nils-herrmann/clean_scirepeval huggingface dataset:
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-toend deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden – diabetes and mental health – the results show improved modeling and risk prediction accuracy.
Quantum Recommendation Systems

A recommendation system uses the past purchases or ratings of n products by a group of m users, in order to provide personalized recommendations to individual users. The information is modeled as an m×n preference matrix which is assumed to have a good rank-k approximation, for a small constant k. In this work, we present a quantum algorithm for recommendation systems that has running time O(poly(k)polylog(mn)). All known classical algorithms for recommendation systems that work through reconstructing an approximation of the preference matrix run in time polynomial in the matrix dimension. Our algorithm provides good recommendations by sampling efficiently from an approximation of the preference matrix, without reconstructing the entire matrix. For this, we design an efficient quantum procedure to project a given vector onto the row space of a given matrix. This is the first algorithm for recommendation systems that runs in time polylogarithmic in the dimensions of the matrix and provides an example of a quantum machine learning algorithm for a real world application.
The optimal design of GaN-based Dual Active Bridge for bi-directional Plug-IN Hybrid Electric Vehicle (PHEV) charger

A high frequency, high efficiency bi-directional battery charger for Plug-in Hybrid Electric Vehicle (PHEV) is built with high voltage normally-off GaN-on-Si HFETs. This paper characterized the multi-chip-model both statically and dynamically. The optimal design of the isolated 500 kHz Dual Active Bridge DC/DC stage is detailed, taking account the wide battery voltage range and sinusoidal charging, to eliminate large DC link capacitor. Experimentally result shows a 500 kHz DAB converter with discrete inductor and transformer can achieved 97.2% efficiency at 1kW and 96.4% efficiency at 2.4 kW. By integrating the inductor into the transformer, 98.2% efficiency is achieved at 1 kW.
Individual user characteristics and information visualization: connecting the dots through eye tracking

There is increasing evidence that users' characteristics such as cognitive abilities and personality have an impact on the effectiveness of information visualization techniques. This paper investigates the relationship between such characteristics and fine-grained user attention patterns. In particular, we present results from an eye tracking user study involving bar graphs and radar graphs, showing that a user's cognitive abilities such as perceptual speed and verbal working memory have a significant impact on gaze behavior, both in general and in relation to task difficulty and visualization type. These results are discussed in view of our long-term goal of designing information visualisation systems that can dynamically adapt to individual user characteristics.
A Minimally Invasive Tendril Robot for In-Space Inspection

Current work at NASA's Johnson Space Center is focusing on the identification and design of novel robotic archetypes to fill roles complimentary to current space robots during in-space assembly and maintenance tasks. Tendril, NASA's latest robot designed for minimally invasive inspection, is one system born of this effort. Inspired by the biology of snakes, tentacles, and climbing plants, the Tendril robot is a long slender manipulator that can extend deep into crevasses and under thermal blankets to inspect areas largely inaccessible by conventional means. The design of the Tendril, with its multiple bending segments and 1 cm diameter, also serves as an initial step in exploring the whole body control known to continuum robots coupled with the small scale and dexterity found in medical and commercial minimally invasive devices. An overview of Tendril's design is presented along with preliminary results from testing that seeks to improve Tendril's performance through an iterative design process
An Automatic Inequality Prover and Instance Optimal Identity Testing

