nils-herrmann/clean_scirepeval huggingface dataset:
Optimization of computer simulation models with rare events

Discrete event simulation systems (DESS) are widely used in many diverse areas such as computer-communication networks, exible manufacturing systems, project evaluation and review techniques (PERT), and ow networks. Because of their complexity , such systems are typically analyzed via Monte Carlo simulation methods. This paper deals with optimization of complex computer simulation models involving rare events. A classic example is to nd an optimal (s; S) policy in a multi-item, multi-commodity inventory system, when quality standards require the backlog probability to be extremely small. Our approach is based on change of the probability measure techniques, also called likelihood ratio (LR) and importance sampling (IS) methods. Unfortunately, for arbitrary probability measures the LR estimators and the resulting optimal solution often tend to be unstable and may have large variances. Therefore, the choice of the corresponding importance sampling distribution and in particular its parameters in an optimal way is an important task. We consider the case where the IS distribution comes from the same parametric family as the original (true) one and use the stochastic counterpart method, to handle simulation based optimization models. More speciically, we use a two-stage procedure: at the rst stage we identify (estimate) the optimal parameter vector at the IS distribution, while at the second stage we estimate the optimal solution of the underlying constrained optimization problem. Particular emphasis will be placed on estimation of rare events and on integration of the associated performance function into stochastic optimization programs. Supporting numerical results are provided as well.
Structure and function of auditory cortex: music and speech

We examine the evidence that speech and musical sounds exploit different acoustic cues: speech is highly dependent on rapidly changing broadband sounds, whereas tonal patterns tend to be slower, although small and precise changes in frequency are important. We argue that the auditory cortices in the two hemispheres are relatively specialized, such that temporal resolution is better in left auditory cortical areas and spectral resolution is better in right auditory cortical areas. We propose that cortical asymmetries might have developed as a general solution to the need to optimize processing of the acoustic environment in both temporal and frequency domains.
A Security Metrics Taxonomization Model for Software-Intensive Systems

We introduce a novel high-level security metrics objective taxonomization model for software-intensive systems. The model systematizes and organizes security metrics development activities. It focuses on the security level and security performance of technical systems while taking into account the alignment of metrics objectives with different business and other management goals. The model emphasizes the roles of security-enforcing mechanisms, the overall security quality of the system under investigation, and secure system lifecycle, project and business management. Security correctness, effectiveness and efficiency are seen as the fundamental measurement objectives, determining the directions for more detailed security metrics development. Integration of the proposed model with riskdriven security metrics development approaches is also discussed.
Teaching critical thinking for transfer across domains. Dispositions, skills, structure training, and metacognitive monitoring.

Advances in technology and changes in necessary workplace skills have made the ability to think critically more important than ever before, yet there is ample evidence that many adults consistently engage in flawed thinking. Numerous studies have shown that critical thinking, defined as the deliberate use of skills and strategies that increase the probability of a desirable outcome, can be learned in ways that promote transfer to novel contexts. A 4-part empirically based model is proposed to guide teaching and learning for critical thinking: (a) a dispositional component to prepare learners for effortful cognitive work, (b) instruction in the skills of critical thinking, (c) training in the structural aspects of problems and arguments to promote transcontextual transfer of critical-thinking skills, and (d) a metacognitive component that includes checking for accuracy and monitoring progress toward the goal.
A Two-Stage Parsing Method for Text-Level Discourse Analysis

Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance. In this paper, we propose that transition-based model is more appropriate for parsing the naked discourse tree (i.e., identifying span and nuclearity) due to data sparsity. At the same time, we argue that relation labeling can benefit from naked tree structure and should be treated elaborately with consideration of three kinds of relations including within-sentence, across-sentence and across-paragraph relations. Thus, we design a pipelined two-stage parsing method for generating an RST tree from text. Experimental results show that our method achieves state-of-the-art performance, especially on span and nuclearity identification.
Generalization Bounds for Transfer Learning under Model Shift

