nils-herrmann/clean_scirepeval huggingface dataset:
Sympathy, empathy, and compassion: A grounded theory study of palliative care patients’ understandings, experiences, and preferences

Compassion is considered an essential element in quality patient care. One of the conceptual challenges in healthcare literature is that compassion is often confused with sympathy and empathy. Studies comparing and contrasting patients' perspectives of sympathy, empathy, and compassion are largely absent.



The aim of this study was to investigate advanced cancer patients' understandings, experiences, and preferences of "sympathy," "empathy," and "compassion" in order to develop conceptual clarity for future research and to inform clinical practice.



Data were collected via semi-structured interviews and then independently analyzed by the research team using the three stages and principles of Straussian grounded theory.



Data were collected from 53 advanced cancer inpatients in a large urban hospital.



Constructs of sympathy, empathy, and compassion contain distinct themes and sub-themes. Sympathy was described as an unwanted, pity-based response to a distressing situation, characterized by a lack of understanding and self-preservation of the observer. Empathy was experienced as an affective response that acknowledges and attempts to understand individual's suffering through emotional resonance. Compassion enhanced the key facets of empathy while adding distinct features of being motivated by love, the altruistic role of the responder, action, and small, supererogatory acts of kindness. Patients reported that unlike sympathy, empathy and compassion were beneficial, with compassion being the most preferred and impactful.



Although sympathy, empathy, and compassion are used interchangeably and frequently conflated in healthcare literature, patients distinguish and experience them uniquely. Understanding patients' perspectives is important and can guide practice, policy reform, and future research.
A Unified Sentence Space for Categorical Distributional-Compositional Semantics: Theory and Experiments

This short paper summarizes a faithful implementation of the categorical framework of Coecke et al. (2010), the aim of which is to provide compositionality in distributional models of lexical semantics. Based on Frobenius Algebras, our method enable us to (1) have a unifying meaning space for phrases and sentences of different structure and word vectors, (2) stay faithful to the linguistic types suggested by the underlying type-logic, and (3) perform the concrete computations in lower dimensions by reducing the space complexity. We experiment with two different parameters of the model and apply the setting to a verb disambiguation and a term/definition classification task with promising results.
Load Balancing Routing for Wireless Access Networks

Widespread use of wireless devices presents new challenges for network operators, who need to provide service to ever larger numbers of mobile end users, while ensuring Quality-of-Service guarantees. In this paper we describe a new distributed routing algorithm that performs dynamic load-balancing for wireless access networks. The algorithm constructs a load-balanced backbone tree, which simplifies routing and avoids per-destination state for routing and per-flow state for QoS reservations. We evaluate the performance of the algorithm using several metrics including adaptation to mobility, degree of load-balance, bandwidth blocking rate, and convergence speed. We find that the algorithm achieves better network utilization by lowering bandwidth blocking rates than other methods.
Who Will Reply to/Retweet This Tweet?: The Dynamics of Intimacy from Online Social Interactions

Friendships are dynamic. Previous studies have converged to suggest that social interactions, in both online and offline social networks, are diagnostic reflections of friendship relations (also called social ties). However, most existing approaches consider a social tie as either a binary relation, or a fixed value (named tie strength). In this paper, we investigate the dynamics of dyadic friend relationships through online social interactions, in terms of a variety of aspects, such as reciprocity, temporality, and contextuality. In turn, we propose a model to predict repliers and retweeters given a particular tweet posted at a certain time in a microblog-based social network. More specifically, we have devised a learning-to-rank approach to train a ranker that considers elaborate user-level and tweet-level features (like sentiment, self-disclosure, and responsiveness) to address these dynamics. In the prediction phase, a tweet posted by a user is deemed a query and the predicted repliers/retweeters are retrieved using the learned ranker. We have collected a large dataset containing 73.3 million dyadic relationships with their interactions (replies and retweets). Extensive experimental results based on this dataset show that by incorporating the dynamics of friendship relations, our approach significantly outperforms state-of-the-art models in terms of multiple evaluation metrics, such as MAP, NDCG and Topmost Accuracy. In particular, the advantage of our model is even more promising in predicting the exact sequence of repliers/retweeters considering their orders. Furthermore, the proposed approach provides emerging implications for many high-value applications in online social networks.
A Lambda Calculus for Quantum Computation

