nils-herrmann/clean_scirepeval huggingface dataset:
Diversity of Dopaminergic Neural Circuits in Response to Drug Exposure

Addictive substances are known to increase dopaminergic signaling in the mesocorticolimbic system. The origin of this dopamine (DA) signaling originates in the ventral tegmental area (VTA), which sends afferents to various targets, including the nucleus accumbens, the medial prefrontal cortex, and the basolateral amygdala. VTA DA neurons mediate stimuli saliency and goal-directed behaviors. These neurons undergo robust drug-induced intrinsic and extrinsic synaptic mechanisms following acute and chronic drug exposure, which are part of brain-wide adaptations that ultimately lead to the transition into a drug-dependent state. Interestingly, recent investigations of the differential subpopulations of VTA DA neurons have revealed projection-specific functional roles in mediating reward, aversion, and stress. It is now critical to view drug-induced neuroadaptations from a circuit-level perspective to gain insight into how differential dopaminergic adaptations and signaling to targets of the mesocorticolimbic system mediates drug reward. This review hopes to describe the projection-specific intrinsic characteristics of these subpopulations, the differential afferent inputs onto these VTA DA neuron subpopulations, and consolidate findings of drug-induced plasticity of VTA DA neurons and highlight the importance of future projection-based studies of this system.
Deep LSTM for Large Vocabulary Continuous Speech Recognition

Recurrent neural networks (RNNs), especially long shortterm memory (LSTM) RNNs, are effective network for sequential task like speech recognition. Deeper LSTM models perform well on large vocabulary continuous speech recognition, because of their impressive learning ability. However, it is more difficult to train a deeper network. We introduce a training framework with layer-wise training and exponential moving average methods for deeper LSTM models. It is a competitive framework that LSTM models of more than 7 layers are successfully trained on Shenma voice search data in Mandarin and they outperform the deep LSTM models trained by conventional approach. Moreover, in order for online streaming speech recognition applications, the shallow model with low real time factor is distilled from the very deep model. The recognition accuracy have little loss in the distillation process. Therefore, the model trained with the proposed training framework reduces relative 14% character error rate, compared to original model which has the similar real-time capability. Furthermore, the novel transfer learning strategy with segmental Minimum Bayes-Risk is also introduced in the framework. The strategy makes it possible that training with only a small part of dataset could outperform full dataset training from the beginning.
Diagnosing Coronary Heart Disease using Ensemble Machine Learning

Globally, heart disease is the leading cause of death for both men and women. One in every four people is afflicted with and dies of heart disease. Early and accurate diagnoses of heart disease thus are crucial in improving the chances of longterm survival for patients and saving millions of lives. In this research, an advanced ensemble machine learning technology, utilizing an adaptive Boosting algorithm, is developed for accurate coronary heart disease diagnosis and outcome predictions. The developed ensemble learning classification and prediction models were applied to 4 different data sets for coronary heart disease diagnosis, including patients diagnosed with heart disease from Cleveland Clinic Foundation (CCF), Hungarian Institute of Cardiology (HIC), Long Beach Medical Center (LBMC), and Switzerland University Hospital (SUH). The testing results showed that the developed ensemble learning classification and prediction models achieved model accuracies of 80.14% for CCF, 89.12% for HIC, 77.78% for LBMC, and 96.72% for SUH, exceeding the accuracies of previously published research. Therefore, coronary heart disease diagnoses derived from the developed ensemble learning classification and prediction models are reliable and clinically useful, and can aid patients globally, especially those from developing countries and areas where there are few heart disease diagnostic specialists. accuracy; adaptive Boosting algorithm; AUC; classifier; classification error; coronary heart disease; diagnosis; ensemble learning; F-score; K-S measure; machine learning; precision; prediction; recall; ROC; sensitivity; specificity
An ultra-wideband monocycle pulse generator with good performance

A new ultra-wideband monocycle pulse generator with good performance is designed and demonstrated. The pulse generator circuits employ SRD(step recovery diode), Schottky diode, and simple RC coupling and decoupling circuit, and are completely fabricated on the planar microstrip structure, which have the characteristic of low cost and small size. Through SRD modeling, the accuracy of the simulation is improved, which save the design period greatly. The generated monocycle pulse has the peak-to-peak amplitude 1.3V, pulse width 370ps and pulse repetition rate of 10MHz, whose waveform features are symmetric well and low ringing level. Good agreement between the measured and calculated results is achieved.
Automatic On-line Monitoring of animal Health and welfare by Precision livestock farming

