nils-herrmann/clean_scirepeval huggingface dataset:
Innervation du muscle rétracteur antérieur du byssus (ABRM) de Mytilus edulis L. et de Mytilus galloprovincialis Lmk.

Specific and non specific cholinesterase activities were demonstrated in the ABRM of Mytilus edulis L. and Mytilus galloprovincialis L. by means of different techniques. The results were found identical for both species: neuromuscular junctions “en grappe”-type scarcely distributed within the ABRM, contain AChE. According to the histochemical inhibition tests, (a) the eserine inhibits AChE activity of the ABRM with a level of 5·10−5 M or higher, (b) the ChE non specific activities are inhibited by iso-OMPA level between 5·10−5 to 10−4 M. The histo- and cytochemical observations were completed by showing the existence of neuromuscular junctions containing small clear vesicles: they probably are the morphological support for ACh presence. Moreover, specific and non specific ChE activities were localized in the glio-interstitial cells. AChE precipitates were developped along the ABRM sarcolemma, some muscle mitochondria and in the intercellular spaces remain enigmatic.
Unsupervised Learning of Task-Specific Tree Structures with Tree-LSTMs

For years, recursive neural networks (RvNNs) have shown to be suitable for representing text into fixed-length vectors and achieved good performance on several natural language processing tasks. However, the main drawback of RvNN is that it requires explicit tree structure (e.g. parse tree), which makes data preparation and model implementation hard. In this paper, we propose a novel tree-structured long short-term memory (Tree-LSTM) architecture that efficiently learns how to compose task-specific tree structures only from plain text data. To achieve this property, our model uses Straight-Through (ST) Gumbel-Softmax estimator to decide the parent node among candidates and to calculate gradients of the discrete decision. We evaluate the proposed model on natural language interface and sentiment analysis and show that our model outperforms or at least comparable to previous Tree-LSTM-based works. Especially in the natural language interface task, our model establishes the new state-of-the-art accuracy of 85.4%. We also find that our model converges significantly faster and needs less memory than other models of complex structures.
Parallel functional arrays

The goal of this paper is to develop a form of functional arrays (sequences) that are as efficient as imperative arrays, can be used in parallel, and have well defined cost-semantics. The key idea is to consider sequences with functional value semantics but non-functional cost semantics. Because the value semantics is functional, "updating" a sequence returns a new sequence. We allow operations on "older" sequences (called interior sequences) to be more expensive than operations on the "most recent" sequences (called leaf sequences). 
 We embed sequences in a language supporting fork-join parallelism. Due to the parallelism, operations can be interleaved non-deterministically, and, in conjunction with the different cost for interior and leaf sequences, this can lead to non-deterministic costs for a program. Consequently the costs of programs can be difficult to analyze. The main result is the derivation of a deterministic cost dynamics which makes analyzing the costs easier. The theorems are not specific to sequences and can be applied to other data types with different costs for operating on interior and leaf versions. 
 We present a wait-free concurrent implementation of sequences that requires constant work for accessing and updating leaf sequences, and logarithmic work for accessing and linear work for updating interior sequences. We sketch a proof of correctness for the sequence implementation. The key advantages of the present approach compared to current approaches is that our implementation requires no changes to existing programming languages, supports nested parallelism, and has well defined cost semantics. At the same time, it allows for functional implementations of algorithms such as depth-first search with the same asymptotic complexity as imperative implementations.
FPGA-based 6-DoF pose estimation with a monocular camera using non co-planer marker and application on micro quadcopter

Self-localization using visual markers is widely used for precise control of Unmanned aerial vehicles (UAVs). This paper presents a novel method of 6-DoF pose estimation using a monocular camera and non co-planar markers. We conducted numerical simulations comparing pose estimation using co-planar markers and using non co-planar markers, and designed a non co-planar marker feasible for robotics applications. Our method uses a single CPU core and a field programmable gate array (FPGA) and can conduct fast and precise 6-DoF pose estimation at the same frequency of the camera input (100 fps). In addition, to show the performance of our method, we applied our 6-DoF pose estimation algorithm to a palm-sized quadcopter and flew it autonomously using only an on-board system with a monocular camera and three-axis gyro sensor. To the authors' knowledge, this is the first study on fully on-board autonomous flight control of an MAV in 6-DoF space without an accelerometer.
Inhibitory Modulation of Orbitofrontal Cortex on Medial Prefrontal Cortex-Amygdala Information Flow.