Transfer learning (sometimes also referred to as domain-adaptation) algorithms are often used when one tries to apply a model learned from a fully labeled source domain, to an unlabeled target domain, that is similar but not identical to the source. Previous work on covariate shift focuses on matching the marginal distributions on observations X across domains while assuming the conditional distribution P (Y |X) stays the same. Relevant theory focusing on covariate shift has also been developed. Recent work on transfer learning under model shift deals with different conditional distributions P (Y |X) across domains with a few target labels, while assuming the changes are smooth. However, no analysis has been provided to say when these algorithms work. In this paper, we analyze transfer learning algorithms under the model shift assumption. Our analysis shows that when the conditional distribution changes, we are able to obtain a generalization error bound of O( 1 λ∗nl ) with respect to the labeled target sample size nl, modified by the smoothness of the change (λ∗) across domains. Our analysis also sheds light on conditions when transfer learning works better than no-transfer learning (learning by labeled target data only). Furthermore, we extend the transfer learning algorithm from a single source to multiple sources.
SimDoc: Topic Sequence Alignment based Document Similarity Framework

Document similarity is the problem of estimating the degree to which a given pair of documents has similar semantic content. An accurate document similarity measure can improve several enterprise relevant tasks such as document clustering, text mining, and question-answering. In this paper, we show that a document's thematic flow, which is often disregarded by bag-of-word techniques, is pivotal in estimating their similarity. To this end, we propose a novel semantic document similarity framework, called SimDoc. We model documents as topic-sequences, where topics represent latent generative clusters of related words. Then, we use a sequence alignment algorithm to estimate their semantic similarity. We further conceptualize a novel mechanism to compute topic-topic similarity to fine tune our system. In our experiments, we show that SimDoc outperforms many contemporary bag-of-words techniques in accurately computing document similarity, and on practical applications such as document clustering.
The coupling of emotion and cognition in the eye: introducing the pupil old/new effect.

The study presented here investigated the effects of emotional valence on the memory for words by assessing both memory performance and pupillary responses during a recognition memory task. Participants had to make speeded judgments on whether a word presented in the test phase of the experiment had already been presented ("old") or not ("new"). An emotion-induced recognition bias was observed: Words with emotional content not only produced a higher amount of hits, but also elicited more false alarms than neutral words. Further, we found a distinct pupil old/new effect characterized as an elevated pupillary response to hits as opposed to correct rejections. Interestingly, this pupil old/new effect was clearly diminished for emotional words. We therefore argue that the pupil old/new effect is not only able to mirror memory retrieval processes, but also reflects modulation by an emotion-induced recognition bias.
Psychedelics and the essential importance of context.

Psychedelic drugs are making waves as modern trials support their therapeutic potential and various media continue to pique public interest. In this opinion piece, we draw attention to a long-recognised component of the psychedelic treatment model, namely ‘set’ and ‘setting’ – subsumed here under the umbrella term ‘context’. We highlight: (a) the pharmacological mechanisms of classic psychedelics (5-HT2A receptor agonism and associated plasticity) that we believe render their effects exceptionally sensitive to context, (b) a study design for testing assumptions regarding positive interactions between psychedelics and context, and (c) new findings from our group regarding contextual determinants of the quality of a psychedelic experience and how acute experience predicts subsequent long-term mental health outcomes. We hope that this article can: (a) inform on good practice in psychedelic research, (b) provide a roadmap for optimising treatment models, and (c) help tackle unhelpful stigma still surrounding these compounds, while developing an evidence base for long-held assumptions about the critical importance of context in relation to psychedelic use that can help minimise harms and maximise potential benefits.
W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis

