nils-herrmann/clean_scirepeval huggingface dataset:
A Practical and Highly Optimized Convolutional Neural Network for Classifying Traffic Signs in Real-Time

Classifying traffic signs is an indispensable part of Advanced Driver Assistant Systems. This strictly requires that the traffic sign classification model accurately classifies the images and consumes as few CPU cycles as possible to immediately release the CPU for other tasks. In this paper, we first propose a new ConvNet architecture. Then, we propose a new method for creating an optimal ensemble of ConvNets with highest possible accuracy and lowest number of ConvNets. Our experiments show that the ensemble of our proposed ConvNets (the ensemble is also constructed using our method) reduces the number of arithmetic operations 88 and $$73\,\%$$ 73 % compared with two state-of-art ensemble of ConvNets. In addition, our ensemble is $$0.1\,\%$$ 0.1 % more accurate than one of the state-of-art ensembles and it is only $$0.04\,\%$$ 0.04 % less accurate than the other state-of-art ensemble when tested on the same dataset. Moreover, ensemble of our compact ConvNets reduces the number of the multiplications 95 and $$88\,\%$$ 88 % , yet, the classification accuracy drops only 0.2 and $$0.4\,\%$$ 0.4 % compared with these two ensembles. Besides, we also evaluate the cross-dataset performance of our ConvNet and analyze its transferability power in different layers. We show that our network is easily scalable to new datasets with much more number of traffic sign classes and it only needs to fine-tune the weights starting from the last convolution layer. We also assess our ConvNet through different visualization techniques. Besides, we propose a new method for finding the minimum additive noise which causes the network to incorrectly classify the image by minimum difference compared with the highest score in the loss vector.
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning

Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node. While most existing approaches need to specify the neighborhood and the dependence form to the neighborhood, which may significantly degrades the flexibility of representation, we propose a novel graph node embedding method (namely GESF) via the set function technique. Our method can 1) learn an arbitrary form of representation function from neighborhood, 2) automatically decide the significance of neighbors at different distances, and 3) be applied to heterogeneous graph embedding, which may contain multiple types of nodes. Theoretical guarantee for the representation capability of our method has been proved for general homogeneous and heterogeneous graphs and evaluation results on benchmark data sets show that the proposed GESF outperforms the state-of-the-art approaches on producing node vectors for classification tasks.
A Dexterous and Self-adaptive Humanoid Robot Hand: Gesture-Changeable Under-Actuated Hand

A novel concept called gesture-changeable under-actuated (GCUA) function is proposed to improve the dexterities of traditional under-actuated hands and reduce the control difficulties of dexterous hands. Based on the GCUA function, a new humanoid robot hand, GCUA Hand is designed and manufactured. The GCUA Hand can grasp different objects self-adaptively and change its initial gesture dexterously before contacting objects. The hand has 5 fingers and 15 DOFs, each finger is based on screw-nut transmission, flexible drawstring constraint and belt-pulley under-actuated mechanism to realize GCUA function. The analyses on grasping static forces and grasping stabilities are put forward. The analyses and Experimental results show that the GCUA function is very nice and valid. The hands with the GCUA function can meet the requirements of grasping and operating with lower control and cost, which is the middle road between traditional under-actuated hands and dexterous hands.
Learning Policy Representations in Multiagent Systems

Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems. Prior work in agent modeling has largely been task-specific and driven by handengineering domain-specific prior knowledge. We propose a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data. Our framework casts agent modeling as a representation learning problem. Consequently, we construct a novel objective inspired by imitation learning and agent identification and design an algorithm for unsupervised learning of representations of agent policies. We demonstrate empirically the utility of the proposed framework in (i) a challenging highdimensional competitive environment for continuous control and (ii) a cooperative environment for communication, on supervised predictive tasks, unsupervised clustering, and policy optimization using deep reinforcement learning.
Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis.

