Miracle-dz/UniFAR-FTUs huggingface dataset:
Giant Gate-Tunability of Complex Refractive Index in Semiconducting Carbon Nanotubes

Electrically-tunable optical properties in materials are desirable for many applications ranging from displays to lasing and optical communication. In most two-dimensional thin-films and other quantum confined materials, these constants have been measured accurately. However, the optical constants of single wall nanotubes (SWCNT) as a function of electrostatic tuning are yet to be measured due to lack of electronic purity and spatial homogeneity over large areas. Here, we measure the basic optical constants of ultrathin high-purity (>99%) semiconducting single wall carbon nanotube (s-SWCNT) films with spectroscopic ellipsometry. We extract the gate-tunable complex refractive index of s-SWCNT films and observe giant modulation of the real refractive index (~11.2% or an absolute value of >0.2) and extinction coefficient (~11.6%) in the near-infrared (IR) region (1.3-1.55 {\mu}m) induced by the applied electric field significantly higher than all existing electro-optic semiconductors in this wavelength range. We further design a multilayer IR reflection phase modulator stack by combining s-SWCNT and monolayer MoS2 heterostructures that can attain >45° reflection phase modulation at 1600 nm wavelength for < 200 nm total stack thickness. Our results highlight s-SWCNT as a promising material system for infrared photonics and electro-optics in telecommunication applications.
A Case for Partitioned Bloom Filters

In a partitioned Bloom Filter (PBF) the bit vector is split into disjoint parts, one per hash function. Contrary to hardware designs, where they prevail, software implementations mostly ignore PBFs, considering them worse than standard Bloom filters (SBF), due to the slightly larger false positive rate (FPR). In this paper, by performing an in-depth analysis, first we show that the FPR advantage of SBFs is smaller than thought; more importantly, by deriving the per-element FPR, we show that SBFs have weak spots in the domain: elements that test as false positives much more frequently than expected. This is relevant in scenarios where an element is tested against many filters. Moreover, SBFs are prone to exhibit extremely weak spots if naive double hashing is used, something occurring in mainstream libraries. PBFs exhibit a uniform distribution of the FPR over the domain, with no weak spots, even using naive double hashing. Finally, we survey scenarios beyond set membership testing, identifying many advantages of having disjoint parts, in designs using SIMD techniques, for filter size reduction, test of set disjointness, and duplicate detection in streams. PBFs are better, and should replace SBFs, in general purpose libraries and as the base for novel designs.
Population Dynamics of Leptinotarsa decemlineata (Coleoptera: Chrysomelidae): Measuring the Effects of Methyl Salicylate and Predator Recruitment in Potato

Abstract Methyl salicylate is an organic compound produced by potato and other plants in response to insect herbivory. In other studies the compound has been shown to be attractive to numerous predatory arthropods. Experiments were conducted in Virginia to evaluate methyl salicylate lures for attracting natural enemies in potato plots to manage populations of Leptinotarsa decemlineata (Say). Abundance of predatory arthropods and L. decemlineata life stages were measured in plots treated with and without 90 day, 5 g slow-release packets of methyl salicylate (95% methyl salicylate [Predalure™]). Mortality of L. decemlineata eggs and small larvae was estimated by calculating the difference in numbers of individuals recruited to subsequent stages using a stage-specific life table approach. Methyl salicylate treatment had no impact on predator recruitment or mortality of L. decemlineata eggs and small larvae, compared with nontreated plots. Cumulative mortality of L. decemlineata ranged from 87.9 - 89.2% in 2010 and from 81.9 - 94.8% in 2011. The dominant arthropod predators observed on potatoes included Hippodamia convergens Guerin-Meneville, Coccinella septempunctata L. , and Perillus bioculatus (F.).
Fuel rod classification from Passive Gamma Emission Tomography (PGET) of spent nuclear fuel assemblies

