fuels and petroleum articles. Sun light can be utilized through solar cells which fulfills the need of energy of the utilizer instead of energy generation by NRES. The development of solar cells has crossed by a number of modifications from one age to another. The cost and efficiency of solar cells are the obstacles in the advancement. In order to select suitable solar photovoltaic (PV) cells for a particular area, operators are needed to sense the basic mechanisms and topologies of diverse solar PV with maximum power point tracking (MPPT) methodologies that are checked to a great degree. In this article, authors reviewed and analyzed a successive growth in the solar PV cell research from one decade to other, and explained about their coming fashions and behaviors. This article also attempts to emphasize on many experiments and technologies to contribute the perks of solar energy.
Miniaturized UWB Antipodal Vivaldi Antenna and Its Application for Detection of Void Inside Concrete Specimens

A miniaturized antipodal Vivaldi antenna to operate from 1 to 30 GHz is designed for nondestructive testing and evaluation of construction materials, such as concrete, polymers, and dielectric composites. A step-by-step procedure has been employed to design and optimize performance of the proposed antenna. First, a conventional antipodal Vivaldi antenna (CAVA) is designed as a reference. Second, the CAVA is shortened to have a small size of the CAVA. Third, to extend the low end of frequency band, the inner edges of the top and bottom radiators of the shortened CAVA have been bent. To enhance gain at lower frequencies, regular slit edge technique is employed. Finally, a half elliptical-shaped dielectric lens as an extension of the antenna substrate is added to the antenna to feature high gain and front-to-back ratio. A prototype of the antenna is employed as a part of the microwave imaging system to detect voids inside concrete specimen. High-range resolution images of voids are achieved by applying synthetic aperture radar algorithm.
The use of natural and synthetic phospholipids as pharmaceutical excipients*

In pharmaceutical formulations, phospholipids obtained from plant or animal sources and synthetic phospholipids are used. Natural phospholipids are purified from, e.g., soybeans or egg yolk using non-toxic solvent extraction and chromatographic procedures with low consumption of energy and minimum possible waste. Because of the use of validated purification procedures and sourcing of raw materials with consistent quality, the resulting products differing in phosphatidylcholine content possess an excellent batch to batch reproducibility with respect to phospholipid and fatty acid composition. The natural phospholipids are described in pharmacopeias and relevant regulatory guidance documentation of the Food and Drug Administration (FDA) and European Medicines Agency (EMA). Synthetic phospholipids with specific polar head group, fatty acid composition can be manufactured using various synthesis routes. Synthetic phospholipids with the natural stereochemical configuration are preferably synthesized from glycerophosphocholine (GPC), which is obtained from natural phospholipids, using acylation and enzyme catalyzed reactions. Synthetic phospholipids play compared to natural phospholipid (including hydrogenated phospholipids), as derived from the number of drug products containing synthetic phospholipids, a minor role. Only in a few pharmaceutical products synthetic phospholipids are used. Natural phospholipids are used in oral, dermal, and parenteral products including liposomes. Natural phospholipids instead of synthetic phospholipids should be selected as phospholipid excipients for formulation development, whenever possible, because natural phospholipids are derived from renewable sources and produced with more ecologically friendly processes and are available in larger scale at relatively low costs compared to synthetic phospholipids.  For selection of phospholipid excipients for pharmaceutical formulations, natural phospholipids are preferred compared to synthetic phospholipids because they are available at large scale with reproducible quality at lower costs of goods. They are well accepted by regulatory authorities and are produced using less chemicals and solvents at higher yields. In order to avoid scale up problems during pharmaceutical development and production, natural phospholipid excipients instead of synthetic phospholipids should be selected whenever possible.
STICKY BONE IN DEHISCENCE DEFECT AROUND DENTAL IMPLANT

