nils-herrmann/clean_scirepeval huggingface dataset:
AskWorld: Budget-Sensitive Query Evaluation for Knowledge-on-Demand

Recently, several Web-scale knowledge harvesting systems have been built, each of which is competent at extracting information from certain types of data (e.g., unstructured text, structured tables on the web, etc.). In order to determine the response to a new query posed to such systems (e.g., is sugar a healthy food?), it is useful to integrate opinions from multiple systems. If a response is desired within a specific time budget (e.g., in less than 2 seconds), then maybe only a subset of these resources can be queried. In this paper, we address the problem of knowledge integration for on-demand time-budgeted query answering. We propose a new method, AskWorld, which learns a policy that chooses which queries to send to which resources, by accommodating varying budget constraints that are available only at query (test) time. Through extensive experiments on real world datasets, we demonstrate AskWorld’s capability in selecting most informative resources to query within test-time constraints, resulting in improved performance compared to competitive baselines.
Plug-and-Play priors for model based reconstruction

Model-based reconstruction is a powerful framework for solving a variety of inverse problems in imaging. In recent years, enormous progress has been made in the problem of denoising, a special case of an inverse problem where the forward model is an identity operator. Similarly, great progress has been made in improving model-based inversion when the forward model corresponds to complex physical measurements in applications such as X-ray CT, electron-microscopy, MRI, and ultrasound, to name just a few. However, combining state-of-the-art denoising algorithms (i.e., prior models) with state-of-the-art inversion methods (i.e., forward models) has been a challenge for many reasons. In this paper, we propose a flexible framework that allows state-of-the-art forward models of imaging systems to be matched with state-of-the-art priors or denoising models. This framework, which we term as Plug-and-Play priors, has the advantage that it dramatically simplifies software integration, and moreover, it allows state-of-the-art denoising methods that have no known formulation as an optimization problem to be used. We demonstrate with some simple examples how Plug-and-Play priors can be used to mix and match a wide variety of existing denoising models with a tomographic forward model, thus greatly expanding the range of possible problem solutions.
Solving the 0-1 Knapsack Problem with Genetic Algorithms

This paper describes a research project on using Genetic Algorithms (GAs) to solve the 0-1 Knapsack Problem (KP). The Knapsack Problem is an example of a combinatorial optimization problem, which seeks to maximize the benefit of objects in a knapsack without exceeding its capacity. The paper contains three sections: brief description of the basic idea and elements of the GAs, definition of the Knapsack Problem, and implementation of the 0-1 Knapsack Problem using GAs. The main focus of the paper is on the implementation of the algorithm for solving the problem. In the program, we implemented two selection functions, roulette-wheel and group selection. The results from both of them differed depending on whether we used elitism or not. Elitism significantly improved the performance of the roulette-wheel function. Moreover, we tested the program with different crossover ratios and single and double crossover points but the results given were not that different.
Digital Facial Engraving

This contribution introduces the basic techniques for digital facial engraving, which imitates traditional copperplate engraving. Inspired by traditional techniques, we first establish a set of basic rules thanks to which separate engraving layers are built on the top of the original photo. Separate layers are merged according to simple merging rules and according to range shift/scale masks specially introduced for this purpose. We illustrate the introduced technique by a set of black/white and color engravings, showing different features such as engraving-specific image enhancements, mixing different regular engraving lines with mezzotint, irregular perturbations of engraving lines etc. We introduce the notion of engraving style which comprises a set of separate engraving layers together with a set of associated range shift/scale masks. The engraving style helps to port the look and feel of one engraving to another. Once different libraries of pre-defined mappable engraving styles and an appropriate user interface are added to the basic system, producing a decent gravure starting from a simple digital photo will be a matter of seconds. The engraving technique described in this contribution opens new perspectives for digital art, adding unprecedented power and precision to the engraver's work. 1.  Engraving is among the most important traditional graphical techniques. It first appeared in the fifteenth century as an illustrative support for budding book-printing, but very quickly became an art in its own right, thanks to its specific expressive power. Actually, four main classes of engraving are used by artists: letterpress or relief printing, intaglio or in-hollow printing, silk screen process and lithography, with several different techniques in each class. The history of printmaking was punctuated by prosperous periods of techniques which later declined for various reasons. Facial engraving is one such example. Extremely popular in seventeenth and eighteenth centuries, when photography did not exist, this wonderful art became almost unused, due to the extreme technical demands that it made on the engraver. Professional copperplate engravers are rare today, and the cost of true engravings is simply too prohibitive to be used in everyday printing. At the same time, traditional facial engraving has no doubt very specific appeal: its neat, sharp appearance distinguishes it advantageously from photos. To appreciate the graphical impact of engravings it's enough to compare the engraved portraits in the Wall Street Journal with portraits in other newspapers produced with traditional impersonal screening. Does it mean that this enjoyable art is condemned to disappear …
Recovering Risk Aversion from Option Prices and Realized Returns

