nils-herrmann/clean_scirepeval huggingface dataset:
Verification for Machine Learning, Autonomy, and Neural Networks Survey

This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components (LECs) that accomplish tasks from classification to control. Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this article presents a survey of many of these recent
Efficient exciton concentrators built from colloidal core/crown CdSe/CdS semiconductor nanoplatelets.

We present the synthesis and the optical properties of a new type of two-dimensional heterostructure: core/crown CdSe/CdS nanoplatelets. They consist of CdSe nanoplatelets that are extended laterally with CdS. Both the CdSe core and the CdS crown dimensions can be controlled. Their thickness is controlled at the monolayer level. These novel nanoplatelet-based heterostructures have spectroscopic properties that can be similar to nanoplatelets or closer to quantum dots, depending on the CdSe core lateral size.
Immunological aspects of intestinal mucus and mucins

A number of mechanisms ensure that the intestine is protected from pathogens and also against our own intestinal microbiota. The outermost of these is the secreted mucus, which entraps bacteria and prevents their translocation into the tissue. Mucus contains many immunomodulatory molecules and is largely produced by the goblet cells. These cells are highly responsive to the signals they receive from the immune system and are also able to deliver antigens from the lumen to dendritic cells in the lamina propria. In this Review, we will give a basic overview of mucus, mucins and goblet cells, and explain how each of these contributes to immune regulation in the intestine.
An energy efficient inverter based readout circuit for capacitive sensor

This paper presents a high energy efficient, parasitic free and low complex readout integrated circuit for capacitive sensors. A very low power consumption is achieved by replacing a power hungry operation amplifier by a subthreshold inverter instead in a switched capacitor amplifier(SC-amp) and reducing the supply voltage of all digital circuits in the system. A fast respond finite gain compensation method is utilized to reduce the gain error of the SC-amp and increase the energy efficiency of the readout circuit. A two-step auto calibration is applied to eliminate the offset from nonideal effects of the SC-amp and comparator delay. The readout system is implemented and simulated in TSMC 90 nm CMOS technology. With supply voltage of 1 V, simulation shows that the circuit can achieve 10.4 bit resolution while consuming only 3 μW during 640 μs conversion time. The digital output code has little sensitivity to temperature variation.
Global Spread of Vancomycin-resistant Enterococcus faecium from Distinct Nosocomial Genetic Complex

Vancomycin-resistant enterococci (VRE) have caused hospital outbreaks worldwide, and the vancomycin-resistance gene (vanA) has crossed genus boundaries to methicillin-resistant Staphylococcus aureus. Spread of VRE, therefore, represents an immediate threat for patient care and creates a reservoir of mobile resistance genes for other, more virulent pathogens. Evolutionary genetics, population structure, and geographic distribution of 411 VRE and vancomycin-susceptible Enterococcus faecium isolates, recovered from human and nonhuman sources and community and hospital reservoirs in 5 continents, identified a genetic lineage of E. faecium (complex-17) that has spread globally. This lineage is characterized by 1) ampicillin resistance, 2) a pathogenicity island, and 3) an association with hospital outbreaks. Complex-17 is an example of cumulative evolutionary processes that improved the relative fitness of bacteria in hospital environments. Preventing further spread of this epidemic E. faecium subpopulation is critical, and efforts should focus on the early disclosure of ampicillin-resistant complex-17 strains.
A convolutional approach to reflection symmetry

We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edgebased pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. Code and a new database for 2D symmetry detection is available.
Precision agriculture using remote monitoring systems in Brazil

Soil and nutrient depletion from intensive use of land is a critical issue for food production. An understanding of whether the soil is adequately treated with appropriate crop management practices in real-time during production cycles could prevent soil erosion and the overuse of natural or artificial resources to keep the soil healthy and suitable for planting. Precision agriculture traditionally uses expensive techniques to monitor the health of soil and crops including images from satellites and airplanes. Recently there are several studies using drones and a multitude of sensors connected to farm machinery to observe and measure the health of soil and crops during planting and harvesting. This paper describes a real-time, in-situ agricultural internet of things (IoT) device designed to monitor the state of the soil and the environment. This device was designed to be compatible with open hardware and it is composed of temperature and humidity sensors (soil and environment), electrical conductivity of the soil and luminosity, Global Positioning System (GPS) and a ZigBee radio for data communication. The field trial involved soil testing and measurements of the local climate in Sao Paulo, Brazil. The measurements of soil temperature, humidity and conductivity are used to monitor soil conditions. The local climate data could be used to support decisions about irrigation and other activities related to crop health. On-going research includes methods to reduce the consumption of energy and increase the number of sensors. Future applications include the use of the IoT device to detect fire in crops, a common problem in sugar cane crops and the integration of the IoT device with irrigation management systems to improve water usage.
Intracellular amyloid-β in Alzheimer's disease

