nils-herrmann/clean_scirepeval huggingface dataset:
CONNECTING IMAGES AND NATURAL LANGUAGE A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

A long-standing goal in the field of artificial intelligence is to develop agents that can perceive and understand the rich visual world around us and who can communicate with us about it in natural language. Significant strides have been made towards this goal over the last few years due to simultaneous advances in computing infrastructure, data gathering and algorithms. The progress has been especially rapid in visual recognition, where computers can now classify images into categories with a performance that rivals that of humans, or even surpasses it in some cases such as classifying breeds of dogs. However, despite much encouraging progress, most of the advances in visual recognition still take place in the context of assigning one or a few discrete labels to an image (e.g. person, boat, keyboard, etc.). In this dissertation we develop models and techniques that allow us to connect the domain of visual data and the domain of natural language utterances, enabling translation between elements of the two domains. In particular, first we introduce a model that embeds both images and sentences into a common multi-modal embedding space. This space then allows us to identify images that depict an arbitrary sentence description and conversely, we can identify sentences that describe any image. Second, we develop an image captioning model that takes an image and directly generates a sentence description without being constrained a finite collection of human-written sentences to choose from. Lastly, we describe a model that can take an image and both localize and describe all if its salient parts. We demonstrate that this model can also be used backwards to take any arbitrary description (e.g. white tennis shoes) and e ciently localize the described concept in a large collection of images. We argue that these models, the techniques they take advantage of internally and the interactions they enable are a stepping stone towards artificial intelligence and that connecting images and natural language o↵ers many practical benefits and immediate valuable applications. From the modeling perspective, instead of designing and staging explicit algorithms to process images and sentences in complex processing pipelines, our contribution lies in the design of hybrid convolutional and recurrent neural network architectures that connect visual data and natural language utterances with a single network. Therefore, the computational processing of images,
Unsupervised scene segmentation using sparse coding context

This paper presents an approach to image understanding on the aspect of unsupervised scene segmentation. With the goal of image understanding in mind, we consider ‘unsupervised scene segmentation’ a task of dividing a given image into semantically meaningful regions without using annotation or other human-labeled information. We seek to investigate how well an algorithm can achieve at partitioning an image with limited human-involved learning procedures. Specifically, we are interested in developing an unsupervised segmentation algorithm that only relies on the contextual prior learned from a set of images. Our algorithm incorporates a small set of images that are similar to the input image in their scene structures. We use the sparse coding technique to analyze the appearance of this set of images; the effectiveness of sparse coding allows us to derive a priori the context of the scene from the set of images. Gaussian mixture models can then be constructed for different parts of the input image based on the sparse-coding contextual prior, and can be combined into an Markov-random-field-based segmentation process. The experimental results show that our unsupervised segmentation algorithm is able to partition an image into semantic regions, such as buildings, roads, trees, and skies, without using human-annotated information. The semantic regions generated by our algorithm can be useful, as pre-processed inputs for subsequent classification-based labeling algorithms, in achieving automatic scene annotation and scene parsing.
Contrast Stretching Recursively Separated Histogram Equalization for Brightness Preservation and Contrast Enhancement

Contrast enhancement of an image can efficiently performed by Histogram Equalization. However, this method tends to introduce unnecessary visual deterioration such as saturation effect. One of the solutions to overcome this weakness is by preserving the mean brightness of the input image inside the output image. This paper proposes a new histogram equalization method called Contrast Stretching Recursively Separated Histogram Equalization (CSRSHE), for brightness preservation and image contrast enhancement. This algorithm applies a two stage approach: 1) A new intensity is assigned to each pixel according to an adaptive transfer function that is designed on the basis of the global and local statistics of the input image. 2) Performing recursive mean separate histogram equalization based on a modified local contrast stretching manipulation. We show that compared to other existent methods, CSRSHE preserves the image brightness more accurately and produces images with better contrast enhancement.
United States Health Care Reform: Progress to Date and Next Steps.

The Affordable Care Act is the most important health care legislation enacted in the United States since the creation of Medicare and Medicaid in 1965. The law implemented comprehensive reforms designed to improve the accessibility, affordability, and quality of health care.



To review the factors influencing the decision to pursue health reform, summarize evidence on the effects of the law to date, recommend actions that could improve the health care system, and identify general lessons for public policy from the Affordable Care Act.