A relationship exists between aggregate risk-neutral and subjective probability distributions and risk aversion functions. We empirically derive risk aversion functions implied by option prices and realized returns on the S&P500 index simultaneously. These risk aversion functions dramatically change shapes around the 1987 crash: Precrash, they are positive and decreasing in wealth and largely consistent with standard assumptions made in economic theory. Postcrash, they are partially negative and partially increasing and irreconcilable with those assumptions. Mispricing in the option market is the most likely cause. Simulated trading strategies exploiting this mispricing show excess returns, even after accounting for the possibility of further crashes, transaction costs, and hedges against the downside risk.
"The part of me that you bring out": ideal similarity and the Michelangelo phenomenon.

This work examines the Michelangelo phenomenon, an interpersonal model of the means by which people move closer to (vs. further from) their ideal selves. The authors propose that partner similarity--similarity to the ideal self, in particular--plays an important role in this process. Across 4 studies employing diverse designs and measurement techniques, they observed consistent evidence that when partners possess key elements of one another's ideal selves, each person affirms the other by eliciting important aspects of the other's ideals, each person moves closer to his or her ideal self, and couple well-being is enhanced. Partner similarity to the actual self also accounts for unique variance in key elements of this model. The associations of ideal similarity and actual similarity with couple well-being are fully attributable to the Michelangelo process, to partner affirmation and target movement toward the ideal self. The authors also performed auxiliary analyses to rule out several alternative interpretations of these findings.
Deep supervised learning for hyperspectral data classification through convolutional neural networks

Spectral observations along the spectrum in many narrow spectral bands through hyperspectral imaging provides valuable information towards material and object recognition, which can be consider as a classification task. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on the construction of complex handcrafted features. However, it is rarely known which features are important for the problem at hand. In contrast to these approaches, we propose a deep learning based classification method that hierarchically constructs high-level features in an automated way. Our method exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task. Experimental results and quantitative validation on widely used datasets showcasing the potential of the developed approach for accurate hyperspectral data classification.
Hyperbolic Plykin Attractor Can Exist in Neuron Models

Hyperbolic Plykin attractor can exist in neuron models Strange hyperbolic attractors are hard to find in real physical systems. This paper provides the first example of a realistic system, a canonical three-dimensional (3D) model of bursting neurons, that is likely to have a strange hyperbolic attractor. Using a geometrical approach to the study of the neuron model, we derive a flow-defined Poincare map giving ail accurate account of the system's dynamics. In a parameter region where the neuron system undergoes bifurcations causing transitions between tonic spiking and bursting, this two-dimensional map becomes a map of a disk with several periodic holes. A particular case is the map of a disk with three holes, matching the Plykin example of a planar hyperbolic attractor. The corresponding attractor of the 3D neuron model appears to be hyperbolic (this property is not verified in the present paper) and arises as a result of a two-loop (secondary) homoclinic bifurcation of a saddle. This type of bifurcation, and the complex behavior it can produce, have not been previously examined.
Semi-Stochastic Gradient Descent Methods

In this paper we study the problem of minimizing the average of a large number (n) of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full gradient and a random number of stochastic gradients is computed, following a geometric law. The total work needed for the method to output an ε-accurate solution in expectation, measured in the number of passes over data, or equivalently, in units equivalent to the computation of a single gradient of the loss, is O((κ/n) log(1/ε)), where κ is the condition number. This is achieved by running the method for O(log(1/ε)) epochs, with a single gradient evaluation and O(κ) stochastic gradient evaluations in each. The SVRG method of Johnson and Zhang  arises as a special case. If our method is limited to a single epoch only, it needs to evaluate at most O((κ/ε) log(1/ε)) stochastic gradients. In contrast, SVRG requires O(κ/ε) stochastic gradients. To illustrate our theoretical results, S2GD only needs the workload equivalent to about 2.1 full gradient evaluations to find an 10−6-accurate solution for a problem with n = 10 and κ = 10.
Reconstructing 3D Human Pose from 2D Image Landmarks

