nils-herrmann/clean_scirepeval huggingface dataset:
Active Learning with Partial Feedback

While many active learning papers assume that the learner can simply ask for a label and receive it, real annotation often presents a mismatch between the form of a label (say, one among many classes), and the form of an annotation (typically yes/no binary feedback). To annotate examples corpora for multiclass classification, we might need to ask multiple yes/no questions, exploiting a label hierarchy if one is available. To address this more realistic setting, we propose active learning with partial feedback (ALPF), where the learner must actively choose both which example to label and which binary question to ask. At each step, the learner selects an example, asking if it belongs to a chosen (possibly composite) class. Each answer eliminates some classes, leaving the learner with a partial label. The learner may then either ask more questions about the same example (until an exact label is uncovered) or move on immediately, leaving the first example partially labeled. Active learning with partial labels requires (i) a sampling strategy to choose (example, class) pairs, and (ii) learning from partial labels between rounds. Experiments on Tiny ImageNet demonstrate that our most effective method improves 26% (relative) in top-1 classification accuracy compared to i.i.d. baselines and standard active learners given 30% of the annotation budget that would be required (naively) to annotate the dataset. Moreover, ALPF-learners fully annotate TinyImageNet at 42% lower cost. Surprisingly, we observe that accounting for per-example annotation costs can alter the conventional wisdom that active learners should solicit labels for hard examples.
Robot Motion Planning in Learned Latent Spaces

This paper presents Latent Sampling-based Motion Planning (L-SBMP), a methodology towards computing motion plans for complex robotic systems by learning a plannable latent representation. Recent works in control of robotic systems have effectively leveraged local, low-dimensional embeddings of high-dimensional dynamics. In this paper we combine these recent advances with techniques from samplingbased motion planning (SBMP) in order to design a methodology capable of planning for high-dimensional robotic systems beyond the reach of traditional approaches (e.g., humanoids, or even systems where planning occurs in the visual space). Specifically, the learned latent space is constructed through an autoencoding network, a dynamics network, and a collision checking network, which mirror the three main algorithmic primitives of SBMP, namely state sampling, local steering, and collision checking. Notably, these networks can be trained through only raw data of the system’s states and actions along with a supervising collision checker. Building upon these networks, an RRT-based algorithm is used to plan motions directly in the latent space – we refer to this exploration algorithm as Learned Latent RRT (L2RRT). This algorithm globally explores the latent space and is capable of generalizing to new environments. The overall methodology is demonstrated on two planning problems, namely a visual planning problem, whereby planning happens in the visual (pixel) space, and a humanoid robot planning problem.
A Stochastic Decoder for Neural Machine Translation

The process of translation is ambiguous, in that there are typically many valid translations for a given sentence. This gives rise to significant variation in parallel corpora, however, most current models of machine translation do not account for this variation, instead treating the problem as a deterministic process. To this end, we present a deep generative model of machine translation which incorporates a chain of latent variables, in order to account for local lexical and syntactic variation in parallel corpora. We provide an indepth analysis of the pitfalls encountered in variational inference for training deep generative models. Experiments on several different language pairs demonstrate that the model consistently improves over strong baselines.
FluidNet: A Flexible Cloud-Based Radio Access Network for Small Cells

Cloud-based radio access networks (C-RAN) have been proposed as a cost-efficient way of deploying small cells. Unlike conventional RANs, a C-RAN decouples the baseband processing unit (BBU) from the remote radio head (RRH), allowing for centralized operation of BBUs and scalable deployment of light-weight RRHs as small cells. In this work, we argue that the intelligent configuration of the front-haul network between the BBUs and RRHs, is essential in delivering the performance and energy benefits to the RAN and the BBU pool, respectively. We then propose FluidNet - a scalable, light-weight framework for realizing the full potential of C-RAN. FluidNet deploys a logically re-configurable front-haul to apply appropriate transmission strategies in different parts of the network and hence cater effectively to both heterogeneous user profiles and dynamic traffic load patterns. 's algorithms determine configurations that maximize the traffic demand satisfied on the RAN, while simultaneously optimizing the compute resource usage in the BBU pool. We prototype FluidNet on a 6 BBU, 6 RRH WiMAX C-RAN testbed. Prototype evaluations and large-scale simulations reveal that FluidNet's ability to re-configure its front-haul and tailor transmission strategies provides a 50% improvement in satisfying traffic demands, while reducing the compute resource usage in the BBU pool by 50% compared to baseline transmission schemes.
Design of an Autonomous Precision Pollination Robot