The primal role that the amyloid-β (Aβ) peptide has in the development of Alzheimer's disease is now almost universally accepted. It is also well recognized that Aβ exists in multiple assembly states, which have different physiological or pathophysiological effects. Although the classical view is that Aβ is deposited extracellularly, emerging evidence from transgenic mice and human patients indicates that this peptide can also accumulate intraneuronally, which may contribute to disease progression.
Let me in: Control of HIV nuclear entry at the nuclear envelope.

The nuclear envelope is a physical barrier that isolates the cellular DNA from the rest of the cell, thereby limiting pathogen invasion. The Human Immunodeficiency Virus (HIV) has a remarkable ability to enter the nucleus of non-dividing target cells such as lymphocytes, macrophages and dendritic cells. While this step is critical for replication of the virus, it remains one of the less understood aspects of HIV infection. Here, we review the viral and host factors that favor or inhibit HIV entry into the nucleus, including the viral capsid, integrase, the central viral DNA flap, and the host proteins CPSF6, TNPO3, Nucleoporins, SUN1, SUN2, Cyclophilin A and MX2. We review recent perspectives on the mechanism of action of these factors, and formulate fundamental questions that remain. Overall, these findings deepen our understanding of HIV nuclear import and strengthen the favorable position of nuclear HIV entry for antiviral targeting.
Quantum Cryptography: A New Generation of Information Technology Security System

Quantum cryptography is an emerging technology in which two parties can secure network communications by applying the phenomena of quantum physics. The security of these transmissions is based on the inviolability of the laws of quantum mechanics. Quantum cryptography was born in the early seventies when Steven Wiesner wrote "Conjugate Coding", which took more than ten years to end this paper. The quantum cryptography relies on two important elements of quantum mechanics - the Heisenberg Uncertainty principle and the principle of photon polarization. The Heisenberg Uncertainty principle states that, it is not possible to measure the quantum state of any system without distributing that system. The principle of photon polarization states that, an eavesdropper can not copy unknown qubits i.e. unknown quantum states, due to no-cloning theorem which was first presented by Wootters and Zurek in 1982. This research paper concentrates on the theory of  quantum cryptography, and how this technology contributes to the network security. This research paper summarizes the current state of quantum cryptography, and the real–world application implementation of this technology, and finally the future direction in which the quantum cryptography is headed forwards.
High Performance Graph Processing with Locality Oriented Design

Many distributed graph processing frameworks have emerged for helping doing large scale data analysis for many applications including social network and data mining. The existing frameworks usually focus on the system scalability without consideration of local computing performance. We have observed two locality issues which greatly influence the local computing performance in existing systems. One is the locality of the data associated with each vertex/edge. The data are often considered as a logical undividable unit and put into continuous memory. However, it is quite common that for some computing steps, only some portions of data (called as some properties) are needed. The current data layout incurs large amount of interleaved memory access. The other issue is their execution engine applies computation at a granularity of vertex. Making optimization for the locality of source vertex of each edge will often hurt the locality of target vertex or vice versa. We have built a distributed graph processing framework called Photon to address the above issues. Photon employs Property View to store the same type of property for all vertices and edges together. This will improve the locality while doing computation with a portion of properties. Photon also employs an edge-centric execution engine with Hilbert-Order that improve the locality during computation. We have evaluated Photon with five graph applications using five real-world graphs and compared it with four existing systems. The results show that Property View and edge-centric execution design improve graph processing by 2.4X.
African higher education : Challenges for the 21 st century

African higher education, at the beginning of the new millennium, faces unprecedented challenges. Not only is the demand for access unstoppable, especially in the context of Africa’s traditionally low postsecondary attendance levels, but higher education is recognized as a key force for modernization and development. Africa’s academic institutions face obstacles in providing the education, research, and service needed if the continent is to advance. Generalizing about a continent as large and diverse as Africa is difficult. Yet there are some common elements – and there are certainly some common challenges. In our discussion, we are not generally optimistic either in analyzing the current reality in much of Africa or in pointing to future prospects. The fact is that African universities currently function in very difficult circumstances, both in terms of the social, economic, and political problems facing the continent and in the context of globalization, and the road to future success will not be an
An Extended Fixed Priority Scheme for Mixed Criticality Systems

