nils-herrmann/clean_scirepeval huggingface dataset:
Global analysis of the double-gimbal mechanism

The double-gimbal mechanism (DGM) is a multibody mechanical device composed of three rigid bodies, namely, a base, an inner gimbal, and an outer gimbal, interconnected by two revolute joints. A typical DGM, where the cylindrical base is connected to the outer gimbal by a revolute joint, and the inner gimbal, which is the disk-shaped payload, is connected to the outer gimbal by a revolute joint. The DGM is an integral component of an inertially stabilized platform, which provides motion to maintain line of sight between a target and a platform payload sensor. Modern, commercially available gimbals use two direct-drive or gear-driven motors on orthogonal axes to actuate the joints. Many of these mechanisms are constrained to a reduced operational region, while moresophisticated models use a slip ring to allow continuous rotation about an axis. Angle measurements for each axis are obtained from either a rotary encoder or a resolver. The DGM is a fundamental component of pointing and tracking applications that include missile guidance systems, ground-based telescopes, antenna assemblies, laser communication systems, and close-in weapon systems (CIWSs) such as the Phalanx 1B.
A review of Parkinson's disease.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. Sources of data Literature search using Medline with keywords Parkinson's disease supplemented with previously published papers known to the author.


AREAS OF AGREEMENT
There have been significant recent advances in the understanding of the pathogenesis of the disease. There has also been a greater realization that the disorder may be associated with significant non-motor disturbances in addition to the more commonly recognized motor complications.


AREAS OF CONTROVERSY
Although there is growing circumstantial evidence, it remains to be proven whether any of the current treatments for PD have a neuroprotective effect.


AREAS TIMELY FOR DEVELOPING RESEARCH
Although there is no cure, there are several management options for the early treatment of PD. As the disease progresses, further treatment options are available; however, the management of late-stage motor complications and non-motor symptoms remains particularly challenging and will benefit from further clinical research.
True 3D display

Realizing a true three-dimensional (3D) display environment has been an ultimate goal of visual computing communities. Burton Inc. in Japan and others built upon the modern laser-plasma technology to come up with 3D Aerial Display device in 2006, with which the users are allowed to plot a unicursal series of illuminants freely in the midair, and thus the surrounding audience can enjoy watching different aspects of the 3D image from different positions, without any eye strain [Kimura et al. 2006].
DICE: THE INFINITELY DIFFERENTIABLE MONTE

The score function estimator is widely used for estimating gradients of stochastic objectives in Stochastic Computation Graphs (SCG), e.g., in reinforcement learning and meta-learning. While deriving the first order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and conceptually simple, using the same approach for higher order gradients is more challenging. Firstly, analytically deriving and implementing such estimators is laborious and not compliant with automatic differentiation. Secondly, repeatedly applying SL to construct new objectives for each order gradient involves increasingly cumbersome graph manipulations. Lastly, to match the first order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for higher order gradient estimators. To address all these shortcomings in a unified way, we introduce DICE, which provides a single objective that can be differentiated repeatedly, generating correct gradient estimators of any order in SCGs. Unlike SL, DICE relies on automatic differentiation for performing the requisite graph manipulations. We verify the correctness of DICE both through a proof and through numerical evaluation of the DICE gradient estimates. We also use DICE to propose and evaluate a novel approach for multi-agent learning. Our code is available at https://goo.gl/xkkGxN.
Reconsidering Network Effect Theory

According to Network Effect literature network externalities lead to market failure due to Pareto-inferior coordination results. We show that the assumptions and simplifications implicitly used for modeling standardization processes fail to explain the real-world variety of diffusion courses in today’s dynamic IT markets and derive requirements for a more general model of network effects. We argue that Agent-based Computational Economics provides a solid basis for meeting these requirements by integrating evolutionary models from Game Theory and Institutional Economics.
How management teams can have a good fight.

Top-level managers know that conflict over issues is natural and even necessary. Management teams that challenge one another's thinking develop a more complete understanding of their choices, create a richer range of options, and make better decisions. But the challenge--familiar to anyone who has ever been part of a management team--is to keep constructive conflict over issues from degenerating into interpersonal conflict. From their research on the interplay of conflict, politics, and speed in the decision--making process of management teams, the authors have distilled a set of six tactics characteristic of high-performing teams: They work with more, rather than less, information. They develop multiple alternatives to enrich debate. The establish common goals. They make an effort to inject humor into the workplace. They maintain a balanced corporate power structure. They resolve issues without forcing a consensus. These tactics work because they keep conflict focused on issues; foster collaborative, rather than competitive, relations among team members; and create a sense of fairness in the decision-making process. Without conflict, groups lose their effectiveness. Managers often become withdrawn and only superficially harmonious. The alternative to conflict is not usually agreement but rather apathy and disengagement, which open the doors to a primary cause of major corporate debacles: groupthink.
A Planning Approach to Declarer Play in Contract Bridge

