nils-herrmann/clean_scirepeval huggingface dataset:
Approximation of backward stochastic differential equations using Malliavin weights and least squares regression

We design a numerical scheme for solving a Dynamic Programming equation with Malliavin weights arising from the time discretization of backward stochastic differential equations with the integration by parts representation of the $Z$ component by ( (2002) 1390 1418) When the sequence of conditional expectations is computed using empirical least squares regressions, we establish, under general conditions, tight error bounds as the time average of local regression errors only (up to logarithmic factors) We compute the algorithm complexity by a suitable optimization of the parameters, depending on the dimension and the smoothness of value functions, in the limit as the number of grid times goes to infinity. The estimates take into account the regularity of the terminal function.
Language effects in second language learners: A longitudinal electrophysiological study of spanish classroom learning
Venue: Brain Research

How do the neural mechanisms involved in word recognition evolve over the course of word learning in adult learners of a new second language? The current study sought to closely track language effects, which are differences in electrophysiological indices of word processing between one's native and second languages, in beginning university learners over the course of a single semester of learning. Monolingual L1 English speakers enrolled in introductory Spanish were first trained on a list of 228 Spanish words chosen from the vocabulary to be learned in class. Behavioral data from the training session and the following experimental sessions spaced over the course of the semester showed expected learning effects. In the three laboratory sessions participants read words in three lists (English, Spanish and mixed) while performing a go/no go lexical decision task in which event related potentials (ERPs) were recorded. As observed in previous studies there were ERP language effects with larger N400s to native than second language words. Importantly, this difference declined over the course of L2 learning with N400 amplitude increasing for new second language words. These results suggest that even over a single semester of learning that new second language words are rapidly incorporated into the word recognition system and begin to take on lexical and semantic properties similar to native language words. Moreover, the results suggest that electrophysiological measures can be used as sensitive measures for tracking the acquisition of new linguistic knowledge.
CogALex V Shared Task: GHHH Detecting Semantic Relations via Word Embeddings
Venue: CogALex@COLING

This paper describes our system submission to the CogALex 2016 Shared Task on Corpus Based Identification of Semantic Relations. Our system won first place for Task 1 and second place for Task 2. The evaluation results of our system on the test set is 88.1% (79.0% for TRUE only) f measure for Task 1 on detecting semantic similarity, and 76.0% (42.3% when excluding RANDOM) for Task 2 on identifying finer grained semantic relations. In our experiments, we try word analogy, linear regression, and multi task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. We found that linear regression performs better in the binary classification (Task 1) while CNNs have better performance in the multi class semantic classification (Task 2) We assume that word analogy is more suited for deterministic answers rather than handling the ambiguity of one to many and many to many relationships. We also show that classifier performance could benefit from balancing the distribution of labels in the training data.
Positive impacts in social life cycle assessment: state of the art and the way forward
Venue: The International Journal of Life Cycle Assessment

PurposeSocial life cycle assessment (SLCA) is a methodology under continuous development, which may be applied at different scales: from products to economic sectors up to systems at region (meso) and country (macro) scales. Traditionally, SLCA has been focusing on the assessment of negative social externalities, whereas also positive social impacts could be associated to human interventions. The purpose of the present study is to understand how positive impacts are defined in published literature and how they could be assessed through indicators. The aim is to clarify the concept among scholars and to support decision making in business and policy context.MethodsThe study uses a systematic review approach in order to analyse the types of indicators adopted. In the field of SLCA and according to Paragahawewa et al. (2009) "[I]ndicators are 'pointers' to the state of the impact categories (and/or subcategories) being evaluated by the SLCA" Indicators can be quantitative, semi quantitative or qualitative (UNEP/SETAC 2009) This review was carried out in order to identify and analyse positive impacts and indicators. After careful scrutiny, 47 papers containing theoretical frameworks were considered, as well as 46 papers presenting case studies.Results and discussionCompared to environmental life cycle assessment (E LCA) where the presence of positive impacts is lower, evaluating benefits or positive impacts can still play a major role in SLCA (Benoit et al. 2010) A quarter of the analysed papers on theoretical frameworks take into account the topic of positive impacts and indicators. Results from case study analysis highlight as "workers" was the most considered stakeholder (in 100 of the analysed papers) and as the majority of positive indicators used in the case study analysed are recorded in relation to "other value chain actors" Within the concept of "positive impacts" no reference should be made merely to the utility of a product or service. In a broader sense, we could refer to solutions improving the conditions of one or various stakeholders involved. In other words, these are solutions that carry a positive contribution to one or more stakeholders without harming others.ConclusionsSo far, positive impacts are barely covered in literature. There is a clear need of streamlining definition and indicators, especially if they should be applied in a policy context complementing traditional and often monetary based, cost benefit analysis (CBA)
Quantification of Carbon Nanotubes in Environmental Matrices: Current Capabilities, Case Studies, and Future Prospects.
Venue: Environmental science technology