The classical lambda calculus may be regarded both as a programming language and as a formal algebraic system for reasoning about computation. It provides a computational model equivalent to the Turing machine, and continues to be of enormous benefit in the classical theory of computation. We propose that quantum computation, like its classical counterpart, may benefit from a version of the lambda calculus suitable for expressing and reasoning about quantum algorithms. In this paper we develop a quantum lambda calculus as an alternative model of quantum computation, which combines some of the benefits of both the quantum Turing machine and the quantum circuit models. The calculus turns out to be closely related to the linear lambda calculi used in the study of Linear Logic. We set up a computational model and an equational proof system for this calculus, and we argue that it is equivalent to the quantum Turing machine. PACS: 03.67.Lx, 02.10.-v, 02.70.-c
Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency

We present a generative model for the unsupervised learning of dependency structures. We also describe the multiplicative combination of this dependency model with a model of linear constituency. The product model outperforms both components on their respective evaluation metrics, giving the best published figures for unsupervised dependency parsing and unsupervised constituency parsing. We also demonstrate that the combined model works and is robust cross-linguistically, being able to exploit either attachment or distributional reg ularities that are salient in the data.
Game theory in communications: motivation, explanation, and application to power control

Game theory is a set of tools developed to model interactions between agents with conflicting interests, and is thus well-suited to address some problems in communications systems. In this paper we present some of the basic concepts of game theory and show why it is an appropriate tool for analyzing some communication problems and providing insights into how communication systems should be designed. We then provided a detailed example in which game theory is applied to the power control problem in a
Polar Codes for Channel and Source Coding

The two central topics of information theory are the compression and the transmission of data. Shannon, in his seminal work, formalized both these problems and determined their fundamental limits. Since then the main goal of coding theory has been to find practical schemes that approach these limits. Polar codes, recently invented by Arıkan, are the first “practical” codes that are known to achieve the capacity for a large class of channels. Their code construction is based on a phenomenon called “channel polarization”. The encoding as well as the decoding operation of polar codes can be implemented with O(N log N) complexity, where N is the blocklength of the code. We show that polar codes are suitable not only for channel coding but also achieve optimal performance for several other important problems in information theory. The first problem we consider is lossy source compression. We construct polar codes that asymptotically approach Shannon’s rate-distortion bound for a large class of sources. We achieve this performance by designing polar codes according to the “test channel”, which naturally appears in Shannon’s formulation of the rate-distortion function. The encoding operation combines the successive cancellation algorithm of Arıkan with a crucial new ingredient called “randomized rounding”. As for channel coding, both the encoding as well as the decoding operation can be implemented with O(N log N) complexity. This is the first known “practical” scheme that approaches the optimal rate-distortion trade-off. We also construct polar codes that achieve the optimal performance for the Wyner-Ziv and the Gelfand-Pinsker problems. Both these problems can be tackled using “nested” codes and polar codes are naturally suited for this purpose. We further show that polar codes achieve the capacity of asymmetric channels, multi-terminal scenarios like multiple access channels, and degraded broadcast channels. For each of these problems, our constructions are the first known “practical” schemes that approach the optimal performance. The original polar codes of Arıkan achieve a block error probability decaying exponentially in the square root of the block length. For source coding, the gap between the achieved distortion and the limiting distortion also vanishes exponentially in the square root of the blocklength. We explore other polarlike code constructions with better rates of decay. With this generalization,
Fast context-aware recommendations with factorization machines

The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So far, the best performing method for context-aware rating prediction in terms of predictive accuracy is Multiverse Recommendation based on the Tucker tensor factorization model. However this method has two drawbacks: (1) its model complexity is exponential in the number of context variables and polynomial in the size of the factorization and (2) it only works for categorical context variables. On the other hand there is a large variety of fast but specialized recommender methods which lack the generality of context-aware methods.
 We propose to apply Factorization Machines (FMs) to model contextual information and to provide context-aware rating predictions. This approach results in fast context-aware recommendations because the model equation of FMs can be computed in linear time both in the number of context variables and the factorization size. For learning FMs, we develop an iterative optimization method that analytically finds the least-square solution for one parameter given the other ones. Finally, we show empirically that our approach outperforms Multiverse Recommendation in prediction quality and runtime.
Multipath Sparse Coding Using Hierarchical Matching Pursuit

