nils-herrmann/clean_scirepeval huggingface dataset:
A proposal for including nomophobia in the new DSM-V

The Diagnostic and Statistical Manual of Mental Disorders (DSM) is considered to be the gold standard manual for assessing the psychiatric diseases and is currently in its fourth version (DSM-IV), while a fifth (DSM-V) has just been released in May 2013. The DSM-V Anxiety Work Group has put forward recommendations to modify the criteria for diagnosing specific phobias. In this manuscript, we propose to consider the inclusion of nomophobia in the DSM-V, and we make a comprehensive overview of the existing literature, discussing the clinical relevance of this pathology, its epidemiological features, the available psychometric scales, and the proposed treatment. Even though nomophobia has not been included in the DSM-V, much more attention is paid to the psychopathological effects of the new media, and the interest in this topic will increase in the near future, together with the attention and caution not to hypercodify as pathological normal behaviors.
Towards Automatic Visual Obstacle Avoidance

University This report describes ongoing research on a working system which drives a vehicle through cluttered environments under computer control, guided by images perceived through an onboard tv camera. The emphasis is on reliable and fast low level visual techniques which determine the existence and location of objects in the world, but do not identify them. Included are an interest operator for choosing distinctive regions in images, a correlator for finding matching regions in similar images, a camera solver which determines camera displacement and distance to objects from stereo information tGennery, D.B., this Proceedings] and an automatic geometric distortion corrector for camera nonlinearities. Many of these use pictures reduced in linear dimension by powers of 2 by summation of pixels. Other operators are a high pass filter. a point noise remover, a contrast normalizes a vertical roll corrector, a picture comparator and an operator for reducing pictures by other than powers of two. Our hardware includes an electric vehicle, called the cart, remotely controlled over a CB radio link by a PDP-KL10. It carries a b/w tv camera whose picture is broadcast over a UHF channel, and occasionally digitized by the computer. It has motors for the wheels, steering and camera pan. Each can be made to run forward or backward. There are potentiometers on the steering and pan which enables them to be commanded to point straight ahead. Budgetary and personnel limitations have resulted in crude mechanical arrangements. The motor speeds are poorly regulated, and video is the only feedback to the computer. Dead reckoning errors are about 30%. Our small resources have been spent gaining software experience before undertaking serious hardware work. In my opinion our major hardware limitation is one shared by all other vision work, and Al in general, namely a critical shortage of raw processing power. For instance it would take about 100,000 efficiently programmed PDP-10's to match the human visual system.  Early versions of the routines described below were used in a program which drove the vehicle in straight lines or uniform arcs. It acquired and tracked distant features, using their motion from frame to frame to build up a model of vehicle response, and to servo on the desired path. With the cart outdoors on a dirty road, it worked well. Ten runs of about 60 steps were completed. The runs were usually terminated by serious hiccups of the radio control link. …
Four years' experience with a miniaturized extracorporeal circulation system and its influence on clinical outcome.

It has been suggested that the morbidity associated with cardiopulmonary bypass can be attributed in part to the blood-material and blood-air interactions in the extracorporeal circulation (ECC). A recently introduced minimized ECC-system (MECC System) should be able to reduce these negative effects associated with ECC. A retrospective analysis was performed comprising 485 patients who were operated on for elective coronary artery bypass grafting (CABG) using the MECC System with intermittent antegrade warm blood cardioplegia (group 1) from January 2000 to February 2004. A control group consisted of 485 patients (group 2) undergoing elective CABG in the same period using a conventional ECC and cold crystalloid cardioplegia. There were no significant differences between the two groups in terms of the duration of intubation following surgery, the length of intensive care unit-stay and the total hospital stay. Although the 30-day mortality was similar between the two groups, the incidence of postoperative complications and the perioperative use of blood products were significantly higher in the control group compared to the MECC group. The MECC System may serve as an alternative and less invasive approach to conventional ECC.
Discover breaking events with popular hashtags in twitter

In this paper, we utilize tags in Twitter (the hashtags) as an indicator of events. We first study the properties of hashtags for event detection. Based on several observations, we proposed three attributes of hashtags, including (1) instability for temporal analysis, (2) Twitter meme possibility to distinguish social events from virtual topics or memes, and (3) authorship entropy for mining the most contributed authors. Based on these attributes, breaking events are discovered with hashtags, which cover a wide range of social events among different languages in the real world.
Inverse Reward Design

