nils-herrmann/clean_scirepeval huggingface dataset:
Population Cost Prediction on Public Healthcare Datasets

The increasing availability of digital health records should ideally improve accountability in healthcare. In this context, the study of predictive modeling of healthcare costs forms a foundation for accountable care, at both population and individual patient-level care. In this research we use machine learning algorithms for accurate predictions of healthcare costs on publicly available claims and survey data. Specifically, we investigate the use of the regression trees, M5 model trees and random forest, to predict healthcare costs of individual patients given their prior medical (and cost) history.
 Overall, three observations showcase the utility of our research: (a) prior healthcare cost alone can be a good indicator for future healthcare cost, (b) M5 model tree technique led to very accurate future healthcare cost prediction, and (c) although state-of-the-art machine learning algorithms are also limited by skewed cost distributions in healthcare, for a large fraction (75%) of population, we were able to predict with higher accuracy using these algorithms. In particular, using M5 model trees we were able to accurately predict costs within less than $125 for 75% of the population when compared to prior techniques. Since models for predicting healthcare costs are often used to ascertain overall population health, our work is useful to evaluate future costs for large segments of disease populations with reasonably low error as demonstrated in our results on real-world publicly available datasets.
Evolving an expert checkers playing program without using human expertise

An evolutionary algorithm has taught itself how to play the game of checkers without using features that would normally require human expertise. Using only the raw positions of pieces on the board and the piece differential, the evolutionary program optimized artificial neural networks to evaluate alternative positions in the game. Over the course of several hundred generations, the program taught itself to play at a level that is competitive with human experts (one level below human masters). This was verified by playing the best-evolved neural network against 165 human players on an Internet gaming zone. The neural network’s performance earned a rating that is better than 99.61 percent of all registered players at the website. Control experiments between the best-evolved neural network and a program that relies on material advantage indicate the superiority of the neural network both at equal levels of look-ahead and CPU time. The results suggest that the principles of Darwinian evolution may be usefully applied to solving problems that have not yet been solved by human expertise.
Applying PCA for Traffic Anomaly Detection: Problems and Solutions

Spatial Principal Component Analysis (PCA) has been proposed for network-wide anomaly detection. A recent work has shown that PCA is very sensitive to calibration settings, unfortunately, the authors did not provide further explanations for this observation. In this paper, we fill this gap and provide the reasoning behind the found discrepancies. First, we revisit PCA for anomaly detection and evaluate its performance on our data. We develop a slightly modified version of PCA that uses only data from a single router. Instead of correlating data across different spatial measurement points, we correlate the data across different metrics. With the help of the analyzed data, we explain the pitfalls of PCA and underline our argumentation with measurement results. We show that the main problems that make PCA difficult to apply are (i) the temporal correlation in the data; (ii) the non-stationarity of the data; and (iii) the difficulty about choosing the right number of components. Moreover, we propose a solution to deal with the most dominant problem, the temporal correlation in data. We find that when we consider temporal correlation, PCA detection results are significantly improved.
Personalized input: improving ten-finger touchscreen typing through automatic adaptation

Although typing on touchscreens is slower than typing on physical keyboards, touchscreens offer a critical potential advantage: they are software-based, and, as such, the keyboard layout and classification models used to interpret key presses can dynamically adapt to suit each user's typing pattern. To explore this potential, we introduce and evaluate two novel personalized keyboard interfaces, both of which adapt their underlying key-press classification models. The first keyboard also visually adapts the location of keys while the second one always maintains a visually stable rectangular layout. A three-session user evaluation showed that the keyboard with the stable rectangular layout significantly improved typing speed compared to a control condition with no personalization. Although no similar benefit was found for the keyboard that also offered visual adaptation, overall subjective response to both new touchscreen keyboards was positive. As personalized keyboards are still an emerging area of research, we also outline a design space that includes dimensions of adaptation and key-press classification features.
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals

Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data. Here we describe EEG-GAN as a framework to generate electroencephalographic (EEG) brain signals. We introduce a modification to the improved training of Wasserstein GANs to stabilize training and investigate a range of architectural choices critical for time series generation (most notably upand down-sampling). For evaluation we consider and compare different metrics such as Inception score, Frechet inception distance and sliced Wasserstein distance, together showing that our EEG-GAN framework generated naturalistic EEG examples. It thus opens up a range of new generative application scenarios in the neuroscientific and neurological context, such as data augmentation in brain-computer interfacing tasks, EEG super-sampling, or restoration of corrupted data segments. The possibility to generate signals of a certain class and/or with specific properties may also open a new avenue for research into the underlying structure of brain signals.
Semantic Compositionality through Recursive Matrix-Vector Spaces

Single-word vector space models have been very successful at learning lexical information. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length. Our model assigns a vector and a matrix to every node in a parse tree: the vector captures the inherent meaning of the constituent, while the matrix captures how it changes the meaning of neighboring words or phrases. This matrix-vector RNN can learn the meaning of operators in propositional logic and natural language. The model obtains state of the art performance on three different experiments: predicting fine-grained sentiment distributions of adverb-adjective pairs; classifying sentiment labels of movie reviews and classifying semantic relationships such as cause-effect or topic-message between nouns using the syntactic path between them.
The PASCAL Recognising Textual Entailment Challenge

This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark. The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.
Prefrontal neuropsychological effects of sleep deprivation in young adults--a model for healthy aging?

Neuropsychological testing and brain imaging show that healthy aging leads to a preferential impairment of the prefrontal cortex (PFC). Interestingly, in young adults sleep deprivation (SD) has similar effects. Psychological tasks not so oriented to the PFC are less sensitive both to SD and aging. The PFC is a cortical region working particularly hard during wakefulness, which may make it more vulnerable to "deterioration," whether this is through aging or SD. In these respects SD in young adults may offer a model for aging. No study has directly compared aging with SD. We compared groups comprising (equal sexes): YOUNG (av. 23y), MIDDLE AGED (av. 60y) and OLD (av. 73y). Young were subdivided into SD and non-sleep deprived groups. All participants were carefully screened, were healthy, good sleepers and with a similar educational background. A battery of PFC-oriented, short and straightforward neuropsychological tests was used to compare the effects of 36h of SD in the young group, with findings from the healthy, alert, non-sleep deprived groups. Tests relied on accuracy rather than speed (which took into account the problem of "global slowing" in the older participants), and were administered once (i.e., were novel). Test outcomes were significantly affected by: (1) SD in the young groups, and (2) by age. A non-PFC component of one test was not affected by SD or by age. It was concluded that 36h SD in young adults produces effects on the PFC similar to those found in normal, alert people aged about 60 years. However, it can not be concluded that an aged brain is a sleep-deprived brain.
Oxytocin increases trust in humans

Trust pervades human societies. Trust is indispensable in friendship, love, families and organizations, and plays a key role in economic exchange and politics. In the absence of trust among trading partners, market transactions break down. In the absence of trust in a country's institutions and leaders, political legitimacy breaks down. Much recent evidence indicates that trust contributes to economic, political and social success. Little is known, however, about the biological basis of trust among humans. Here we show that intranasal administration of oxytocin, a neuropeptide that plays a key role in social attachment and affiliation in non-human mammals, causes a substantial increase in trust among humans, thereby greatly increasing the benefits from social interactions. We also show that the effect of oxytocin on trust is not due to a general increase in the readiness to bear risks. On the contrary, oxytocin specifically affects an individual's willingness to accept social risks arising through interpersonal interactions. These results concur with animal research suggesting an essential role for oxytocin as a biological basis of prosocial approach behaviour.
U-Net: Machine Reading Comprehension with Unanswerable Questions

Machine reading comprehension with unanswerable questions is a new challenging task for natural language processing. A key subtask is to reliably predict whether the question is unanswerable. In this paper, we propose a unified model, called U-Net, with three important components: answer pointer, no-answer pointer, and answer verifier. We introduce a universal node and thus process the question and its context passage as a single contiguous sequence of tokens. The universal node encodes the fused information from both the question and passage, and plays an important role to predict whether the question is answerable and also greatly improves the conciseness of the U-Net. Different from the stateof-art pipeline models, U-Net can be learned in an end-to-end fashion. The experimental results on the SQuAD 2.0 dataset show that U-Net can effectively predict the unanswerability of questions and achieves an F1 score of 71.7 on SQuAD 2.0.
Considerations on deploying high-performance container-based NFV