With the increase of online customer opinions in specialised websites and social networks, the necessity of automatic systems to help to organise and classify customer reviews by domain-specific aspect/categories and sentiment polarity is more important than ever. Supervised approaches for Aspect Based Sentiment Analysis obtain good results for the domain/language they are trained on, but having manually labelled data for training supervised systems for all domains and languages is usually very costly and time consuming. In this work we describe W2VLDA, an almost unsupervised system based on topic modelling, that combined with some other unsupervised methods and a minimal configuration, performs aspect/category classification, aspectterms/opinion-words separation and sentiment polarity classification for any given domain and language. We evaluate the performance of the aspect and sentiment classification in the multilingual SemEval 2016 task 5 (ABSA) dataset. We show competitive results for several languages (English, Spanish, French and Dutch) and domains (hotels, restaurants, electronic devices).
The truth about lying in online dating profiles

Online dating is a popular new tool for initiating romantic relationships, although recent research and media reports suggest that it may also be fertile ground for deception. Unlike previous studies that rely solely on self-report data, the present study establishes ground truth for 80 online daters' height, weight and age, and compares ground truth data to the information provided in online dating profiles. The results suggest that deception is indeed frequently observed, but that the magnitude of the deceptions is usually small. As expected, deceptions differ by gender. Results are discussed in light of the Hyperpersonal model and the self-presentational tensions experienced by online dating participants.
Machine learning and time series: Real world applications

There are in-numerous applications that deal with real scenarios where data are captured over time making them potential candidates for time series analysis. Time series contain temporal dependencies that divide different points in time into different classes. This paper aims at reviewing marriage of a concept i.e. time series modeling with an approach i.e. Machine learning in tackling real life problems. Like time series is ubiquitous and has found extensive usage in our daily life, machine learning approaches have found its applicability in dealing with complex real world scenarios where approximation, uncertainty, chaotic data are prime characteristics.
YOLO9000: Better, Faster, Stronger

We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. Using a novel, multi-scale training method the same YOLOv2 model can run at varying sizes, offering an easy tradeoff between speed and accuracy. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster. Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that dont have labelled detection data. We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. YOLO9000 predicts detections for more than 9000 different object categories, all in real-time.
Active visual SLAM for robotic area coverage: Theory and experiment

This paper reports on an integrated navigation algorithm for the visual simultaneous localization and mapping (SLAM) robotic area coverage problem. In the robotic area coverage problem, the goal is to explore and map a given target area within a reasonable amount of time. This goal necessitates the use of minimally redundant overlap trajectories for coverage efficiency; however, visual SLAM’s navigation estimate will inevitably drift over time in the absence of loop-closures. Therefore, efficient area coverage and good SLAM navigation performance represent competing objectives. To solve this decision-making problem, we introduce perception-driven navigation, an integrated navigation algorithm that automatically balances between exploration and revisitation using a reward framework. This framework accounts for SLAM localization uncertainty, area coverage performance, and the identification of good candidate regions in the environment for visual perception. Results are shown for both a hybrid simulation and real-world demonstration of a visual SLAM system for autonomous underwater ship hull inspection.
Effectiveness of problem-based learning curricula: research and theory.

This article provides a critical overview of problem-based learning (PBL), its effectiveness for knowledge acquisition and clinical performance, and the underlying educational theory. The focus of the paper is on (1) the credibility of claims (both empirical and theoretical) about the ties between PBL and educational outcomes and (2) the magnitude of the effects.



The author reviewed the medical education literature, starting with three reviews published in 1993 and moving on to research published from 1992 through 1998 in the primary sources for research in medical education. For each study the author wrote a summary, which included study design, outcome measures, effect sizes, and any other information relevant to the research conclusion.



