nils-herrmann/clean_scirepeval huggingface dataset:
Brains and brands : Developing mutually informative research in neuroscience and marketing

Cop dvances in neuroimaging technology have led to an explosion in the number of studies investigating the living human brain, and thereby our understanding of its structure and function.With the proliferation of dazzling images from brain scans in both scientific and popular media, researchers from other fields in the social and behavioral sciences have naturally become interested in the application of neuroimaging to their own research. Commercial enterprises have long been interested in the prospects of literally ‘‘getting inside the heads’’ of customers and partners, with a variety of goals in mind. Here we consider the ways in which scholars of consumer behavior may draw upon neuroscientific advances to inform their own research. We describe the motivation of neuroscientific inquiry from the point of view of neuroscientists, including an introduction to the technologies and methodologies available; correspondingly, we consider major questions in consumer behavior that are likely to be of interest to neuroscientists and why. Recent key discoveries in neuroscience are presented which will likely have a direct impact on the development of a neuromarketing subdiscipline and for neuroimaging as a marketing research technique. We discuss where and how neuroscience methodologies may reasonably be added to the research inventory of marketers. In sum, we aim to show not only that a neuromarketing subdiscipline may fruitfully contribute to our understanding of the biological bases of human behavior, but also that developing this as a productive research field will rest largely in framing marketing research questions in the braincentric mindset of neuroscientists.
Combining Content-based and Collaborative Filters in an Online Newspaper Combining Content-based and Collaborative Filters in an Online Newspaper

The explosive growth of mailing lists, Web sites and Usenet news demands eeective ltering solutions. Collaborative ltering combines the informed opinions of humans to make personalized, accurate predictions. Content-based ltering uses the speed of computers to make complete, fast predictions. In this work, we present a new ltering approach that combines the coverage and speed of content-lters with the depth of collaborative ltering. We apply our research approach to an online newspaper, an as yet untapped opportunity for lters useful to the widespread news reading populace. We present the design of our ltering system and describe the results from preliminary experiments that suggest merits to our approach.
Compressive sensing

1 Scope The Shannon/Nyquist sampling theorem tells us that in order to not lose information when uniformly sampling a signal we must sample at least two times faster than its bandwidth. In many applications, including digital image and video cameras, the Nyquist rate can be so high that we end up with too many samples and must compress in order to store or transmit them. In other applications, including imaging systems (medical scanners, radars) and high-speed analog-to-digital converters, increasing the sampling rate or density beyond the current state-of-the-art is very expensive. In this lecture, we will learn about a new technique that tackles these issues using compressive sensing [1, 2]. We will replace the conventional sampling and reconstruction operations with a more general linear measurement scheme coupled with an optimization in order to acquire certain kinds of signals at a rate significantly below Nyquist.
Cross-Lingual Ontology Mapping - An Investigation of the Impact of Machine Translation

Ontologies are at the heart of knowledge management and make use of information that is not only written in English but also in many other natural languages. In order to enable knowledge discovery, sharing and reuse of these multilingual ontologies, it is necessary to support ontology mapping despite natural language barriers. This paper examines the soundness of a generic approach that involves machine translation tools and monolingual ontology matching techniques in cross-lingual ontology mapping scenarios. In particular, experimental results collected from case studies which engage mappings of independent ontologies that are labeled in English and Chinese are presented. Based on findings derived from these studies, limitations of this generic approach are discussed. It is shown with evidence that appropriate translations of conceptual labels in ontologies are of crucial importance when applying monolingual matching techniques in cross-lingual ontology mapping. Finally, to address the identified challenges, a semantic-oriented cross-lingual ontology mapping (SOCOM) framework is proposed and discussed.
AMR-to-text Generation with Synchronous Node Replacement Grammar

EDUCATION PhD 2001 Computer Science, University of California, Berkeley. Directed by Nelson Morgan, Daniel Jurafsky, Charles Fillmore, Jerome Feldman.  Statistical Language Understanding Using Frame Semantics MS 1999 Computer Science, University of California, Berkeley.  Topic-Based Language Models Using EM BA 1995 Double major in Linguistics and Computer Science, University of California, Berkeley.
A multilevel relaxation algorithm for simultaneous localization and mapping

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.
The promise of machine learning in cybersecurity

