nils-herrmann/clean_scirepeval huggingface dataset:
Quantum statistical mechanics over function fields

In this paper we construct a noncommutative space of “pointed Drinfeld modules” that generalizes to the case of function fields the noncommutative spaces of commensurability classes of Q-lattices. It extends the usual moduli spaces of Drinfeld modules to possibly degenerate level structures. In the second part of the paper we develop some notions of quantum statistical mechanics in positive characteristic and we show that, in the case of Drinfeld modules of rank one, there is a natural time evolution on the associated noncommutative space, which is closely related to the positive characteristic L-functions introduced by Goss. The points of the usual moduli space of Drinfeld modules define KMS functionals for this time evolution. We also show that the scaling action on the dual system is induced by a Frobenius action, up to a Wick rotation to imaginary time.
Learning an Optimizer for Image Deconvolution

As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature of the inverse problem. The predominant approach is based on optimization subject to regularization functions that are either manually designed, or learned from examples. Existing learning based methods have shown superior restoration quality but are not practical enough due to their restricted model design. They solely focus on learning a prior and require to know the noise level for deconvolution. We address the gap between the optimizationbased and learning-based approaches by learning an optimizer. We propose a Recurrent Gradient Descent Network (RGDN) by systematically incorporating deep neural networks into a fully parameterized gradient descent scheme. A parameterfree update unit is used to generate updates from the current estimates, based on a convolutional neural network. By training on diverse examples, the Recurrent Gradient Descent Network learns an implicit image prior and a universal update rule through recursive supervision. Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed method is effective and robust to produce favorable results as well as practical for realworld image deblurring applications.
Solving the Credit Assignment Problem : The interaction of Explicit and Implicit learning with Internal and External State Information

In most problem-solving activities, feedback is received at the end of an action sequence. This creates a credit-assignment problem where the learner must associate the feedback with earlier actions, and the interdependencies of actions require the learner to either remember past choices of actions (internal state information) or rely on external cues in the environment (external state information) to select the right actions. We investigated the nature of explicit and implicit learning processes in the credit-assignment problem using a probabilistic sequential choice task with and without external state information. We found that when explicit memory encoding was dominant, subjects were faster to select the better option in their first choices than in the last choices; when implicit reinforcement learning was dominant subjects were faster to select the better option in their last choices than in their first choices. However, implicit reinforcement learning was only successful when distinct external state information was available. The results suggest the nature of learning in credit assignment: an explicit memory encoding process that keeps track of internal state information and a reinforcement-learning process that uses state information to propagate reinforcement backwards to previous choices. However, the implicit reinforcement learning process is effective only when the valences can be attributed to the appropriate states in the system – either internally generated states in the cognitive system or externally presented stimuli in the environment.
Trading the Twitter Sentiment with Reinforcement Learning

This paper is to explore the possibility to use alternative data and artificial intelligence techniques to trade stocks. The efficacy of the daily Twitter sentiment on predicting the stock return is examined using machine learning methods. Reinforcement learning(Q-learning) is applied to generate the optimal trading policy based on the sentiment signal. The predicting power of the sentiment signal is more significant if the stock price is driven by the expectation on the company growth and when the company has a major event that draws the public attention. The optimal trading strategy based on reinforcement learning outperforms the trading strategy based on the machine learning prediction.
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector

Over the years, several spatio-temporal interest point detectors have been proposed. While some detectors can only extract a sparse set of scaleinvariant features, others allow for the detection of a larger amount of features at user-defined scales. This paper presents for the first time spatio-temporal interest points that are at the same time scale-invariant (both spatially and temporally) and densely cover the video content. Moreover, as opposed to earlier work, the features can be computed efficiently. Applying scale-space theory, we show that this can be achieved by using the determinant of the Hessian as the saliency measure. Computations are speeded-up further through the use of approximative box-filter operations on an integral video structure. A quantitative evaluation and experimental results on action recognition show the strengths of the proposed detector in terms of repeatability, accuracy and speed, in comparison with previously proposed detectors.
Exploring the impact of artificial intelligence on teaching and learning in higher education

This paper explores the phenomena of the emergence of the use of artificial intelligence in teaching and learning in higher education. It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve. Recent technological advancements and the increasing speed of adopting new technologies in higher education are explored in order to predict the future nature of higher education in a world where artificial intelligence is part of the fabric of our universities. We pinpoint some challenges for institutions of higher education and student learning in the adoption of these technologies for teaching, learning, student support, and administration and explore further directions for research.
Field Mice: Extracting Hand Geometry from Electric Field Measurements

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Quantifying Long-term Scientific Impact

The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.
Robust Higher Order Potentials for Enforcing Label Consistency

This paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. Our method is based on higher order conditional random fields and uses potentials defined on sets of pixels (image segments) generated using unsupervised segmentation algorithms. These potentials enforce label consistency in image regions and can be seen as a generalization of the commonly used pairwise contrast sensitive smoothness potentials. The higher order potential functions used in our framework take the form of the Robust P n model and are more general than the P n Potts model recently proposed by Kohli et al. We prove that the optimal swap and expansion moves for energy functions composed of these potentials can be computed by solving a st-mincut problem. This enables the use of powerful graph cut based move making algorithms for performing inference in the framework. We test our method on the problem of multi-class object segmentation by augmenting the conventional crf used for object segmentation with higher order potentials defined on image regions. Experiments on challenging data sets show that integration of higher order potentials quantitatively and qualitatively improves results leading to much better definition of object boundaries. We believe that this method can be used to yield similar improvements for many other labelling problems.
An accurate contactless position sensor with planar resonators

In this paper, we report the development of a contactless position sensor with thin and planar structures for both sensor and target. The target is designed to be a compact resonator with resonance near the operating frequency, which improves the signal strength and increases the sensing range. The sensor is composed of a source coil and a pair of symmetrically arranged detecting coils. With differential measurement technique, highly accurate edge detection can be realized. Experiment results show that the sensor operates at varying gap size between the target and the sensor, even when the target is at 30 mm away, and the achieved accuracy is within 2% of the size of the sensing coil.
Multimodal MR Synthesis via Modality-Invariant Latent Representation

We propose a multi-input multi-output fully convolutional neural network model for MRI synthesis. The model is robust to missing data, as it benefits from, but does not require, additional input modalities. The model is trained end-to-end, and learns to embed all input modalities into a shared modality-invariant latent space. These latent representations are then combined into a single fused representation, which is transformed into the target output modality with a learnt decoder. We avoid the need for curriculum learning by exploiting the fact that the various input modalities are highly correlated. We also show that by incorporating information from segmentation masks the model can both decrease its error and generate data with synthetic lesions. We evaluate our model on the ISLES and BRATS data sets and demonstrate statistically significant improvements over state-of-the-art methods for single input tasks. This improvement increases further when multiple input modalities are used, demonstrating the benefits of learning a common latent space, again resulting in a statistically significant improvement over the current best method. Finally, we demonstrate our approach on non skull-stripped brain images, producing a statistically significant improvement over the previous best method. Code is made publicly available at https://github.com/agis85/multimodal_brain_synthesis.
Arabic Text Categorization Algorithm using Vector Evaluation Method

Text categorization is the process of grouping documents into categories based on their contents. This process is important to make information retrieval easier, and it became more important due to the huge textual information available online. The main problem in text categorization is how to improve the classification accuracy. Although Arabic text categorization is a new promising field, there are a few researches in this field. This paper proposes a new method for Arabic text categorization using vector evaluation. The proposed method uses a categorized Arabic documents corpus, and then the weights of the tested document's words are calculated to determine the document keywords which will be compared with the keywords of the corpus categorizes to determine the tested document's best category.
Autoscanning for coupled scene reconstruction and proactive object analysis

Detailed scanning of indoor scenes is tedious for humans. We propose autonomous scene scanning by a robot to relieve humans from such a laborious task. In an autonomous setting, detailed scene acquisition is inevitably coupled with scene analysis at the required level of detail. We develop a framework for object-level scene reconstruction coupled with object-centric scene analysis. As a result, the autoscanning and reconstruction will be object-aware, guided by the object analysis. The analysis is, in turn, gradually improved with progressively increased object-wise data fidelity. In realizing such a framework, we drive the robot to execute an iterative analyze-and-validate algorithm which interleaves between object analysis and guided validations.
 The object analysis incorporates online learning into a robust graph-cut based segmentation framework, achieving a global update of object-level segmentation based on the knowledge gained from robot-operated local validation. Based on the current analysis, the robot performs proactive validation over the scene with physical push and scan refinement, aiming at reducing the uncertainty of both object-level segmentation and object-wise reconstruction. We propose a joint entropy to measure such uncertainty based on segmentation confidence and reconstruction quality, and formulate the selection of validation actions as a maximum information gain problem. The output of our system is a reconstructed scene with both object extraction and object-wise geometry fidelity.
Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes

In the context of multi-class single-label classification, the loss function of deep learning methods compares the predicted class distribution versus the ground truth class distribution. The commonly used cross-entropy loss ignores the intricate inter-class relationships that often exist in real-life tasks such as age classification. We propose to leverage these relationships between classes by training deep nets with the exact squared Earth Mover’s Distance (also known as Wasserstein distance), assuming that the classes are ordered: one can put all classes in a onedimensional space such that the dissimilarities between classes are represented by the euclidean distances between them. The EMD loss uses the predicted probabilities of all classes and penalizes the miss-predictions according to the dissimilarities between classes. Our exact EMD loss yields state-of-the-art results with limited computational overhead on age estimation and image aesthetics datasets.
Potential Fields for Maintaining Connectivity of Mobile Networks

The control of mobile networks of multiple agents raises fundamental and novel problems in controlling the structure of the resulting dynamic graphs. In this paper, we consider the problem of controlling a network of agents so that the resulting motion always preserves the connectivity property of the network. In particular, the connectivity condition is translated to differentiable constraints on individual agent motion by considering the dynamics of the Laplacian matrix and its spectral properties. Artificial potential fields are then used to drive the agents to configurations away from the undesired space of disconnected networks while avoiding collisions with each other. We conclude by illustrating a class of interesting problems that can be achieved while preserving connectivity constraints.
Predicting the orientation of invisible stimuli from activity in human primary visual cortex