Carbon nanotubes (CNTs) have numerous exciting potential applications and some that have reached commercialization. As such, quantitative measurements of CNTs in key environmental matrices (water, soil, sediment, and biological tissues) are needed to address concerns about their potential environmental and human health risks and to inform application development. However, standard methods for CNT quantification are not yet available. We systematically and critically review each component of the current methods for CNT quantification including CNT extraction approaches, potential biases, limits of detection, and potential for standardization. This review reveals that many of the techniques with the lowest detection limits require uncommon equipment or expertise, and thus, they are not frequently accessible. Additionally, changes to the CNTs (e.g. agglomeration) after environmental release and matrix effects can cause biases for many of the techniques, and biasing factors vary among the techniques. Five case studies are provided to illustrate how to use this information to inform responses to real world scenarios such as monitoring potential CNT discharge into a river or ecotoxicity testing by a testing laboratory. Overall, substantial progress has been made in improving CNT quantification during the past ten years, but additional work is needed for standardization, development of extraction techniques from complex matrices, and multimethod comparisons of standard samples to reveal the comparability of techniques.
Real Time 3D Image Guidance Using a Standard LINAC: Measured Motion, Accuracy, and Precision of the First Prospective Clinical Trial of Kilovoltage Intrafraction Monitoring Guided Gating for Prostate Cancer Radiation Therapy.
Venue: International journal of radiation oncology, biology, physics

Kilovoltage intrafraction monitoring (KIM) is a new real time 3 dimensional image guidance method. Unlike previous real time image guidance methods, KIM uses a standard linear accelerator without any additional equipment needed. The first prospective clinical trial of KIM is underway for prostate cancer radiation therapy. In this paper we report on the measured motion accuracy and precision using real time KIM guided gating.  Imaging and motion information from the first 200 fractions from 6 patient prostate cancer radiation therapy volumetric modulated arc therapy treatments were analyzed. A 3 mm/5 second action threshold was used to trigger a gating event where the beam is paused and the couch position adjusted to realign the prostate to the treatment isocenter. To quantify the in vivo accuracy and precision, KIM was compared with simultaneously acquired kV/MV triangulation for 187 fractions.  KIM was successfully used in 197 of 200 fractions. Gating events occurred in 29 fractions (14.5% In these 29 fractions, the percentage of beam on time, the prostate displacement was >3 mm from the isocenter position, reduced from 73% without KIM to 24% with KIM guided gating. Displacements >5 mm were reduced from 16% without KIM to 0% with KIM. The KIM accuracy was measured at <0.3 mm in all 3 dimensions. The KIM precision was <0.6 mm in all 3 dimensions.  Clinical implementation of real time KIM image guidance combined with gating for prostate cancer eliminates large prostate displacements during treatment delivery. Both in vivo KIM accuracy and precision are well below 1 mm.
Fast Traffic Sign Recognition Using Color Segmentation and Deep Convolutional Networks
Venue: ACIVS

The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelligent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Oriented Gradients (HOG) and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a preprocessing step to reduce the search space, while classification is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well known German Traffic Sign data set and on the novel Data set of Italian Traffic Signs (DITS) which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night time. Experimental results demonstrate the effectiveness of the proposed approach in terms of both classification accuracy and computational speed.
Dual Neural Network Model for the Evolution of Speech and Language
Venue: Trends in Neurosciences

