nils-herrmann/clean_scirepeval huggingface dataset:
Predicting Suicidal Behavior From Longitudinal Electronic Health Records.

The purpose of this article was to determine whether longitudinal historical data, commonly available in electronic health record (EHR) systems, can be used to predict patients' future risk of suicidal behavior.



Bayesian models were developed using a retrospective cohort approach. EHR data from a large health care database spanning 15 years (1998-2012) of inpatient and outpatient visits were used to predict future documented suicidal behavior (i.e., suicide attempt or death). Patients with three or more visits (N=1,728,549) were included. ICD-9-based case definition for suicidal behavior was derived by expert clinician consensus review of 2,700 narrative EHR notes (from 520 patients), supplemented by state death certificates. Model performance was evaluated retrospectively using an independent testing set.



Among the study population, 1.2% (N=20,246) met the case definition for suicidal behavior. The model achieved sensitive (33%-45% sensitivity), specific (90%-95% specificity), and early (3-4 years in advance on average) prediction of patients' future suicidal behavior. The strongest predictors identified by the model included both well-known (e.g., substance abuse and psychiatric disorders) and less conventional (e.g., certain injuries and chronic conditions) risk factors, indicating that a data-driven approach can yield more comprehensive risk profiles.



Longitudinal EHR data, commonly available in clinical settings, can be useful for predicting future risk of suicidal behavior. This modeling approach could serve as an early warning system to help clinicians identify high-risk patients for further screening. By analyzing the full phenotypic breadth of the EHR, computerized risk screening approaches may enhance prediction beyond what is feasible for individual clinicians.
CULTURE AND COGNITION

Recent work in cognitive psychology and social cognition bears heavily on concerns of sociologists of culture. Cognitive research confirms views of culture as fragmented; clarifies the roles of institutions and agency; and illuminates supraindividual aspects of culture. Individuals experience culture as disparate bits of information and as schematic structures that organize that information. Culture carried by institutions, networks, and social movements diffuses, activates, and selects among available schemata. Implications for the study of identity, collective memory, social classification, and logics of action are developed.
Media Bias and Reputation

A Bayesian consumer who is uncertain about the quality of an information source will infer that the source is of higher quality when its reports conform to the consumer’s prior expectations. We use this fact to build a model of media bias in which firms slant their reports toward the prior beliefs of their customers in order to build a reputation for quality. Bias emerges in our model even though it can make all market participants worse off. The model predicts that bias will be less severe when consumers receive independent evidence on the true state of the world and that competition between independently owned news outlets can reduce bias. We present a variety of empirical evidence consistent with these predictions.
Exploring shared control in automated driving

Automated driving systems that share control with human drivers by using haptic feedback through the steering wheel have been shown to have advantages over fully automated systems and manual driving. Here, we describe an experiment to elicit tacit expectations of behavior from such a system. A gaming steering wheel electronically coupled to the steering wheel in a full-car driving simulator allows two participants to share control of the vehicle. One participant was asked to use the gaming wheel to act as the automated driving agent while another participant acted as the car driver. The course provided different information and visuals to the driving agent and the driver to simulate possible automation failures and conflict situations between automation and the driver. The driving agent was also given prompts that specified a communicative goal at various points along the course. Both participants were interviewed before and after the drive, and vehicle data and drive video were collected. Our results suggest that drivers were able to interpret simple trajectory intentions, such as a lane change, conveyed by the driving agent. However, the driving agent was not able to effectively communicate more nuanced, higher level ideas such as availability, primarily due to the steering wheel being the control mechanism. Torque on the steering wheel without warning was seen most often as a failure of automation. Gentle and steady steering movements were viewed more favorably.
Problem Set # 2 Solutions : Naive Bayes , SVMs , and