Over the last few years' machine learning has migrated from the laboratory to the forefront of operational systems. Amazon, Google and Facebook use machine learning every day to improve customer experiences, suggested purchases or connect people socially with new applications and facilitate personal connections. Machine learning's powerful capability is also there for cybersecurity. Cybersecurity is positioned to leverage machine learning to improve malware detection, triage events, recognize breaches and alert organizations to security issues. Machine learning can be used to identify advanced targeting and threats such as organization profiling, infrastructure vulnerabilities and potential interdependent vulnerabilities and exploits. Machine learning can significantly change the cybersecurity landscape. Malware by itself can represent as many as 3 million new samples an hour. Traditional malware detection and malware analysis is unable to pace with new attacks and variants. New attacks and sophisticated malware have been able to bypass network and end-point detection to deliver cyber-attacks at alarming rates. New techniques like machine learning must be leveraged to address the growing malware problem. This paper describes how machine learning can be used to detect and highlight advanced malware for cyber defense analysts. The results of our initial research and a discussion of future research to extend machine learning is presented.
New technology and health care costs--the case of robot-assisted surgery.

n engl j med 363;8 nejm.org august 19, 2010 701 stood. These technologies can lead to increases in costs, either because they are simply more expensive than previous treatments or because their introduction leads to an expansion in the types and numbers of patients treated. We examined these patterns as they apply to the case of robot-assisted surgery. Robotic surgical devices allow a surgeon at a console to operate remote-controlled robotic arms, which may facilitate the performance of laparoscopic procedures. Laparoscopic surgery, in turn, is associated with shorter hospital stays than open surgery, as well as with less postoperative pain and scarring and lower risks of infection and need for blood transfusion. Robotic technology has been adopted rapidly over the past 4 years in both the United States and Europe. The number of robot-assisted procedures that are performed worldwide has nearly tripled since 2007, from 80,000 to 205,000. Between 2007 and 2009, the number of da Vinci systems, the leading robotic technology, that were installed in U.S. hospitals grew by approximately 75%, from almost 800 to around 1400, and the number that were installed in other countries doubled, from 200 to nearly 400, according to Intuitive Surgical, da Vinci’s manufacturer. A wide range of procedures are now performed by means of robot-assisted surgery. Some of these procedures were already being performed laparoscopically before robots were introduced; the introduction of robotic technology affects expenditures associated with such procedures primarily by increasing the cost per procedure. For procedures that were more often performed as open surgeries, the introduction of robots may affect both the cost and the volume of surgeries performed. Robotic surgical systems have high fixed costs, with prices ranging from $1 million to $2.5 million for each unit. Surgeons must perform 150 to 250 procedures to become adept in their use. The systems also require costly maintenance and demand the use of additional consumables (singleuse robotic appliances). The use of robotic systems may also require more operating time than alternatives. In the case of procedures that had previously been performed as open surgery, however, some of the new costs will New Technology and Health Care Costs — The Case of Robot-Assisted Surgery
Potentials and Challenges of C-RAN Supporting Multi-RATs Toward 5G Mobile Networks

This paper presents an overview of the cloud radio access network (C-RAN), which is a key enabler for future mobile networks in order to meet the explosive capacity demand of mobile traffic, and reduce the capital and operating expenditure burden faced by operators. We start by reviewing the requirements of future mobile networks, called 5G, followed by a discussion on emerging network concepts for 5G network architecture. Then, an overview of C-RAN and related works are presented. As a significant scenario of a 5G system, the ultra dense network deployment based on C-RAN is discussed with focuses on flexible backhauling, automated network organization, and advanced mobility management. Another import feature of a 5G system is the long-term coexistence of multiple radio access technologies (multi-RATs). Therefore, we present some directions and preliminary thoughts for future C-RAN-supporting Multi-RATs, including joint resource allocation, mobility management, as well as traffic steering and service mapping.
Financial socialization of first-year college students: the roles of parents, work, and education.