Explaining the evolution of speech and language poses one of the biggest challenges in biology. We propose a dual network model that posits a volitional articulatory motor network (VAMN) originating in the prefrontal cortex (PFC; including Broca's area) that cognitively controls vocal output of a phylogenetically conserved primary vocal motor network (PVMN) situated in subcortical structures. By comparing the connections between these two systems in human and nonhuman primate brains, we identify crucial biological preadaptations in monkeys for the emergence of a language system in humans. This model of language evolution explains the exclusiveness of non verbal communication sounds (e.g. cries) in infants with an immature PFC, as well as the observed emergence of non linguistic vocalizations in adults after frontal lobe pathologies.
Twentieth Century Thinkers In Adult Education

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Convolutional Matrix Factorization for Document Context Aware Recommendation
Venue: RecSys

Sparseness of user to item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In particular, when rating data is sparse, document modeling based approaches have improved the accuracy by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag of words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents. This paper proposes a novel context aware recommendation model, convolutional matrix factorization (ConvMF) that integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF) Consequently, ConvMF captures contextual information of documents and further enhances the rating prediction accuracy. Our extensive evaluations on three real world datasets show that ConvMF significantly outperforms the state of the art recommendation models even when the rating data is extremely sparse. We also demonstrate that ConvMF successfully captures subtle contextual difference of a word in a document. Our implementation and datasets are available at http:/dm.postech.ac.kr/ConvMF.
STCAPLRS

Newly emerging location based social media network services (LBSMNS) provide valuable resources to understand users' behaviors based on their location histories. The location based behaviors of a user are generally influenced by both user intrinsic interest and the location preference, and moreover are spatial temporal context dependent. In this article, we propose a spatial temporal context aware personalized location recommendation system (STCAPLRS) which offers a particular user a set of location items such as points of interest or venues (e.g. restaurants and shopping malls) within a geospatial range by considering personal interest, local preference, and spatial temporal context influence. STCAPLRS can make accurate recommendation and facilitate people's local visiting and new location exploration by exploiting the context information of user behavior, associations between users and location items, and the location and content information of location items. Specifically, STCAPLRS consists of two components: offline modeling and online recommendation. The core module of the offline modeling part is a context aware regression mixture model that is designed to model the location based user behaviors in LBSMNS to learn the interest of each individual user, the local preference of each individual location, and the context aware influence factors. The online recommendation part takes a querying user along with the corresponding querying spatial temporal context as input and automatically combines the learned interest of the querying user, the local preference of the querying location, and the context aware influence factor to produce the top k recommendations. We evaluate the performance of STCAPLRS on two real world datasets: Dianping and Foursquare. The results demonstrate the superiority of STCAPLRS in recommending location items for users in terms of both effectiveness and efficiency. Moreover, the experimental analysis results also illustrate the excellent interpretability of STCAPLRS.
On the Complexity of Best Arm Identification in Multi Armed Bandit Models
Venue: J. Mach. Learn. Res.

The stochastic multi armed bandit model is a simple abstraction that has proven useful in many different contexts in statistics and machine learning. Whereas the achievable limit in terms of regret minimization is now well known, our aim is to contribute to a better understanding of the performance in terms of identifying the m best arms. We introduce generic notions of complexity for the two dominant frameworks considered in the literature: fixed budget and fixed confidence settings. In the fixed confidence setting, we provide the first known distribution dependent lower bound on the complexity that involves information theoretic quantities and holds when m 1 under general assumptions. In the specific case of two armed bandits, we derive refined lower bounds in both the fixedcon fidence and fixed budget settings, along with matching algorithms for Gaussian and Bernoulli bandit models. These results show in particular that the complexity of the fixed budget setting may be smaller than the complexity of the fixed confidence setting, contradicting the familiar behavior observed when testing fully specified alternatives. In addition, we also provide improved sequential stopping rules that have guaranteed error probabilities and shorter average running times. The proofs rely on two technical results that are of independent interest: a deviation lemma for self normalized sums (Lemma 7) and a novel change of measure inequality for bandit models (Lemma 1)
DPDK based Improvement of Packet Forwarding

