nils-herrmann/clean_scirepeval huggingface dataset:
Student Performance Prediction with Optimum Multilabel Ensemble Model

One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.
Truthful and Faithful Monetary Policy for a Stablecoin Conducted by a Decentralised, Encrypted Artificial Intelligence

The Holy Grail of a decentralised stablecoin is achieved on rigorous mathematical frameworks, obtaining multiple advantageous proofs: stability, convergence, truthfulness, faithfulness, and malicious-security. These properties could only be attained by the novel and interdisciplinary combination of previously unrelated fields: model predictive control, deep learning, alternating direction method of multipliers (consensus-ADMM), mechanism design, secure multi-party computation, and zero-knowledge proofs. For the first time, this paper proves: 
- the feasibility of decentralising the central bank while securely preserving its independence in a decentralised computation setting 
- the benefits for price stability of combining mechanism design, provable security, and control theory, unlike the heuristics of previous stablecoins 
- the implementation of complex monetary policies on a stablecoin, equivalent to the ones used by central banks and beyond the current fixed rules of cryptocurrencies that hinder their price stability 
- methods to circumvent the impossibilities of Guaranteed Output Delivery (G.O.D.) and fairness: standing on truthfulness and faithfulness, we reach G.O.D. and fairness under the assumption of rational parties 
As a corollary, a decentralised artificial intelligence is able to conduct the monetary policy of a stablecoin, minimising human intervention.
Three-in-a-tree in near linear time

The three-in-a-tree problem is to determine if a simple undirected graph contains an induced subgraph which is a tree connecting three given vertices. Based on a beautiful characterization that is proved in more than twenty pages, Chudnovsky and Seymour [Combinatorica 2010] gave the previously only known polynomial-time algorithm, running in O(mn 2) time, to solve the three-in-a-tree problem on an n-vertex m-edge graph. Their three-in-a-tree algorithm has become a critical subroutine in several state-of-the-art graph recognition and detection algorithms. In this paper we solve the three-in-a-tree problem in O(mlog2 n) time, leading to improved algorithms for recognizing perfect graphs and detecting thetas, pyramids, beetles, and odd and even holes. Our result is based on a new and more constructive characterization than that of Chudnovsky and Seymour. Our new characterization is stronger than the original, and our proof implies a new simpler proof for the original characterization. The improved characterization gains the first factor n in speed. The remaining improvement is based on dynamic graph algorithms.
Reproducibility of an airway tapering measurement in computed tomography with application to bronchiectasis

We propose a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on computed tomography (CT). We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure. We generate a spline from the centerline of an airway to measure the area and arclength at contiguous intervals. The tapering measurement is the gradient of the linear regression between area in log space and arclength. The reproducibility of the measure was assessed by analyzing different radiation doses, voxel sizes, and reconstruction kernel on single timepoint and longitudinal CT scans and by evaluating the effect of airway bifurcations. Using 74 airways from 10 CT scans, we show a statistical difference, p  =  3.4  ×  10  −  4, in tapering between healthy airways (n  =  35) and those affected by bronchiectasis (n  =  39). The difference between the mean of the two populations is 0.011  mm  −  1, and the difference between the medians of the two populations was 0.006  mm  −  1. The tapering measurement retained a 95% confidence interval of ±0.005  mm  −  1 in a simulated 25 mAs scan and retained a 95% confidence of ±0.005  mm  −  1 on simulated CTs up to 1.5 times the original voxel size. We have established an estimate of the precision of the tapering measurement and estimated the effect on precision of the simulated voxel size and CT scan dose. We recommend that the scanner calibration be undertaken with the phantoms as described, on the specific CT scanner, radiation dose, and reconstruction algorithm that are to be used in any quantitative studies.
Constructing Dynamic Knowledge Graph for Visual Semantic Understanding and Applications in Autonomous Robotics