Safeguarding the disposal of spent nuclear fuel in a geological repository needs an effective, efficient, reliable and robust non-destructive assay (NDA) system to ensure the integrity of the fuel prior to disposal. In the context of the Finnish geological repository, Passive Gamma Emission Tomography (PGET) will be a part of such an NDA system. We report here on the results of PGET measurements at the Finnish nuclear power plants during the years 2017-2020. Gamma activity profiles are recorded from all angles by rotating the detector arrays around the fuel assembly that has been inserted into the center of the torus. Image reconstruction from the resulting tomographic data is defined as a constrained minimization problem with a data fidelity term and regularization terms. The activity and attenuation maps, as well as detector sensitivity corrections, are the variables in the minimization process. The regularization terms ensure that prior information on the (possible) locations of fuel rods and their diameter are taken into account. Fuel rod classification, the main purpose of the PGET method, is based on the difference of the activity of a fuel rod from its immediate neighbors, taking into account its distance from the assembly center. The classification is carried out by a support vector machine. We report on the results for ten different fuel types with burnups between 5.72 and 55.0 GWd/tU, cooling times between 1.87 and 34.6 years and initial enrichments between 1.9 and 4.4%. For all fuel assemblies measured, missing fuel rods, partial fuel rods and water channels were correctly classified. Burnable absorber fuel rods were classified as fuel rods. On rare occasions, a fuel rod that is present was falsely classified as missing. We conclude that the combination of the PGET device and our image reconstruction method provides a reliable base for fuel rod classification.
Sketch-based community detection in evolving networks

We consider an approach for community detection in time-varying networks. At its core, this approach maintains a small sketch graph to capture the essential community structure found in each snapshot of the full network. We demonstrate how the sketch can be used to explicitly identify six key community events which typically occur during network evolution: growth, shrinkage, merging, splitting, birth, and death. Based on these detection techniques, we formulate a community detection algorithm which can process a network concurrently exhibiting all processes. One advantage afforded by the sketch-based algorithm is the efficient handling of large networks. Whereas detecting events in the full graph may be computationally expensive, the small size of the sketch allows changes to be quickly assessed. A second advantage occurs in networks containing clusters of disproportionate size. The sketch is constructed such that there is equal representation of each cluster, thus reducing the possibility that the small clusters are lost in the estimate. We present a new standardized benchmark based on the stochastic block model which models the addition and deletion of nodes, as well as the birth and death of communities. When coupled with existing benchmarks, this new benchmark provides a comprehensive suite of tests encompassing all six community events. We provide analysis and a set of numerical results demonstrating the advantages of our approach both in runtime and in the handling of small clusters.
Properties of self-gravitating quasi-stationary states

Initially far out-of-equilibrium, self-gravitating systems form quasi-stationary states (QSS) through a collisionless relaxation dynamics. These may arise from a bottom-up aggregation of structures or in a top-down frame; their quasi-equilibrium properties are well described by the Jeans equation and are not universal. These QSS depend on initial conditions. To understand the origin of such dependence, we present the results of numerical experiments of initially cold and spherical systems characterized by various choices of the spectrum of initial density fluctuations. The amplitude of such fluctuations determines whether the system relaxes in a top-down or bottom-up manner. We find that statistical properties of the resulting QSS mainly depend upon the amount of energy exchanged during the formation process. In particular, in the violent top-down collapses the energy exchange is large and the QSS show an inner core with an almost flat density profile and a quasi Maxwell-Boltzmann (isotropic) velocity distribution, while their outer regions display a density profile ρ(r) ∝ r−α (α > 0) with radially elongated orbits. We show analytically that α = 4, in agreement with numerical experiments. In the less violent bottom-up dynamics, the energy exchange is much smaller, the orbits are less elongated, and 0 < α(r) ≤ 4, where the density profile is well fitted by the Navarro-Frenk-White behavior. Such a dynamical evolution is shown by both nonuniform spherical isolated systems and by halos extracted from cosmological simulations. We consider the relation of these results with the core-cusp problem and conclude that this can be solved naturally if galaxies form through a monolithic collapse.
Nonlinearly ghost-free higher curvature gravity

We find unitary and local theories of higher curvature gravity in the vielbein formalism, known as the Poincar\'{e} gauge theory by utilizing the equivalence to the ghost-free massive bigravity. We especially focus on three and four dimensions but extensions into a higher dimensional spacetime are straightforward. In three dimensions, a quadratic gravity $\mathcal{L}=R+T^2+R^2$, where $R$ is the curvature and $T$ is the torsion with indices omitted, is shown to be equivalent to zwei-dreibein gravity and free from the ghost at fully non-linear orders. In a special limit, new massive gravity is recovered. When the model is applied to the AdS/CFT correspondence, unitarity both in the bulk theory and in the boundary theory implies that the torsion must not vanish. On the other hand, in four dimensions, the absence of ghost at non-linear orders requires an infinite number of higher curvature terms, and these terms can be given by a schematic form $R(1+R/\alpha m^2)^{-1}R$ where $m$ is the mass of the massive spin-2 mode originated from the higher curvature terms and $\alpha$ is an additional parameter that determines the amplitude of the torsion. We also provide another four-dimensional ghost-free higher curvature theory that contains a massive spin-0 mode as well as the massive spin-2 mode.
Dynamic Buffer Sizing for Out-of-order Event Compensation for Time-sensitive Applications

