nils-herrmann/clean_scirepeval huggingface dataset:
Algorithmic Aspects of Inverse Problems Using Generative Models

The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled by generative adversarial networks, or GANs). In this work, we study the algorithmic aspects of such a learning-based approach from a theoretical perspective. For certain generative network architectures, we establish a simple non-convex algorithmic approach that (a) theoretically enjoys linear convergence guarantees for certain inverse problems, and (b) empirically improves upon conventional techniques such as back-propagation. We also propose an extension of our approach that can handle model mismatch (i.e., situations where the generative network prior is not exactly applicable.) Together, our contributions serve as further building blocks towards an algorithmic theory of generative models in inverse problems.
Transformation of mammalian cells to antibiotic resistance with a bacterial gene under control of the SV40 early region promoter.

A bacterial gene (neo) conferring resistance to neomycin-kanamycin antibiotics has been inserted into SV40 hybrid plasmid vectors and introduced into cultured mammalian cells by DNA transfusion. Whereas normal cells are killed by the antibiotic G418, those that acquire and express neo continue to grow in the presence of G418. In the course of the selection, neo DNA becomes associated with high molecular weight cellular DNA and is retained even when cells are grown in the absence of G418 for extended periods. Since neo provides a marker for dominant selections, cell transformation to G418 resistance is an efficient means for cotransformation of nonselected genes.
Fighting money laundering with technology: A case study of Bank X in the UK

Article history: Received 4 December 2016 Received in revised form 29 October 2017  This paper presents a longitudinal interpretive case study of a UK bank's efforts to combat Money Laundering (ML) by expanding the scope of its profiling ofML behaviour. The concept of structural coupling, taken from systems theory, is used to reflect on the bank's approach to theorize about the nature of ML-profiling. The paper offers a practical contribution by laying a path towards the improvement of money laundering detection in an organizational context while a set of evaluation measures is extracted from the case study. Generalizing from the case of the bank, the paper presents a systems-oriented conceptual framework for ML monitoring.
WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking

We present WISER, a new semantic search engine for expert finding in academia. Our system is unsupervised and it jointly combines classical language modeling techniques, based on text evidences, with the Wikipedia Knowledge Graph, via entity linking. WISER indexes each academic author through a novel profiling technique which models her expertise with a small, labeled and weighted graph drawn from Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the author’s publications, whereas the weighted edges express the semantic relatedness among these entities computed via textual and graph-based relatedness functions. Every node is also labeled with a relevance score which models the pertinence of the corresponding entity to author’s expertise, and is computed by means of a proper random-walk calculation over that graph; and with a latent vector representation which is learned via entity and other kinds of structural embeddings derived from Wikipedia. At query time, experts are retrieved by combining classic document-centric approaches, which exploit the occurrences of query terms in the author’s documents, with a novel set of profile-centric scoring strategies, which compute the semantic relatedness between the author’s expertise and the query topic via the above graph-based profiles. The effectiveness of our system is established over a largescale experimental test on a standard dataset for this task. We show that WISER achieves better performance than all the other competitors, thus proving the effectiveness of modelling author’s profile via our “semantic” graph of entities. Finally, we comment on the use of WISER for indexing and profiling the whole research community within the University of Pisa, and its application to technology transfer in our University.
Trends in Microgrid Control

The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.
Substrate integrated waveguide and its applications to leaky wave antennas

Substrate Integrated Waveguide has been an emerging technology for the realization of microwave and millimeter wave regions. It is the planar form of the conventional rectangular waveguide. It has profound applications at higher frequencies, since prevalent platforms like microstrip and coplanar waveguide have loss related issues. This paper discusses basic concepts of SIW, design aspects and their applications to leaky wave antennas. A brief overview of recent works on Substrate integrated Waveguide based Leaky Wave Antennas has been provided.
Survey of Game Theory and Future Trends with Application to Emerging Wireless Data Communication Networks