Precision robotic pollination systems can not only fill the gap of declining natural pollinators, but can also surpass them in efficiency and uniformity, helping to feed the fast-growing human population on Earth. This paper presents the design and ongoing development of an autonomous robot named “BrambleBee”, which aims at pollinating bramble plants in a greenhouse environment. Partially inspired by the ecology and behavior of bees, BrambleBee employs state-of-the-art localization and mapping, visual perception, path planning, motion control, and manipulation techniques to create an efficient and robust autonomous pollination system.
Definition of descriptors for semantic image interpretation

A lot of effort has been put into researching image interpretation, but there is still no universally accepted approach to map low-level feature into high level image semantic interpretation . In this paper, a method for continuous low-level features vector quantization is presented so as to define appropriate values for descriptive variables. The similarity among different concepts of the domain is examined and compared by using the measure of similarity which is based on the probabilistic model and the measure of distance. Also, an abstract image description vector suitable for image analysis is given.
Rifaximin treatment in hepatic encephalopathy.

Hepatic encephalopathy is a chronically debilitating complication of hepatic cirrhosis. The efficacy of rifaximin, a minimally absorbed antibiotic, is well documented in the treatment of acute hepatic encephalopathy, but its efficacy for prevention of the disease has not been established.



In this randomized, double-blind, placebo-controlled trial, we randomly assigned 299 patients who were in remission from recurrent hepatic encephalopathy resulting from chronic liver disease to receive either rifaximin, at a dose of 550 mg twice daily (140 patients), or placebo (159 patients) for 6 months. The primary efficacy end point was the time to the first breakthrough episode of hepatic encephalopathy. The key secondary end point was the time to the first hospitalization involving hepatic encephalopathy.



Rifaximin significantly reduced the risk of an episode of hepatic encephalopathy, as compared with placebo, over a 6-month period (hazard ratio with rifaximin, 0.42; 95% confidence interval [CI], 0.28 to 0.64; P<0.001). A breakthrough episode of hepatic encephalopathy occurred in 22.1% of patients in the rifaximin group, as compared with 45.9% of patients in the placebo group. A total of 13.6% of the patients in the rifaximin group had a hospitalization involving hepatic encephalopathy, as compared with 22.6% of patients in the placebo group, for a hazard ratio of 0.50 (95% CI, 0.29 to 0.87; P=0.01). More than 90% of patients received concomitant lactulose therapy. The incidence of adverse events reported during the study was similar in the two groups, as was the incidence of serious adverse events.



Over a 6-month period, treatment with rifaximin maintained remission from hepatic encephalopathy more effectively than did placebo. Rifaximin treatment also significantly reduced the risk of hospitalization involving hepatic encephalopathy.
Single Image Super-Resolution via Non Sub-sample Contourlet Transform based Learning and a Gabor Prior

Enhancing the quality of image is a continuous process in image processing related research activities. For some applications it becomes essential to have best quality of image such as in forensic department, where in order to retrieve maximum possible information, image has to be enlarged in terms of size, with higher resolution and other features associated with it. Such obtained high quality images have also a concern in satellite imaging, medical science, High Definition Television (HDTV), etc. In this paper a novel approach of getting high resolution image from a single low resolution image is discussed. The Non Sub-sampled Contourlet Transform (NSCT) based learning is used to learn the NSCT coefficients at the finer scale of the unknown high-resolution image from a dataset of high resolution images. The cost function consisting of a data fitting term and a Gabor prior term is optimized using an Iterative Back Projection (IBP). By making use of directional decomposition property of the NSCT and the Gabor filter bank with various orientations, the proposed method is capable to reconstruct an image with less edge artifacts. The validity of the proposed approach is proven through simulation on several images. RMS measures, PSNR measures and illustrations show the success of the proposed method.
The Comovement of Investor Attention

Prior literature has documented that investor attention is associated with the pricing of stocks. We examine attention comovement, the extent to which investor attention for a firm is affected by attention paid to the industry and market in general. We find that attention comovement is positively related to comovement in firm fundamentals, and is also related to firm characteristics, such as size and visibility. We also find that the comovement of investor attention has market consequences: attention comovement is positively associated with excess stock return and trading volume comovement. Finally, we show that a prominent information release (a firm’s earnings announcement) contributes to attention comovement. Our results aid in understanding the industry and market-wide nature of investor attention and its market consequences. We thank Mary Barth (editor), an anonymous associate editor, two anonymous referees, Richard Willis, and workshop participants at Vanderbilt University for helpful comments and suggestions. We thank Yung-Yu Chen for assistance in acquiring Google search data and Nick Guest for research assistance. The financial support of the Fisher College of Business, Foster School of Business, Olin Business School, and the Marriott School of Management is gratefully acknowledged.
Iran’s Land Suitability for Agriculture