Autonomous agents optimize the reward function we give them. What they don’t know is how hard it is for us to design a reward function that actually captures what we want. When designing the reward, we might think of some specific training scenarios, and make sure that the reward will lead to the right behavior in those scenarios. Inevitably, agents encounter new scenarios (e.g., new types of terrain) where optimizing that same reward may lead to undesired behavior. Our insight is that reward functions are merely observations about what the designer actually wants, and that they should be interpreted in the context in which they were designed. We introduce inverse reward design (IRD) as the problem of inferring the true objective based on the designed reward and the training MDP. We introduce approximate methods for solving IRD problems, and use their solution to plan risk-averse behavior in test MDPs. Empirical results suggest that this approach can help alleviate negative side effects of misspecified reward functions and mitigate reward hacking.
Instructable Intelligent Personal Agent

Unlike traditional machine learning methods, humans often learn from natural language instruction. As users become increasingly accustomed to interacting with mobile devices using speech, their interest in instructing these devices in natural language is likely to grow. We introduce our Learning by Instruction Agent (LIA), an intelligent personal agent that users can teach to perform new action sequences to achieve new commands, using solely natural language interaction. LIA uses a CCG semantic parser to ground the semantics of each command in terms of primitive executable procedures defining sensors and effectors of the agent. Given a natural language command that LIA does not understand, it prompts the user to explain how to achieve the command through a sequence of steps, also specified in natural language. A novel lexicon induction algorithm enables LIA to generalize across taught commands, e.g., having been taught how to “forward an email to Alice,” LIA can correctly interpret the command “forward this email to Bob.” A user study involving email tasks demonstrates that users voluntarily teach LIA new commands, and that these taught commands significantly reduce task completion time. These results demonstrate the potential of natural language instruction as a significant, under-explored paradigm for machine
Predicting Behavior From Actions in the Past : Repeated Decision Making or a Matter of Habit ?

This paper summarizes research on determinants of repeated behaviors, and the decision processes underlying them. The present research focuses on travel mode choices as an example ofsuch behaviors. It is proposed that when behavior is performed repeatedly and becomes habitual, it is guided by automated cognitive processes, rather than being preceded by elaborate decision processes (i.e,, a decision based on attitudes and intentions). First, current attitude-behavior models are discussed, and the role of habit in these models is examined. Second, research is presented on the decision processes preceding travel mode choices. Based on the present theoretical and empirical overview, it is concluded that frequently performed behavior is often a matter of habit, thereby establishing a boundary condition for the applicability of attitude-behavior models. However, more systematic research is required to disentangle the role of habit in attitude-behavior models and to learn more about the cognitive processes underlying habitual behavior.
A Closer Look at Memorization in Deep Networks

We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differences in gradient-based optimization of deep neural networks (DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned explicit regularization (e.g., dropout) we can degrade DNN training performance on noise datasets without compromising generalization on real data. Our analysis suggests that the notions of effective capacity which are dataset independent are unlikely to explain the generalization performance of deep networks when trained with gradient based methods because training data itself plays an important role in determining the degree of memorization.
Anxiety and depression in transgender individuals: the roles of transition status, loss, social support, and coping.

The purpose of the current study was to examine facilitative and avoidant coping as mediators between distress and transition status, social support, and loss.



A total of 351 transgender individuals (n = 226 transgender women and n = 125 transgender men) participated in this study. Participants completed measures on transgender identity, family history of mental health concerns, perceptions of loss, coping, depression, and anxiety.



The rates of depressive symptoms (51.4% for transgender women; 48.3% for transgender men) and anxiety (40.4% for transgender women; 47.5% for transgender men) within the current study far surpass the rates of those for the general population. Structural equation modeling (SEM) was used to analyze the data-2 separate models were hypothesized, based on reports of anxiety or depression. The SEM results suggest that the processes for transgender women and transgender men are primarily similar for depression and anxiety; avoidant coping served as a mediator between transition status and both distress variables. Social support was directly related to distress variables, as well as indirectly related through avoidant coping.