This paper considers a novel implementation scheme for fixed priority (FP) uniprocessor scheduling of mixed criticality systems. The scheme requires that jobs have their execution times monitored. If system behavior inconsistent with lower criticality levels is detected during run-time via such monitoring, (i) tasks of lower criticalities are discarded (this is already done by current FP mixed-criticality scheduling algorithms); and (ii) the priorities of the remaining tasks may be re-ordered. Evaluations illustrate the benefits of this scheme.
Input ( d ) Input ( a ) Input ( b ) Input A ct iv at

The activation function for neurons is a prominent element in the deep learning architecture for obtaining high performance. Inspired by neuroscience findings, we introduce and define two types of neurons with different activation functions for artificial neural networks: excitatory and inhibitory neurons, which can be adaptively selected by selflearning. Based on the definition of neurons, in the paper we not only unify the mainstream activation functions, but also discuss the complementariness among these types of neurons. In addition, through the cooperation of excitatory and inhibitory neurons, we present a compositional activation function that leads to new state-of-the-art performance comparing to rectifier linear units. Finally, we hope that our framework not only gives a basic unified framework of the existing activation neurons to provide guidance for future design, but also contributes neurobiological explanations which can be treated as a window to bridge the gap between biology and computer science.
Entrepreneurial Orientation and E-business Capabilities of Manufacturing SMEs: An Absorptive Capacity Lens

This research uses the absorptive capacity (ACAP) concept as a theoretical lens to study the effect of e-business upon the competitive performance of SMEs, addressing the following research issue: To what extent are manufacturing SMEs successful in developing their potential and realized ACAP in line with their entrepreneurial orientation? A survey study of 588 manufacturing SMEs found that their e-business capabilities, considered as knowledge acquisition and assimilation capabilities have an indirect effect on their competitive performance that is mediated by their knowledge transformation and exploitation capabilities, and insofar as these capabilities are developed as a result of a more entrepreneurial orientation on their part. Finally, the effect of this orientation on the SMEs' competitive performance appears to be totally mediated by their ACAP.
Analysis of Cascaded Integrator Comb ( CIC ) Decimation Filter in Efficient Compensation

Decimation filter has wide application in both the analog and digital system for data rate conversion as well as filtering. In this paper, we have discussed about efficient structure of a decimation filter. We have three class of filters FIR, IIR and CIC filters. IIR filters are simpler in structure but do not satisfy linear phase requirements which are required in time sensitive features like a video or a speech. FIR filters have a well defined frequency response but they require lot of hardware to store the filter coefficients. CIC filters don’t have this drawback they are coefficient less so hardware requirement is much reduced but as they don’t have well defined frequency response. So another structure is proposed which takes advantage of good feature of both the structures and thus have a cascade of CIC and FIR filters. They exhibit both the advantage of FIR and CIC filters and hence more efficient over all in terms of hardware and frequency response requirements.
A survey of traceability in requirements engineering and model-driven development

Traceability—the ability to follow the life of software artifacts—is a topic of great interest to software developers in general, and to requirements engineers and model-driven developers in particular. This article aims to bring those stakeholders together by providing an overview of the current state of traceability research and practice in both areas. As part of an extensive literature survey, we identify commonalities and differences in these areas and uncover several unresolved challenges which affect both domains. A good common foundation for further advances regarding these challenges appears to be a combination of the formal basis and the automated recording opportunities of MDD on the one hand, and the more holistic view of traceability in the requirements engineering domain on the other hand.
An Hybrid Approach for Spoken Natural Language Understanding Applied to a Mobile Intelligent Robot

The greater sophistication and complexity of machines increases the necessity to equip them with human friendly interfaces. As we know, voice is the main support for human-human communication, so it is desirable to interact with machines, namely robots, using voice. In this paper we present the recent evolution of the Natural Language Understanding capabilities of Carl, our mobile intelligent robot capable of interacting with humans using spoken natural language. The new design is based on a hybrid approach, combining a robust parser with Memory Based Learning. This hybrid architecture is capable of performing deep analysis if the sentence is (almost) completely accepted by the grammar, and capable of performing a shallow analysis if the sentence has severe errors.
Software Feature Request Detection in Issue Tracking Systems

