nils-herrmann/clean_scirepeval huggingface dataset:
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits

We present efficient algorithms for the problem of contextual bandits with i.i.d. covariates, an arbitrary sequence of rewards, and an arbitrary class of policies. Our algorithm BISTRO requires d calls to the empirical risk minimization (ERM) oracle per round, where d is the number of actions. The method uses unlabeled data to make the problem computationally simple. When the ERM problem itself is computationally hard, we extend the approach by employing multiplicative approximation algorithms for the ERM. The integrality gap of the relaxation only enters in the regret bound rather than the benchmark. Finally, we show that the adversarial version of the contextual bandit problem is learnable (and efficient) whenever the full-information supervised online learning problem has a non-trivial regret guarantee (and efficient).
Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition

Autocorrelation domain is a proper domain for clean speech signal and noise separation. In this paper, a method is proposed to decrease effects of noise on the clean speech signal, autocorrelation-based noise subtraction (ANS). Then to deal with the error introduced by assumption that noise and clean speech signal are uncorrelated, two methods are proposed. Also to improve recognition rate of speech recognition system, overestimation parameter is used. Finally, with the addition of energy and cepstral mean and variance normalization to features of speech, recognition rate has improved considerably in comparison to standard features and other correlation-based methods. The proposed methods are tested on the Aurora 2 database. Between different proposed methods, autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization (ANSSOEMV) method has a best recognition rate improvement in average than MFCC features which is 64.91% on the Aurora 2 database.
Advances in neuro-oncology imaging

Despite the fact that MRI has evolved to become the standard method for diagnosis and monitoring of patients with brain tumours, conventional MRI sequences have two key limitations: the inability to show the full extent of the tumour and the inability to differentiate neoplastic tissue from nonspecific, treatment-related changes after surgery, radiotherapy, chemotherapy or immunotherapy. In the past decade, PET involving the use of radiolabelled amino acids has developed into an important diagnostic tool to overcome some of the shortcomings of conventional MRI. The Response Assessment in Neuro-Oncology working group — an international effort to develop new standardized response criteria for clinical trials in brain tumours — has recommended the additional use of amino acid PET imaging for brain tumour management. Concurrently, a number of advanced MRI techniques such as magnetic resonance spectroscopic imaging and perfusion weighted imaging are under clinical evaluation to target the same diagnostic problems. This Review summarizes the clinical role of amino acid PET in relation to advanced MRI techniques for differential diagnosis of brain tumours; delineation of tumour extent for treatment planning and biopsy guidance; post-treatment differentiation between tumour progression or recurrence versus treatment-related changes; and monitoring response to therapy. An outlook for future developments in PET and MRI techniques is also presented.
GPU-based Real-Time Approximation of the Ablation Zone for Radiofrequency Ablation

Percutaneous radiofrequency ablation (RFA) is becoming a standard minimally invasive clinical procedure for the treatment of liver tumors. However, planning the applicator placement such that the malignant tissue is completely destroyed, is a demanding task that requires considerable experience. In this work, we present a fast GPU-based real-time approximation of the ablation zone incorporating the cooling effect of liver vessels. Weighted distance fields of varying RF applicator types are derived from complex numerical simulations to allow a fast estimation of the ablation zone. Furthermore, the heat-sink effect of the cooling blood flow close to the applicator's electrode is estimated by means of a preprocessed thermal equilibrium representation of the liver parenchyma and blood vessels. Utilizing the graphics card, the weighted distance field incorporating the cooling blood flow is calculated using a modular shader framework, which facilitates the real-time visualization of the ablation zone in projected slice views and in volume rendering. The proposed methods are integrated in our software assistant prototype for planning RFA therapy. The software allows the physician to interactively place virtual RF applicator models. The real-time visualization of the corresponding approximated ablation zone facilitates interactive evaluation of the tumor coverage in order to optimize the applicator's placement such that all cancer cells are destroyed by the ablation.
Recent advances in rice biotechnology--towards genetically superior transgenic rice.