Results suggest the need for practitioners to focus on interventions that reduce avoidant coping strategies, while simultaneously increasing social support, in order to improve mental health for transgender individuals. Individuals who are in the beginning stages of their transition will use different coping strategies than those who are in later stages; interventions should be adjusted on the basis of the transition status of transgender clients.
A least squares formulation for canonical correlation analysis

Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multi-dimensional variables. It projects both sets of variables into a lower-dimensional space in which they are maximally correlated. CCA is commonly applied for supervised dimensionality reduction, in which one of the multi-dimensional variables is derived from the class label. It has been shown that CCA can be formulated as a least squares problem in the binaryclass case. However, their relationship in the more general setting remains unclear. In this paper, we show that, under a mild condition which tends to hold for high-dimensional data, CCA in multi-label classifications can be formulated as a least squares problem. Based on this equivalence relationship, we propose several CCA extensions including sparse CCA using 1-norm regularization. Experiments on multi-label data sets confirm the established equivalence relationship. Results also demonstrate the effectiveness of the proposed CCA extensions.
Toward a Cognitive Neuroscience of Metacognition

The relationship between metacognition and executive control is explored. According to an analysis by Fernandez-Duque, Baird, and Posner (this issue), metacognitive regulation involves attention, conflict resolution, error correction, inhibitory control, and emotional regulation. These aspects of metacognition are presumed to be mediated by a neural circuit involving midfrontal brain regions. An evaluation of the proposal by Fernandez-Duque et al. is made, and it is suggested that there is considerable convergence of issues associated with metacognition, executive control, working memory, and frontal lobe function. By integrating these domains and issues, significant progress could be made toward a cognitive neuroscience of metacognition.
Culture fusion and English language teaching

This paper is first to exemplify the problems caused by culture difference. After this, a survey is made on Chinese-western culture difference and its main aspects, followed by the Chinese and western culture difference on the layer of language. In the second part the discussion of the relationship between language and culture reveals the importance of culture education in language teaching. The author is of the opinion that one cannot really master a target language without adequate knowledge of the culture related to that language. The paper concludes with several suggestions about useful ways to get culture fusion in ELT and points out that human beings do not live in the objective world alone, nor alone in the world of social activity as ordinarily understood but are very much at the mercy of the particular language which has become the medium of expression for their society. It is quite an illusion that language is merely an incidental means of solving specific problems of communication or reflection.
Residual and plain convolutional neural networks for 3D brain MRI classification

In the recent years there have been a number of studies that applied deep learning algorithms to neuroimaging data. Pipelines used in those studies mostly require multiple processing steps for feature extraction, although modern advancements in deep learning for image classification can provide a powerful framework for automatic feature generation and more straightforward analysis. In this paper, we show how similar performance can be achieved skipping these feature extraction steps with the residual and plain 3D convolutional neural network architectures. We demonstrate the performance of the proposed approach for classification of Alzheimer's disease versus mild cognitive impairment and normal controls on the Alzheimers Disease National Initiative (ADNI) dataset of 3D structural MRI brain scans.
Evidence for infants' internal working models of attachment.

Nearly half a century ago, psychiatrist John Bowlby proposed that the instinctual behavioral system that underpins an infant’s attachment to his or her mother is accompanied by ‘‘internal working models’’ of the social world—models based on the infant’s own experience with his or her caregiver (Bowlby, 1958, 1969/1982). These mental models were thought to mediate, in part, the ability of an infant to use the caregiver as a buffer against the stresses of life, as well as the later development of important self-regulatory and social skills. Hundreds of studies now testify to the impact of caregivers’ behavior on infants’ behavior and development: Infants who most easily seek and accept support from their parents are considered secure in their attachments and are more likely to have received sensitive and responsive caregiving than insecure infants; over time, they display a variety of socioemotional advantages over insecure infants (Cassidy & Shaver, 1999). Research has also shown that, at least in older children and adults, individual differences in the security of attachment are indeed related to the individual’s representations of social relations (Bretherton & Munholland, 1999). Yet no study has ever directly assessed internal working models of attachment in infancy. In the present study, we sought to do so.
Blood glucose-insulin regulation and management system using MATLAB/SIMULINK