Notes: (1) These questions require thought, but do not require long answers. Please be as concise as possible. (2) If you have a question about this homework, we encourage you to post your question on our Piazza forum, at https://piazza.com/class#fall2012/cs229. (3) If you missed the first lecture or are unfamiliar with the collaboration or honor code policy, please read the policy on Handout #1 (available from the course website) before starting work. (4) For problems that require programming, please include in your submission a printout of your code (with comments) and any figures that you are asked to plot. (5) Please indicate the submission time and number of late dates clearly in your submission. SCPD students: Please email your solutions to cs229-qa@cs.stanford.edu with the subject line " Problem Set 2 Submission ". The first page of your submission should be the homework routing form, which can be found on the SCPD website. Your submission (including the routing form) must be a single pdf file, or we may not be able to grade it. If you are writing your solutions out by hand, please write clearly and in a reasonably large font using a dark pen to improve legibility. 1.  Constructing kernels In class, we saw that by choosing a kernel K(x, z) = φ(x) T φ(z), we can implicitly map data to a high dimensional space, and have the SVM algorithm work in that space. One way to generate kernels is to explicitly define the mapping φ to a higher dimensional space, and then work out the corresponding K. However in this question we are interested in direct construction of kernels. I.e., suppose we have a function K(x, z) that we think gives an appropriate similarity measure for our learning problem, and we are considering plugging K into the SVM as the kernel function. However for K(x, z) to be a valid kernel, it must correspond to an inner product in some higher dimensional space resulting from some feature mapping φ. Mercer's theorem tells us that K(x, z) is a (Mercer) kernel if and only if for any finite set {x (1) ,. .. , x (m) }, the matrix K is symmetric and positive semidefinite, where the square matrix K ∈ R m×m is given by K ij = K(x (i) , x (j)). Now here comes the question: Let K 1 , K 2 be kernels …
INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process

Copious sequential event data has consistently increased in various high-impact domains such as social media and sharing economy. When events start to take place in a sequential fashion, an important question arises: “what type of event will happen at what time in the near future?” To answer the question, a class of mathematical models called the marked temporal point process is often exploited as it can model the timing and properties of events seamlessly in a joint framework. Recently, various recurrent neural network (RNN) models are proposed to enhance the predictive power of mark temporal point process. However, existing marked temporal point models are fundamentally based on the Maximum Likelihood Estimation (MLE) framework for the training, and inevitably suffer from the problem resulted from the intractable likelihood function. Surprisingly, little attention has been paid to address this issue. In this work, we propose INITIATOR a novel training framework based on noise-contrastive estimation to resolve this problem. Theoretically, we show the exists a strong connection between the proposed INITIATOR and the exact MLE. Experimentally, the efficacy of INITIATOR is demonstrated over the state-of-the-art approaches on several real-world datasets from various areas.
Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance

In this work, a Modified Vector Field Histogram (MVFH) has been developed to improve path planning and obstacle avoidance for a wheeled driven mobile robot. It permits the detection of unknown obstacle to avoid collisions by simultaneously a steering the mobile robot toward the target; a regular grid map representation for a work space environment is caurried out. A Neural Network (NN) model is used to learn many critical situations of environment during robot navigation among obstacles using MVFH. Also, digital filter has been utilized for improving the robustness of obstacle avoidance trajectory of mobile robot. The proposed MVFH-NN has been implemented and tested by using MobotSim program simulation and MATLAB . The developed algorithm showed good navigation properties and can be used in complex real world (maze-like environment), it also shows good ability to overcome limitations of the traditional VFH algorithm (like wide candidate valley, narrow hallway and target distance limitation).
Modelling stock-market investors as Reinforcement Learning agents

Decision making in uncertain and risky environments is a prominent area of research. Standard economic theories fail to fully explain human behaviour, while a potentially promising alternative may lie in the direction of Reinforcement Learning (RL) theory. We analyse data for 46 players extracted from a financial market online game and test whether Reinforcement Learning (Q-Learning) could capture these players behaviour using a riskiness measure based on financial modeling. Moreover we test an earlier hypothesis that players are “naíve” (short-sighted). Our results indicate that Reinforcement Learning is a component of the decision-making process. We also find that there is a significant improvement of fitting for some of the players when using a full RL model against a reduced version (myopic), where only immediate reward is valued by the players, indicating that not all players are naíve.
THE INFLUENCE OF CONSUMERS ’ PERCEPTION OF GREEN PRODUCTS ON GREEN PURCHASE INTENTION

Green consumerism has increasingly received attention since the increased level of consumer awareness towards green products. Therefore, the aim of this paper had been to examine the influence of consumer perception of green products on green purchase intention. In this study, perception of green products was conceptualized as a multidimensional variable comprised of green corporate perception, eco-label, green advertising, green packaging, and green product value. By using a survey, a total of 159 questionnaires from respondents aged above 18 in Sabah were collected. The results demonstrated that within consumer perception; green corporate perception, eco-label, and green product value had positive significant influences on green purchase intention. The findings also revealed that eco-label and green product value made the largest contribution in influencing green purchase intention among consumers. In contrast, both green advertising and green packaging had no significant impact on consumer intention to purchase green products.
From algorithms to devices: Enabling machine learning through ultra-low-power VLSI mixed-signal array processing

Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things. As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by the design community and present a new set of design trade-offs. This review focuses on efficient implementation of mixed-signal matrix-vector multiplication as a central computational primitive enabling machine learning and statistical signal processing, with specific examples in spatial filtering for adaptive beamforming. We describe adaptive algorithms amenable for efficient implementation with such primitives in the presence of noise and analog variability. We also briefly highlight current trends in high-density integration in emerging memory device technologies and their use in high-dimensional adaptive computing.
AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference

Channel pruning is an important family of methods to speedup deep model’s inference. Previous filter pruning algorithms regard channel pruning and model fine-tuning as two independent steps. This paper argues that combining them in a single end-to-end trainable system will lead to better results. We propose an efficient channel selection layer, namely AutoPruner, to find less important filters automatically in a joint training manner. AutoPruner takes previous activation responses as input and generates a true binary index code for pruning. Hence, all the filters corresponding to zero index values can be removed safely after training. We empirically demonstrate that the gradient information of this channel selection layer is also helpful for the whole model training. Compared with previous state-ofthe-art pruning algorithms, AutoPruner achieves significantly better performance. Furthermore, ablation experiments show that the proposed novel mini-batch pooling and binarization operations are vital for the success of filter pruning.
Meta-Learning for Low-Resource Neural Machine Translation

In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML, Finn et al., 2017) for lowresource neural machine translation (NMT). We frame low-resource translation as a metalearning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use the universal lexical representation (Gu et al., 2018b) to overcome the input-output mismatch across different languages. We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro, Lv, Fi, Tr and Ko) as target tasks. We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach (Zoph et al., 2016) and enables us to train a competitive NMT system with only a fraction of training examples. For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT’16 by seeing only 16,000 translated words (⇠ 600 parallel sentences).
Design and control of hybrid power and propulsion systems for smart ships : A review of developments

The recent trend to design more efficient and versatile ships has increased the variety in hybrid propulsion and power supply architectures. In order to improve performance with these architectures, intelligent control strategies are required, while mostly conventional control strategies are applied currently. First, this paper classifies ship propulsion topologies into mechanical, electrical and hybrid propulsion, and power supply topologies into combustion, electrochemical, stored and hybrid power supply. Then, we review developments in propulsion and power supply systems and their control strategies, to subsequently discuss opportunities and challenges for these systems and the associated control. We conclude that hybrid architectures with advanced control strategies can reduce fuel consumption and emissions up to 10–35%, while improving noise, maintainability, manoeuvrability and comfort. Subsequently, the paper summarises the benefits and drawbacks, and trends in application of propulsion and power supply technologies, and it reviews the applicability and benefits of promising advanced control strategies. Finally, the paper analyses which control strategies can improve performance of hybrid systems for future smart and autonomous ships and concludes that a combination of torque, angle of attack, and Model Predictive Control with dynamic settings could improve performance of future smart and more
Survey on anonymous communications in computer networks

0140-3664/$ see front matter 2009  A doi:10.1016/j.comcom.2009.11.009 * Corresponding author.  E-mail addresses: renjian@egr.msu.edu (J. Ren), jie Anonymous communications aim to preserve communications privacy within the shared public network environment. It can provide security well beyond content privacy and integrity. The scientific studies of anonymous communications are largely originated from Chaum’s two seminal approaches: mixnet and DC-net. In this paper, we present an overview of the research in this field. We start with the basic definitions of anonymous communications. We then describe the cryptographic primitives, the network protocols, and some of the representative anonymous communication systems. We also describe verifiable mixnets and their applications to electronic voting. Finally, we briefly cite some other anonymous systems.
Removable partial dentures--treatment now and for the future.