Complex real-world signals, such as images, contain discriminative structures that differ in many aspects including scale, invariance, and data channel. While progress in deep learning shows the importance of learning features through multiple layers, it is equally important to learn features through multiple paths. We propose Multipath Hierarchical Matching Pursuit (M-HMP), a novel feature learning architecture that combines a collection of hierarchical sparse features for image classification to capture multiple aspects of discriminative structures. Our building blocks are MI-KSVD, a codebook learning algorithm that balances the reconstruction error and the mutual incoherence of the codebook, and batch orthogonal matching pursuit (OMP), we apply them recursively at varying layers and scales. The result is a highly discriminative image representation that leads to large improvements to the state-of-the-art on many standard benchmarks, e.g., Caltech-101, Caltech-256, MITScenes, Oxford-IIIT Pet and Caltech-UCSD Bird-200.
The WITAS Unmanned Aerial Vehicle Project

The purpose of this paper is to provide a broad overview of the WITAS Unmanned Aerial Vehicle Project. The WITAS UAV project is an ambitious, long-term basic research project with the goal of developing technologies and functionalities necessary for the successful deployment of a fully autonomous UAV operating over diverse geographical terrain containing road and traffic networks. The project is multi-disciplinary in nature, requiring many different research competences, and covering a broad spectrum of basic research issues, many of which relate to current topics in artificial intelligence. A number of topics considered are knowledge representation issues, active vision systems and their integration with deliberative/reactive architectures, helicopter modeling and control, ground operator dialogue systems, actual physical platforms, and a number of simulation techniques.
A 1.2–1.6-GHz Substrate-Integrated-Waveguide RF MEMS Tunable Filter

This paper presents a high-performance substrate-integrated-waveguide RF microelectromechanical systems (MEMS) tunable filter for 1.2-1.6-GHz frequency range. The proposed filter is developed using packaged RF MEMS switches and utilizes a two-layer structure that effectively isolates the cavity filter from the RF MEMS switch circuitry. The two-pole filter implemented on RT/Duroid 6010LM exhibits an insertion loss of 2.2-4.1 dB and a return loss better than 15 dB for all tuning states. The relative bandwidth of the filter is 3.7 ± 0.5% over the tuning range. The measured Qu of the filter is 93-132 over the tuning range, which is the best reported Q in filters using off-the-shelf RF MEMS switches on conventional printed circuit board substrates. In addition, an upper stopband rejection better than 28 dB is obtained up to 4.0 GHz by employing low-pass filters at the bandpass filter terminals at the cost of 0.7-1.0-dB increase in the insertion loss.
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.
Dynamic risk tolerance: Motion planning by balancing short-term and long-term stochastic dynamic predictions

Identifying collision-free paths over long time windows in environments with stochastically moving obstacles is difficult, in part because long-term predictions of obstacle positions typically have low fidelity, and the region of possible obstacle occupancy is typically large. As a result, planning methods that are restricted to identifying paths with a low probability of collision may not be able to find a valid path. However, allowing paths with a higher probability of collision may limit detection of imminent collisions. In this paper, we present Dynamic Risk Tolerance (DRT), a framework that dynamically evaluates risk tolerance, a function which is formulated as a time-varying upper bound on the acceptable likelihood of collision for a given path. DRT is implemented with forward stochastic reachable sets to predict the exact distribution of obstacles in a scalable manner over an arbitrarily long time window. In effect, DRT identifies actions that balance risks posed by both near and far obstacles. We empirically compare DRT to other state of the art methods that are capable of generating real-time solutions in highly crowded environments, and demonstrate the success rates for DRT that is 46% higher than the best performing comparison method, in the most difficult problem tested.
Managing demand uncertainty in supply chain planning

In this work, we provide an overview of our previously published works on incorporating demand uncertainty in midterm planning of multisite supply chains. A stochastic programming based approach is described to model the planning process as it reacts to demand realizations unfolding over time. In the proposed bilevel-framework, the manufacturing decisions are modeled as ‘here-and-now’ decisions, which are made before demand realization. Subsequently, the logistics decisions are postponed in a ‘waitand-see’ mode to optimize in the face of uncertainty. In addition, the trade-off between customer satisfaction level and production costs is also captured in the model. The proposed model provides an effective tool for evaluating and actively managing the exposure of an enterprises assets (such as inventory levels and profit margins) to market uncertainties. The key features of the proposed framework are highlighted through a supply chain planning case study.
Quantum computers ready to leap out of the lab in 2017

