nils-herrmann/clean_scirepeval huggingface dataset:
DESIGN OF A LARGE RANGE XY NANOPOSITIONING SYSTEM

Achieving large motion range (> 1mm) along with nanometric motion quality (< 10nm), simultaneously, has been a key challenge in nanopositioning systems. Practical limitations associated with the individual physical components (bearing, actuators, and sensors) and their integration, particularly in the case of multi-axis systems, have restricted the range of currently available nanopositioning systems to approximately 100μm per axis. This paper presents a novel physical system layout, comprising a bearing, actuators, and sensors, that enables large range XY nanopositioning. The bearing is based on a parallel-kinematic XY flexure mechanism that provides a high degree of geometric decoupling between the two motion axes by avoiding geometric over-constraint, provides actuator isolation that allows the use of large-stroke single-axis actuators, and enables a complementary end-point sensing scheme using commonly available sensors. These attributes help achieve 10mm × 10mm motion range in the proposed nanopositioning system. Having overcome the physical system design challenges, a dynamic model of proposed nanopositioning system is created and verified via system identification. In particular, dynamic nonlinearities associated with the large displacements of the flexure mechanism and resulting controls challenges are identified. The physical system is fabricated, assembled, and tested to validate its simultaneous large range and nanometric motion capabilities. Preliminary closed-loop test results, which highlight the potential as well as pending challenges associated with this new design configuration, are presented.
TUT: a statistical model for detecting trends, topics and user interests in social media

The rapid development of online social media sites is accompanied by the generation of tremendous web contents. Web users are shifting from data consumers to data producers. As a result, topic detection and tracking without taking users' interests into account is not enough. This paper presents a statistical model that can detect interpretable trends and topics from document streams, where each trend (short for trending story) corresponds to a series of continuing events or a storyline. A topic is represented by a cluster of words frequently co-occurred. A trend can contain multiple topics and a topic can be shared by different trends. In addition, by leveraging a Recurrent Chinese Restaurant Process (RCRP), the number of trends in our model can be determined automatically without human intervention, so that our model can better generalize to unseen data. Furthermore, our proposed model incorporates user interest to fully simulate the generation process of web contents, which offers the opportunity for personalized recommendation in online social media. Experiments on three different datasets indicated that our proposed model can capture meaningful topics and trends, monitor rise and fall of detected trends, outperform baseline approach in terms of perplexity on held-out dataset, and improve the result of user participation prediction by leveraging users' interests to different trends.
Compact Implementations of ARX-Based Block Ciphers on IoT Processors

In this article, we present implementations for Addition, Rotation, and eXclusive-or (ARX)-based block ciphers, including LEA and HIGHT, on IoT devices, including 8-bit AVR, 16-bit MSP, 32-bit ARM, and 32-bit ARM-NEON processors. We optimized 32-/8-bitwise ARX operations for LEA and HIGHT block ciphers by considering variations in word size, the number of general purpose registers, and the instruction set of the target IoT devices. Finally, we achieved the most compact implementations of LEA and HIGHT block ciphers. The implementations were fairly evaluated through the Fair Evaluation of Lightweight Cryptographic Systems framework, and implementations won the competitions in the first and the second rounds.
DO LANGUAGE MODELS HAVE COMMON SENSE?

It has been argued that current machine learning models do not have common sense, and therefore must be hard-coded with prior knowledge (Marcus, 2018). Here we show surprising evidence that language models can already learn to capture certain common sense knowledge. Our key observation is that a language model can compute the probability of any statement, and this probability can be used to evaluate the truthfulness of that statement. On the Winograd Schema Challenge (Levesque et al., 2011), language models are 11% higher in accuracy than previous state-of-the-art supervised methods. Language models can also be fine-tuned for the task of Mining Commonsense Knowledge on ConceptNet to achieve an F1 score of 0.912 and 0.824, outperforming previous best results (Jastrzebski et al., 2018). Further analysis demonstrates that language models can discover unique features of Winograd Schema contexts that decide the correct answers without explicit supervision.
Implementing Gentry's Fully-Homomorphic Encryption Scheme