Reconstructing an arbitrary configuration of 3D points from their projection in an image is an ill-posed problem. When the points hold semantic meaning, such as anatomical landmarks on a body, human observers can often infer a plausible 3D configuration, drawing on extensive visual memory. We present an activity-independent method to recover the 3D configuration of a human figure from 2D locations of anatomical landmarks in a single image, leveraging a large motion capture corpus as a proxy for visual memory. Our method solves for anthropometrically regular body pose and explicitly estimates the camera via a matching pursuit algorithm operating on the image projections. Anthropometric regularity (i.e., that limbs obey known proportions) is a highly informative prior, but directly applying such constraints is intractable. Instead, we enforce a necessary condition on the sum of squared limblengths that can be solved for in closed form to discourage implausible configurations in 3D. We evaluate performance on a wide variety of human poses captured from different viewpoints and show generalization to novel 3D configurations and robustness to missing data.
An online identification method for both stator resistance and back-EMF coefficient of PMSMs without rotational transducers

This letter describes an effective online method for identifying both the stator resistance and the back-electromotive-force (EMF) coefficient of a permanent-magnet synchronous motor (PMSM) without rotational transducers. Using only the information of stator currents and voltages, it can provide fairly good identification accuracy under various load conditions and can be easily incorporated into most sensorless speed control schemes proposed in the prior literature.
Are Slice-Based Cohesion Metrics Actually Useful in Effort-Aware Post-Release Fault-Proneness Prediction? An Empirical Study

Slice-based cohesion metrics leverage program slices with respect to the output variables of a module to quantify the strength of functional relatedness of the elements within the module. Although slice-based cohesion metrics have been proposed for many years, few empirical studies have been conducted to examine their actual usefulness in predicting fault-proneness.  We aim to provide an in-depth understanding of the ability of slice-based cohesion metrics in effort-aware post-release fault-proneness prediction, i.e. their effectiveness in helping practitioners find post-release faults when taking into account the effort needed to test or inspect the code.  We use the most commonly used code and process metrics, including size, structural complexity, Halstead's software science, and code churn metrics, as the baseline metrics. First, we employ principal component analysis to analyze the relationships between slice-based cohesion metrics and the baseline metrics. Then, we use univariate prediction models to investigate the correlations between slice-based cohesion metrics and post-release fault-proneness. Finally, we build multivariate prediction models to examine the effectiveness of slice-based cohesion metrics in effort-aware post-release fault-proneness prediction when used alone or used together with the baseline code and process metrics.  Based on open-source software systems, our results show that: 1) slice-based cohesion metrics are not redundant with respect to the baseline code and process metrics; 2) most slice-based cohesion metrics are significantly negatively related to post-release fault-proneness; 3) slice-based cohesion metrics in general do not outperform the baseline metrics when predicting post-release fault-proneness; and 4) when used with the baseline metrics together, however, slice-based cohesion metrics can produce a statistically significant and practically important improvement of the effectiveness in effort-aware post-release fault-proneness prediction.  Slice-based cohesion metrics are complementary to the most commonly used code and process metrics and are of practical value in the context of effort-aware post-release fault-proneness prediction.
Comparing and Aligning Process Representations

Processes within organizations can be highly complex chains of inter-related steps, involving numerous stakeholders and information systems. Due to this complexity, having access to the right information is vital to the proper execution and effective management of an organization’s business processes. A major challenge in this regard is that information on a single process is often spread out over numerous models, documents, and systems. This phenomenon results from efforts to provide a variety of process stakeholders with the information that is relevant to them, in a suitable format. However, this disintegration of process information also has considerable disadvantages for organizations. In particular, it can lead to severe maintenance issues, reduced execution efficiency, and negative effects on the quality of process results. Against this background, this doctoral thesis focuses on the spread of process information in organizations and, in particular, on the mitigation of the negative aspects associated with this phenomenon. The main contributions of this thesis are five techniques that focus on the alignment and comparison of process information from different informational artifacts. Each of these techniques tackles a specific scenario involving multiple informational artifacts that contain process information in different representation formats. Among others, we present automated techniques for the detection of inconsistencies between process models and textual process descriptions, the alignment of process performance measurements to process models, conformance-checking in the context of uncertainty, and the matching of process models through the analysis of event-log information. We demonstrate the efficacy and usefulness of these techniques through quantitative evaluations involving data obtained from real-world settings. Altogether, the presented work provides important contributions for the analysis, comparison, and alignment of process information in various representation formats through the development of novel concepts and techniques. The contributions, furthermore, provide a means for organizations to improve the efficiency and quality of their processes.
A Social-Network-Based Cryptocurrency Wallet-Management Scheme