Interpreting semantic knowledge describing entities, relations and attributes explicitly with visuals and implicitly with in behind-scene common senses gain more attention in autonomous robotics. By incorporating vision and language modeling with common-sense knowledge, we can provide rich features indicating strong semantic meanings for human and robot action relationships, which can be utilized further in autonomous robotic controls. In this paper, we propose a systematic scheme to generate high-conceptual dynamic knowledge graphs representing Entity-Relation-Entity (E-R-E) and Entity-Attribute-Value (E-A-V) knowledges by "watching" a video clip. A combination of Vision-Language model and static ontology tree is used to illustrate workspace, configurations, functions and usages for both human and robot. The proposed method is flexible and well-versed. It will serve as our first positioning investigation for further research in various applications for autonomous robots.
The chemical composition of HIP 34407/HIP 34426 and other twin-star comoving pairs

We conducted a high-precision elemental abundance analysis of the twin-star comoving pair HIP 34407/HIP 34426. With mean error of 0.013 dex in the differential abundances (Δ[X/H]), a significant difference was found: HIP 34407 is more metal rich than HIP 34426. The elemental abundance differences correlate strongly with condensation temperature, with the lowest for the volatile elements like carbon around 0.05 ± 0.02 dex, and the highest up to about 0.22 ± 0.01 dex for the most refractory elements like aluminium. Dissimilar chemical composition for stars in twin-star comoving pairs is not uncommon, thus we compile previously published results like ours and look for correlations between abundance differences and stellar parameters, finding no significant trends with average effective temperature, surface gravity, iron abundance, or their differences. Instead, we found a weak correlation between the absolute value of abundance difference and the projected distance between the stars in each pair that appears to be more important for elements that have a low absolute abundance. If confirmed, this correlation could be an important observational constraint for binary star system formation scenarios.
Visualizing How Embeddings Generalize

Deep metric learning is often used to learn an embedding function that captures the semantic differences within a dataset. A key factor in many problem domains is how this embedding generalizes to new classes of data. In observing many triplet selection strategies for Metric Learning, we find that the best performance consistently arises from approaches that focus on a few, well selected triplets.We introduce visualization tools to illustrate how an embedding generalizes beyond measuring accuracy on validation data, and we illustrate the behavior of a range of triplet selection strategies.
Multimodal Dataset of Human-Robot Hugging Interaction

A hug is a tight embrace and an expression of warmth, sympathy and camaraderie. Despite the fact that a hug often only takes a few seconds, it is filled with details and nuances and is a highly complex process of coordination between two agents. For human-robot collaborative tasks, it is necessary for humans to develop trust and see the robot as a partner to perform a given task together. Datasets representing agent-agent interaction are scarce and, if available, of limited quality. To study the underlying phenomena and variations in a hug between a person and a robot, we deployed Baxter humanoid robot and wearable sensors on persons to record 353 episodes of hugging activity. 33 people were given minimal instructions to hug the humanoid robot for as natural hugging interaction as possible. In the paper, we present our methodology and analysis of the collected dataset. The use of this dataset is to implement machine learning methods for the humanoid robot to learn to anticipate and react to the movements of a person approaching for a hug. In this regard, we show the significance of the dataset by highlighting certain features in our dataset.
The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition

Recent advances in artificial intelligence have been strongly driven by the use of game environments for training and evaluating agents. Games are often accessible and versatile, with well-defined state-transitions and goals allowing for intensive training and experimentation. However, agents trained in a particular environment are usually tested on the same or slightly varied distributions, and solutions do not necessarily imply any understanding. If we want AI systems that can model and understand their environment, we need environments that explicitly test for this. Inspired by the extensive literature on animal cognition, we present an environment that keeps all the positive elements of standard gaming environments, but is explicitly designed for the testing of animal-like artificial cognition.
Ultracold polar molecules as qudits

We discuss how the internal structure of ultracold molecules, trapped in the motional ground state of optical tweezers, can be used to implement qudits. We explore the rotational, fine and hyperfine structure of 40Ca19F and 87Rb133Cs, which are examples of molecules with 2Σ and 1Σ electronic ground states, respectively. In each case we identify a subset of levels within a single rotational manifold suitable to implement a four-level qudit. Quantum gates can be implemented using two-photon microwave transitions via levels in a neighboring rotational manifold. We discuss limitations to the usefulness of molecular qudits, arising from off-resonant excitation and decoherence. As an example, we present a protocol for using a molecular qudit of dimension d = 4 to perform the Deutsch algorithm.
Probing the merger history of red early-type galaxies with their faint stellar substructures