We describe a working implementation of a variant of Gentry’s fully homomorphic encryption scheme , similar to the variant used in an earlier implementation effort by Smart and Vercauteren (PKC 2010). Smart and Vercauteren implemented the underlying “somewhat homomorphic” scheme, but were not able to implement the bootstrapping functionality that is needed to get the complete scheme to work. We show a number of optimizations that allow us to implement all aspects of the scheme, including the bootstrapping functionality. Our main optimization is a key-generation method for the underlying somewhat homomorphic encryption, that does not require full polynomial inversion. This reduces the asymptotic complexity from Õ(n) to Õ(n) when working with dimension-n lattices (and practically reducing the time from many hours/days to a few seconds/minutes). Other optimizations include a batching technique for encryption, a careful analysis of the degree of the decryption polynomial, and some space/time trade-offs for the fully-homomorphic scheme. We tested our implementation with lattices of several dimensions, corresponding to several security levels. From a “toy” setting in dimension 512, to “small,” “medium,” and “large” settings in dimensions 2048, 8192, and 32768, respectively. The public-key size ranges in size from 70 Megabytes for the “small” setting to 2.3 Gigabytes for the “large” setting. The time to run one bootstrapping operation (on a 1-CPU 64bit machine with large memory) ranges from 30 seconds for the “small” setting to 30 minutes for the “large” setting.
HIV-1 Capsid-Cyclophilin Interactions Determine Nuclear Import Pathway, Integration Targeting and Replication Efficiency

Lentiviruses such as HIV-1 traverse nuclear pore complexes (NPC) and infect terminally differentiated non-dividing cells, but how they do this is unclear. The cytoplasmic NPC protein Nup358/RanBP2 was identified as an HIV-1 co-factor in previous studies. Here we report that HIV-1 capsid (CA) binds directly to the cyclophilin domain of Nup358/RanBP2. Fusion of the Nup358/RanBP2 cyclophilin (Cyp) domain to the tripartite motif of TRIM5 created a novel inhibitor of HIV-1 replication, consistent with an interaction in vivo. In contrast to CypA binding to HIV-1 CA, Nup358 binding is insensitive to inhibition with cyclosporine, allowing contributions from CypA and Nup358 to be distinguished. Inhibition of CypA reduced dependence on Nup358 and the nuclear basket protein Nup153, suggesting that CypA regulates the choice of the nuclear import machinery that is engaged by the virus. HIV-1 cyclophilin-binding mutants CA G89V and P90A favored integration in genomic regions with a higher density of transcription units and associated features than wild type virus. Integration preference of wild type virus in the presence of cyclosporine was similarly altered to regions of higher transcription density. In contrast, HIV-1 CA alterations in another patch on the capsid surface that render the virus less sensitive to Nup358 or TRN-SR2 depletion (CA N74D, N57A) resulted in integration in genomic regions sparse in transcription units. Both groups of CA mutants are impaired in replication in HeLa cells and human monocyte derived macrophages. Our findings link HIV-1 engagement of cyclophilins with both integration targeting and replication efficiency and provide insight into the conservation of viral cyclophilin recruitment.
Complexity Leadership Theory : Shifting leadership from the industrial age to the knowledge era ☆

Leadership models of the last century have been products of top-down, bureaucratic paradigms. These models are eminently effective for an economy premised on physical production but are not well-suited for a more knowledge-oriented economy. Complexity science suggests a different paradigm for leadership—one that frames leadership as a complex interactive dynamic from which adaptive outcomes (e.g., learning, innovation, and adaptability) emerge. This article draws from complexity science to develop an overarching framework for the study of Complexity Leadership Theory, a leadership paradigm that focuses on enabling the learning, creative, and adaptive capacity of complex adaptive systems (CAS) within a context of knowledge-producing organizations. This conceptual framework includes three entangled leadership roles (i.e., adaptive leadership, administrative leadership, and enabling leadership) that reflect a dynamic relationship between the bureaucratic, administrative functions of the organization and the emergent, informal dynamics of complex adaptive systems (CAS).
Quantum generalisation of feedforward neural networks

We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e. unitary. (The classical networks we generalise are called feedforward, and have step-function activation functions.) The quantum network can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks. We demonstrate numerically that it can: (i) compress quantum states onto a minimal number of qubits, creating a quantum autoencoder, and (ii) discover quantum communication protocols such as teleportation. Our general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.
Addressing Challenges with Augmented Reality Applications on Smartphones

The popularity of smartphones equipped with GPS and geomagnetic sensors has spurred mobile application developer interest in augmented reality (AR), which presents highly contexualized, spatially relevant information that enhances user knowledge of their immediate surroundings. AR applications typically mesh relevant information with user views of the physical world. Prior research has focused on interfaces built with custom hardware, but a smartphone equipped with GPS, a camera, and a geomagnetic sensor is an attractive alternative to traditional solutions. These devices can be programmed to present contextsensitive information to users without needing custom hardware. This paper examines three key challenges facing AR developers on mobile devices and presents solutions applicable to modern mobile platforms, such as Apple’s iPhone and Google Android-based smartphones. First, we investigate methods of filtering raw sensor data and present an algorithm that eliminates sensor noise. Second, we explore the process of implementing a “magic lens” interaction metaphor by overlaying perspective-rendered graphics on the device’s camera using OpenGL and UIKit. Third, we provide an efficient technique for fetching and caching geographically tagged points of interest from a server.
Where are the hard knapsack problems?