PUBLISHING Thousands of scientists start year without journal access p.13 2017 SNEAK PEEK What the new year holds for science p.14 ECOLOGY What is causing the deaths of so many shorebirds? p.16 PHYSICS Quantum computers ready to leap out of the lab The race is on to turn scientific curiosities into working machines. A front runner in the pursuit of quantum computing uses single ions trapped in a vacuum. Q uantum computing has long seemed like one of those technologies that are 20 years away, and always will be. But 2017 could be the year that the field sheds its research-only image. Computing giants Google and Microsoft recently hired a host of leading lights, and have set challenging goals for this year. Their ambition reflects a broader transition taking place at start-ups and academic research labs alike: to move from pure science towards engineering. " People are really building things, " says Christopher Monroe, a physicist at the University of Maryland in College Park who co-founded the start-up IonQ in 2015. " I've never seen anything like that. It's no longer just research. " Google started working on a form of quantum computing that harnesses super-conductivity in 2014. It hopes this year, or shortly after, to perform a computation that is beyond even the most powerful 'classical' supercomputers — an elusive milestone known as quantum supremacy. Its rival, Microsoft, is betting on an intriguing but unproven concept, topological quantum computing, and hopes to perform a first demonstration of the technology. The quantum-computing start-up scene is also heating up. Monroe plans to begin hiring in earnest this year. Physicist Robert Schoelkopf at Yale University in New Haven, Connecticut, who co-founded the start-up Quantum Circuits, and former IBM applied physicist Chad Rigetti, who set up Rigetti in
Learning and Reasoning in Cognitive Radio Networks

Cognitive radio networks challenge the traditional wireless networking paradigm by introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, i.e., learning and reasoning. This fosters optimal resource usage and management allowing a plethora of potential applications such as secondary spectrum access, cognitive wireless backbones, cognitive machine-to-machine etc. The majority of overview works in the field of cognitive radio networks deal with the notions of observation and adaptations, which are not a distinguished cognitive radio networking aspect. Therefore, this paper provides insight into the mechanisms for obtaining and inferring knowledge that clearly set apart the cognitive radio networks from other wireless solutions.
The identification of deviance and its impact on retention in a multiplayer game

Deviant behavior in online social systems is a difficult problem to address. Consequences of deviance include driving off users and tarnishing the system's public image. We present an examination of these concepts in a popular online game, League of Legends. Using a large collection of game records and player-given feedback, we develop a metric, toxicity index, to identify deviant players. We then look at the effects of interacting with deviant players, including effects on retention. We find that toxic players have several significant predictive patterns, such as playing in more competitive game modes and playing with friends. We also show that toxic players drive away new players, but that experienced players are more resilient to deviant behavior. Based on our findings, we suggest methods to better identify and counteract the negative effects of deviance.
AV@CAR: A Spanish Multichannel Multimodal Corpus for In-Vehicle Automatic Audio-Visual Speech Recognition

This paper describes the acquisition of the multichannel multimodal database AV@CAR for automatic audio-visual speech recognition in cars. Automatic speech recognition (ASR) plays an important role inside vehicles to keep the driver away from distraction. It is also known that visual information (lip-reading) can improve accuracy in ASR under adverse conditions as those within a car. The corpus described here is intended to provide training and testing material for several classes of audiovisual speech recognizers including isolated word system, word-spotting systems, vocabulary independent systems, and speaker dependent or speaker independent systems for a wide range of applications. The audio database is composed of seven audio channels including, clean speech (captured using a close talk microphone), noisy speech from several microphones placed on the overhead of the cabin, noise only signal coming from the engine compartment and information about the speed of the car. For the video database, a small video camera sensible to the visible and the near infrared bands is placed on the windscreen and used to capture the face of the driver. This is done under different light conditions both during the day and at night. Additionally, the same individuals are recorded in laboratory, under controlled environment conditions to obtain noise free speech signals, 2D images and 3D + texture face models.
Automatic bug triage using text categorization

Bug triage, deciding what to do with an incoming bug report, is taking up increasing amount of developer resources in large open-source projects. In this paper, we propose to apply machine learning techniques to assist in bug triage by using text categorization to predict the developer that should work on the bug based on the bug’s description. We demonstrate our approach on a collection of 15,859 bug reports from a large open-source project. Our evaluation shows that our prototype, using supervised Bayesian learning, can correctly predict 30% of the report assignments to