Although game-tree search works well in perfect-information games, it is less suitable for imperfect-information games such as contract bridge. The lack of knowledge about the opponents' possible moves gives the game tree a very large branching factor, making it impossible to search a signiicant portion of this tree in a reasonable amount of time. This paper describes our approach for overcoming this problem. We represent information about bridge in a task network that is extended to represent multi-agency and uncertainty. Our game-playing procedure uses this task network to generate game trees in which the set of alternative choices is determined not by the set of possible actions, but by the set of available tactical and strategic schemes. We have tested this approach on declarer play in the game of bridge, in an implementation called Tignum 2. On 5000 randomly generated notrump deals, Tignum 2 beat the strongest commercially available program by 1394 to 1302, with 2304 ties. These results are statistically signiicant at the = 0:05 level. Tignum 2 searched an average of only 8745.6 moves per deal in an average time of only 27.5 seconds per deal on a Sun SPARCstation 10. Further enhancements to Tignum 2 are currently underway.
A Split-and-Conquer Approach for Analysis of Extraordinarily Large Data

If there are extraordinarily large data, too large to fit into a single computer or too expensive to perform a computationally intensive data analysis, what should we do? To deal with this problem, we propose in this paper a split-andconquer approach and illustrate it using several computationally intensive penalized regression methods, along with a theoretical support. Consider a regression setting of generalized linear models with n observations and p covariates, in which n is extraordinarily large and p is either bounded or goes to ∞ at a certain rate of n. We propose to randomly split the data of size n into K subsets of size O(n/K). For each subset of data, we perform a penalized regression analysis and the results from each of the K subsets are then combined to obtain an overall result. We show that under mild conditions the combined overall result still retains desired properties of many commonly used penalized estimators, such as the model selection consistency and asymptotic normality. When K is well controlled, we also show that the combined result is asymptotically equivalent to the result of analyzing the entire data all at once (assuming that there is a super computer that could carry out such an analysis). In addition, the split-and-conquer approach involves a random splitting and a systemic combining. We demonstrate that the approach has an inherent advantage of being more resistant to false model selections caused by spurious correlations. Similar to what reported in the literature, we can establish an upper bound for the expected number of falsely selected variables and a lower bound for the expected number for truly selected variables. Furthermore, when a computational intensive algorithm is used in the sense that its computing expense is at the order of O(n), a > 1, we show that the split-andconquer approach can substantially reduce computing time and computer memory requirement. The proposed methodology is illustrated numerically using both simulation and real data examples.
Large-Scale Sentiment Analysis for News and Blogs (system demonstration)

Newspapers and blogs express opinion of news entities (people, places, things) while reporting on recent events. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Our system consists of a sentiment identification phase, which associates expressed opinions with each relevant entity, and a sentiment aggregation and scoring phase, which scores each entity relative to others in the same class. Finally, we evaluate the significance of our scoring techniques over large corpus of news and blogs.
Robust and efficient robotic mapping

Mobile robots are dependent upon a model of the environment for many of their basic functions. Locally accurate maps are critical to collision avoidance, while large-scale maps (accurate both metrically and topologically) are necessary for efficient route planning. Solutions to these problems have immediate and important applications to autonomous vehicles, precision surveying, and domestic robots. Building accurate maps can be cast as an optimization problem: find the map that is most probable given the set of observations of the environment. However, the problem rapidly becomes difficult when dealing with large maps or large numbers of observations. Sensor noise and non-linearities make the problem even more difficult— especially when using inexpensive (and therefore preferable) sensors. This thesis describes an optimization algorithm that can rapidly estimate the maximum likelihood map given a set of observations. The algorithm, which iteratively reduces map error by considering a single observation at a time, scales well to large environments with many observations. The approach is particularly robust to noise and non-linearities, quickly escaping local minima that trap current methods. Both batch and online versions of the algorithm are described. In order to build a map, however, a robot must first be able to recognize places that it has previously seen. Limitations in sensor processing algorithms, coupled with environmental ambiguity, make this difficult. Incorrect place recognitions can rapidly lead to divergence of the map. This thesis describes a place recognition algorithm that can robustly handle ambiguous data. We evaluate these algorithms on a number of challenging datasets and provide quantitative comparisons to other state-of-the-art methods, illustrating the advantages of our methods.
Swarm optimization clustering methods for opinion mining