The amygdala receives cortical inputs from the medial prefrontal cortex (mPFC) and orbitofrontal cortex (OFC) that are believed to affect emotional control and cue-outcome contingencies, respectively. Although mPFC impact on the amygdala has been studied, how the OFC modulates mPFC-amygdala information flow, specifically the infralimbic (IL) division of mPFC, is largely unknown. In this study, combined in vivo extracellular single-unit recordings and pharmacological manipulations were used in anesthetized rats to examine how OFC modulates amygdala neurons responsive to mPFC activation. Compared with basal condition, pharmacological (N-Methyl-D-aspartate) or electrical activation of the OFC exerted an inhibitory modulation of the mPFC-amygdala pathway, which was reversed with intra-amygdala blockade of GABAergic receptors with combined GABAA and GABAB antagonists (bicuculline and saclofen). Moreover, potentiation of the OFC-related pathways resulted in a loss of OFC control over the mPFC-amygdala pathway. These results show that the OFC potently inhibits mPFC drive of the amygdala in a GABA-dependent manner; but with extended OFC pathway activation this modulation is lost. Our results provide a circuit-level basis for this interaction at the level of the amygdala, which would be critical in understanding the normal and pathophysiological control of emotion and contingency associations regulating behavior.
Emerging topic detection for organizations from microblogs

Microblog services have emerged as an essential way to strengthen the communications among individuals and organizations. These services promote timely and active discussions and comments towards products, markets as well as public events, and have attracted a lot of attentions from organizations. In particular, emerging topics are of immediate concerns to organizations since they signal current concerns of, and feedback by their users. Two challenges must be tackled for effective emerging topic detection. One is the problem of real-time relevant data collection and the other is the ability to model the emerging characteristics of detected topics and identify them before they become hot topics. To tackle these challenges, we first design a novel scheme to crawl the relevant messages related to the designated organization by monitoring multi-aspects of microblog content, including users, the evolving keywords and their temporal sequence. We then develop an incremental clustering framework to detect new topics, and employ a range of content and temporal features to help in promptly detecting hot emerging topics. Extensive evaluations on a representative real-world dataset based on Twitter data demonstrate that our scheme is able to characterize emerging topics well and detect them before they become hot topics.
Triple Context-Based Knowledge Graph Embedding

Knowledge graph embedding aims to represent entities and relations of a knowledge graph in continuous vector spaces. It has increasingly drawn attention for its ability to encode semantics in low dimensional vectors as well as its outstanding performance on many applications, such as question answering systems and information retrieval tasks. Existing methods often handle each triple independently, without considering context information of a triple in the knowledge graph, such an information can be useful for inference of new knowledge. Moreover, the relations and paths between an entity pair also provide information for inference. In this paper, we define a novel context-dependent knowledge graph representation model named triple-context-based knowledge embedding, which is based on the notion of triple context used for embedding entities and relations. For each triple, the triple context is composed of two kinds of graph structured information: one is a set of neighboring entities along with their outgoing relations, the other is a set of relation paths which contain a pair of target entities. Our embedding method is designed to utilize the triple context of each triple while learning embeddings of entities and relations. The method is evaluated on multiple tasks in the paper. Experimental results reveal that our method achieves significant improvements over the state-of-the-art methods.
Can color changes alter the neural correlates of recognition memory? Manipulation of processing affects an electrophysiological indicator of conceptual implicit memory.

It has been widely shown that recognition memory includes two distinct retrieval processes: familiarity and recollection. Many studies have shown that recognition memory can be facilitated when there is a perceptual match between the studied and the tested items. Most event-related potential studies have explored the perceptual match effect on familiarity on the basis of the hypothesis that the specific event-related potential component associated with familiarity is the FN400 (300-500 ms mid-frontal effect). However, it is currently unclear whether the FN400 indexes familiarity or conceptual implicit memory. In addition, on the basis of the findings of a previous study, the so-called perceptual manipulations in previous studies may also involve some conceptual alterations. Therefore, we sought to determine the influence of perceptual manipulation by color changes on recognition memory when the perceptual or the conceptual processes were emphasized. Specifically, different instructions (perceptually or conceptually oriented) were provided to the participants. The results showed that color changes may significantly affect overall recognition memory behaviorally and that congruent items were recognized with a higher accuracy rate than incongruent items in both tasks, but no corresponding neural changes were found. Despite the evident familiarity shown in the two tasks (the behavioral performance of recognition memory was much higher than at the chance level), the FN400 effect was found in conceptually oriented tasks, but not perceptually oriented tasks. It is thus highly interesting that the FN400 effect was not induced, although color manipulation of recognition memory was behaviorally shown, as seen in previous studies. Our findings of the FN400 effect for the conceptual but not perceptual condition support the explanation that the FN400 effect indexes conceptual implicit memory.
Role of Artificial Intelligence & Expert System in : Business Competitiveness