Over the last few years, the idea of network function virtualization (NFV) has become widespread, shifting the workload of hardware-based middleboxes onto applications on commodity hardware. Virtual machine is the common building block of the NFV platform, but its overhead on running additional OSes is a major drawback. With smaller overhead than VMs, containers are tested as the NFV platform, but their packet processing throughput is yet unsatisfactory. (≈ 1.1Mpps).
 In this paper, we focus on alternative performance evaluation of container-based NFV. We setup an evaluation system that forwards generated packets to container which returns back the network traffic to host and packet generator. As a result, we construct a container image that processes millions of packets per second (2.6Mpps) with low-latency (≤ 25μs) while keeping its size to the minimum (7.8MB). In addition, chaining of the container-based NF application shows that it preserves the high-performance (process over a million packets per seconds with latency ≈ 100μs) while adding up to 5 containers into the chain. Our evaluation system of lightweight, high-performance NFV requires no kernel modification nor hardware acceleration. To this end, we utilize BESS as a virtual switch for containers. As the main form of our work is not in the application development, we take full advantage of Click for network functions implementation in our evaluation.
Ricci Flow for 3D Shape Analysis

Ricci flow is a powerful curvature flow method, which is invariant to rigid motion, scaling, isometric, and conformal deformations. We present the first application of surface Ricci flow in computer vision. Previous methods based on conformal geometry, which only handle 3D shapes with simple topology, are subsumed by the Ricci flow-based method, which handles surfaces with arbitrary topology. We present a general framework for the computation of Ricci flow, which can design any Riemannian metric by user-defined curvature. The solution to Ricci flow is unique and robust to noise. We provide implementation details for Ricci flow on discrete surfaces of either Euclidean or hyperbolic background geometry. Our Ricci flow-based method can convert all 3D problems into 2D domains and offers a general framework for 3D shape analysis. We demonstrate the applicability of this intrinsic shape representation through standard shape analysis problems, such as 3D shape matching and registration, and shape indexing. Surfaces with large nonrigid anisotropic deformations can be registered using Ricci flow with constraints of feature points and curves. We show how conformal equivalence can be used to index shapes in a 3D surface shape space with the use of Teichmuller space coordinates. Experimental results are shown on 3D face data sets with large expression deformations and on dynamic heart data.
Monocular Visual–Inertial State Estimation With Online Initialization and Camera–IMU Extrinsic Calibration

There have been increasing demands for developing microaerial vehicles with vision-based autonomy for search and rescue missions in complex environments. In particular, the monocular visual–inertial system (VINS), which consists of only an inertial measurement unit (IMU) and a camera, forms a great lightweight sensor suite due to its low weight and small footprint. In this paper, we address two challenges for rapid deployment of monocular VINS: 1) the initialization problem and 2) the calibration problem. We propose a methodology that is able to initialize velocity, gravity, visual scale, and camera–IMU extrinsic calibration on the fly. Our approach operates in natural environments and does not use any artificial markers. It also does not require any prior knowledge about the mechanical configuration of the system. It is a significant step toward plug-and-play and highly customizable visual navigation for mobile robots. We show through online experiments that our method leads to accurate calibration of camera–IMU transformation, with errors less than 0.02 m in translation and 1° in rotation. We compare out method with a state-of-the-art marker-based offline calibration method and show superior results. We also demonstrate the performance of the proposed approach in large-scale indoor and outdoor experiments.
Robust Lane Detection and Tracking in Challenging Scenarios