The knapsack problem is believed to be one of the “easier” N P -hard problems. Not only can it be solved in pseudo-polynomial time, but also decades of algorithmic improvements have made it possible to solve nearly all standard instances from the literature. The purpose of this paper is to give an overview of all recent exact solution approaches, and to show that the knapsack problem still is hard to solve for these algorithms for a variety of new test problems. These problems are constructed either by using standard benchmark instances with larger coefficients, or by introducing new classes of instances for which most upper bounds perform badly. The first group of problems challenge the dynamic programming algorithms while the other group of problems are focused towards branch-and-bound algorithms. Numerous computational experiments with all recent state-of-art codes are used to show that the (KP) is still difficult to solve for a wide number of problems. One could say that the previous benchmark tests were limited to a few highly structured instances, which do not show the full characteristics of knapsack problems.
Differential effects of atomoxetine on executive functioning and lexical decision in attention-deficit/hyperactivity disorder and reading disorder.

The effects of a promising pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD), atomoxetine, were studied on executive functions in both ADHD and reading disorder (RD) because earlier research demonstrated an overlap in executive functioning deficits in both disorders. In addition, the effects of atomoxetine were explored on lexical decision.



Sixteen children with ADHD, 20 children with ADHD + RD, 21 children with RD, and 26 normal controls were enrolled in a randomized placebo-controlled crossover study. Children were measured on visuospatial working memory, inhibition, and lexical decision on the day of randomization and following two 28-day medication periods.



Children with ADHD + RD showed improved visuospatial working memory performance and, to a lesser extent, improved inhibition following atomoxetine treatment compared to placebo. No differential effects of atomoxetine were found for lexical decision in comparison to placebo. In addition, no effects of atomoxetine were demonstrated in the ADHD and RD groups.



Atomoxetine improved visuospatial working memory and to a lesser degree inhibition in children with ADHD + RD, which suggests differential developmental pathways for co-morbid ADHD + RD as compared to ADHD and RD alone.
Deep Biaffine Attention for Neural Dependency Parsing

Andor, D., Alberti, C., Weiss, D., Severyn, A., Presta, A., Ganchev, K., Petrov, S., and Collins, M. (2016). Globally normalized transition-based neural networks. In Association for Computational Linguistics. Ballesteros, M., Goldberg, Y., Dyer, C., and Smith, N. A. (2016). Training with exploration improves a greedy stack-LSTM parser. Proceedings of the conference on empirical methods in natural language processing. Chen, D. and Manning, C. D. (2014). A fast and accurate dependency parser using neural networks. In Proceedings of the conference on empirical methods in natural language processing, pages 740–750. Cheng, H., Fang, H., He, X., Gao, J., and Deng, L. (2016). Bi-directional attention with agreement for dependency parsing.  Hashimoto, K., Xiong, C., Tsuruoka, Y., and Socher, R. (2016). A joint many-task model: Growing a neural network for multiple nlp tasks.  (2016). Simple and accurate dependency parsing using bidirectional LSTM feature representations. Transactions of the Association for Computational Linguistics, 4:313–327. Kuncoro, A., Ballesteros, M., Kong, L., Dyer, C., Neubig, G., and Smith, N. A. (2016). What do recurrent neural network grammars learn about syntax? CoRR, abs/1611.05774. McDonald, R. T. and Pereira, F. C. (2006). Online learning of approximate dependency parsing algorithms. In EACL. Nivre,  Hall, J., and Nilsson,  (2006).  A data-driven parser-generator for dependency parsing. In Proceedings of LREC, volume 6, pages 2216–2219.
Independent Factor Reinforcement Learning for Portfolio Management

In this paper we propose to do portfolio management using reinforcement learning (RL) and independent factor model. Factors in independent factor model are mutually independent and exhibit better predictability. RL is applied to each factor to capture temporal dependence and provide investment suggestion on factor. Optimal weights on factors are found by portfolio optimization method subject to the investment suggestions and general portfolio constraints. Experimental results and analysis are given to show that the proposed method has better performance when compare to two alternative portfolio management systems.
Interactive Narrative: An Intelligent Systems Approach

Interactive narrative is a form of digital interactive experience in which users create or influence a dramatic storyline through their actions. The goal of an interactive narrative system is to immerse the user in a virtual world such that he or she believes that they are an integral part of an unfolding story and that their actions can significantly alter the direction and/or outcome of the story. In this article we review the ways in which artificial intelligence can be brought to bear on the creation of interactive narrative systems. We lay out the landscape of about 20 years of interactive narrative research and explore the successes as well as open research questions pertaining to the novel use of computational narrative intelligence in the pursuit of entertainment, education, and training.
Bootstrap, Review, Decode: Using Out-of-Domain Textual Data to Improve Image Captioning