We consider the problem of verifying the identity of a distribution: Given the description of a distribution over a discrete support p = (p<sub>1</sub>, p<sub>2</sub>, ... , p<sub>n</sub>), how many samples (independent draws) must one obtain from an unknown distribution, q, to distinguish, with high probability, the case that p = q from the case that the total variation distance (L<sub>1</sub> distance) ||p - q||1≥ ϵ? We resolve this question, up to constant factors, on an instance by instance basis: there exist universal constants c, c' and a function f(p, ϵ) on distributions and error parameters, such that our tester distinguishes p = q from ||p-q||1≥ ϵ using f(p, ϵ) samples with success probability > 2/3, but no tester can distinguish p = q from ||p - q||1≥ c · ϵ when given c' · f(p, ϵ) samples. The function f(p, ϵ) is upperbounded by a multiple of ||p||2/3/ϵ<sup>2</sup>, but is more complicated, and is significantly smaller in some cases when p has many small domain elements, or a single large one. This result significantly generalizes and tightens previous results: since distributions of support at most n have L<sub>2/3</sub> norm bounded by √n, this result immediately shows that for such distributions, O(√n/ϵ<sup>2</sup>) samples suffice, tightening the previous bound of O(√npolylog/n<sup>4</sup>) for this class of distributions, and matching the (tight) known results for the case that p is the uniform distribution over support n. The analysis of our very simple testing algorithm involves several hairy inequalities. To facilitate this analysis, we give a complete characterization of a general class of inequalities- generalizing Cauchy-Schwarz, Holder's inequality, and the monotonicity of L<sub>p</sub> norms. Specifically, we characterize the set of sequences (a)<sub>i</sub> = a<sub>1</sub>, . . . , ar, (b)i = b<sub>1</sub>, . . . , br, (c)i = c<sub>1</sub>, ... , cr, for which it holds that for all finite sequences of positive numbers (x)<sub>j</sub> = x<sub>1</sub>,... and (y)<sub>j</sub> = y<sub>1</sub>,...,Π<sub>i=1</sub><sup>r</sup> (Σ<sub>j</sub>x<sup>a</sup><sub>j</sub><sup>i</sup><sub>y</sub><sub>i</sub><sup>b</sup><sup>i</sup>)<sup>ci</sup>≥1. For example, the standard Cauchy-Schwarz inequality corresponds to the sequences a = (1, 0, 1/2), b = (0,1, 1/2), c = (1/2 , 1/2 , -1). Our characterization is of a non-traditional nature in that it uses linear programming to compute a derivation that may otherwise have to be sought throu.gh trial and error, by hand. We do not believe such a characterization has appeared in the literature, and hope its computational nature will be useful to others, and facilitate analyses like the one here.
Understanding quantified-selfers' practices in collecting and exploring personal data

Researchers have studied how people use self-tracking technologies and discovered a long list of barriers including lack of time and motivation as well as difficulty in data integration and interpretation. Despite the barriers, an increasing number of Quantified-Selfers diligently track many kinds of data about themselves, and some of them share their best practices and mistakes through Meetup talks, blogging, and conferences. In this work, we aim to gain insights from these "extreme users," who have used existing technologies and built their own workarounds to overcome different barriers. We conducted a qualitative and quantitative analysis of 52 video recordings of Quantified Self Meetup talks to understand what they did, how they did it, and what they learned. We highlight several common pitfalls to self-tracking, including tracking too many things, not tracking triggers and context, and insufficient scientific rigor. We identify future research efforts that could help make progress toward addressing these pitfalls. We also discuss how our findings can have broad implications in designing and developing self-tracking technologies.
Learning spectral mapping for speech dereverberation

Reverberation distorts human speech and usually has negative effects on speech intelligibility, especially for hearing-impaired listeners. It also causes performance degradation in automatic speech recognition and speaker identification systems. Therefore, the dereverberation problem must be dealt with in daily listening environments. We propose to use deep neural networks (DNNs) to learn a spectral mapping from the reverberant speech to the anechoic speech. The trained DNN produces the estimated spectral representation of the corresponding anechoic speech. We demonstrate that distortion caused by reverberation is substantially attenuated by the DNN whose outputs can be resynthesized to the dereverebrated speech signal. The proposed approach is simple, and our systematic evaluation shows promising dereverberation results, which are significantly better than those of related systems.
Design and Evaluation of MiMaze, a Multi-Player Game on the Internet

This paper describes the design, implementation, and evaluation of MiMaze, a distributed multiplayer game on the Internet. The major contribution of this work is to have designed and implemented a completely distributed communication architecture based on IP multicast. MiMaze uses multicast communication system based on RTP/UDP/ IP, and a distributed synchronization mechanisms to guarantee the consistency of the game, regardless network delay. This paper provides evaluation results on the Mbone
Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels

Superpixels are perceptually meaningful atomic regions that can effectively capture image features. Among various methods for computing uniform superpixels, simple linear iterative clustering (SLIC) is popular due to its simplicity and high performance. In this paper, we extend SLIC to compute content-sensitive superpixels, i.e., small superpixels in content-dense regions (e.g., with high intensity or color variation) and large superpixels in content-sparse regions. Rather than the conventional SLIC method that clusters pixels in ℝ5, we map the image I to a 2-dimensional manifold M ⊂ ℝ5, whose area elements are a good measure of the content density in I. We propose an efficient method to compute restricted centroidal Voronoi tessellation (RCVT) - a uniform tessellation - on M, which induces the content-sensitive superpixels in I. Unlike other algorithms that characterize content-sensitivity by geodesic distances, manifold SLIC tackles the problem by measuring areas of Voronoi cells on M, which can be computed at a very low cost. As a result, it runs 10 times faster than the state-of-the-art content-sensitive superpixels algorithm. We evaluate manifold SLIC and seven representative methods on the BSDS500 benchmark and observe that our method outperforms the existing methods.
Single-chip CMUT-on-CMOS front-end system for real-time volumetric IVUS and ICE imaging