Reel time processing of packets occupies a significant position in the field of computer network security. With theexplosive growth of the backbone link rate,which is consistent with Gilder's law, many bottlenecks of server performance leave the real time data stream unprocessed.Thus, we proposedto take use of DPDK(Data Plan Development Kit) framework to achieve an intelligent NIC packet forwarding system. During this research, we deeply analysis the forwarding process of packet in DPDK and improve its DMA mode.According to the results of experiment, the system greatly enhanced the performance of packet forwarding,and the throughput of forwarding 64 byet or random length packets by 20Gbit NIC reaches13.3Gbps and 18.7Gbps(dual ports forwarding)
Nonlinear Programming Analysis And Methods

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Global optimization advances in Mixed Integer Nonlinear Programming, MINLP, and Constrained Derivative Free Optimization, CDFO
Venue: Eur. J. Oper. Res.

This manuscript reviews recent advances in deterministic global optimization for Mixed Integer Nonlinear Programming (MINLP) as well as Constrained Derivative Free Optimization (CDFO) This work provides a comprehensive and detailed literature review in terms of significant theoretical contributions, algorithmic developments, software implementations and applications for both MINLP and CDFO. Both research areas have experienced rapid growth, with a common aim to solve a wide range of real world problems. We show their individual prerequisites, formulations and applicability, but also point out possible points of interaction in problems which contain hybrid characteristics. Finally, an inclusive and complete test suite is provided for both MINLP and CDFO algorithms, which is useful for future benchmarking.
Accelerated gradient methods for nonconvex nonlinear and stochastic programming
Venue: Math. Program.

In this paper, we generalize the well known Nesterov's accelerated gradient (AG) method, originally designed for convex smooth optimization, to solve nonconvex and possibly stochastic optimization problems. We demonstrate that by properly specifying the stepsize policy, the AG method exhibits the best known rate of convergence for solving general nonconvex smooth optimization problems by using first order information, similarly to the gradient descent method. We then consider an important class of composite optimization problems and show that the AG method can solve them uniformly, i.e. by using the same aggressive stepsize policy as in the convex case, even if the problem turns out to be nonconvex. We demonstrate that the AG method exhibits an optimal rate of convergence if the composite problem is convex, and improves the best known rate of convergence if the problem is nonconvex. Based on the AG method, we also present new nonconvex stochastic approximation methods and show that they can improve a few existing rates of convergence for nonconvex stochastic optimization. To the best of our knowledge, this is the first time that the convergence of the AG method has been established for solving nonconvex nonlinear programming in the literature.
Sequence Level Knowledge Distillation
Venue: EMNLP

Neural machine translation (NMT) offers a novel alternative formulation of translation that is potentially simpler than statistical approaches. However to reach competitive performance, NMT models need to be exceedingly large. In this paper we consider applying knowledge distillation approaches (Bucila et al. 2006; Hinton et al. 2015) that have proven successful for reducing the size of neural models in other domains to the problem of NMT. We demonstrate that standard knowledge distillation applied to word level prediction can be effective for NMT, and also introduce two novel sequence level versions of knowledge distillation that further improve performance, and somewhat surprisingly, seem to eliminate the need for beam search (even when applied on the original teacher model) Our best student model runs 10 times faster than its state of the art teacher with little loss in performance. It is also significantly better than a baseline model trained without knowledge distillation: by 4.2/1.7 BLEU with greedy decoding/beam search. Applying weight pruning on top of knowledge distillation results in a student model that has 13 times fewer parameters than the original teacher model, with a decrease of 0.4 BLEU.
Cross Modal Distillation for Supervision Transfer
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

In this work we propose a technique that transfers supervision between images from different modalities. We use learned representations from a large labeled modality as supervisory signal for training representations for a new unlabeled paired modality. Our method enables learning of rich representations for unlabeled modalities and can be used as a pre training procedure for new modalities with limited labeled data. We transfer supervision from labeled RGB images to unlabeled depth and optical flow images and demonstrate large improvements for both these cross modal supervision transfers.