The use of a removable partial denture (RPD) in clinical practice remains a viable treatment modality. Various advancements have improved the quality of a RPD, subsequently improving the quality of life for the individuals that use them. This article describes four removable partial denture treatment modalities that provide valuable treatment for the partially edentulous patient. These modalities include: the implant supported RPD, attachment use in RPDs, rotational path RPDs, and Titanium and CAD/CAM RPDs. Data on future needs for RPDs indicate that while there is a decline in tooth loss in the U.S., the need for RPDs will actually increase as the population increases and ages. With the growth in the geriatric population, which includes a high percentage of partially edentulous patients, the use of RPDs in clinical treatment will continue to be predictable treatment option in clinical dentistry.
DEVELOPMENT OF A SUSTAINABLE COMPETITIVE ADVANTAGE MODEL BASED ON BALANCED SCORECARD

Nowadays organizations turn to any standard procedure to gain a competitive advantage. If sustainable, competitive advantage can bring about benefit to the organization. The aim of the present study was to introduce competitive advantage as well as to assess the impacts of the balanced scorecard as a means to measure the performance of organizations. The population under study included employees of organizations affiliated to the Social Security Department in North Khorasan Province, of whom a total number of 120 employees were selected as the participants in the research sample. Two researcher-made questionnaires with a 5-point Likert scale were used to measure the competitive advantage and the balanced scorecard. Besides, Cronbach's alpha coefficient was used to measure the reliability of the instruments that was equal to 0.74 and 0.79 for competitive advantage and the balanced scorecard, respectively. The data analysis was performed using the structural equation modeling and the results indicated the significant and positive impact of the implementation of the balanced scorecard on the sustainable competitive advantage.
College Student Technology Use and Academic Performance

As technology use continues to rise, especially among young individuals, there are concerns that excessive use of technology may impact academic performance. Researchers have started to investigate the possible negative effects of technology use on college academic performance, but results have been mixed. The following study seeks to expand upon previous studies by exploring the relationship among the use of a wide variety of technology forms and an objective measure of academic performance (GPA) using a 7-day time diary data collection method. The current study also seeks to examine both underclassmen and upperclassmen to see if these groups differ in how they use technology. Upperclassmen spent significantly more time using technology for academic and workrelated purposes, whereas underclassmen spent significantly more time using cell phones, online chatting, and social networking sites. Significant negative correlations with GPA emerged for television, online gaming, adult site, and total technology use categories.  Technology use, academic performance, post-secondary education.
Idioms and mental imagery: The metaphorical motivation for idiomatic meaning

We conducted three experiments to investigate the mental images associated with idiomatic phrases in English. Our hypothesis was that people should have strong conventional images for many idioms and that the regularity in people's knowledge of their images for idioms is due to the conceptual metaphors motivating the figurative meanings of idioms. In the first study, subjects were asked to form and describe their mental images for different idiomatic expressions. Subjects were then asked a series of detailed questions about their images regarding the causes and effects of different events within their images. We found high consistency in subjects' images of idioms with similar figurative meanings despite differences in their surface forms (e.g., spill the beans and let the cat out of the bag). Subjects' responses to detailed questions about their images also showed a high degree of similarity in their answers. Further examination of subjects' imagery protocols supports the idea that the conventional images and knowledge associated with idioms are constrained by the conceptual metaphors (e.g., the MIND IS A CONTAINER and IDEAS ARE ENTITIES) which motivate the figurative meanings of idioms. The results of two control studies showed that the conventional images associated with idioms are not solely based on their figurative meanings (Experiment 2) and that the images associated with literal phrases (e.g., spill the peas) were quite varied and unlikely to be constrained by conceptual metaphor (Experiment 3). These findings support the view that idioms are not "dead" metaphors with their meanings being arbitrarily determined. Rather, the meanings of many idioms are motivated by speakers' tacit knowledge of the conceptual metaphors underlying the meanings of these figurative phrases.
Knowledge Base Population using Semantic Label Propagation

Training relation extractors for the purpose of automated knowledge base population requires the availability of sufficient training data. The amount of manual labeling can be significantly reduced by applying distant supervision, which generates training data by aligning large text corpora with existing knowledge bases. This typically results in a highly noisy training set, where many training sentences do not express the intended relation. In this paper, we propose to combine distant supervision with minimal human supervision by annotating features (in particular shortest dependency paths) rather than complete relation instances. Such feature labeling eliminates noise from the initial training set, resulting in a significant increase of precision at the expense of recall. We further improve on this approach by introducing the Semantic Label Propagation (SLP) method, which uses the similarity between low-dimensional representations of candidate training instances to again extend the (filtered) training set in order to increase recall while maintaining high precision. Our strategy is evaluated on an established test collection designed for knowledge base population (KBP) from the TAC KBP English slot filling task. The experimental results show that SLP leads to substantial performance gains when compared to existing approaches while requiring an almost negligible human annotation effort.