Several detailed observations, such as those carried out at the Canada-France-Hawaii Telescope (CFHT), have revealed prominent Low Surface Brightness (LSB) fine structures that lead to a change in the apparent morphology of galaxies. Previous photometry surveys have developed observational techniques which make use of the diffuse light detected in the external regions of galaxies. In these studies, the outer perturbations have been identified and classified. These include tidal tails, stellar streams, and shells. These structures serve as tracers for interacting events and merging events and retain some memory of the mass assembly of galaxies. Cosmological numerical simulations are required to estimate their visibility timescale, among other properties, in order to reconstruct the merger history of galaxies. In the present work, we analyze a hydrodynamical cosmological simulation to build up a comprehensive interpretation of the properties of fine structures. We present a census of several types of LSB fine structures compiled using a visual inspection of individual snapshots at various points in time. We reconstruct the evolution of the number of fine structures detected around an early-type galaxy and we compare it with the merger history of the galaxy. We find that most fine structures are associated with major and intermediate mass merger events. Their survival timescale ranges between 0.7 and 4 Gyr. Shells and streams remain visible for a longer time, while tidal tails have a shorter lifetime. These estimates for the survival time of collisional debris provide clues for the interpretation of the shape and frequency of fine structures observed in deep images with regard to their mass assembly. We find that the detectability of stellar streams is most sensitive at the surface brightness limit, demonstrating greater visibility at the deepest surface brightness level used in our simulation. We see between two and three times more streams based on a surface brightness cut of 33 mag arcsec−2 than with 29 mag arcsec−2. We find that the detection of shells is strongly dependent upon the projection angle.
Automatic Detection and Classification of Cognitive Distortions in Mental Health Text

In cognitive psychology, automatic and self-reinforcing irrational thought patterns are known as cognitive distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops of unhealthy thinking, leading to inaccurate perceptions of reality commonly associated with anxiety and depression. In this paper, we present a machine learning framework for the automatic detection and classification of 15 common cognitive distortions in two novel mental health free datasets collected from both crowdsourcing and a real-world online therapy program. We also performed an exploratory analysis using unsupervised content-based clustering and topic modeling algorithms as first efforts towards a data-driven perspective on the thematic relationship between similar cognitive distortions traditionally deemed unique. Finally, we highlight the difficulties in applying mental health-based machine learning in a real-world setting and comment on the implications and benefits of our framework for improving automated delivery of therapeutic treatment in conjunction with traditional cognitive-behavioral therapy.
Towards Unsupervised Segmentation of Extreme Weather Events

Extreme weather is one of the main mechanisms through which climate change will directly impact human society. Coping with such change as a global community requires markedly improved understanding of how global warming drives extreme weather events. While alternative climate scenarios can be simulated using sophisticated models, identifying extreme weather events in these simulations requires automation due to the vast amounts of complex high-dimensional data produced. Atmospheric dynamics, and hydrodynamic flows more generally, are highly structured and largely organize around a lower dimensional skeleton of coherent structures. Indeed, extreme weather events are a special case of more general hydrodynamic coherent structures. We present a scalable physics-based representation learning method that decomposes spatiotemporal systems into their structurally relevant components, which are captured by latent variables known as local causal states. For complex fluid flows we show our method is capable of capturing known coherent structures, and with promising segmentation results on CAM5.1 water vapor data we outline the path to extreme weather identification from unlabeled climate model simulation data.
Data-Efficient Classification of Birdcall through Convolutional Neural Networks Transfer Learning

Deep learning Convolutional Neural Network (CNN) models are powerful classification models but require a large amount of training data. In niche domains such as bird acoustics, it is expensive and difficult to obtain a large number of training samples. One method of classifying data with a limited number of training samples is to employ transfer learning. In this research, we evaluated the effectiveness of birdcall classification using transfer learning from a larger base dataset (2814 samples in 46 classes) to a smaller target dataset (351 samples in 10 classes) using the ResNet-50 CNN. We obtained 79% average validation accuracy on the target dataset in 5-fold cross-validation. The methodology of transfer learning from an ImageNet-trained CNN to a project-specific and a much smaller set of classes and images was extended to the domain of spectrogram images, where the base dataset effectively played the role of the ImageNet.
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning

Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions. One way to avoid local optima is to use a population of agents to ensure coverage of the policy space, yet learning a population with the "best" coverage is still an open problem. In this work, we present a novel approach to population-based RL in continuous control that leverages properties of normalizing flows to perform attractive and repulsive operations between current members of the population and previously observed policies. Empirical results on the MuJoCo suite demonstrate a high performance gain for our algorithm compared to prior work, including Soft-Actor Critic (SAC).
Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs

In this paper, the problem of optimizing the deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs. Therefore, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users. To solve this problem, an algorithm based on the machine learning framework of gated recurrent units (GRUs) is proposed. Using GRUs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. In order to reduce the complexity of the prediction algorithm while accurately predicting the illumination distribution, a Gaussian mixture model (GMM) is used to fit the illumination distribution of the target area at each time slot. Based on the predicted illumination distribution, the optimization problem is proved to be a convex optimization problem that can be solved by using duality. Simulations using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 22.1\% reduction in transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution. The results also show that UAVs must hover at areas having strong illumination, thus providing useful guidelines on the deployment of VLC-enabled UAVs.
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples

Adversarial examples reveal the vulnerability and unexplained nature of neural networks. Studying the defense of adversarial examples is of considerable practical importance. Most adversarial examples that misclassify networks are often undetectable by humans. In this paper, we propose a defense model to train the classifier into a human-perception classification model with shape preference. The proposed model comprising a texture transfer network (TTN) and an auxiliary defense generative adversarial networks (GAN) is called Human-perception Auxiliary Defense GAN (HAD-GAN). The TTN is used to extend the texture samples of a clean image and helps classifiers focus on its shape. GAN is utilized to form a training framework for the model and generate the necessary images. A series of experiments conducted on MNIST, Fashion-MNIST and CIFAR10 show that the proposed model outperforms the state-of-the-art defense methods for network robustness. The model also demonstrates a significant improvement on defense capability of adversarial examples.
Object-Centric Stereo Matching for 3D Object Detection

Safe autonomous driving requires reliable 3D object detection—determining the 6 DoF pose and dimensions of objects of interest. Using stereo cameras to solve this task is a cost-effective alternative to the widely used LiDAR sensor. The current state-of-the-art for stereo 3D object detection takes the existing PSMNet stereo matching network, with no modifications, and converts the estimated disparities into a 3D point cloud, and feeds this point cloud into a LiDAR-based 3D object detector. The issue with existing stereo matching networks is that they are designed for disparity estimation, not 3D object detection; the shape and accuracy of object point clouds are not the focus. Stereo matching networks commonly suffer from inaccurate depth estimates at object boundaries, which we define as streaking, because background and foreground points are jointly estimated. Existing networks also penalize disparity instead of the estimated position of object point clouds in their loss functions. We propose a novel 2D box association and object-centric stereo matching method that only estimates the disparities of the objects of interest to address these two issues. Our method achieves state-of-the-art results on the KITTI 3D and BEV benchmarks.
Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body

For robots to exhibit a high level of intelligence in the real world, they must be able to assess objects for which they have no prior knowledge. Therefore, it is crucial for robots to perceive object affordances by reasoning about physical interactions with the object. In this paper, we propose a novel method to provide robots with an ability to imagine object affordances using physical simulations. The class of chair is chosen here as an initial category of objects to illustrate a more general paradigm. In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical sitting interaction between an articulated human body and the object. This object affordance reasoning is used as a cue for object classification (chair vs non-chair). Moreover, if an object is classified as a chair, the affordance reasoning can also predict the upright pose of the object which allows the sitting interaction to take place. We call this type of poses the functional pose. We demonstrate our method in chair classification on synthetic 3D CAD models. Although our method uses only 30 models for training, it outperforms appearance-based deep learning methods, which require a large amount of training data, when the upright orientation is not assumed to be known a priori. In addition, we showcase that the functional pose predictions of our method align well with human judgments on both synthetic models and real objects scanned by a depth camera.