Analysis of publicly available data, data obtained from government agencies, and published research findings. The period examined extends from 1963 to early 2016.



The Affordable Care Act has made significant progress toward solving long-standing challenges facing the US health care system related to access, affordability, and quality of care. Since the Affordable Care Act became law, the uninsured rate has declined by 43%, from 16.0% in 2010 to 9.1% in 2015, primarily because of the law's reforms. Research has documented accompanying improvements in access to care (for example, an estimated reduction in the share of nonelderly adults unable to afford care of 5.5 percentage points), financial security (for example, an estimated reduction in debts sent to collection of $600-$1000 per person gaining Medicaid coverage), and health (for example, an estimated reduction in the share of nonelderly adults reporting fair or poor health of 3.4 percentage points). The law has also begun the process of transforming health care payment systems, with an estimated 30% of traditional Medicare payments now flowing through alternative payment models like bundled payments or accountable care organizations. These and related reforms have contributed to a sustained period of slow growth in per-enrollee health care spending and improvements in health care quality. Despite this progress, major opportunities to improve the health care system remain.



Policy makers should build on progress made by the Affordable Care Act by continuing to implement the Health Insurance Marketplaces and delivery system reform, increasing federal financial assistance for Marketplace enrollees, introducing a public plan option in areas lacking individual market competition, and taking actions to reduce prescription drug costs. Although partisanship and special interest opposition remain, experience with the Affordable Care Act demonstrates that positive change is achievable on some of the nation's most complex challenges.
Interpersonal Conflict and Its Management in Information System Development

Researchers from a wide range of management areas agree that conflicts are an important part of organizational life and that their study is important. Yet, interpersonal conflict is a neglected topic in information system development (ISD). Based on definitional properties of interpersonal conflict identified in the management and organizational behavior literatures, this paper presents a model of how individuals participating in ISD projects perceive conflict and its influence on ISD outcomes. Questionnaire data was obtained from 265 IS staff (main sample) and 272 users (confirmatory sample) working on 162 ISD projects. Results indicated that the construct of interpersonal conflict was reflected by three key dimensions: disagreement, interference, and negative emotion. While conflict management was found to have positive effects on ISD outcomes, it did not substantially mitigate the negative effects of interpersonal conflict on these outcomes. In other words, the impact of interpersonal conflict was perceived to be negative, regardless of how it was managed or resolved.
A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing

The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem. A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other
User Embodiment in Collaborative Virtual Environments

This paper explores the issue of user embodiment within collaborative virtual environments. By user embodiment we mean the provision of users with appropriate body images so as to represent them to others and also to themselves. By collaborative virtual environments we mean multi-user virtual reality systems which explicitly support co-operative work (although we argue that the results of our exploration may also be applied to other kinds of collaborative system). The main part of the paper identifies a list of embodiment design issues including: presence, location, identity, activity, availability, history of activity, viewpoint, actionpoint, gesture, facial expression, voluntary versus involuntary expression, degree of presence, reflecting capabilities, physical properties, active bodies, time and change, manipulating your view of others, representation across multiple media, autonomous and distributed body parts, truthfulness and efficiency. Following this, we show how these issues are reflected in our own DIVE and MASSIVE prototype systems and also show how they can be used to analyse several other existing collaborative systems.
Impedance Control of an aerial-manipulator: Preliminary results

In this paper, an impedance control scheme for aerial robotic manipulators is proposed, with the aim of reducing the end-effector interaction forces with the environment. The proposed control has a multi-level architecture, in detail the outer loop is composed by a trajectory generator and an impedance filter that modifies the trajectory to achieve a complaint behaviour in the end-effector space; a middle loop is used to generate the joint space variables through an inverse kinematic algorithm; finally the inner loop is aimed at ensuring the motion tracking. The proposed control architecture has been experimentally tested.
A Recommender System for Online Shopping Based on Past Customer Behaviour