Livestock production today has become a very complex process since several requirements have to be combined such as: food safety, animal welfare, animal health, environmental impact and sustainability in a wider sense. The consequence is a growing need to balance many of these variables during the production process. In the past farmers were monitoring their animals in their daily work by normal audio-visual observation like ethologists still do in their research. Today however the number of animals per farm has increased so much that this has become impossible. Another problem is that visual observation never can be done continuously during 24 hours a day. One of the objectives of Precision Livestock Farming (PLF) is to develop the technology and the tools for the on-line monitoring of farm animals and this continuously during their life and in a fully automatic way. This technology will never replace the farmer but can support him as a tool that automatically and continuously delivers him quantitative information about the status of his animals. Like other living organisms farm animals are responding to their environment with several behavioural and physiological variables. Many sensors and sensing techniques are under development to measure such behavioural and biological responses of farm animals. This can be done by new sensors or by sound analysis, image analysis etc. A major problem to monitor animals is the fact that animals themselves are complex systems that are individually different and that are so called time varying dynamic systems since their behaviour and health status can change at any time. Another challenge for PLF is to develop reliable monitoring tools for such Complex Individual Time varying Dynamic systems (“CITD” systems). In this paper we will talk about what is PLF and what is the importance of PLF. Next we will explain the basic principles. Further we will show examples of monitoring tools by PLF such as on-line monitor for health status by analysing continuously the sound produced by pigs. Another example shows the on-line automatic identification of the behaviour of individual laying hens by continuous analysis of 2D images from a top view camera. Next we will demonstrate the potential of PLF for more efficient controlling of biological processes. Finally we will discuss how implementation might be realised and what risk and problems are. The technology that is already available and that is under development today can be used for efficient and continuous monitoring if an engineering approach is combined with the expertise of ethologists, physiologist, veterinarians who are familiar with the animal as a living organism.
Bowden Cable Actuator for Force-Feedback Exoskeletons

This paper introduces a novel type of actuator that is investigated by ESA for force-reflection to a wearable exoskeleton. The actuator consists of a DC motor that is relocated from the joint by means of Bowden cable transmissions. The actuator shall support the development of truly ergonomic and compact wearable man-machine interfaces. Important Bowden cable transmission characteristics are discussed, which dictate a specific hardware design for such an actuator. A first prototype is shown, which was used to analyze these basic characteristics of the transmissions and to proof the overall actuation concept. A second, improved prototype is introduced, which is currently used to investigate the achievable performance as a master actuator in a master-slave control with force-feedback. Initial experimental results are presented, which show good actuator performance in a 4 channel control scheme with a slave joint. The actuator features low movement resistance in free motion and can reflect high torques during hard contact situations. High contact stability can be achieved. The actuator seems therefore well suited to be implemented into the ESA exoskeleton for space-robotic telemanipulation
Complex activity recognition using context-driven activity theory and activity signatures

In pervasive and ubiquitous computing systems, human activity recognition has immense potential in a large number of application domains. Current activity recognition techniques (i) do not handle variations in sequence, concurrency and interleaving of complex activities; (ii) do not incorporate context; and (iii) require large amounts of training data. There is a lack of a unifying theoretical framework which exploits both domain knowledge and data-driven observations to infer complex activities. In this article, we propose, develop and validate a novel Context-Driven Activity Theory (CDAT) for recognizing complex activities. We develop a mechanism using probabilistic and Markov chain analysis to discover complex activity signatures and generate complex activity definitions. We also develop a Complex Activity Recognition (CAR) algorithm. It achieves an overall accuracy of 95.73% using extensive experimentation with real-life test data. CDAT utilizes context and links complex activities to situations, which reduces inference time by 32.5% and also reduces training data by 66%.
A New Clustering Approach for Anomaly Intrusion Detection

Recent advances in technology have made our work easier compare to earlier times. Computer network is growing day by day but while discussing about the security of computers and networks it has always been a major concerns for organizations varying from smaller to larger enterprises. It is true that organizations are aware of the possible threats and attacks so they always prepare for the safer side but due to some loopholes attackers are able to make attacks. Intrusion detection is one of the major fields of research and researchers are trying to find new algorithms for detecting intrusions. Clustering techniques of data mining is an interested area of research for detecting possible intrusions and attacks. This paper presents a new clustering approach for anomaly intrusion detection by using the approach of K-medoids method of clustering and its certain modifications. The proposed algorithm is able to achieve high detection rate and overcomes the disadvantages of K-means algorithm.
How to apply de Bruijn graphs to genome assembly