Increasing population has posed insurmountable challenges to agriculture in the provision of future food security, particularly in the Middle East and North Africa (MENA) region where biophysical conditions are not well-suited for agriculture. Iran, as a major agricultural country in the MENA region, has long been in the quest for food self-sufficiency, however, the capability of its land and water resources to realize this goal is largely unknown. Using very high-resolution spatial data sets, we evaluated the capacity of Iran’s land for sustainable crop production based on the soil properties, topography, and climate conditions. We classified Iran’s land suitability for cropping as (million ha): very good 0.4% (0.6), good 2.2% (3.6), medium 7.9% (12.8), poor 11.4% (18.5), very poor 6.3% (10.2), unsuitable 60.0% (97.4), and excluded areas 11.9% (19.3). In addition to overarching limitations caused by low precipitation, low soil organic carbon, steep slope, and high soil sodium content were the predominant soil and terrain factors limiting the agricultural land suitability in Iran. About 50% of the Iran’s existing croplands are located in low-quality lands, representing an unsustainable practice. There is little room for cropland expansion to increase production but redistribution of cropland to more suitable areas may improve sustainability and reduce pressure on water resources, land, and ecosystem in Iran.
Hanamogera speech robot which makes a person feel a talking is fun

We propose that a robot speaks a Hanamogera (a semantic-free speech) when the robot speaks with a person. Hanamogera is semantic-free speech and the sound of the speech is a sound of the words which consists of phonogram characters. The consisted characters can be changed freely because the Hanamogera speech does not have to have any meaning. Each sound of characters of a Hanamogera is thought to have an impression according to the contained consonant/vowel in the characters. The Hanamogera is expected to make a listener feel that the talking with a robot which speaks a Hanamogera is fun because of a sound of the Hanamogera. We conducted an experiment of talking with a NAO and an experiment of evaluating to Hanamogera speeches. The results of the experiment showed that a talking with a Hanamogera speech robot was better fun than a talking with a nodding robot.
Transfer learning for speech and language processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field1.
Future Directions in Role-Based Access Control Models

In the past five years there has been tremendous activity in role-based access control (RBAC) models. Consensus has been achieved on a standard core RBAC model that is in process of publication by the US National Institute of Standards and Technology (NIST). An early insight was that RBAC cannot be encompassed by a single model since RBAC concepts range from very simple to very sophisticated. Hence a family of models is more appropriate than a single model. The NIST model reflects this approach. In fact RBAC is an open-ended concept which can be extended in many different directions as new applications and systems arise. The consensus embodied in the NIST model is a substantial achievement. All the same it just a starting point. There are important aspects of RBAC models, such as administration of RBAC, on which consensus remains to be reached. Recent RBAC models have studied newer concepts such as delegation and personalization, which are not captured in the NIST model. Applications of RBAC in workflow management systems have been investigated by several researchers. Research on RBAC systems that cross organizational boundaries has also been initiated. Thus RBAC models remain a fertile area for future research. In this paper we discuss some of the directions which we feel are likely to result in practically useful enhancements to the current state of art in RBAC models.
CarFast: achieving higher statement coverage faster

Test coverage is an important metric of software quality, since it indicates thoroughness of testing. In industry, test coverage is often measured as statement coverage. A fundamental problem of software testing is how to achieve higher statement coverage faster, and it is a difficult problem since it requires testers to cleverly find input data that can steer execution sooner toward sections of application code that contain more statements.
 We created a novel fully automatic approach for aChieving higher stAtement coveRage FASTer (CarFast), which we implemented and evaluated on twelve generated Java applications whose sizes range from 300 LOC to one million LOC. We compared CarFast with several popular test case generation techniques, including pure random, adaptive random, and Directed Automated Random Testing (DART). Our results indicate with strong statistical significance that when execution time is measured in terms of the number of runs of the application on different input test data, CarFast outperforms the evaluated competitive approaches on most subject applications.
An FPGA based SIMD processor with a vector memory unit

A SIMD processor that contains a 16-way partitioned data-path is designed for efficient multimedia data processing. In order to automatically align data needed for SIMD processing, the architecture adopts a vector memory unit that consists of 17-bank memory blocks. The vector memory unit also has address generation and rearrangement units for eliminating bank conflicts. The MicroBlaze FPGA based RISC processor is used for program control and scalar data processing. The architecture has been implemented on a Xilinx FPGA, and the implementation performance for several multimedia kernels is obtained
Structure and stability of the Si ( 331 )-( 12 Ã 1 ) surface reconstruction investigated with first-principles density functional theory