This cross-sectional study tests a conceptual financial socialization process model, specifying four-levels that connect anticipatory socialization during adolescence to young adults' current financial learning, to their financial attitudes, and to their financial behavior. A total of 2,098 first-year college students (61.9% females) participated in the survey, representing a diverse ethnic group (32.6% minority participation: Hispanic 14.9%, Asian/Asian American 9%, Black 3.4%, Native American 1.8% and other 3.5%). Structural equation modeling indicated that parents, work, and high school financial education during adolescence predicted young adults' current financial learning, attitude and behavior, with the role played by parents substantially greater than the role played by work experience and high school financial education combined. Data also supported the proposed hierarchical financial socialization four-level model, indicating that early financial socialization is related to financial learning, which in turn is related to financial attitudes and subsequently to financial behavior. The study presents a discussion of how the theories of consumer socialization and planned behavior were combined effectively to depict the financial development of young adults. Several practical implications are also provided for parents, educators and students.
Origami Wheel Transformer: A Variable-Diameter Wheel Drive Robot Using an Origami Structure.

A wheel drive mechanism is simple, stable, and efficient, but its mobility in unstructured terrain is seriously limited. Using a deformable wheel is one of the ways to increase the mobility of a wheel drive robot. By changing the radius of its wheels, the robot becomes able to pass over not only high steps but also narrow gaps. In this article, we propose a novel design for a variable-diameter wheel using an origami-based soft robotics design approach. By simply folding a patterned sheet into a wheel shape, a variable-diameter wheel was built without requiring lots of mechanical parts and a complex assembly process. The wheel's diameter can change from 30 to 68 mm, and it is light in weight at about 9.7 g. Although composed of soft materials (fabrics and films), the wheel can bear more than 400 times its weight. The robot was able to change the wheel's radius in response to terrain conditions, allowing it to pass over a 50-mm gap when the wheel is shrunk and a 50-mm step when the wheel is enlarged.
Formalizing agility, part 2: how an agile organization embraced the CMMI

Most large IT organizations need the best of both worlds - working software that supports ever-changing business needs, and a process for delivering software that is predictable, trainable, and auditable. Organizations with agile software teams often receive timely, cost-effective solutions of sufficient quality. Organizations with formal processes often benefit from industry-recognized certifications and robust process improvement mechanisms. Rarely does a single, large IT organization achieve both. DTE Energy has such a combination: its lightweight yet CMMI-compatible methodology is now used within its 600+ person IT organization to deliver and support working software. Its small teams embrace core agile principles as they provide "just enough" solutions that satisfy maturing business needs. Yet they passed two SCAMPI appraisals toward formal CMMI Level III accreditation, scheduled for mid-2006. This report extends the Agile 2005 experience report on DTE Energy's agile IT organization's journey toward CMMI staged maturity accreditation. This report briefly recaps their seven years of agile experience, presents governance mechanisms and change management techniques, and highlights their four-release, one-year plan for CMMI Level III accreditation. Finally, this report offers suggestions on embracing a formal process framework that are applicable to any organization
Unsupervised Salience Learning for Person Re-identification

Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing similarities of images with existing approaches. Moreover, many existing approaches learn discriminative features and handle drastic viewpoint change in a supervised way and require labeling new training data for a different pair of camera views. In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning. Distinctive features are extracted without requiring identity labels in the training procedure. First, we apply adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations. Second, we learn human salience in an unsupervised manner. To improve the performance of person re-identification, human salience is incorporated in patch matching to find reliable and discriminative matched patches. The effectiveness of our approach is validated on the widely used VIPeR dataset and ETHZ dataset.
SphereFlow: 6 DoF Scene Flow from RGB-D Pairs

We take a new approach to computing dense scene flow between a pair of consecutive RGB-D frames. We exploit the availability of depth data by seeking correspondences with respect to patches specified not as the pixels inside square windows, but as the 3D points that are the inliers of spheres in world space. Our primary contribution is to show that by reasoning in terms of such patches under 6 DoF rigid body motions in 3D, we succeed in obtaining compelling results at displacements large and small without relying on either of two simplifying assumptions that pervade much of the earlier literature: brightness constancy or local surface planarity. As a consequence of our approach, our output is a dense field of 3D rigid body motions, in contrast to the 3D translations that are the norm in scene flow. Reasoning in our manner additionally allows us to carry out occlusion handling using a 6 DoF consistency check for the flow computed in both directions and a patchwise silhouette check to help reason about alignments in occlusion areas, and to promote smoothness of the flow fields using an intuitive local rigidity prior. We carry out our optimization in two steps, obtaining a first correspondence field using an adaptation of PatchMatch, and subsequently using alpha-expansion to jointly handle occlusions and perform regularization. We show attractive flow results on challenging synthetic and real-world scenes that push the practical limits of the aforementioned assumptions.
Smart Semantic Middleware for the Internet of Things