Rice biotechnology has made rapid advances since the first transgenic rice plants were produced 15 years ago. Over the past decade, this progress has resulted in the development of high frequency, routine and reproducible genetic transformation protocols for rice. This technology has been applied to produce rice plants that withstand several abiotic stresses, as well as to gain tolerance against various pests and diseases. In addition, quality improving and increased nutritional value traits have also been introduced into rice. Most of these gains were not possible through conventional breeding technologies. Transgenic rice system has been used to understand the process of transformation itself, the integration pattern of transgene as well as to modulate gene expression. Field trials of transgenic rice, especially insect-resistant rice, have recently been performed and several other studies that are prerequisite for safe release of transgenic crops have been initiated. New molecular improvisations such as inducible expression of transgene and selectable marker-free technology will help in producing superior transgenic product. It is also a step towards alleviating public concerns relating to issues of transgenic technology and to gain regulatory approval. Knowledge gained from rice can also be applied to improve other cereals. The completion of the rice genome sequencing together with a rich collection of full-length cDNA resources has opened up a plethora of opportunities, paving the way to integrate data from the large-scale projects to solve specific biological problems.
Learning the Structure of Generative Models without Labeled Data

Curating labeled training data has become the primary bottleneck in machine learning. Recent frameworks address this bottleneck with generative models to synthesize labels at scale from weak supervision sources. The generative model's dependency structure directly affects the quality of the estimated labels, but selecting a structure automatically without any labeled data is a distinct challenge. We propose a structure estimation method that maximizes the ℓ 1-regularized marginal pseudolikelihood of the observed data. Our analysis shows that the amount of unlabeled data required to identify the true structure scales sublinearly in the number of possible dependencies for a broad class of models. Simulations show that our method is 100× faster than a maximum likelihood approach and selects 1/4 as many extraneous dependencies. We also show that our method provides an average of 1.5 F1 points of improvement over existing, user-developed information extraction applications on real-world data such as PubMed journal abstracts.
Implementing the PPM data compression scheme

The “Prediction by Partial Matching” (PPM) data compression algorithm developed by Cleary and Witten is capable of very high compression rates, encoding English text in as little as 2.2 bits/character. Here it is shown that the estimates made by Cleary and Witten of the resources required to implement the scheme can be revised to allow for a tractable and useful implementation. In particular, a variant is described that encodes and decodes at over 4 kbytes/s on a small workstation, and operates within a few hundred kilobytes of data space, but still obtains compression of about 2.4 bits/character on
ELECTROACTIVE POLYMERS AS ARTIFICIAL MUSCLES-CAPABILITIES , POTENTIALS AND CHALLENGES

For many years, electroactive polymers (EAP) received relatively little attention due to the small number of available materials and their limited actuation capability. The recent emergence of EAP materials with large displacement response changed the paradigm of these materials and their potential capability. The main attractive characteristic of EAP is their operational similarity to biological muscles, particularly their resilience and ability to induce large actuation strains. Unique robotic components and miniature devices are currently being explored, where EAP serve as actuators to enable new capabilities. Generally, the low density and the relative ease of shaping them make polymers highly attractive materials and they are increasingly being chosen for aerospace applications. Early applications included components and structures, where composite materials made a significant impact on the construction of high performance aircraft. Increasingly, the resilience of polymers is finding applications in the emerging field of inflatable structures. Balloons were used to cushion the deployment of the Mars Pathfinder lander on July 4, 1997, paving the way for the recent large number of related initiatives. Inflatables are now being used to construct a rover (Figure l), aerial vehicles, telescopes , radar antennas, and others. Some of these applications have reached space flight experiments, whereas others are now at advanced stages of development. Polymers were also reported as construction elements of such actuators as: (a) McKibben muscle actuators [Schulte, 19611 These are air tubes with an angularly breaded fiber reinforcement that contract significantly when inflated, delivering a large force. (b) Shape memory polymers These materials sustain a volume change of over 40 times when subjected to pressure and heat, and a temperature rise causes the recovery of the pre-pressed shape [Sokolowski, et al, 19991; (c) Electrorhological fluids These are electroactive polymer liquids that that experience dramatic changes in rheological properties in the presence of an electric field. Willis M. Winslow first explained the effect in the 1940s using oil dispersions of fine powders [Winslow, 19491. The fluids are made from suspensions of an insulating base fluid and particles on the order of one tenth to one hundred microns in size. The electro-rheological effect is thought to arise from the difference in the dielectric constants of the fluid and particles. In the presence of an electric field, the particles, due to an induced dipole moment, will form chains along the field lines. This induced structure changes the E m ’ s viscosity, yield stress, and other properties, allowing the E W to change consistency from that of a liquid to something that is viscoelastic with response times on the order of milliseconds.
Discovery and resupply of pharmacologically active plant-derived natural products: A review

Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a "screening hit" through a "drug lead" to a "marketed drug" is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis. While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future.
After Sandy Hook Elementary: A Year in the Gun Control Debate on Twitter

The mass shooting at Sandy Hook elementary school on December 14, 2012 catalyzed a year of active debate and legislation on gun control in the United States. Social media hosted an active public discussion where people expressed their support and opposition to a variety of issues surrounding gun legislation. In this paper, we show how a contentbased analysis of Twitter data can provide insights and understanding into this debate. We estimate the relative support and opposition to gun control measures, along with a topic analysis of each camp by analyzing over 70 million gun-related tweets from 2013. We focus on spikes in conversation surrounding major events related to guns throughout the year. Our general approach can be applied to other important public health and political issues to analyze the prevalence and nature of public opinion.
Acquisition of Linguistic Patterns for Knowledge-based Information Extraction

In this paper we present a new method of automatic acquisition of linguistic patterns for Information Extraction, as implemented in the CICERO system. Our approach combines lexico-semantic information available from the WordNet database with collocating data extracted from training corpora. Due to the open-domain nature of the WordNet information and the immediate availability of large collections of texts, our method can be easily ported to open-domain Information Extraction.
Active Terahertz Imaging Using Schottky Diodes in CMOS: Array and 860-GHz Pixel

Schottky-barrier diodes (SBD's) fabricated in CMOS without process modification are shown to be suitable for active THz imaging applications. Using a compact passive-pixel array architecture, a fully-integrated 280-GHz 4 × 4 imager is demonstrated. At 1-MHz input modulation frequency, the measured peak responsivity is 5.1 kV/W with ±20% variation among the pixels. The measured minimum NEP is 29 pW/Hz1/2. Additionally, an 860-GHz SBD detector is implemented by reducing the number of unit cells in the diode, and by exploiting the efficiency improvement of patch antenna with frequency. The measured NEP is 42 pW/Hz1/2 at 1-MHz modulation frequency. This is competitive to the best reported performance of MOSFET-based pixel measured without attaching an external silicon lens (66 pW/Hz1/2 at 1 THz and 40 pW/Hz1/2 at 650 GHz). Given that incorporating the 280-GHz detector into an array increased the NEP by ~ 20%, the 860-GHz imager array should also have the similar NEP as that for an individual detector. The circuits were utilized in a setup that requires neither mirrors nor lenses to form THz images. These suggest that an affordable and portable fully-integrated CMOS THz imager is possible.
Axiomatic Analysis of Smoothing Methods in Language Models for Pseudo-Relevance Feedback

Pseudo-Relevance Feedback (PRF) is an important general technique for improving retrieval effectiveness without requiring any user effort. Several state-of-the-art PRF models are based on the language modeling approach where a query language model is learned based on feedback documents. In all these models, feedback documents are represented with unigram language models smoothed with a collection language model. While collection language model-based smoothing has proven both effective and necessary in using language models for retrieval, we use axiomatic analysis to show that this smoothing scheme inherently causes the feedback model to favor frequent terms and thus violates the IDF constraint needed to ensure selection of discriminative feedback terms. To address this problem, we propose replacing collection language model-based smoothing in the feedback stage with additive smoothing, which is analytically shown to select more discriminative terms. Empirical evaluation further confirms that additive smoothing indeed significantly outperforms collection-based smoothing methods in multiple language model-based PRF models.
An Introduction to Parallel Algorithms