Game Theory (GT) has been used with excellent results to model and optimize the operation of a huge number of real-world systems, including in communications and networking. Using a tutorial style, this paper surveys and updates the literature contributions that have applied a diverse set of theoretical games to solve a variety of challenging problems, namely in wireless data communication networks. During our literature discussion, the games are initially divided into three groups: classical, evolutionary, and incomplete information. Then, the classical games are further divided into three subgroups: non-cooperative, repeated, and cooperative. This paper reviews applications of games to develop adaptive algorithms and protocols for the efficient operation of some standardized uses cases at the edge of emerging heterogeneous networks. Finally, we highlight the important challenges, open issues, and future research directions where GT can bring beneficial outcomes to emerging wireless data networking applications.
Leadership Past , Present , and Future

L eadership matters, according to prominent leadership scholars (see also Bennis, 2007). But what is leadership? That turns out to be a challenging question to answer. Leadership is a complex and diverse topic, and trying to make sense of leadership research can be an intimidating endeavor. One comprehensive handbook of leadership (Bass, 2008), covering more than a century of scientific study, comprises more than 1,200 pages of text and more than 200 additional pages of references! There is clearly a substantial scholarly body of leadership theory and research that continues to grow each year. Given the sheer volume of leadership scholarship that is available, our purpose is not to try to review it all. That is why our focus is on the nature or essence of leadership as we and our chapter authors see it. But to fully understand and appreciate the nature of leadership, it is essential that readers have some background knowledge of the history of leadership research, the various theoretical streams that have evolved over the years, and emerging issues that are pushing the boundaries of the leadership frontier. Further complicating our task is that more than one hundred years of leadership research have led to several paradigm shifts and a voluminous body of knowledge. On several occasions, scholars of leadership became quite frustrated by the large amount of false starts, incremental theoretical advances, and contradictory findings. As stated more than five decades ago by Warren Bennis (1959, pp. 259–260), “Of all the hazy and confounding areas in social psychology, leadership theory undoubtedly contends for Leadership: Past, Present, and Future
A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Online social data like user-generated content, expressed or implicit relations among people, and behavioral traces are at the core of many popular web applications and platforms, driving the research agenda of researchers in both academia and industry. The promises of social data are many, including the understanding of "what the world thinks»» about a social issue, brand, product, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. However, many academics and practitioners are increasingly warning against the naive usage of social data. They highlight that there are biases and inaccuracies occurring at the source of the data, but also introduced during data processing pipeline; there are methodological limitations and pitfalls, as well as ethical boundaries and unexpected outcomes that are often overlooked. Such an overlook can lead to wrong or inappropriate results that can be consequential.
Text comprehension, memory, and learning.

People are often able to reproduce a text quite well but are unable to use the information in the text for other purposes. Factors that help people to reproduce a text have been studied for some time. This article explores ways that enable people to learn from texts. Content overlap between a text and the reader's prior knowledge is identified as one factor, and methods are proposed to identify whether a text is suitable for readers with given background knowledge. For readers with low background knowledge, a text should be as coherent and explicit as possible to facilitate learning. However, data are presented to show that for readers with adequate background knowledge, texts with coherence gaps that stimulate constructive activities are in fact better for learning.
Online Speed Adaptation Using Supervised Learning for High-Speed, Off-Road Autonomous Driving

The mobile robotics community has traditionally addressed motion planning and navigation in terms of steering decisions. However, selecting the best speed is also important – beyond its relationship to stopping distance and lateral maneuverability. Consider a high-speed (35 mph) autonomous vehicle driving off-road through challenging desert terrain. The vehicle should drive slowly on terrain that poses substantial risk. However, it should not dawdle on safe terrain. In this paper we address one aspect of risk – shock to the vehicle. We present an algorithm for trading-off shock and speed in realtime and without human intervention. The trade-off is optimized using supervised learning to match human driving. The learning process is essential due to the discontinuous and spatially correlated nature of the control problem – classical techniques do not directly apply. We evaluate performance over hundreds of miles of autonomous driving, including performance during the 2005 DARPA Grand Challenge. This approach was the deciding factor in our vehicle’s speed for nearly 20% of the DARPA competition – more than any other constraint except the DARPA-imposed speed limits – and resulted in the fastest finishing time.
The dynamics of transitions in socio-technical systems: A multi-level analysis of the transition pathway from horse-drawn carriages to automobiles (1860-1930)