Biomedical signal processing has exponentially been expanding and demanding area for research and subsequent applications in its respective quarters. Exploration on diabetes is one of the main segments of this area which is being addressed for the decades. One of the main functions of the pancreas is to regulate glucose concentration in blood through release of the enzyme insulin. Some theoretical analysis of the control of blood glucose level in diabetic individuals is undertaken using a simple mathematical model of the dynamics of glucose and insulin interaction in the blood system developed by Stolwijk and Hardy dynamic model. This paper is focused to envisage the regulation and management of the concentration of glucose and insulin in the blood of a diabetic. The model was modified by adding a term for exogenous insulin infusion. Using this model, a closed-loop feed back system which regulates and manages the blood glucose-insulin has been designed, implemented and analyzed using Matlab/Simulink. This system imitates as an artificial pancreas on day-to-day basis. A time-consuming and tedious series of diabetes experiments was carried out and results were analyzed on the basis of medical parameters. The obtained results showed significant regulation and management of the glucose-insulin delivery in the blood. Hence, we are reasonably confident of having contribution in the subject area.
Performance Evaluation of Container-Based Virtualization for High Performance Computing Environments

The use of virtualization technologies in high performance computing (HPC) environments has traditionally been avoided due to their inherent performance overhead. However, with the rise of container-based virtualization implementations, such as Linux VServer, OpenVZ and Linux Containers (LXC), it is possible to obtain a very low overhead leading to near-native performance. In this work, we conducted a number of experiments in order to perform an in-depth performance evaluation of container-based virtualization for HPC. We also evaluated the trade-off between performance and isolation in container-based virtualization systems and compared them with Xen, which is a representative of the traditional hypervisor-based virtualization systems used today.
Framing-effects approach : A theoretical and methodological critique

The article deals with research on framing effects. First, I will start with classifying different approaches on framing. Subsequently, I will provide a definition of the concepts of frame, schema and framing, expand on framing research conducted so far both theoretically and operationally. Having this equipment at hand, I will initiate a discussion on studies of framingeffects in terms of theory, methods and empirical results. This discussion leads to the conclusion that studies on framing effects are insufficiently concerned with the more recent psychological constructs and theories. In merely focusing on the activation of schemata, most studies ignore the more elaborate types of framing-effects. Therefore, several empirical questions remain unanswered and some methodical chances seem to be wasted.
Dynamics of light harvesting in photosynthesis.

We review recent theoretical and experimental advances in the elucidation of the dynamics of light harvesting in photosynthesis, focusing on recent theoretical developments in structure-based modeling of electronic excitations in photosynthetic complexes and critically examining theoretical models for excitation energy transfer. We then briefly describe two-dimensional electronic spectroscopy and its application to the study of photosynthetic complexes, in particular the Fenna-Matthews-Olson complex from green sulfur bacteria. This review emphasizes recent experimental observations of long-lasting quantum coherence in photosynthetic systems and the implications of quantum coherence in natural photosynthesis.
Safety Stock Positioning in Supply Chains with Stochastic Lead Times

W study the safety stock positioning problem in single-product multistage supply chains with tree network structures, where each stage controls its inventory using an installation continuous-time base-stock policy. External demands follow independent Poisson processes, and unsatisfied demands at each stage are fully backordered. The processing (e.g., production) cycle times and transportation lead times are assumed to be stochastic, sequential, and exogenously determined. We derive recursive equations for the backorder delays (because of stockout) at all stages in the supply chain. Based on the recursive equations, we characterize the dependencies of the backorder delays across different stages in the network, and develop insights into the impact of safety stock positioning in various supply chain topologies. We present approximations and algorithms to coordinate the base-stock levels in these supply chains, so as to minimize systemwide inventory cost subject to meeting certain service-level requirements of the external customers.
Improving Graph Convolutional Networks with Non-Parametric Activation Functions

Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform inference on data that is associated to a graph structure, such as, e.g., citation networks or knowledge graphs. While several variants of GNNs have been proposed, they only consider simple nonlinear activation functions in their layers, such as rectifiers or squashing functions. In this paper, we investigate the use of graph convolutional networks (GCNs) when combined with more complex activation functions, able to adapt from the training data. More specifically, we extend the recently proposed kernel activation function, a non-parametric model which can be implemented easily, can be regularized with standard $\ell_{p}$ -norms techniques, and is smooth over its entire domain. Our experimental evaluation shows that the proposed architecture can significantly improve over its baseline, while similar improvements cannot be obtained by simply increasing the depth or size of the original GCN.