We recently proposed a structural model for the Si!331"-!12!1" surface reconstruction containing silicon pentamers and adatoms as elementary structural building blocks. Using first-principles density functional theory we here investigate the stability of a variety of adatom configurations and determine the lowest-energy configuration. We also present a detailed comparison of the energetics between our model for Si!331"-!12 !1" and the adatom-tetramer-interstitial model for Si!110"-!16!2", which shares the same structural building blocks.
Towards a Mathematical Operational Semantics

We present a categorical theory of ‘well-behaved’ operational semantics which aims at complementing the established theory of domains and denotational semantics to form a coherent whole. It is shown that, if the operational rules of a programming language can be modelled as a natural transformation of a suitable general form, depending on functorial notions of syntax and behaviour, then one gets the following for free: an operational model satisfying the rules and a canonical, internally fully abstract denotational model which satisfies the operational rules. The theory is based on distributive laws and bialgebras; it specialises to the known classes of well-behaved rules for structural operational semantics, such as GSOS.
Exploring part-aware segmentation for fine-grained visual categorization

It is challenge to segment fine-grained objects due to appearance variations and clutter of backgrounds. Most of existing segmentation methods hardly separate small parts of the instance from its background with sufficient accuracy. However, such small parts usually contain important semantic information, which is crucial in fine-grained categorization. Observing that fine-grained objects almost share the same configuration of parts, we present a novel part-aware segmentation method, which explicitly detects semantic parts and preserve these parts during segmentation. We firstly design a hybrid part localization method, which generates accurate part proposals with moderate computation. Then we iteratively update the segmentation outputs and the part proposals, which obtains better foreground segmentation results. Experiments demonstrate the superiority of the proposed method, as compared to state-of-the-art segmentation approaches for fine-grained categorization.
A decision support system for determining the optimal size of a new expressway service area : Focused on the pro fi tability

a r t i c l e i n f o Since the early 1990s, South Korea has been expanding its expressways. As of July 2013, a total of 173 expressway service areas (ESAs) have been established. Among these, 31 ESAs were closed due to financial deficits. To address this challenge, this study aimed to develop a decision support system for determining the optimal size of a new ESA, focusing on the profitability of the ESA. This study adopted a case-based reasoning approach as the main research method because it is necessary to provide the historical data as a reference in determining the optimal size of a new ESA, which is more suitable for the decision-making process from the practical perspective. This study used a total of 106 general ESAs to develop the proposed system. Compared to the conventional process (i.e., direction estimation), the prediction accuracy of the improved process (i.e., three-phase estimation process) was improved by 9.84%. The computational time required for the optimization of the proposed system was determined to be less than 10 min (from 1.75 min to 9.93 min). The proposed system could be useful for the final decision-maker as the following purposes: (i) the probability estimation model for determining the optimal size of a new ESA during the planning stage; (ii) the approximate initial construction cost estimation model for a new ESA by using the estimated sales in the ESA; and (iii) the comparative assessment model for evaluating the sales per the building area of the existing ESA.
Antidiabetic medicinal plants as a source of alpha glucosidase inhibitors.

The aim of this study is to collate all available data on antidiabetic plants that inhibit alpha glucosidase, reported mainly by Medline (PubMed) these last years. In the present study, interest is focused on experimental researches conducted on hypoglycemic plants particularly those which show alpha glucosidase inhibitor activity alongside bioactive components. This study describes 47 species that belong to 29 families. The plant families, which enclose the species, studied most as inhibitors of alphaglucosidase, are Fabaceae (6 species.), Crassulaceae (3 species), Hippocrateacaea (3 species), Lamiaceae (3 species), and Myrtaceae (3 species), with most studied species being Salacia reticulata (Hippocrateaceae) and Morus alba (Moraceae). The study also covers natural products (active natural components and crude extracts) isolated from the medicinal plants which inhibit alpha glucosidase as reported this last decade. Many kinds of these isolated natural products show strong activity such as, Alkaloids, stilbenoids (polyphenol), triterpene, acids (chlorogenic acid, betulinic acid, syringic acid, vanillic acid, bartogenic acid, oleanolic acid, dehydrotrametenolic acid, corosolic acid, ellagic acid, ursolic acid, gallic acid), phytosterol, myoinositol, flavonoids, Flavonolignans, anthraquinones, anthrones, and xanthones, Feruloylglucosides, flavanone glucosides, acetophenone glucosides, glucopyranoside derivatives, genine derivatives, flavonol, anthocyanin and others.