A number of recent advances have been achieved in the study of midbrain dopaminergic neurons. Understanding these advances and how they relate to one another requires a deep understanding of the computational models that serve as an explanatory framework and guide ongoing experimental inquiry. This intertwining of theory and experiment now suggests very clearly that the phasic activity of the midbrain dopamine neurons provides a global mechanism for synaptic modification. These synaptic modifications, in turn, provide the mechanistic underpinning for a specific class of reinforcement learning mechanisms that now seem to underlie much of human and animal behavior. This review describes both the critical empirical findings that are at the root of this conclusion and the fantastic theoretical advances from which this conclusion is drawn.
Bayesian Optimization with Gradients

Bayesian optimization has been successful at global optimization of expensiveto-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimization typically does not use derivative information. In this paper we show how Bayesian optimization can exploit derivative information to find good solutions with fewer objective function evaluations. In particular, we develop a novel Bayesian optimization algorithm, the derivative-enabled knowledgegradient (d-KG), which is one-step Bayes-optimal, asymptotically consistent, and provides greater one-step value of information than in the derivative-free setting. d-KG accommodates noisy and incomplete derivative information, comes in both sequential and batch forms, and can optionally reduce the computational cost of inference through automatically selected retention of a single directional derivative. We also compute the d-KG acquisition function and its gradient using a novel fast discretization-free technique. We show d-KG provides state-of-the-art performance compared to a wide range of optimization procedures with and without gradients, on benchmarks including logistic regression, deep learning, kernel learning, and k-nearest neighbors.
Subordinating and coordinating discourse relations

This paper studies the distinction between subordinating and coordinating discourse relations, a distinction that governs the hierarchical structure of discourse. We provide linguistic tests to clarify which discourse relations are subordinating and which are coordinating. We argue that some relations are classified as subordinating or coordinating by default, a default that can be overridden in specific contexts. The distinction between subordinating and coordinating relations thus belongs to the level of information packaging in discourse and not to the level of information content or the semantics of the relations themselves.
Current status of adoption of no-till farming in the world and some of its main benefits

In 1999 no-tillage farming, synonymous of zero tillage farming or conservation agriculture, was adopted on about 45 million ha world wide, growing to 72 million ha in 2003 and to 111 million ha in 2009, corresponding to an growth rate of 6 million ha per annum. Fastest adoption rates have been experienced in South America where some countries are using no-tillage farming on about 70% of the total cultivated area. Opposite to countries like the USA where often fields under no-tillage farming are tilled every now and then, more than two thirds of the area under no-tillage systems in South America is permanently not tilled; in other words once adopted, the soil is never tilled again. The spread of no-tillage systems on more than 110 million ha world-wide shows the great adaptability of the systems to all kinds of climates, soils and cropping conditions. No-tillage is now being practiced from the artic circle over the tropics to about 50olatitude south, from sea level to 3,000 m altitude, from extremely rainy areas with 2,500 mm a year to extremely dry conditions with 250 mm a year. No-till farming offers a way of optimizing productivity and ecosystem services, offering a wide range of economic, environmental and social benefits to the producer and to the society. At the same time, no-till farming is enabling agriculture to respond to some of the global challenges associated with climate change, land and environmental degradation, and increasing cost of food, energy and production inputs. The wide recognition of no-till farming as a truly sustainable system should ensure the spread of the no-till technology and the associated practices of organic soil cover and crop rotation, as soon as the barriers to its adoption have been overcome, to areas where adoption is currently still low. The widespread adoption globally also shows that no-tillage farming cannot any more be considered a temporary fashion or a craze; instead largely through farmers’own effort, the system has established itself as a farming practice and a different way of thinking about sustainable agro-ecosystem management that can no longer be ignored by scientists, academics, extension workers, farmers at large as well as equipment and machine manufacturers and politicians.
Development and validation of the Delaying Gratification Inventory.

Deficits in gratification delay are associated with a broad range of public health problems, such as obesity, risky sexual behavior, and substance abuse. However, 6 decades of research on the construct has progressed less quickly than might be hoped, largely because of measurement issues. Although past research has implicated 5 domains of delay behavior, involving food, physical pleasures, social interactions, money, and achievement, no published measure to date has tapped all 5 components of the content domain. Existing measures have been criticized for limitations related to efficiency, reliability, and construct validity. Using an innovative Internet-mediated approach to survey construction, we developed the 35-item 5-factor Delaying Gratification Inventory (DGI). Evidence from 4 studies and a large, diverse sample of respondents (N = 10,741) provided support for the psychometric properties of the measure. Specifically, scores on the DGI demonstrated strong internal consistency and test-retest reliability for the 35-item composite, each of the 5 domains, and a 10-item short form. The 5-factor structure fit the data well and had good measurement invariance across subgroups. Construct validity was supported by correlations with scores on closely related self-control measures, behavioral ratings, Big Five personality trait measures, and measures of adjustment and psychopathology, including those on the Minnesota Multiphasic Personality Inventory-2-Restructured Form. DGI scores also showed incremental validity in accounting for well-being and health-related variables. The present investigation holds implications for improving public health, accelerating future research on gratification delay, and facilitating survey construction research more generally by demonstrating the suitability of an Internet-mediated strategy.
Deep Convolutional Network Based on Pyramid Architecture