Communication about requirements is often handled in issue tracking systems, especially in a distributed setting. As issue tracking systems also contain bug reports or programming tasks, the software feature requests of the users are often difficult to identify. This paper investigates natural language processing and machine learning features to detect software feature requests in natural language data of issue tracking systems. It compares traditional linguistic machine learning features, such as "bag of words", with more advanced features, such as subject-action-object, and evaluates combinations of machine learning features derived from the natural language and features taken from the issue tracking system meta-data. Our investigation shows that some combinations of machine learning features derived from natural language and the issue tracking system meta-data outperform traditional approaches. We show that issues or data fields (e.g. descriptions or comments), which contain software feature requests, can be identified reasonably well, but hardly the exact sentence. Finally, we show that the choice of machine learning algorithms should depend on the goal, e.g. maximization of the detection rate or balance between detection rate and precision. In addition, the paper contributes a double coded gold standard and an open-source implementation to further pursue this topic.
Security Issues and Solutions in Wireless Sensor Networks

This paper focuses and talks about the wide and varied areas of applications wireless sensor networks have taken over today, right from military surveillance and smart home automation to medical and environmental monitoring. It also gives a gist why security is a primary issue of concern even today for the same, discussing the existing solutions along with outlining the security issues and suggesting possible directions of research over the same. This paper is about the security of wireless sensor networks. These networks create new security threats in comparison to the traditional methods due to some unique characteristics of these networks. A detailed study of the threats, risks and attacks need to be done in order to come up with proper security solutions. Here the paper presents the unique characteristics of these networks and how they pose new security threats. There are several security goals of these networks. These goals and requirements must be kept in mind while designing of security solutions for these networks. It also describes the various attacks that are possible at important layers such as data-link, network, physical and transport layer.
A de novo non-sense mutation in ZBTB18 in a patient with features of the 1q43q44 microdeletion syndrome

The phenotype of patients with a chromosome 1q43q44 microdeletion (OMIM; 612337) is characterized by intellectual disability with no or very limited speech, microcephaly, growth retardation, a recognizable facial phenotype, seizures, and agenesis of the corpus callosum. Comparison of patients with different microdeletions has previously identified ZBTB18 (ZNF238) as a candidate gene for the 1q43q44 microdeletion syndrome. Mutations in this gene have not yet been described. We performed exome sequencing in a patient with features of the 1q43q44 microdeletion syndrome that included short stature, microcephaly, global developmental delay, pronounced speech delay, and dysmorphic facial features. A single de novo non-sense mutation was detected, which was located in ZBTB18. This finding is consistent with an important role for haploinsufficiency of ZBTB18 in the phenotype of chromosome 1q43q44 microdeletions. The corpus callosum is abnormal in mice with a brain-specific knock-out of ZBTB18. Similarly, most (but not all) patients with the 1q43q44 microdeletion syndrome have agenesis or hypoplasia of the corpus callosum. In contrast, the patient with a ZBTB18 point mutation reported here had a structurally normal corpus callosum on brain MRI. Incomplete penetrance or haploinsufficiency of other genes from the critical region may explain the absence of corpus callosum agenesis in this patient with a ZBTB18 point mutation. The findings in this patient with a mutation in ZBTB18 will contribute to our understanding of the 1q43q44 microdeletion syndrome.
Split Hamiltonian Monte Carlo

We show how the Hamiltonian Monte Carlo algorithm can sometimes be speeded up by “splitting” the Hamiltonian in a way that allows much of the movement around the state space to be done at low computational cost. One context where this is possible is when the log density of the distribution of interest (the potential energy function) can be written as the log of a Gaussian density, which is a quadratic function, plus a slowly-varying function. Hamiltonian dynamics for quadratic energy functions can be analytically solved. With the splitting technique, only the slowlyvarying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics. Another context where splitting helps is when the most important terms of the potential energy function and its gradient can be evaluated quickly, with only a slowlyvarying part requiring costly computations. With splitting, the quick portion can be handled with a small stepsize, while the costly portion uses a larger stepsize. We show that both of these splitting approaches can reduce the computational cost of sampling from the posterior distribution for a logistic regression model, using either a Gaussian approximation centered on the posterior mode, or a Hamiltonian split into a B. Shahbaba ( ) Department of Statistics and Department of Computer Science, University of California, Irvine, CA 92697, USA e-mail: babaks@uci.edu S. Lan · W.O. Johnson Department of Statistics, University of California, Irvine, CA 92697, USA R.M. Neal Department of Statistics and Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G3, Canada term that depends on only a small number of critical cases, and another term that involves the larger number of cases whose influence on the posterior distribution is small.