T development of algorithmic ideas for next-generation sequencing can be traced back 300 years to the Prussian city of Königsberg (present-day Kaliningrad, Russia), where seven bridges joined the four parts of the city located on opposing banks of the Pregel River and two river islands . At the time, Königsberg’s residents enjoyed strolling through their city, and they wondered if every part of the city could be visited by walking across each of the seven bridges exactly once and returning to one’s starting location. The solution came in 1735, when the great mathematician Leonhard Euler1 made a conceptual breakthrough that would solve this ‘Bridges of Königsberg problem’. Euler’s first insight was to represent each landmass as a point (called a node) and each bridge as a line segment (called an edge) connecting the appropriate two points. This creates a graph—a network of nodes connected by edges . By describing a procedure for determining whether an arbitrary graph contains a path that visits every edge exactly once and returns to where it started, Euler not only resolved the Bridges of Königsberg problem but also effectively launched the entire branch of mathematics known today as graph theory2. Since Euler’s original description, the use of graph theory has turned out to have many additional practical applications, most of which have greater scientific importance than the development of walking itineraries. Specifically, Euler’s ideas were subsequently adapted by Dutch mathematician Nicolaas de Bruijn to find a cyclic sequence of letters taken from a given alphabet for which every possible word of a certain length (k) appears as a string of consecutive characters in the cyclic sequence exactly once (Box 1 and Fig. 2). Application of the de Bruijn graph has also proven invaluable in the field of molecular biology where researchers are faced with the problem of assembling billions of short sequencing reads into a single genome. In the following article, we describe the problems faced when constructing a genome and how the de Bruijn graph approach can be applied to assemble short-read sequences.
The State of Elliptic Curve Cryptography

Since the introduction of public-key cryptography by Diffie and Hellman in 1976, the potential for the use of the discrete logarithm problem in public-key cryptosystems has been recognized. Although the discrete logarithm problem as first employed by Diffie and Hellman was defined explicitly as the problem of finding logarithms with respect to a generator in the multiplicative group of the integers modulo a prime, this idea can be extended to arbitrary groups and, in particular, to elliptic curve groups. The resulting public-key systems provide relatively small block size, high speed, and high security. This paper surveys the development of elliptic curve cryptosystems from their inception in 1985 by Koblitz and Miller to present day implementations.
Streaming Surface Reconstruction Using Wavelets

We present a streaming method for reconstructing surfaces from large data sets generated by a laser range scanner using wavelets. Wavelets provide a localized, multiresolution representation of functions and this makes them ideal candidates for streaming surface reconstruction algorithms. We show how wavelets can be used to reconstruct the indicator function of a shape from a cloud of points with associated normals. Our method proceeds in several steps. We first compute a low-resolution approximation of the indicator function using an octree followed by a second pass that incrementally adds fine resolution details. The indicator function is then smoothed using a modified octree convolution step and contoured to produce the final surface. Due to the local, multiresolution nature of wavelets, our approach results in an algorithm over 10 times faster than previous methods and can process extremely large data sets in the order of several hundred million points in only an hour.
A Formal Architecture Pattern for Real-Time Distributed Systems

Pattern solutions for software and architectures have significantly reduced design, verification, and validation times by mapping challenging problems into a solved generic problem. In the paper, we present an architecture pattern for ensuring synchronous computation semantics using the PALS protocol. We develop a modeling framework in AADL to automatically transform a synchronous design of a real-time distributed system into an asynchronous design satisfying the PALS protocol. We present a detailed example of how the PALS transformation works for a dual-redundant system. From the example, we also describe the general transformation in terms of intuitively defined AADL semantics. Furthermore, we develop a static analysis checker to find necessary conditions that must be satisfied in order for the PALS transformation to work correctly. The transformations and static checks that we have described are implemented in OSATE using the generated EMF metamodel API for model manipulation.
Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features