Deep convolutional network demonstrates that the classification accuracy can be remarkably improved by increasing the number of network layers, however, increases the accuracy by 1% of costs nearly doubling the number of layers. Meanwhile, gradient dispersion will occur in the training process, which leads to performance degradation. In order to solve the problem of training difficulty with the increased number of layers, we focus on network architecture and propose a deep convolutional network based on the pyramid structure. In the network architecture, as the number of layers increased, the feature map dimensions (i.e., the number of channels) are gradually increased at each layer to distribute the burden concentrated at locations of structural units affected by downsampling, such that all units are equally distributed. By exploring the sequence between the stacked elements inside the structural unit, we design a pyramidal building block, as its shape gradually widens from the top downwards, which is called the deep pyramid convolutional network (DPCNet). Experimental results on CIFAR-10 and CIFAR-100 datasets have shown that DPCNet has the superior generalization capability and can effectively improve the image classification accuracy.
How DBS Bank Pursued a Digital Business Strategy

Business digitalization is changing the competitive landscape in many industries. Digitally savvy customers are demanding more while threats of digital disruptions from new entrants are rising. The full article describes how DBS, a large Asian bank, responded to digital threats and opportunities by adopting a digital business strategy. It identifies the capabilities needed and provides lessons for organizations aspiring to pursue a successful digital business strategy. Most organizations respond to new digital threats and opportunities in an ad hoc manner within some organizational functions, but there is a growing sense that functionally oriented initiatives fail to maximize the potential of digital business strategy. To respond effectively to the threats and opportunities arising from digitalization, companies need a more holistic and integrated approach that develops capabilities in the areas of leadership, operations, customer needs and innovation. This is the approach followed by DBS.
Imaging cognitive anatomy

One of the major challenges in imaging neuroscience is the integration of cognitive science with the empiricism of neurophysiology. The cognitive architectures and principles offered by cognitive science have been essential in shaping experimental design and image analysis strategies from the outset. Now some of the cognitive models and their assumptions (for example, cognitive subtraction) are being re-evaluated in the light of how the brain actually implements putative components and processes. In this review we will consider experimental designs that go beyond cognitive subtraction and also consider how functional imaging can be used to assess the context-sensitivity of cognitive processing (using conjunction analyses), and the integration of different processes (in terms of interactions, using factorial designs) and how both these themes can be developed in the context of parametric designs. These new approaches reflect an ongoing discourse between cognitive science and the emerging principles of functional anatomy.
Nationwide Survey of Work Environment, Work-Life Balance and Burnout among Psychiatrists in Japan

Psychiatry has been consistently shown to be a profession characterised by 'high-burnout'; however, no nationwide surveys on this topic have been conducted in Japan.



The objective of this study was to estimate the prevalence of burnout and to ascertain the relationship between work environment satisfaction, work-life balance satisfaction and burnout among psychiatrists working in medical schools in Japan.



We mailed anonymous questionnaires to all 80 psychiatry departments in medical schools throughout Japan. Work-life satisfaction, work-environment satisfaction and social support assessments, as well as the Maslach Burnout Inventory (MBI), were used.



Sixty psychiatric departments (75.0%) responded, and 704 psychiatrists provided answers to the assessments and MBI. Half of the respondents (n = 311, 46.0%) experienced difficulty with their work-life balance. Based on the responses to the MBI, 21.0% of the respondents had a high level of emotional exhaustion, 12.0% had a high level of depersonalisation, and 72.0% had a low level of personal accomplishment. Receiving little support, experiencing difficulty with work-life balance, and having less work-environment satisfaction were significantly associated with higher emotional exhaustion. A higher number of nights worked per month was significantly associated with higher depersonalisation.