Since the early 1990s, there has been a significant research activity in efficient parallel algorithms and novel computer architectures for problems that have been already solved sequentially (sorting, maximum flow, searching, etc). In this handout, we are interested in parallel algorithms and we avoid particular hardware details. The primary architectural model for our algorithms is a simplified machine called Parallel RAM (or PRAM). In essence, the PRAM model consists of a number p of processors that can read and/or write on a shared “global” memory in parallel (i.e., at the same time). The processors can also perform various arithmetic and logical operations in parallel.
An open source part-of-speech tagger for Norwegian: Building on existing language resources

This paper presents an open source part-of-speech tagger for the Norwegian language. It describes how an existing language processing library was used to build a new part-of-speech tagger for this language. This part-of-speech tagger has been built on already available resources, in particular a Norwegian dictionary and gold standard corpus, which were partly customized for the purposes of this paper. The results of a careful evaluation show that this tagger yields an accuracy close to state-of-the-art taggers for other languages.
A tendon skeletal finger model for evaluation of pinching effort

In this paper, we propose a tendon skeletal finger model and discuss which finger postures the human feels easy to pinch based on the tendon forces and the human experimental results. The finger model mimics a human tendon skeletal structure. The tendon forces during the pinching motion were simulated using the finger model. Simulation results show that the tendon forces closely mirror the human muscle activity. Sensory evaluation of subjective pinching effort was conducted with five subjects. The subject pinched five kinds of cylinders, from 20 [mm] to 100 [mm]. The pinching force and the surface EMGs were simultaneously measured in the experiment. Based on the human questionnaire tests, we investigated which finger postures the human feels easy to pinch a cylinder. The results show that the pattern of the EMGs measured by the experiment is very similar to that of the tendon forces calculated by the finger model simulation. This indicates that the tendon force is a useful index of the subjective pinching effort and it can be used for the quantitative evaluation instead of EMGs.
Efficient and Extensible Algorithms for Multi Query Optimization

Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multiquery optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms were exhaustive, and explore a doubly exponential search space.
In this paper we demonstrate that multi-query optimization using heuristics is practical, and provides significant benefits. We propose three cost-based heuristic algorithms: Volcano-SH and Volcano-RU, which are based on simple modifications to the Volcano search strategy, and a greedy heuristic. Our greedy heuristic incorporates novel optimizations that improve efficiency greatly. Our algorithms are designed to be easily added to existing optimizers. We present a performance study comparing the algorithms, using workloads consisting of queries from the TPC-D benchmark. The study shows that our algorithms provide significant benefits over traditional optimization, at a very acceptable overhead in optimization time.
Social connectedness: concept and measurement

Social connectedness, i.e. the experience of belonging and relatedness between people, is a central concept in understanding and evaluating communication media, in particular awareness systems. A generic measure based on this construct can support the design of such systems. The current paper describes the construction of two questionnaires for the measurement of this concept. These questionnaires were subsequently applied in survey studies in order to establish the structure of the concept and to identify the items that are suited for the measurement of its dimensions. One questionnaire was subjected to an initial validation. We conclude with some preliminary suggestions regarding (design) approaches to foster social connectedness.
Clustering with Bregman Divergences: an Asymptotic Analysis

Clustering, in particular k-means clustering, is a central topic in data analysis. Clustering with Bregman divergences is a recently proposed generalization of k-means clustering which has already been widely used in applications. In this paper we analyze theoretical properties of Bregman clustering when the number of the clusters k is large. We establish quantization rates and describe the limiting distribution of the centers as k →∞, extending well-known results for k-means clustering.