Intravascular ultrasound (IVUS) and intracardiac echography (ICE) catheters with real-time volumetric ultrasound imaging capability can provide unique benefits to many interventional procedures used in the diagnosis and treatment of coronary and structural heart diseases. Integration of capacitive micromachined ultrasonic transducer (CMUT) arrays with front-end electronics in single-chip configuration allows for implementation of such catheter probes with reduced interconnect complexity, miniaturization, and high mechanical flexibility. We implemented a single-chip forward-looking (FL) ultrasound imaging system by fabricating a 1.4-mm-diameter dual-ring CMUT array using CMUT-on-CMOS technology on a front-end IC implemented in 0.35-μm CMOS process. The dual-ring array has 56 transmit elements and 48 receive elements on two separate concentric annular rings. The IC incorporates a 25-V pulser for each transmitter and a low-noise capacitive transimpedance amplifier (TIA) for each receiver, along with digital control and smart power management. The final shape of the silicon chip is a 1.5-mm-diameter donut with a 430-μm center hole for a guide wire. The overall front-end system requires only 13 external connections and provides 4 parallel RF outputs while consuming an average power of 20 mW. We measured RF A-scans from the integrated single- chip array which show full functionality at 20.1 MHz with 43% fractional bandwidth. We also tested and demonstrated the image quality of the system on a wire phantom and an ex vivo chicken heart sample. The measured axial and lateral point resolutions are 92 μm and 251 μm, respectively. We successfully acquired volumetric imaging data from the ex vivo chicken heart at 60 frames per second without any signal averaging. These demonstrative results indicate that single-chip CMUT-on-CMOS systems have the potential to produce realtime volumetric images with image quality and speed suitable for catheter-based clinical applications.
The developmental psychometrics of big five self-reports: acquiescence, factor structure, coherence, and differentiation from ages 10 to 20.

How do youths' personality reports differ from those of adults? To identify the year-by-year timing of developmental trends from late childhood (age 10) to early adulthood (age 20), the authors examined Big Five self-report data from a large and diverse Internet sample. At younger ages within this range, there were large individual differences in acquiescent responding, and acquiescence variability had pronounced effects on psychometric characteristics. Beyond the effects of acquiescence, self-reports generally became more coherent within domains, and better differentiated across domains, at older ages. Importantly, however, different Big Five domains showed different developmental trends. Extraversion showed especially pronounced age gains in coherence but no gains in differentiation. In contrast, Agreeableness and Conscientiousness showed large age gains in differentiation but only trivial gains in coherence. Neuroticism and Openness showed moderate gains in both coherence and differentiation. Comparisons of items that were relatively easy versus difficult to comprehend indicated that these patterns were not simply due to verbal comprehension. These findings have important implications for the study of personality characteristics and other psychological attributes in childhood and adolescence.
Dynamic Reasoning Systems

A dynamic reasoning system (DRS) is an adaptation of a conventional formal logical system that explicitly portrays reasoning as a temporal activity, with each extralogical input to the system and each inference rule application being viewed as occurring at a distinct timestep. Every DRS incorporates some well-defined logic together with a controller that serves to guide the reasoning process in response to user inputs. Logics are generic, whereas controllers are application specific. Every controller does, nonetheless, provide an algorithm for nonmonotonic belief revision. The general notion of a DRS comprises a framework within which one can formulate the logic and algorithms for a given application and prove that the algorithms are correct, that is, that they serve to (1) derive all salient information and (2) preserve the consistency of the belief set. This article illustrates the idea with ordinary first-order predicate calculus, suitably modified for the present purpose, and two examples. The latter example revisits some classic nonmonotonic reasoning puzzles (Opus the Penguin, Nixon Diamond) and shows how these can be resolved in the context of a DRS, using an expanded version of first-order logic that incorporates typed predicate symbols. All concepts are rigorously defined and effectively computable, thereby providing the foundation for a future software implementation.
Quantifying urban attractiveness from the distribution and density of digital footprints