Humans can experience aftereffects from oriented stimuli that are not consciously perceived, suggesting that such stimuli receive cortical processing. Determining the physiological substrate of such effects has proven elusive owing to the low spatial resolution of conventional human neuroimaging techniques compared to the size of orientation columns in visual cortex. Here we show that even at conventional resolutions it is possible to use fMRI to obtain a direct measure of orientation-selective processing in V1. We found that many parts of V1 show subtle but reproducible biases to oriented stimuli, and that we could accumulate this information across the whole of V1 using multivariate pattern recognition. Using this information, we could then successfully predict which one of two oriented stimuli a participant was viewing, even when masking rendered that stimulus invisible. Our findings show that conventional fMRI can be used to reveal feature-selective processing in human cortex, even for invisible stimuli.
Group-regularized individual prediction: theory and application to pain

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study.
A Knowledge Compilation Map

We propose a perspective on knowledge compilation which calls for analyzing different compilation approaches according to two key dimensions: the succinctness of the target compilation language, and the class of queries and transformations that the language supports in polytime. We then provide a knowledge compilation map, which analyzes a large number of existing target compilation languages according to their succinctness and their polytime transformations and queries. We argue that such analysis is necessary for placing new compilation approaches within the context of existing ones. We also go beyond classical, flat target compilation languages based on CNF and DNF, and consider a richer, nested class based on directed acyclic graphs (such as OBDDs), which we show to include a relatively large number of target compilation languages.
The Animacy Continuum in the Human Ventral Vision Pathway

Major theories for explaining the organization of semantic memory in the human brain are premised on the often-observed dichotomous dissociation between living and nonliving objects. Evidence from neuroimaging has been interpreted to suggest that this distinction is reflected in the functional topography of the ventral vision pathway as lateral-to-medial activation gradients. Recently, we observed that similar activation gradients also reflect differences among living stimuli consistent with the semantic dimension of graded animacy. Here, we address whether the salient dichotomous distinction between living and nonliving objects is actually reflected in observable measured brain activity or whether previous observations of a dichotomous dissociation were the illusory result of stimulus sampling biases. Using fMRI, we measured neural responses while participants viewed 10 animal species with high to low animacy and two inanimate categories. Representational similarity analysis of the activity in ventral vision cortex revealed a main axis of variation with high-animacy species maximally different from artifacts and with the least animate species closest to artifacts. Although the associated functional topography mirrored activation gradients observed for animate–inanimate contrasts, we found no evidence for a dichotomous dissociation. We conclude that a central organizing principle of human object vision corresponds to the graded psychological property of animacy with no clear distinction between living and nonliving stimuli. The lack of evidence for a dichotomous dissociation in the measured brain activity challenges theories based on this premise.
How Much of Driving Is Preattentive?

Driving a car in an urban setting is an extremely difficult problem, incorporating a large number of complex visual tasks; however, this problem is solved daily by most adults with little apparent effort. This paper proposes a novel vision-based approach to autonomous driving that can predict and even anticipate a driver's behavior in real time, using preattentive vision only. Experiments on three large datasets totaling over 200 000 frames show that our preattentive model can (1) detect a wide range of driving-critical context such as crossroads, city center, and road type; however, more surprisingly, it can (2) detect the driver's actions (over 80% of braking and turning actions) and (3) estimate the driver's steering angle accurately. Additionally, our model is consistent with human data: First, the best steering prediction is obtained for a perception to action delay consistent with psychological experiments. Importantly, this prediction can be made before the driver's action. Second, the regions of the visual field used by the computational model strongly correlate with the driver's gaze locations, significantly outperforming many saliency measures and comparable to state-of-the-art approaches.
Central retinal artery occlusion in Wegener's granulomatosis: a diagnostic dilemma

To report a case of central retinal artery occlusion (CRAO) in a patient with biopsy-verified Wegener's granulomatosis (WG) with positive C-ANCA.



A 55-year-old woman presented with a 3-day history of acute painless bilateral loss of vision; she also complained of fever and weight loss. Examination showed a CRAO in the left eye and angiographically documented choroidal ischemia in both eyes.



The possibility of systemic vasculitis was not kept in mind until further studies were carried out; methylprednisolone pulse therapy was then started. Renal biopsy disclosed focal and segmental necrotizing vasculitis of the medium-sized arteries, supporting the diagnosis of WG, and cyclophosphamide pulse therapy was administered with gradual improvement, but there was no visual recovery.



CRAO as presenting manifestation of WG, in the context of retinal vasculitis, is very uncommon, but we should be aware of WG in the etiology of CRAO. This report shows the difficulty of diagnosing Wegener's granulomatosis; it requires a high index of suspicion, and we should obtain an accurate medical history and repeat serological and histopathological examinations. It emphasizes that inflammation of arteries leads to irreversible retinal infarction, and visual loss may occur.