As ubiquitous systems become increasingly complex, traditional solutions to manage and control them reach their limits and pose a need for self-manageability. Also, heterogeneity of the ubiquitous components, standards, data formats, etc, creates significant obstacles for interoperability in such complex systems. The promising technologies to tackle these problems are the Semantic technologies, for interoperability, and the Agent technologies for management of complex systems. This paper describes our vision of a middleware for the Internet of Things, which will allow creation of self-managed complex systems, in particular industrial ones, consisting of distributed and heterogeneous components of different nature. We also present an analysis of issues to be resolved to realize such a middleware.
Deep Learning for the Classification of Lung Nodules

Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging. Compared to the conventional machine learning methods, deep learning requires no hand-tuned feature extractor, and has shown a superior performance in many visual object recognition applications. In this study, we develop a deep convolutional neural network (CNN) and apply it to thoracic CT images for the classification of lung nodules. We present the CNN architecture and classification accuracy for the original images of lung nodules. In order to understand the features of lung nodules, we further construct new datasets, based on the combination of artificial geometric nodules and some transformations of the original images, as well as a stochastic nodule shape model. It is found that simplistic geometric nodules cannot capture the important features of lung nodules.
Ridge Preservation with Modified “Socket-Shield” Technique: A Methodological Case Series

After tooth extraction, the alveolar bone undergoes a remodeling process, which leads to horizontal and vertical bone loss. These resorption processes complicate dental rehabilitation, particularly in connection with implants. Various methods of guided bone regeneration (GBR) have been described to retain the original dimension of the bone after extraction. Most procedures use filler materials and membranes to support the buccal plate and soft tissue, to stabilize the coagulum and to prevent epithelial ingrowth. It has also been suggested that resorption of the buccal bundle bone can be avoided by leaving a buccal root segment (socket shield technique) in place, because the biological integrity of the buccal periodontium (bundle bone) remains untouched. This method has also been described in connection with immediate implant placement. The present case report describes three consecutive cases in which a modified method was applied as part of a delayed implantation. The latter was carried out after six months, and during re-entry the new bone formation in the alveolar bone and the residual ridge was clinically evaluated as proof of principle. It was demonstrated that the bone was clinically preserved with this method. Possibilities and limitations are discussed and directions for future research are disclosed.
A leg-wheel robot-based approach to the solution of flipper-track robot kinematics

This paper presents the method of solving kinematics of a flipper-track robot. A general kinematics framework based on the screw theory is introduced for the leg-wheel robots. Focusing on the contact point between a flipper and the ground surface, the flipper-track robot kinematics is presented with the equivalent hybrid leg-wheel robot kinematics. For a studied four flipper-track robot, several robot control modes and corresponding kinematics solutions are identified. The algorithm for robot posture and tracking control is presented respecting robot's mechanical constraints. The kinematics and robot posture control were tested by simulation on a virtual robot in the MATLAB-ODE simulation environment.
An automated framework for generating variable-accuracy battery models from datasheet information

Models based on an electrical circuit equivalent have become the most popular choice for modeling the behavior of batteries, thanks to their ease of co-simulation with other parts of a digital system. Such circuit models are actually model templates: the specific values of their electrical elements must be derived by the analysis of the specific battery devices to be modeled. This process requires either to measure the battery characteristics or to derive them from the datasheet. In the latter case, however, very often not all information are available and the model fitting becomes then unfeasible.
 In this paper we present a methodology for deriving, in a semi-automatic way, circuit equivalent battery models solely from data available in a battery datasheet. In order to account for the different amount of information available, we introduce the concept of "level" of a model, so that models with different accuracy can be derived depending on the available data. The methodology requires only minimal intervention by the designer and it automatically generates MATLAB models once the required data for the corresponding model level are transcribed from the datasheet.
 Simulation results show that our methodology allows to accurately reconstruct the information reported in the datasheet as well as to derive missing ones.