In the past, sensors networks in cities have been limited to fixed sensors, embedded in particular locations, under centralised control. Today, new applications can leverage wireless devices and use them as sensors to create aggregated information. In this paper, we show that the emerging patterns unveiled through the analysis of large sets of aggregated digital footprints can provide novel insights into how people experience the city and into some of the drivers behind these emerging patterns. We particularly explore the capacity to quantify the evolution of the attractiveness of urban space with a case study of in the area of the New York City Waterfalls, a public art project of four man-made waterfalls rising from the New York Harbor. Methods to study the impact of an event of this nature are traditionally based on the collection of static information such as surveys and ticket-based people counts, which allow to generate estimates about visitors’ presence in specific areas over time. In contrast, our contribution makes use of the dynamic data that visitors generate, such as the density and distribution of aggregate phone calls and photos taken in different areas of interest and over time. Our analysis provides novel ways to quantify the impact of a public event on the distribution of visitors and on the evolution of the attractiveness of the points of interest in proximity. This information has potential uses for local authorities, researchers, as well as service providers such as mobile network operators.
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large data sets, such as three-dimensional medical images. We propose an adaptive sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in improved learning. Our contribution is threefold: 1) We give a detailed description of the proposed sampling algorithm to speed up and improve learning performance on large images. 2) We propose a deep dual path CNN that captures information at fine and coarse scales, resulting in a network with a large field of view and high resolution outputs. 3) We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark.
A Data Mining Approach to Predict Forest Fires using Meteorological Data

Forest fires are a major environmental issue, creating economical and ecological damage while endangering human lives. Fast detection is a key element for controlling such phenomenon. To achieve this, one alternative is to use automatic tools based on local sensors, such as provided by meteorological stations. In effect, meteorological conditions (e.g. temperature, wind) are known to influence forest fires and several fire indexes, such as the forest Fire Weather Index (FWI), use such data. In this work, we explore a Data Mining (DM) approach to predict the burned area of forest fires. Five different DM techniques, e.g. Support Vector Machines (SVM) and Random Forests, and four distinct feature selection setups (using spatial, temporal, FWI components and weather attributes), were tested on recent real-world data collected from the northeast region of Portugal. The best configuration uses a SVM and four meteorological inputs (i.e. temperature, relative humidity, rain and wind) and it is capable of predicting the burned area of small fires, which are more frequent. Such knowledge is particularly useful for improving firefighting resource management (e.g. prioritizing targets for air tankers and ground crews).
Bounding the Partition Function using Holder's Inequality

We describe an algorithm for approximate inference in graphical models based on Hölder’s inequality that provides upper and lower bounds on common summation problems such as computing the partition function or probability of evidence in a graphical model. Our algorithm unifies and extends several existing approaches, including variable elimination techniques such as minibucket elimination and variational methods such as tree reweighted belief propagation and conditional entropy decomposition. We show that our method inherits benefits from each approach to provide significantly better bounds on sum-product tasks.
An auditory-based feature for robust speech recognition

A conventional automatic speech recognizer does not perform well in the presence of noise, while human listeners are able to segregate and recognize speech in noisy conditions. We study a novel feature based on an auditory periphery model for robust speech recognition. Specifically, gammatone frequency cepstral coefficients are derived by applying a cepstral analysis on gammatone filterbank responses. Our evaluations show that the proposed feature performs considerably better than conventional acoustic features. We further demonstrate that integrating the proposed feature with a computational auditory scene analysis system yields promising recognition performance.
Reasoning about Meaning in Natural Language with Compact Closed Categories and Frobenius Algebras

Compact closed categories have found applications in modeling quantum information protocols by Abramsky-Coecke. They also provide semantics for Lambek’s pregroup algebras, applied to formalizing the grammatical structure of natural language, and are implicit in a distributional model of word meaning based on vector spaces. Specifically, in previous work Coecke-Clark-Sadrzadeh used the product category of pregroups with vector spaces and provided a distributional model of meaning for sentences. We recast this theory in terms of strongly monoidal functors and advance it via Frobenius algebras over vector spaces. The former are used to formalize topological quantum field theories by Atiyah and Baez-Dolan, and the latter are used to model classical data in quantum protocols by Coecke-PavlovicVicary. The Frobenius algebras enable us to work in a single space in which meanings of words, phrases, and sentences of any structure live. Hence we can compare meanings of different language constructs and enhance the applicability of the theory. We report on experimental results on a number of language tasks and verify the theoretical predictions.