The review of the literature revealed no convincing evidence that PBL improves knowledge base and clinical performance, at least not of the magnitude that would be expected given the resources required for a PBL curriculum. The results were considered in light of the educational theory that underlies PBL and its basic research. The author concludes that the ties between educational theory and research (both basic and applied) are loose at best.
Linear Regression for Face Recognition

In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single-object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class-specific galleries. The inverse problem is solved using the least-squares method and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Linear Regression Classification (LRC) algorithm falls in the category of nearest subspace classification. The algorithm is extensively evaluated on several standard databases under a number of exemplary evaluation protocols reported in the face recognition literature. A comparative study with state-of-the-art algorithms clearly reflects the efficacy of the proposed approach. For the problem of contiguous occlusion, we propose a Modular LRC approach, introducing a novel Distance-based Evidence Fusion (DEF) algorithm. The proposed methodology achieves the best results ever reported for the challenging problem of scarf occlusion.
Capacity Control of ReLU Neural Networks by Basis-path Norm

Recently, path norm was proposed as a new capacity measure for neural networks with Rectified Linear Unit (ReLU) activation function, which takes the rescaling-invariant property of ReLU into account. It has been shown that the generalization error bound in terms of the path norm explains the empirical generalization behaviors of the ReLU neural networks better than that of other capacity measures. Moreover, optimization algorithms which take path norm as the regularization term to the loss function, like Path-SGD, have been shown to achieve better generalization performance. However, the path norm counts the values of all paths, and hence the capacity measure based on path norm could be improperly influenced by the dependency among different paths. It is also known that each path of a ReLU network can be represented by a small group of linearly independent basis paths with multiplication and division operation, which indicates that the generalization behavior of the network only depends on only a few basis paths. Motivated by this, we propose a new norm Basis-path Norm based on a group of linearly independent paths to measure the capacity of neural networks more accurately. We establish a generalization error bound based on this basis path norm, and show it explains the generalization behaviors of ReLU networks more accurately than previous capacity measures via extensive experiments. In addition, we develop optimization algorithms which minimize the empirical risk regularized by the basis-path norm. Our experiments on benchmark datasets demonstrate that the proposed regularization method achieves clearly better performance on the test set than the previous regularization approaches.
Tumor Lymphangiogenesis as a Potential Therapeutic Target

Metastasis the spread of cancer cells to distant organs, is the main cause of death for cancer patients. Metastasis is often mediated by lymphatic vessels that invade the primary tumor, and an early sign of metastasis is the presence of cancer cells in the regional lymph node (the first lymph node colonized by metastasizing cancer cells from a primary tumor). Understanding the interplay between tumorigenesis and lymphangiogenesis (the formation of lymphatic vessels associated with tumor growth) will provide us with new insights into mechanisms that modulate metastatic spread. In the long term, these insights will help to define new molecular targets that could be used to block lymphatic vessel-mediated metastasis and increase patient survival. Here, we review the molecular mechanisms of embryonic lymphangiogenesis and those that are recapitulated in tumor lymphangiogenesis, with a view to identifying potential targets for therapies designed to suppress tumor lymphangiogenesis and hence metastasis.
Specialising Word Vectors for Lexical Entailment

We present LEAR (, a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymyhypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNetstyle hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection, and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed asymmetric specialisation model.
Multi-view deep learning for consistent semantic mapping with RGB-D cameras

Visual scene understanding is an important capability that enables robots to purposefully act in their environment. In this paper, we propose a novel deep neural network approach to predict semantic segmentation from RGB-D sequences. The key innovation is to train our network to predict multi-view consistent semantics in a self-supervised way. At test time, its semantics predictions can be fused more consistently in semantic keyframe maps than predictions of a network trained on individual views. We base our network architecture on a recent single-view deep learning approach to RGB and depth fusion for semantic object-class segmentation and enhance it with multi-scale loss minimization. We obtain the camera trajectory using RGB-D SLAM and warp the predictions of RGB-D images into ground-truth annotated frames in order to enforce multi-view consistency during training. At test time, predictions from multiple views are fused into keyframes. We propose and analyze several methods for enforcing multi-view consistency during training and testing. We evaluate the benefit of multi-view consistency training and demonstrate that pooling of deep features and fusion over multiple views outperforms single-view baselines on the NYUDv2 benchmark for semantic segmentation. Our end-to-end trained network achieves state-of-the-art performance on the NYUDv2 dataset in single-view segmentation as well as multi-view semantic fusion.