Back in time dentists used to place implants in locations with sufficient bone-dimensions only, with less regard to placement of final definitive restoration but most of the times, the placement of implant is not as accurate as intended and even a minor variation in comparison to ideal placement causes difficulties in fabrication of final prosthesis. The use of bone substitutes and membranes is now one of the standard therapeutic approaches. In order to accelerate healing of bone graft over the bony defect, numerous techniques utilizing platelet and fibrinogen concentrates have been introduced in the literature..  This study was designed to evaluate the efficacy of using Autologous Concentrated Growth Factors (CGF) Enriched Bone Graft Matrix (Sticky Bone) and CGF-Enriched Fibrin Membrane in management of dehiscence defect around dental implant in narrow maxillary anterior ridge.  Eleven DIO implants were inserted in six adult patients presenting an upper alveolar ridge width of less than 4mm determined by cone beam computed tomogeraphy (CBCT). After implant placement, the resultant vertical labial dehiscence defect was augmented utilizing Sticky Bone and CGF-Enriched Fibrin Membrane. Three CBCTs were made, pre-operatively, immediately postoperatively and six-months post-operatively. The change in vertical defect size was calculated radiographically then statistically analyzed.  Vertical dehiscence defect was sufficiently recovered in 5 implant-sites while in the other 6 sites it was decreased to mean value of 1.25 mm ± 0.69 SD, i.e the defect coverage in 6 implants occurred with mean value of 4.59 mm ±0.49 SD. Also the results of the present study showed that the mean of average implant stability was 59.89 mm ± 3.92  The combination of PRF mixed with CGF with bone graft (allograft) can increase the quality (density) of the newly formed bone and enhance the rate of new bone formation.
The Dynamic Capabilities of Firms 2 . Toward a Dynamic Capabilities Framework

An expanded paradigm is needed to explain how competitive advantage is gained and held. Firms resorting to 'resource-based strategy' attempt to accumulate valuable technology assets and employ an aggressive intellectual property stance. However, winners in the global marketplace have been firms demonstrating timely responsiveness and rapid and flexible product innovation, along with the management capability to effectively coordinate and redeploy internal and external competences. This source of competitive advantage, 'dynamic capabilities', emphasizes two aspects. First, it refers to the shifting character of the environment; second, it emphasizes the key role of strategic management in appropriately adapting, integrating, and re-configuring internal and external organizational skills, resources, and functional competences toward changing environment. Only recently have researchers begun to focus on the specifics of developing firm-specific capabilities and the manner in which competences are renewed to respond to shifts in the business environment. The dynamic capabilities \ approach provides a coherent framework to integrate existing conceptual and empirical ^ knowledge, and facilitate prescription. This paper argues that the competitive advanis tage of firms stems from dynamic capabilities rooted in high performance routines z operating inside the firm, embedded in the firm's processes, and conditioned by its •*> history. It offers dynamic capabilities as an emerging paradigm of the modern business J firm that draws on multiple disciplines and advances, with the help of industry studies > in the USA and elsewhere.
Distributed leadership : developing theory from practice

Taylor and Francis Ltd CUS103837.sgm 10.1080/00220270500038545 Journal of Curri ulum Studies 0 00-0 00 (p i t)/0 -0000 (online) Original Article 2 5 & Francis Group Ltd 002 5 Hele Timperley School f Edu a onUniversity of AucklandPrivate Bag 92019AucklandNew Zealandh.timperley@auckland.ac.nz Hopes that the transformation of schools lies with exceptional leaders have proved both unrealistic and unsustainable. The idea of leadership as distributed across multiple people and situations has proven to be a more useful framework for understanding the realities of schools and how they might be improved. However, empirical work on how leadership is distributed within more and less successful schools is rare. This paper presents key concepts related to distributed leadership and illustrates them with an empirical study in a schoolimprovement context in which varying success was evident. Grounding the theory in this practice-context led to the identification of some risks and benefits of distributing leadership and to a challenge of some key concepts presented in earlier theorizing about leadership and its distribution.
A Hybrid Approach for Detecting Suspicious Accounts in Money Laundering Using Data Mining Techniques

Money laundering is a criminal activity to disguise black money as white money. It is a process by which illegal funds and assets are converted into legitimate funds and assets. Money Laundering occurs in three stages: Placement, Layering, and Integration. It leads to various criminal activities like Political corruption, smuggling, financial frauds, etc. In India there is no successful Anti Money laundering techniques which are available. The Reserve Bank of India (RBI), has issued guidelines to identify the suspicious transactions and send it to Financial Intelligence Unit (FIU). FIU verifies if the transaction is actually suspicious or not. This process is time consuming and not suitable to identify the illegal transactions that occurs in the system. To overcome this problem we propose an efficient Anti Money Laundering technique which can able to identify the traversal path of the Laundered money using Hash based Association approach and successful in identifying agent and integrator in the layering stage of Money Laundering by Graph Theoretic Approach.
A context-based theory of recency and contiguity in free recall.