Today’s global economy requires increased attention to the issue of business competitiveness. Business information system or Artificial Intelligence and expert system raise the competitiveness of enterprises in the global market. Business intelligence as the basis for the development and application in business information is becoming an important information technology framework that can help organization to manage, develop and communicate their intangible assets, such as information and knowledge based economy. Competency Based Management (CBM) has become vital to any firm’s strategic position and organizational decision making. The role of Artificial intelligence & Expert System (ES) is to provide a knowledge based information system that is expected to have human attributes in order to replicate human capacity in ethical decision making. In this paper a holistic framework is proposed to review the ES approach that will be practically feasible for organizational settings. It will also provide executives and scholars with pragmatic understanding about integrating knowledge management strategy and technologies in business processes for successful performance. It is based on the psychological conceptions of human competence and performance in the workplace. Using an ES approach to Educational Institute with CBM will be able to more effectively use their limited resources to reap the more benefits from their investments in both people and technology. The idea of robotic domestic workers is still farfetched but companies are making progress even here. There is already a Robot Vacuum Cleaner marketed by Electrolux and doubtless improved systems with better functionality.
Poker hand classification

Artificial Neural Network (ANN) forms a useful tool in pattern recognition tasks. Collection of five, eight or more cards in a cards game are normally called poker hands. There are various poker variations, each with different poker hands ranking. In the present paper, an attempt is made to solve poker hand classification problem using different learning paradigms and architectures of artificial neural network: multi-layer feed-forward Backpropagation (supervised) and self-organizing map (un-supervised). Poker data set is touted to be a difficult dataset for classification algorithms. Experimental results are presented to demonstrate the performance of the proposed system. The paper also aims to suggest about training algorithms and training parameters that must be chosen in order to solve poker hand classification problem using neural network model. As neural networks are the most convenient tools for handling complicated data sets with real values, one of the most important objectives of the paper is to explain how a neural network can also be used successfully for classification kind of problems involving categorical attributes. The proposed model succeeded in classification of poker hands with 94% classification accuracy.
Autonomic disorders predicting Parkinson's disease.

It is now well recognized that there is a premotor phase of Parkinson's disease (PD) with hyposmia and REM sleep behavior disorder caused by degeneration of specific CNS neurons. Most patients with PD describe autonomic symptoms at the time of diagnosis suggesting that these features may have potential sensitivity as clinical biomarkers of the premotor phase. The recognition that damage to peripheral autonomic neurons is present in the early stages of PD has led to a search for specific abnormalities in autonomic function that could serve as predictive biomarkers. There is evidence that constipation, urinary and sexual dysfunction and more recently decreased cardiac chronotropic response during exercise, are part of the premotor parkinsonian phenotype. The sensitivity and specificity of these features has yet to be accurately assessed. We briefly review the evidence for autonomic dysfunction as biomarker of premotor PD.
DeepMatching: Hierarchical Deformable Dense Matching

We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a hierarchical, multi-layer, correlational architecture designed for matching images and was inspired by deep convolutional approaches. The proposed matching algorithm can handle non-rigid deformations and repetitive textures and efficiently determines dense correspondences in the presence of significant changes between images. We evaluate the performance of DeepMatching, in comparison with state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al. A comparison of affine region detectors, 2005), the MPI-Sintel (Butler et al. A naturalistic open source movie for optical flow evaluation, 2012) and the Kitti (Geiger et al.  The KITTI dataset, 2013) datasets. DeepMatching outperforms the state-of-the-art algorithms and shows excellent results in particular for repetitive textures. We also apply DeepMatching to the computation of optical flow, called DeepFlow, by integrating it in the large displacement optical flow (LDOF) approach of Brox and Malik (Large displacement optical flow: descriptor matching in variational motion estimation, 2011). Additional robustness to large displacements and complex motion is obtained thanks to our matching approach. DeepFlow obtains competitive performance on public benchmarks for optical flow estimation.
What is Market News ?