Supervised machine learning and opinion lexicon are the most frequent approaches for opinion mining, but they require considerable effort to prepare the training data and to build the opinion lexicon, respectively. In this paper, a novel unsupervised clustering approach is proposed for opinion mining. Three swarm algorithms based on Particle Swarm Optimization are evaluated using three corpora with different levels of complexity with respect to size, number of opinions, domains, languages, and class balancing. K-means and Agglomerative clustering algorithms, as well as, the Artificial Bee Colony and Cuckoo Search swarm-based algorithms were selected for comparison. The proposed swarm-based algorithms achieved better accuracy using the word bigram feature model as the pre-processing technique, the Global Silhouette as optimization function, and on datasets with two classes: positive and negative. Although the swarm-based algorithms obtained lower result for datasets with three classes, they are still competitive considering that neither labeled data, nor opinion lexicons are required for the opinion clustering approach.
Receptors for luteinizing hormone-releasing hormone (GnRH) as therapeutic targets in triple negative breast cancers (TNBC)

Triple negative breast cancers express receptors for gonadotropin-releasing hormone (GnRH) in more than 50 % of the cases, which can be targeted with peptidic analogs of GnRH, such as triptorelin. The current study investigates cytotoxic activity of triptorelin as a monotherapy and in treatment combinations with chemotherapeutic agents and inhibitors of the PI3K and the ERK pathways in in vitro models of triple negative breast cancers (TNBC). GnRH receptor expression of TNBC cell lines MDA-MB-231 and HCC1806 was investigated. Cells were treated with triptorelin, chemotherapeutic agents (cisplatin, docetaxel, AEZS-112), PI3K/AKT inhibitors (perifosine, AEZS-129), an ERK inhibitor (AEZS-134), and dual PI3K/ERK inhibitor AEZS-136 applied as single agent therapies and in combinations. MDA-MB-231 and HCC1806 TNBC cells both expressed receptors for GnRH on messenger (m)RNA and protein level and were found sensitive to triptorelin with a respective median effective concentration (EC50) of 31.21 ± 0.21 and 58.50 ± 19.50. Synergistic effects occurred when triptorelin was combined with cisplatin. In HCC1806 cells, synergy occurred when triptorelin was applied with PI3K/AKT inhibitors perifosine and AEZS-129. In MDA-MB-231 cells, synergy was observed after co-treatment with triptorelin and ERK inhibitor AEZS-134 and dual PI3K/ERK inhibitor AEZS-136. GnRH receptors on TNBC cells can be used for targeted therapy of these cancers with GnRH agonist triptorelin. Treatment combinations based on triptorelin and PI3K and ERK inhibitors and chemotherapeutic agent cisplatin have synergistic effects in in vitro models of TNBC. If confirmed in vivo, clinical trials based on triptorelin and cisplatin could be quickly carried out, as triptorelin is FDA approved for other indications and known to be well tolerated.
Statistical techniques for online anomaly detection in data centers

Online anomaly detection is an important step in data center management, requiring light-weight techniques that provide sufficient accuracy for subsequent diagnosis and management actions. This paper presents statistical techniques based on the Tukey and Relative Entropy statistics, and applies them to data collected from a production environment and to data captured from a testbed for multi-tier web applications running on server class machines. The proposed techniques are lightweight and improve over standard Gaussian assumptions in terms of performance.
Science overlay maps: A new tool for research policy and library management

We present a novel approach to visually locate bodies of research within the sciences, both at each moment of time and dynamically. This article describes how this approach fits with other efforts to locally and globally map scientific outputs. We then show how these science overlay maps help benchmark, explore collaborations, and track temporal changes, using examples of universities, corporations, funding agencies, and research topics. We address their conditions of application and discuss advantages, downsides and limitations. Overlay maps especially help investigate the increasing number of scientific developments and organisations that do not fit within traditional disciplinary categories. We make these tools available (at the Internet) to enable researchers to explore the ongoing socio-cognitive transformations of science and technology systems.
Pain, agitation, and behavioural problems in people with dementia admitted to general hospital wards: a longitudinal cohort study