A low level of personal accomplishment was quite prevalent among Japanese psychiatrists compared with the results of previous studies. Poor work-life balance was related to burnout, and social support was noted to mitigate the impact of burnout.
1 CROWDFUNDING : TRANSFORMING CUSTOMERS INTO INVESTORS THROUGH INNOVATIVE

Consumers have recently begun to play a new role in some markets: that of providing capital and investment support to the offering. This phenomenon, called crowdfunding, is a collective effort by people who network and pool their money together, usually via the Internet, in order to invest in and support efforts initiated by other people or organizations. Successful service businesses that organize crowdfunding and act as intermediaries are emerging, attesting to the viability of this means of attracting investment. Employing a “Grounded Theory” approach, this paper performs an in-depth qualitative analysis of three cases involving crowdfunding initiatives: SellaBand in the music business, Trampoline in financial services, and Kapipal in non-profit services. These cases were selected to represent a diverse set of crowdfunding operations that vary in terms of risk/return for the investorconsumer and the type of consumer involvement. The analysis offers important insights about investor behaviour in crowdfunding service models, the potential determinants of such behaviour, and variations in behaviour and determinants across different service models. The findings have implications for service managers interested in launching and/or managing crowdfunding initiatives, and for service theory in terms of extending the consumer’s role from co-production and co-creation to investment.
Screening for Psychological Distress in Adult Primary Brain Tumor Patients and Caregivers: Considerations for Cancer Care Coordination

This study aimed to assess psychological distress (PD) as scored by the Distress Thermometer (DT) in adult primary brain tumor patients and caregivers (CGs) in a clinic setting and ascertain if any high-risk subgroups for PD exist.



From May 2012 to August 2013, n = 96 patients and n = 32 CG underwent DT screening at diagnosis, and a differing cohort of n = 12 patients and n = 14 CGs at first recurrence. Groups were described by diagnosis (high grade, low grade, and benign) and English versus non English speaking. Those with DT score ≥4 met caseness criteria for referral to psycho-oncology services. One-way ANOVA tests were conducted to test for between-group differences where appropriate.



At diagnosis and first recurrence, 37.5 and 75.0% (respectively) of patients had DT scores above the cutoff for distress. At diagnosis, 78.1% of CGs met caseness criteria for distress. All CGs at recurrence met distress criterion. Patients with high-grade glioma had significantly higher scores than those with a benign tumor. For patients at diagnosis, non English speaking participants did not report significantly higher DT scores than English speaking participants.



Psychological distress is particularly elevated in CGs and in patients with high-grade glioma at diagnosis. Effective PD screening, triage, and referral by skilled care coordinators are vital to enable timely needs assessment, psychological support, and effective intervention.
Expert finding in community question answering: a review

The rapid development of Community Question Answering (CQA) satisfies users’ quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. The new features of CQA (such as huge volume, sparse data and crowdsourcing) violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify the recent solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the crowdsourcing situation. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boost the performance. Further, we compare the performance of different models on different types of matching tasks, including text vs. text, graph vs. text, audio vs. text and video vs. text. The results will help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA.
A high-efficiency runtime reconfigurable IP for CNN acceleration on a mid-range all-programmable SoC

Convolutional Neural Networks (CNNs) are a nature-inspired model, extensively employed in a broad range of applications in computer vision, machine learning and pattern recognition. The CNN algorithm requires execution of multiple layers, commonly called convolution layers, that involve application of 2D convolution filters of different sizes over a set of input image features. Such a computation kernel is intrinsically parallel, thus significantly benefits from acceleration on parallel hardware. In this work, we propose an accelerator architecture, suitable to be implemented on mid-to high-range FPGA devices, that can be re-configured at runtime to adapt to different filter sizes in different convolution layers. We present an accelerator configuration, mapped on a Xilinx Zynq XC-Z7045 device, that achieves up to 120 GMAC/s (16 bit precision) when executing 5×5 filters and up to 129 GMAC/s when executing 3×3 filters, consuming less than 10W of power, reaching more than 97% DSP resource utilizazion at 150MHz operating frequency and requiring only 16B/cycle I/O bandwidth.