Knowing the location of a social media user and their posts is important for various purposes, such as the recommendation of location-based items/services, and locality detection of crisis/disasters. This paper describes our submission to the shared task “Geolocation Prediction in Twitter” of the 2nd Workshop on Noisy User-generated Text. In this shared task, we propose an algorithm to predict the location of Twitter users and tweets using a multinomial Naive Bayes classifier trained on Location Indicative Words and various textual features (such as city/country names, #hashtags and @mentions). We compared our approach against various baselines based on Location Indicative Words, city/country names, #hashtags and @mentions as individual feature sets, and experimental results show that our approach outperforms these baselines in terms of classification accuracy, mean and median error distance.
A novel social media competitive analytics framework with sentiment benchmarks

In today’s competitive business environment, there is a strong need for businesses to collect, monitor, and analyze user-generated data on their own and on their competitors’ social media sites, such as Facebook, Twitter, and blogs. To achieve a competitive advantage, it is often necessary to listen to and understand what customers are saying about competitors’ products and services. Current social media analytics frameworks do not provide benchmarks that allow businesses to compare customer sentiment on social media to easily understand where businesses are doing well and where they need to improve. In this paper, we present a social media competitive analytics framework with sentiment benchmarks that can be used to glean industry-specific marketing intelligence. Based on the idea of the proposed framework, new social media competitive analytics with sentiment benchmarks can be developed to enhance marketing intelligence and to identify specific actionable areas in which businesses are leading and lagging to further improve their customers’ experience using customer opinions gleaned from social media. Guided by the proposed framework, an innovative business-driven social media competitive analytics tool named VOZIQ is developed. We use VOZIQ to analyze tweets associated with five large retail sector companies and to generate meaningful business insight reports.
Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing

Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should bring in no new vulnerabilities towards user data privacy. In this paper, we utilize and uniquely combine the public key based homomorphic authenticator with random masking to achieve the privacy-preserving public cloud data auditing system, which meets all above requirements. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.
The NIDS Cluster: Scalable, Stateful Network Intrusion Detection on Commodity Hardware

In this work we present a NIDS cluster as a scalable solution for realizing high-performance, stateful network intrusion detection on commodity hardware. The design addresses three challenges: (i) distributing traffic evenly across an extensible set of analysis nodes in a fashion that minimizes the communication required for coordination, (ii) adapting the NIDS’s operation to support coordinating its low-level analysis rather than just aggregating alerts; and (iii) validating that the cluster produces sound results. Prototypes of our NIDS cluster now operate at the Lawrence Berkeley National Laboratory and the University of California at Berkeley. In both environments the clusters greatly enhance the power of the network security monitoring.
Combined posterior and delayed staged mini-open anterior short-segment fusion for thoracolumbar burst fractures.

A prospective study.



To assess the outcome of patients with a single thoracolumbar burst fracture treated with circumferential short-segment fusion consisting of posterior reduction, short-segment fusion, and delayed staged mini-open anterior short-segment fusion.



The surgical treatment of thoracolumbar burst fractures remains controversial. In attempting to combine the advantages of posterior procedures, including initial correction of kyphosis and early decompression, and those of anterior procedures, including direct decompression and restoration of anterior column support, a combined posterior and delayed staged anterior procedure seems to be a reasonable choice. However, conventional combined procedures are invasive.



We prospectively selected 28 consecutive patients with single thoracolumbar burst fracture for circumferential short-segment fusion consisting of posterior reduction, short-segment fusion, and delayed staged mini-open anterior short-segment fusion. The pedicle screw systems were removed after confirmation of posterior bony fusion to preserve as many motion segments as possible in those patients who could be treated with circumferential monosegmental fusion. Radiographic and clinical assessment of 28 patients who received this treatment was carried out.



The mean loss of correction of kyphosis between the time of the combined procedure and final follow-up was 3.7 degrees (range, 0 to 10.2 degrees). Bony fusion was eventually achieved in all patients. There were 15 cases with monosegmental and 13 cases with bisegmental circumferential fusion. All 10 patients with initial neurological deficit improved by at least 1 Frankel grade: 3 improved by 1 grade, 5 improved by 2 grades, and 2 improved by 3 grades. In total, 27 patients, who were P1 or P2 on the Denis pain scale, were considered to have obtained clinically satisfactory results.



This combined procedure is less invasive than the conventional combined one, and finally achieves shorter stabilization, resulting in preservation of motion segments. It thus seems to be a reasonable treatment option for thoracolumbar burst fractures.