Effective cryptocurrency key management has become an urgent requirement for modern cryptocurrency. Although a large body of cryptocurrency wallet-management schemes has been proposed, they are mostly constructed for specific application scenarios and often suffer from weak security. In this paper, we propose a more effective, usable, and secure cryptocurrency wallet-management system based on semi-trusted social networks, therein allowing users to collaborate with involved parties to achieve some powerful functions and recovery under certain circumstances. Furthermore, we employ an identity-based hierarchical key-insulated encryption scheme to achieve time-sharing authorization and present a semi-trusted portable social-network-based wallet-management scheme that provides the features of security-enhanced storage, portable login on different devices, no-password authentication, flexible key delegation, and so on. The performance analysis shows that our proposed schemes require minimal additional overhead and have low time delays, making them sufficiently efficient for real-world deployment.
Routing, scheduling and channel assignment in Wireless Mesh Networks: Optimization models and algorithms

Wireless Mesh networks (WMNs) can partially replace the wired backbone of traditional wireless access networks and, similarly, they require to carefully plan radio resource assignment in order to provide the same quality guarantees to traffic flows. In this paper we study the radio resource assignment optimization problem in Wireless Mesh Networks assuming a time division multiple access (TDMA) scheme, a dynamic power control able to vary emitted power slot-by-slot, and a rate adaptation mechanism that sets transmission rates according to the signalto-interference-and-noise ratio (SINR). The proposed optimization framework includes routing, scheduling and channel assignment. Quality requirements of traffic demands are expressed in terms of minimum bandwidth and modeled with constraints defining the number of information units (packets) that must be delivered per frame. We consider an alternative problem formulation where decision variables represent compatible sets of links active in the same slot and channel, called configurations. We propose a two phases solution approach where a set of configurations is first selected to meet traffic requirements along the best available paths, and then configurations are assigned to channels according to device characteristics and constraints. The optimization goal is to minimize the number of used slots, which is directly related to the global resource allocation efficiency. We provide a lower bound of the optimal solution solving the continuous relaxation of problem formulation. Moreover, we propose a heuristic approach to determine practical integer solutions (upper bound). Since configuration variables are exponentially many, our solution approaches are based on the Column Generation technique. In order to assess the effectiveness of the proposed algorithms we show the numerical results obtained on a set of realistic-size randomly generated instances.
Breast cancer molecular subtypes respond differently to preoperative chemotherapy.

Molecular classification of breast cancer has been proposed based on gene expression profiles of human tumors. Luminal, basal-like, normal-like, and erbB2+ subgroups were identified and were shown to have different prognoses. The goal of this research was to determine if these different molecular subtypes of breast cancer also respond differently to preoperative chemotherapy.



Fine needle aspirations of 82 breast cancers were obtained before starting preoperative paclitaxel followed by 5-fluorouracil, doxorubicin, and cyclophosphamide chemotherapy. Gene expression profiling was done with Affymetrix U133A microarrays and the previously reported "breast intrinsic" gene set was used for hierarchical clustering and multidimensional scaling to assign molecular class.



The basal-like and erbB2+ subgroups were associated with the highest rates of pathologic complete response (CR), 45% [95% confidence interval (95% CI), 24-68] and 45% (95% CI, 23-68), respectively, whereas the luminal tumors had a pathologic CR rate of 6% (95% CI, 1-21). No pathologic CR was observed among the normal-like cancers (95% CI, 0-31). Molecular class was not independent of conventional cliniocopathologic predictors of response such as estrogen receptor status and nuclear grade. None of the 61 genes associated with pathologic CR in the basal-like group were associated with pathologic CR in the erbB2+ group, suggesting that the molecular mechanisms of chemotherapy sensitivity may vary between these two estrogen receptor-negative subtypes.



The basal-like and erbB2+ subtypes of breast cancer are more sensitive to paclitaxel- and doxorubicin-containing preoperative chemotherapy than the luminal and normal-like cancers.