Pain is underdetected and undertreated in people with dementia. We aimed to investigate the prevalence of pain in people with dementia admitted to general hospitals and explore the association between pain and behavioural and psychiatric symptoms of dementia (BPSD). We conducted a longitudinal cohort study of 230 people, aged above 70, with dementia and unplanned medical admissions to 2 UK hospitals. Participants were assessed at baseline and every 4 days for self-reported pain (yes/no question and FACES scale) and observed pain (Pain Assessment in Advanced Dementia scale [PAINAD]) at movement and at rest, for agitation (Cohen-Mansfield Agitating Inventory [CMAI]) and BPSD (Behavioural Pathology in Alzheimer Disease Scale [BEHAVE-AD]). On admission, 27% of participants self-reported pain rising to 39% on at least 1 occasion during admission. Half of them were able to complete the FACES scale, this proportion decreasing with more severe dementia. Using the PAINAD, 19% had pain at rest and 57% had pain on movement on at least 1 occasion (in 16%, this was persistent throughout the admission). In controlled analyses, pain was not associated with CMAI scores but was strongly associated with total BEHAVE-AD scores, both when pain was assessed on movement (β = 0.20, 95% confidence interval [CI] = 0.07-0.32, P = 0.002) and at rest (β = 0.41, 95% CI = 0.14-0.69, P = 0.003). The association was the strongest for aggression and anxiety. Pain was common in people with dementia admitted to the acute hospital and associated with BPSD. Improved pain management may reduce distressing behaviours and improve the quality of hospital care for people with dementia.
Connectionist models of language production: lexical access and grammatical encoding

Theories of language production have long been expressed as connectionist models. We outline the issues and challenges that must be addressed by connectionist models of lexical access and grammatical encoding, and review three recent models. The models illustrate the value of an interactive activation approach to lexical access in production, the need for sequential output in both phonological and grammatical encoding, and the potential for accounting for structural effects on errors and structural priming from learning.
The Impact of Leadership Style on Knowledge-Sharing Intentions in China

Knowledge management (KM) is a dominant theme in the behavior of contemporary organizations. While KM has been extensively studied in developed economies, it is much less well understood in developing economies, notably those that are characterized by different social and cultural traditions to the mainstream of Western societies. This is notably the case in China. This chapter develops and tests a theoretical model that explains the impact of leadership style and interpersonal trust on the intention of information and knowledge workers in China to share their knowledge with their peers. All the hypotheses are supported, showing that both initiating structure and consideration have a significant effect on employees’ intention to share knowledge through trust building: 28.2% of the variance in employees’ intention to share knowledge is explained. The authors discuss the theoretical contributions of the chapter, identify future research opportunities, and highlight the implications for practicing managers.  10.4018/978-1-60566-920-5.ch009
Automatic segmentation of left ventricular myocardium by deep convolutional and de-convolutional neural networks

Deep learning has been integrated into several existing left ventricle (LV) endocardium segmentation methods to yield impressive accuracy improvements. However, challenges remain for segmentation of LV epicardium due to its fuzzier appearance and complications from the right ventricular insertion points. Segmenting the myocardium collectively (i.e., endocardium and epicardium together) confers the potential for better segmentation results. In this work, we develop a computational platform based on deep learning to segment the whole LV myocardium simultaneously from a cardiac magnetic resonance (CMR) image. The deep convolutional network is constructed using Caffe platform, which consists of 6 convolutional layers, 2 pooling layers, and 1 de-convolutional layer. A preliminary result with Dice metric of 0.75±0.04 is reported on York MR dataset. While in its current form, our proposed one-step deep learning method cannot compete with state-of-art myocardium segmentation methods, it delivers promising first pass segmentation results.
Barriers to Effective Municipal Solid Waste Management in a Rapidly Urbanizing Area in Thailand

This study focused on determining the barriers to effective municipal solid waste management (MSWM) in a rapidly urbanizing area in Thailand. The Tha Khon Yang Subdistrict Municipality is a representative example of many local governments in Thailand that have been facing MSWM issues. In-depth interviews with individuals and focus groups were conducted with key informants including the municipality staff, residents, and external organizations. The major influences affecting waste management were categorized into six areas: social-cultural, technical, financial, organizational, and legal-political barriers and population growth. SWOT analysis shows both internal and external factors are playing a role in MSWM: There is good policy and a reasonably sufficient budget. However, there is insufficient infrastructure, weak strategic planning, registration, staff capacity, information systems, engagement with programs; and unorganized waste management and fee collection systems. The location of flood prone areas has impacted on location and operation of landfill sites. There is also poor communication between the municipality and residents and a lack of participation in waste separation programs. However, external support from government and the nearby university could provide opportunities to improve the situation. These findings will help inform municipal decision makers, leading to better municipal solid waste management in newly urbanized areas.