With current projections regarding the growth of Internet sales, online retailing raises many questions about how to market on the Net. While convenience impels consumers to purchase items on the web, quality remains a significant factor in deciding where to shop online. The competition is increasing and personalization is considered to be the competitive advantage that will determine the winners in the market of online shopping in the following years. Recommender systems are a means of personalizing a site and a solution to the customer’s information overload problem. As such, many e-commerce sites already use them to facilitate the buying process. In this paper we present a recommender system for online shopping focusing on the specific characteristics and requirements of electronic retailing. We use a hybrid model supporting dynamic recommendations, which eliminates the problems the underlying techniques have when applied solely. At the end, we conclude with some ideas for further development and research in this area.
Visualizing Dynamic Hierarchies in Graph Sequences

Graphs are used to model relations between objects, where these objects can be grouped hierarchically based on their connectivity. In many applications, the relations change over time and so does the hierarchical group structure. We developed a visualization technique that supports the analysis of the topology and the hierarchical group structure of a dynamic graph and the tracking of changes over time. Each graph of a sequence is visualized by an adjacency matrix, where the hierarchical group structure is encoded within the matrix using indentation and nested contours, complemented by icicle plots attached to the matrices. The density within and between subgroups of the hierarchy is represented within the matrices using a gray scale. To visualize changes, transitions and dissimilarities between the hierarchically structured graphs are shown using a flow metaphor and color coding. The design of our visualization technique allows us to show more than one hierarchical group structure of the same graph by stacking the sequences, where hierarchy comparison is supported not only within but also between sequences. To improve the readability, we minimize the number of crossing curves within and between sequences based on a sorting algorithm that sweeps through the sequences of hierarchies.
Mixed MOSFET-IGBT bridge for high-efficient Medium-Frequency Dual-Active-Bridge converter in Solid State Transformers

High power DC-DC conversion is a key element within the Solid-State-Transformer concept. In order to reduce the switching losses of the Medium-Voltage side semiconductors, a Triangular-Current-Mode modulation scheme presents an attractive option. This modulation scheme, however introduces considerable challenges in the design of the low-voltage side power electronic bridges, which need to deal with high conducted and high switched currents. In order to increase the converter's efficiency, a combination of IGBTs and MOSFETs in a full-bridge configuration is considered. Practical hardware realizations are utilized in order to quantify the improvements introduced by the combination of these switches. Furthermore, the MOSFET's current conduction phase is supported by parallel connected IGBTs, which are used in order to further increase the full-bridge's efficiency, as shown by the provided experimental verification.
MicroRNA-based biotechnology for plant improvement.

MicroRNAs (miRNAs) are an extensive class of newly discovered endogenous small RNAs, which negatively regulate gene expression at the post-transcription levels. As the application of next-generation deep sequencing and advanced bioinformatics, the miRNA-related study has been expended to non-model plant species and the number of identified miRNAs has dramatically increased in the past years. miRNAs play a critical role in almost all biological and metabolic processes, and provide a unique strategy for plant improvement. Here, we first briefly review the discovery, history, and biogenesis of miRNAs, then focus more on the application of miRNAs on plant breeding and the future directions. Increased plant biomass through controlling plant development and phase change has been one achievement for miRNA-based biotechnology; plant tolerance to abiotic and biotic stress was also significantly enhanced by regulating the expression of an individual miRNA. Both endogenous and artificial miRNAs may serve as important tools for plant improvement.
Equity Style Timing : A Multi-Style Rotation Model for the Russell Large-Cap and SmallCap Growth and Value Style Indexes

Researchers and practitioners have devoted considerable attention to devising market-timing strategies as potential value-enhancement tools. The success of such active or tactical asset allocation strategies is dependent on their ability to capture either inefficiencies, to the extent that they exist, or disequilibria associated with changes in the investor opportunity set. Much of the equity-style timing literature focuses on the shifting between pairs of risky assets or between one risky and one riskless asset class, using a binomial approach. This paper develops a multinomial timing model based on macroeconomic and fundamental public information using Frank Russell large-cap and small-cap style indexes. We model four different market segments simultaneously. Out-of-sample tests demonstrate that active multi-style rotation strategies can be devised that outperform the best performing buy-and-hold portfolio. The profitability of such strategies is robust to reasonable levels of transaction costs. Journal of Asset Management (2007) 8, 9–23. doi:10.1057/palgrave.jam.2250056
Detection and Tracking in Thermal Infrared Imagery

Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing price and size during recent years have, however, opened up new application areas. They are now widely used for civilian applications, e.g., within industry, to search for missing persons, in automotive safety, as well as for medical applications. Thermal cameras are useful as soon as it is possible to measure a temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy. This thesis addresses the problem of detection and tracking in thermal infrared imagery. Visual detection and tracking of objects in video are research areas that have been and currently are subject to extensive research. Indications of their popularity are recent benchmarks such as the annual Visual Object Tracking (VOT) challenges, the Object Tracking Benchmarks, the series of workshops on Performance Evaluation of Tracking and Surveillance (PETS), and the workshops on Change Detection. Benchmark results indicate that detection and tracking are still challenging problems. A common belief is that detection and tracking in thermal infrared imagery is identical to detection and tracking in grayscale visual imagery. This thesis argues that the preceding allegation is not true. The characteristics of thermal infrared radiation and imagery pose certain challenges to image analysis algorithms. The thesis describes these characteristics and challenges as well as presents evaluation results confirming the hypothesis. Detection and tracking are often treated as two separate problems. However, some tracking methods, e.g. template-based tracking methods, base their tracking on repeated specific detections. They learn a model of the object that is adaptively updated. That is, detection and tracking are performed jointly. The thesis includes a template-based tracking method designed specifically for thermal infrared imagery, describes a thermal infrared dataset for evaluation of templatebased tracking methods, and provides an overview of the first challenge on shortterm, single-object tracking in thermal infrared video. Finally, two applications employing detection and tracking methods are presented.
English Language Premium : Evidence from a policy experiment in India

A key question facing education policymakers in many emerging economies is whether to promote the local language, as opposed to English, in elementary schools. The dilemma is particularly strong in countries that underwent rapid globalization making English a lingua franca for international as well as domestic exchange. In this paper, we estimate the English premium in globalization globalizing economy, by exploiting an exogenous language policy intervention in India. English training was revoked from the primary grades of all public schools in the state of West Bengal. In a two-way fixed effects model we combine differences across birth cohorts and districts in the exposure to English education, to estimate the effect of the language policy on wage premium. In addition, since the policy was introduced only in the state of West Bengal, we combine other states with no such intervention to address the potential threat of differential district trends confounding our two-way estimates. Our results indicate a remarkably high English skill premium in the labor market. A 1% increase in the probability of learning English raises weekly wages by 1.6%. On the average this implies a 68% reduction in wages for those that do not learn English due to the change in language policy. We provide further evidence that occupational choice played a decisive role in determining the wage gap.  We also thank Daifeng He and Michael Plotzke for their feedback. We are grateful to the Bradley Foundation for providing research support and Center for Research in Economics and Strategy (CRES), in the Olin Business School, Washington University in St. Louis, for travel grants. All errors are ours. 2 Department of Economics, Washington University in St Louis, email: tchakrab@artsci.wustl.edu 3 Department of Economics, Washington University in St Louis, email: skapur@artsci.wustl.edu
Topological Analysis and Gaussian Decision Tree: Effective Representation and Classification of Biosignals of Small Sample Size

Bucking the trend of big data, in microdevice engineering, small sample size is common, especially when the device is still at the proof-of-concept stage. The small sample size, small interclass variation, and large intraclass variation, have brought biosignal analysis new challenges. Novel representation and classification approaches need to be developed to effectively recognize targets of interests with the absence of a large training set.  Moving away from the traditional signal analysis in the spatiotemporal domain, we exploit the biosignal representation in the topological domain that would reveal the intrinsic structure of point clouds generated from the biosignal. Additionally, we propose a Gaussian-based decision tree (GDT), which can efficiently classify the biosignals even when the sample size is extremely small.  This study is motivated by the application of mastitis detection using low-voltage alternating current electrokinetics (ACEK) where five categories of bisignals need to be recognized with only two samples in each class. Experimental results demonstrate the robustness of the topological features as well as the advantage of GDT over some conventional classifiers in handling small dataset.  Our method reduces the voltage of ACEK to a safe level and still yields high-fidelity results with a short assay time.  This paper makes two distinctive contributions to the field of biosignal analysis, including performing signal processing in the topological domain and handling extremely small dataset. Currently, there have been no related works that can efficiently tackle the dilemma between avoiding electrochemical reaction and accelerating assay process using ACEK.