A lane-detection system is an important component of many intelligent transportation systems. We present a robust lane-detection-and-tracking algorithm to deal with challenging scenarios such as a lane curvature, worn lane markings, lane changes, and emerging, ending, merging, and splitting lanes. We first present a comparative study to find a good real-time lane-marking classifier. Once detection is done, the lane markings are grouped into lane-boundary hypotheses. We group left and right lane boundaries separately to effectively handle merging and splitting lanes. A fast and robust algorithm, based on random-sample consensus and particle filtering, is proposed to generate a large number of hypotheses in real time. The generated hypotheses are evaluated and grouped based on a probabilistic framework. The suggested framework effectively combines a likelihood-based object-recognition algorithm with a Markov-style process (tracking) and can also be applied to general-part-based object-tracking problems. An experimental result on local streets and highways shows that the suggested algorithm is very reliable.
A Look at Several Memory Management Units, TLB-Refill Mechanisms, and Page Table Organizations

Virtual memory is a staple in modem systems, though there is little agreement on how its functionality is to be implemented on either the hardware or software side of the interface. The myriad of design choices and incompatible hardware mechanisms suggests potential performance problems, especially since increasing numbers of systems (even embedded systems) are using memory management. A comparative study of the implementation choices in virtual memory should therefore aid system-level designers.This paper compares several virtual memory designs, including combinations of hierarchical and inverted page tables on hardware-managed and software-managed translation lookaside buffers (TLBs). The simulations show that systems are fairly sensitive to TLB size; that interrupts already account for a large portion of memory-management overhead and can become a significant factor as processors execute more concurrent instructions; and that if one includes the cache misses inflicted on applications by the VM system, the total VM overhead is roughly twice what was thought (10--20% rather than 5--10%).
Beware, Your Hands Reveal Your Secrets!

Research on attacks which exploit video-based side-channels to decode text typed on a smartphone has traditionally assumed that the adversary is able to leverage some information from the screen display (say, a reflection of the screen or a low resolution video of the content typed on the screen). This paper introduces a new breed of side-channel attack on the PIN entry process on a smartphone which entirely relies on the spatio-temporal dynamics of the hands during typing to decode the typed text. Implemented on a dataset of 200 videos of the PIN entry process on an HTC One phone, we show, that the attack breaks an average of over 50% of the PINs on the first attempt and an average of over 85% of the PINs in ten attempts. Because the attack can be conducted in such a way not to raise suspicion (i.e., since the adversary does not have to direct the camera at the screen), we believe that it is very likely to be adopted by adversaries who seek to stealthily steal sensitive private information. As users conduct more and more of their computing transactions on mobile devices in the open, the paper calls for the community to take a closer look at the risks posed by the now ubiquitous camera-enabled devices.
Stability and control of a quadrocopter despite the complete loss of one, two, or three propellers

This paper presents periodic solutions for a quadrocopter maintaining a height around a position in space despite having lost a single, two opposing, or three propellers. In each case the control strategy consists of the quadrocopter spinning about a primary axis, fixed with respect to the vehicle, and tilting this axis for translational control. A linear, timeinvariant description of deviations from the attitude equilibrium is derived, allowing for a convenient cascaded control design. The results for the cases of losing one and two propellers are validated in experiment, while the case of losing three propellers is validated in a nonlinear simulation. These results have application in multicopter fault-tolerant control design, and also point to possible design directions for novel flying vehicles.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field.
Long-term Mapping Techniques for Ship Hull Inspection and Surveillance using an Autonomous Underwater Vehicle

This paper reports on a system for an autonomous underwater vehicle to perform in-situ, multiple session, hull inspection using long-term simultaneous localization and mapping (SLAM). Our method assumes very little a-priori knowledge, and does not require the aid of acoustic beacons for navigation, which is a typical mode of navigation in this type of application. Our system combines recent techniques in underwater saliency-informed visual SLAM and a method for representing the ship hull surface as a collection of many locally planar surface features. This methodology produces accurate maps that can be constructed in real-time on consumer-grade computing hardware. A single-session SLAM result is initially used as a prior map for later sessions, where the robot automatically merges the multiple surveys into a common hull-relative reference frame. To perform the re-localization step, we use a particle filter that leverages the locally planar representation of the ship hull surface, and a fast visual descriptor matching algorithm. Finally, we apply the recentlydeveloped graph sparsification tool, generic linear constraints (GLC), as a way to manage the computational complexity of the SLAM system as the robot accumulates information across multiple sessions. We show results for 20 SLAM sessions for two large vessels over the course of days, months, and even up to three years, with a total path length of approximately 10.2 km.