The authors present a new model of free recall on the basis of M. W. Howard and M. J. Kahana's temporal context model and M. Usher and J. L. McClelland's leaky-accumulator decision model. In this model, contextual drift gives rise to both short-term and long-term recency effects, and contextual retrieval gives rise to short-term and long-term contiguity effects. Recall decisions are controlled by a race between competitive leaky accumulators. The model captures the dynamics of immediate, delayed, and continual distractor free recall, demonstrating that dissociations between short- and long-term recency can naturally arise from a model in which an internal contextual state is used as the sole cue for retrieval across time scales.
Blockchain and Scalability

Bitcoin has shown great utility around the world with the drastic increase in its value and global consensus method of proof-of-work (POW). Over the years after the revolution in the digital transaction space, we are looking at major scalability issue with old POW consensus method and bitcoin peak limit of processing only 7 transactions per second. With more companies trying to adopt blockchain to modify their existing systems, blockchain working on old consensus methods and with scalability issues can't deliver the optimal solution. Specifically, with new trends like smart contracts and DAPPs, much better performance is needed to support any actual business applications. Such requirements are pushing the new platforms away from old methods of consensus and adoption of off-chain solutions. In this paper, we discuss various scalability issues with the Bitcoin and Ethereum blockchain and recent proposals like the lighting protocol, sharding, super quadratic sharding, DPoS to solve these issues. We also draw the comparison between these proposals on their ability to overcome scalability limits and highlighting major problems in these approaches. In the end, we propose our solution to suffice the scalability issue and conclude with the fact that with better scalability, blockchain has the potential to outrageously support varied domains of the industry.
Continuous passive motion (CPM): theory and principles of clinical application.

Stiffness following surgery or injury to a joint develops as a progression of four stages: bleeding, edema, granulation tissue, and fibrosis. Continuous passive motion (CPM) properly applied during the first two stages of stiffness acts to pump blood and edema fluid away from the joint and periarticular tissues. This allows maintenance of normal periarticular soft tissue compliance. CPM is thus effective in preventing the development of stiffness if full motion is applied immediately following surgery and continued until swelling that limits the full motion of the joint no longer develops. This concept has been applied successfully to elbow rehabilitation, and explains the controversy surrounding CPM following knee arthroplasty. The application of this concept to clinical practice requires a paradigm shift, resulting in our attention being focused on preventing the initial or delayed accumulation of periarticular interstitial fluids.
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation

Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1% in human
Surviving or Flourishing ? Integrating Business Resilience and Sustainability

Businesses are always seeking resilient strategies so they can weather unpredictable competitive environments. One source of unpredictability is the unsustainability of commerce’s environmental, economic or social impacts and the limitations this places on businesses. Another is poor resilience causing erroneous and unexpected outputs. Companies prospering long-term must have both resilience and sustainability, existing in a symbiotic state. This paper explores the two concepts and their relationship, their combined benefits and proposes an approach for supporting decision-makers to proactively build both characteristics.  The paper looks at businesses as complex adaptive systems, how their resilience and sustainability can be defined and how these might be exhibited. It then explores how they can be combined in practice.  The two qualities are related but have different purposes, moreover resilience has two major forms related to timescales. Both kinds of resilience are identified as key for delivering sustainability, yet the reverse is also found to be true. Both are needed to deliver either and to let businesses flourish.  Although the ideal state of resilient sustainability is difficult to define or achieve, pragmatic ways exist to deliver the right direction of change in organisational decisions. A novel approach to this is explored based on Transition Engineering and Robustness Engineering.  This paper links resilience and sustainability explicitly and develops a holistic pragmatic approach for working through their implications in strategic decision-making.
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge

The Winograd Schema Challenge is a popular alternative Turing test, comprising a binarychoice coreference-resolution task that requires significant common-sense and world knowledge to solve. In this paper, we propose a novel framework that successfully resolves many Winograd questions while imposing minimal restrictions on their form and difficulty. Our method works by (i) generating queries from a parsed representation of a Winograd question, (ii) acquiring relevant knowledge using Information Retrieval, and (iii) reasoning on the gathered knowledge. Our approach improves the F1 performance by 0.16 over previous works, without taskspecific supervised training.
A resilience-based framework for evaluating adaptive co-management : Linking ecology , economics and society in a complex world

2006 Received in revised form 25 September 2006  Adaptive co-management brings together collaborative and adaptive approaches in pursuit of sustainable resource use and social–ecological resilience. Enthusiasm for this management approach, however, is countered by recent critiques regarding outcomes. A lack of evidence from consistent evaluation of adaptive co-management further exacerbates this situation. This paper revisits the issue of evaluation in natural resource management and recasts it in light of complex adaptive systems thinking. An evaluative framework for adaptive co-management is developed which directs attention toward three broad components: ecosystem conditions, livelihood outcomes and process and institutional conditions. Scale-specific parameters are offered for each component to facilitate systematic learning from experience and encourage cross-site comparisons. Conclusions highlight the importance of systematically incorporating evaluation into the adaptive co-management process and recognize the challenge for resource agencies and researchers to shift from a conventional to a complex adaptive system perspective.
Anomaly Detection using One-Class Neural Networks

We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines the ability of deep networks to extract progressively rich representation of data with the one-class objective of creating a tight envelope around normal data. The OC-NN approach breaks new ground for the following crucial reason: data representation in the hidden layer is driven by the OC-NN objective and is thus customized for anomaly detection. This is a departure from other approaches which use a hybrid approach of learning deep features using an autoencoder and then feeding the features into a separate anomaly detection method like one-class SVM (OC-SVM). The hybrid OC-SVM approach is sub-optimal because it is unable to influence representational learning in the hidden layers. A comprehensive set of experiments demonstrate that on complex data sets (like CIFAR and GTSRB), OC-NN performs on par with state-of-the-art methods and outperformed conventional shallow methods in some scenarios.
Quantum Error-Correcting Codes for Qudit Amplitude Damping

Traditional quantum error-correcting codes are designed for the depolarizing channel modeled by generalized Pauli errors occurring with equal probability. Amplitude damping channels model, in general, the decay process of a multilevel atom or energy dissipation of a bosonic system with Markovian bath at zero temperature. We discuss quantum error-correcting codes adapted to amplitude damping channels for higher dimensional systems (qudits). For multi-level atoms, we consider a natural kind of decay process, and for bosonic systems, we consider the qudit amplitude damping channel obtained by truncating the Fock basis of the bosonic modes (e.g., the number of photons) to a certain maximum occupation number. We construct families of single-error-correcting quantum codes that can be used for both cases. Our codes have larger code dimensions than the previously known single-error-correcting codes of the same lengths. In addition, we present families of multi-error correcting codes for these two channels, as well as generalizations of our construction technique to error-correcting codes for the qutrit <inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\Lambda $ </tex-math></inline-formula> channels.
Segmentation-Aware Convolutional Networks Using Local Attention Masks

We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision. To obtain segmentation information, we set up a CNN to provide an embedding space where region co-membership can be estimated based on Euclidean distance. We use these embeddings to compute a local attention mask relative to every neuron position. We incorporate such masks in CNNs and replace the convolution operation with a “segmentation-aware” variant that allows a neuron to selectively attend to inputs coming from its own region. We call the resulting network a segmentation-aware CNN because it adapts its filters at each image point according to local segmentation cues, while at the same time remaining fully-convolutional. We demonstrate the merit of our method on two widely different dense prediction tasks, that involve classification (semantic segmentation) and regression (optical flow). Our results show that in semantic segmentation we can replace DenseCRF inference with a cascade of segmentation-aware filters, and in optical flow we obtain clearly sharper responses than the ones obtained with comparable networks that do not use segmentation. In both cases segmentation-aware convolution yields systematic improvements over strong baselines.