We propose a novel way of using out-of-domain textual data to enhance the performance of existing image captioning systems. We evaluate this learning approach on a newly designed model that uses – and improves upon – building blocks from state-of-the-art methods. This model starts from detecting visual concepts present in an image which are then fed to a reviewer-decoder architecture with an attention mechanism. Unlike previous approaches that encode visual concepts using word embeddings, we instead suggest using regional image features which capture more intrinsic information. The main benefit of this architecture is that it synthesizes meaningful thought vectors that capture salient image properties and then applies a soft attentive decoder to decode the thought vectors and generate image captions. We evaluate our model on both Microsoft COCO and Flickr30K datasets and demonstrate that this model combined with our bootstrap learning method can largely improve performance and help the model to generate more accurate and diverse captions.
A Survey on Application of Bio-Inspired Algorithms

The algorithms that are inspired by the principles of natural biological evolution and distributed collective behaviour of social colonies have shown excellence in dealing with complex optimization problems and are becoming more popular nowadays. This paper surveys the recent advances in biologically inspired swarm optimization methods, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in various fields. KeywordsBiologically inspired algorithms, Swarm Intelligence, application of Bio-inspired algorithms.
Digital anthropometry for human body measurement on android platform

Anthropometry is the science of measuring the size and proportions of the human body. Based on the current researches, callipers and tape measures are the instruments used in measuring body length of an individual. Anthropometry is mostly conducted by the tailors as well as interior designers. This study aims to develop Digital Anthropometry for Human Body Measurement Using OpenCV on Android Platform which automatically measures human body using android phone. These body parts to be measured are represented by a marker; shoulder width, sleeve length, body length, back-waist length, back-neck-to-cuff, cross-back, outseam and inseam. For the accuracy, the distance requirement of the device is 30 inches away from the subject for upper body and 62 inches for the lower body. The required color shirt of the subject is a white and black pants or shorts, the required background color is white and bright surrounding is required.
Frequency invariant transformation of periodic signals (FIT-PS) for signal representation in NILM

It is the aim of non-intrusive load monitoring (NILM) to determine the individual energy consumption of multiple devices on the basis of the total energy consumption. The energy consumption of a single device is measured at a central point without the need of individual measuring instruments on the devices themselves. We present a new method for signal separation, decomposition of the signal into individual states and feature extraction. Frequency invariant transformation of periodic signals (FIT-PS) is based on a signal diagram similar to the concept of trajectories [1]-[3] utilizing the periodicity of voltage and current, and their correlation. A major advantage of this approach is the dimensional expansion of the signal. Thereby smallest changes cif the current can be identified easily and the associated change of state can be detected. This approach breaks down the current signal into its individual periods using the voltage as a reference signal for the determination of trigger points. Thereby, the phase information between current and voltage is maintained and is inherently part of the new signal representation. The efficiency of the new signal representation and signal separation is demonstrated by the example of an event detection algorithm. For testing the BLUED dataset [4] was used reaching a sensitivity in event detection of 99.31%.
Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: Inverse relationships during postadolescent brain maturation.

Recent in vivo structural imaging studies have shown spatial and temporal patterns of brain maturation between childhood, adolescence, and young adulthood that are generally consistent with postmortem studies of cellular maturational events such as increased myelination and synaptic pruning. In this study, we conducted detailed spatial and temporal analyses of growth and gray matter density at the cortical surface of the brain in a group of 35 normally developing children, adolescents, and young adults. To accomplish this, we used high-resolution magnetic resonance imaging and novel computational image analysis techniques. For the first time, in this report we have mapped the continued postadolescent brain growth that occurs primarily in the dorsal aspects of the frontal lobe bilaterally and in the posterior temporo-occipital junction bilaterally. Notably, maps of the spatial distribution of postadolescent cortical gray matter density reduction are highly consistent with maps of the spatial distribution of postadolescent brain growth, showing an inverse relationship between cortical gray matter density reduction and brain growth primarily in the superior frontal regions that control executive cognitive functioning. Inverse relationships are not as robust in the posterior temporo-occipital junction where gray matter density reduction is much less prominent despite late brain growth in these regions between adolescence and adulthood. Overall brain growth is not significant between childhood and adolescence, but close spatial relationships between gray matter density reduction and brain growth are observed in the dorsal parietal and frontal cortex. These results suggest that progressive cellular maturational events, such as increased myelination, may play as prominent a role during the postadolescent years as regressive events, such as synaptic pruning, in determining the ultimate density of mature frontal lobe cortical gray matter.