We investigate the market for news under two assumptions: that readers hold beliefs which they like to see confirmed, and that newspapers can slant stories toward these beliefs. We show that, on the topics where readers share common beliefs, one should not expect accuracy even from competitive media: competition results in lower prices, but common slanting toward reader biases. On topics where reader beliefs diverge (such as politically divisive issues), however, newspapers segment the market and slant toward extreme positions. Yet in the aggregate, a reader with access to all news sources could get an unbiased perspective. Generally speaking, reader heterogeneity is more important for accuracy in media than competition per se.
An Interpretable Knowledge Transfer Model for Knowledge Base Completion

Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, ITransF, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets— WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information.
Estimation accuracy of a vector-controlled frequency converter used in the determination of the pump system operating state

Pump, compressor and fan systems often have a notable energy savings potential, which can be identified by monitoring their operation for instance by a frequency converter and model-based estimation methods. In such cases, sensorless determination of the system operating state relies on the accurate estimation of the motor rotational speed and shaft torque, which is commonly available in vector- and direct-torque-controlled frequency converters. However, frequency converter manufacturers seldom publish the expected estimation accuracies for the rotational speed and shaft torque. In this paper, the accuracy of these motor estimates is studied by laboratory measurements for a vector-controlled frequency converter both in the steady and dynamical states. In addition, the effect of the flux optimization feature on the estimation accuracy is studied. Finally, the impact of erroneous motor estimates on the flow rate estimation is demonstrated in the paper.
Energy Efficient Cooperative Computing in Mobile Wireless Sensor Networks

Advances in future computing to support emerging sensor applications are becoming more important as the need to better utilize computation and communication resources and make them energy efficient. As a result, it is predicted that intelligent devices and networks, including mobile wireless sensor networks (MWSN), will become the new interfaces to support future applications. In this paper, we propose a novel approach to minimize energy consumption of processing an application in MWSN while satisfying a certain completion time requirement. Specifically, by introducing the concept of cooperation, the logics and related computation tasks can be optimally partitioned, offloaded and executed with the help of peer sensor nodes, thus the proposed solution can be treated as a joint optimization of computing and networking resources. Moreover, for a network with multiple mobile wireless sensor nodes, we propose energy efficient cooperation node selection strategies to offer a tradeoff between fairness and energy consumption. Our performance analysis is supplemented by simulation results to show the significant energy saving of the proposed solution.
Using Mutation Analysis for Assessing and Comparing Testing Coverage Criteria

The empirical assessment of test techniques plays an important role in software testing research. One common practice is to seed faults in subject software, either manually or by using a program that generates all possible mutants based on a set of mutation operators. The latter allows the systematic, repeatable seeding of large numbers of faults, thus facilitating the statistical analysis of fault detection effectiveness of test suites; however, we do not know whether empirical results obtained this way lead to valid, representative conclusions. Focusing on four common control and data flow criteria (block, decision, C-use, and P-use), this paper investigates this important issue based on a middle size industrial program with a comprehensive pool of test cases and known faults. Based on the data available thus far, the results are very consistent across the investigated criteria as they show that the use of mutation operators is yielding trustworthy results: generated mutants can be used to predict the detection effectiveness of real faults. Applying such a mutation analysis, we then investigate the relative cost and effectiveness of the above-mentioned criteria by revisiting fundamental questions regarding the relationships between fault detection, test suite size, and control/data flow coverage. Although such questions have been partially investigated in previous studies, we can use a large number of mutants, which helps decrease the impact of random variation in our analysis and allows us to use a different analysis approach. Our results are then; compared with published studies, plausible reasons for the differences are provided, and the research leads us to suggest a way to tune the mutation analysis process to possible differences in fault detection probabilities in a specific environment
Collaborative Working Architecture for IoT-Based Applications†

The new sensing applications need enhanced computing capabilities to handle the requirements of complex and huge data processing. The Internet of Things (IoT) concept brings processing and communication features to devices. In addition, the Cloud Computing paradigm provides resources and infrastructures for performing the computations and outsourcing the work from the IoT devices. This scenario opens new opportunities for designing advanced IoT-based applications, however, there is still much research to be done to properly gear all the systems for working together. This work proposes a collaborative model and an architecture to take advantage of the available computing resources. The resulting architecture involves a novel network design with different levels which combines sensing and processing capabilities based on the Mobile Cloud Computing (MCC) paradigm. An experiment is included to demonstrate that this approach can be used in diverse real applications. The results show the flexibility of the architecture to perform complex computational tasks of advanced applications.