This article investigates transitions at the level of societal functions (e.g. transport, communication, housing). Societal functions are fulfilled by socio-technical systems, which consist of a cluster of aligned elements, e.g. artefacts, knowledge, user practices and markets, regulation, cultural meaning, infrastructure, maintenance networks and supply networks. To understand how transitions from one socio-technical system to another come about, the article describes a conceptual multi-level perspective. The perspective is illustrated with a historical case study: the transition from horse-drawn carriages to automobiles in the USA (1860–1930). The case study shows that technological substitution approaches to this transition are too simple, because they neglect the electric tram and bicycle, which acted as important stepping stones. The case study also corrects another mistake, namely that the gasoline car won by chance from steam and electric automobiles. It will be shown that particular niches played a crucial role in this competition, as well as the wider socio-technical context. The case study deviates on three points from the multi-level perspective. These deviations are used to conceptualize a particular transition pathway, called ‘de-alignment and re-alignment’.
Blockwise Parallel Decoding for Deep Autoregressive Models

Some recent sequence-to-sequence models like the Transformer (Vaswani et al., 2017) can score all output posiQons in parallel. We propose a simple algorithmic technique that exploits this property to generate mulQple tokens in parallel at decoding Qme with liTle to no loss in quality. Our fastest models exhibit wall-clock speedups of up to 4x over standard greedy decoding on the tasks of machine translaQon and image super-resoluQon.
Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images

Since remote sensing images are captured from the top of the target, such as from a satellite or plane platform, ship targets can be presented at any orientation. When detecting ship targets using horizontal bounding boxes, there will be background clutter in the box. This clutter makes it harder to detect the ship and find its precise location, especially when the targets are in close proximity or staying close to the shore. To solve these problems, this paper proposes a deep learning algorithm using a multiscale rotated bounding box to detect the ship target in a complex background and obtain the location and orientation information of the ship. When labeling the oriented targets, we use the five-parameter method to ensure that the box shape is maintained rectangular. The algorithm uses a pretrained deep network to extract features and produces two divided flow paths to output the result. One flow path predicts the target class, while the other predicts the location and angle information. In the training stage, we match the prior multiscale rotated bounding boxes to the ground-truth bounding boxes to obtain the positive sample information and use it to train the deep learning model. When matching the rotated bounding boxes, we narrow down the selection scope to reduce the amount of calculation. In the testing stage, we use the trained model to predict and obtain the final result after comparing with the score threshold and nonmaximum suppression post-processing. Experiments conducted on a remote sensing dataset show that the algorithm is robust in detecting ship targets under complex conditions, such as wave clutter background, target in close proximity, ship close to the shore, and multiscale varieties. Compared to other algorithms, our algorithm not only exhibits better performance in ship detection but also obtains the precise location and orientation information of the ship.
The Economics of Immediate Gratification

People have self-control problems: We pursue immediate gratification in a way that we ourselves do not appreciate in the long run. Only recently have economists begun to focus on the behavioral and welfare implications of such time-inconsistent preferences. In this paper, we outline a simple formal model of self-control problems, apply this model to some specific economic applications, and discuss some general lessons and open questions in the economic analysis of immediate gratification. We argue that the economic implications of self-control problems depend on the timing of the rewards and costs of an activity, as well as a person's awareness of future self-control problems. We identify situations where knowing about self-control problems can help a person and situations where it can hurt her, and also identify situations where even mild self-control problems can severely damage a person. In the process, we describe some specific implications of self-control problems for addiction, incentive theory, and consumer choice and marketing.  O'Donoghue thanks the Alfred P. Sloan Foundation and Rabin thanks the Alfred P. Sloan and Russell Sage Foundations for financial support. Some of the research for this project was completed when both authors were visiting the Math Center at Northwestern University. We are extremely grateful for their hospitality, intellectual feedback, and financial support. We thank Erik Eyster for valuable research assistance.
A Survey of Metrics for UML Class Diagrams

The demand for increased software quality has resulted in quality being more of differentiator between products than it ever has been before. For this reason, software developers need objective and valid measures for use in the evaluation and improvement of product quality from the initial stages of development. Class diagrams are a key artifact in the development of object-oriented (OO) software because they lay the foundation for all later design and implementation work. It follows that emphasizing class diagram quality may significantly contribute to higher quality OO software systems. The primary aim of this work, therefore, is to present a survey, as complete as possible, of the existing relevant works regarding class diagram metrics. Thus, from works previously published, researchers and practitioners alike may gain broad and ready access to insights for measuring these quality characteristics. Another aim of this work is to help reveal areas of research either lacking completion or yet to undertaken.
Internet of Things: Applications and Challenges in Technology and Standardization

The phrase Internet of Things (IoT) heralds a vision of the future Internet where connecting physical things, from banknotes to bicycles, through a network will let them take an active part in the Internet, exchanging information about themselves and their surroundings. This will give immediate access to information about the physical world and the objects in it leading to innovative services and increase in efficiency and productivity. This paper studies the state-of-the-art of IoT and presents the key technological drivers, potential applications, challenges and future research areas in the domain of IoT. IoT definitions from different perspective in academic and industry communities are also discussed and compared. Finally some major issues of future research in IoT are identified and discussed briefly.
A State-of-the-Art Review of Cloud Forensics

Cloud computing and digital forensics are emerging fields of technology. Unlike traditional digital forensics where the target environment can be almost completely isolated, acquired and can be under the investigators control; in cloud environments, the distribution of computation and storage poses unique and complex challenges to the investigators. Recently, the term “cloud forensics” has an increasing presence in the field of digital forensics. In this state-of-the-art review, we included the most recent research efforts that used “cloud forensics” as a keyword and then classify the literature into three dimensions: (1) survey-based, (2) technology-based and (3) forensics-procedural-based. We discuss widely accepted standard bodies and their efforts to address the current trend of cloud forensics. Our aim is not only to reference related work based on the discussed dimensions, but also to analyse them and generate a mind map that will help in identifying research gaps. Finally, we summarize existing digital forensics tools and the available simulation environments that can be used for evidence acquisition, examination and cloud forensics test purposes.
Psychophysical evidence for separate channels for the perception of form, color, movement, and depth.

Physiological and anatomical findings in the primate visual system, as well as clinical evidence in humans, suggest that different components of visual information processing are segregated into largely independent parallel pathways. Such a segregation leads to certain predictions about human vision. In this paper we describe psychophysical experiments on the interactions of color, form, depth, and movement in human perception, and we attempt to correlate these aspects of visual perception with the different subdivisions of the visual system.
A century of Alzheimer's disease.

One hundred years ago a small group of psychiatrists described the abnormal protein deposits in the brain that define the most common neurodegenerative diseases. Over the past 25 years, it has become clear that the proteins forming the deposits are central to the disease process. Amyloid-beta and tau make up the plaques and tangles of Alzheimer's disease, where these normally soluble proteins assemble into amyloid-like filaments. Tau inclusions are also found in a number of related disorders. Genetic studies have shown that dysfunction of amyloid-beta or tau is sufficient to cause dementia. The ongoing molecular dissection of the neurodegenerative pathways is expected to lead to a true understanding of disease pathogenesis.