Today’s sensor network implementations often comprise various types of nodes connected with different types of networks. These and various other aspects influence the delay of transmitting data and therefore of out-of-order data occurrences. This turns into a crucial problem in time-sensitive applications where data must be processed promptly and decisions must be reliable. In this article, we research dynamic buffer sizing algorithms for multiple, distributed, and independent sources, which reorder event streams, thus enabling subsequent time-sensitive applications to work correctly. To be able to evaluate such algorithms, we had to record datasets first. Five novel dynamic buffer sizing algorithms were implemented and compared to state-of-the-art approaches in this domain. The evaluation has shown that the use of a dynamic time-out buffering method is preferable over a static buffer. The higher the variation of the network or other influences in the environment, the more necessary it becomes to use an algorithm that dynamically adapts its buffer size. These algorithms are universally applicable, easy to integrate in existing architectures, and particularly interesting for time-sensitive applications. Dynamic time-out buffering is still a trade-off between reaction time and out-of-order event compensation.
Proceedings in human metapneumovirus vaccine and treatment development

Human metapneumovirus is a pathogen causing mild-to-severe upper and lower respiratory tract infections. Primary infections occur during early childhood and reinfections happen throughout later life, leading to a severe outcome, especially in the immunocompromised and the elderly. This review briefly summarizes current progress in the development of prophylaxis and treatment options for human metapneumovirus infections.
Multi-Cloud Deployment and Execution of Earth Observation Services

—At this day and age, with the progress in engineering, spatial data is being harvested at a high speed and volume with several formats from a continuously collection of satellites. For scientists to study and utilize this data it ﬁrst requires some transformations from Earth Observation (EO) tools. However, these tools can be resource-heavy, restricting the use to a small and highly skilled community. With the arise of Cloud Computing, scientists do not need to worry with the cost of managing hardware. Furthermore, Cloud Computing releases the scientist from the burden of understanding the infrastructure of their equipment. As a result, Cloud Computing became the natural solution. The procedures of deploying the EO tools on Cloud Computing environments emerges with some critical challenges. It requires to package these tools, provision as well as conﬁgure a cloud instance, deploy and expose them to the web context. Therefore, we aim to automate each of these steps by exploring the already existing technologies. Our focus will begin with the creation of a high degree continuous delivery pipeline. This will be the main base functional aspect of this thesis. In addition, the monetary cost and how to maintain it to a minimum will still be taken into consideration. With this in mind, we aim to utilize an already existing work, titled Workﬂow Engine for
Practical Aspects of Ontology-Based Analysis and Reasoning for Law Information Represented in Textual Form

—Legal domain is an important source of information and includes diverse law texts, court decisions, etc., which is dominated by documents collected in natural language. There is a great demand for the automatic analysis of the legal information in everyday work of lawyers and other people dealing with laws, for which the ontology-based knowledge representation would be of a great advantage. Unfortunately, the current semantic and ontology based technologies cannot be easily applied for the analysis of legal texts due to a certain complexity of those. We present a strategy that allows different categories of tools, such as those for ontology creation, syntactical analysis of texts collected in natural language, and others to interoperate in order to achieve a common goal – creation of domain-specific ontologies and performing complex reasoning over them. A model that can be applied for the legal system knowledge representation is proposed and its implementation for the German Civil Law System in form of an ontology is discussed. The model allows the creation of a common ontology spanning over the knowledge contained in diverse laws, thus paving the way towards a wide adoption of semantic technologies by the experts in the law domain.
Real Time Sign Language Recognition and Translation to Text for Vocally and Hearing Impaired People

- With a population of around 7.8 billion today communication is a strong means for understanding each other. Around 9,000 million individuals are vocally and hearing impaired. Because of the impediment there is a communication gap between the vocally handicapped people and the typical individuals. Gesture based communication is the fundamental method of communication for this section of our society. This language uses a set of representations which are finger sign, expression or mixture of both to precise their information among others. This system presents a completely unique approach of application based translation of sign-action analysis, recognition and generating a text description in English. Where it uses two important steps training and testing. In training set of fifty different domains of video samples are collected, each domain contains 5 samples and assigns a category of words to every video sample and it will be stored in the database. Wherein testing, test sample undergoes preprocessing using median filter, canny operator for edge detection, HOG (Histogram of Oriented Gradients) for feature extraction. SVM (Support Vector Machine) takes input as a HOG features and predicts the class label based on trained SVM model. Finally the text description will be generated in English language. The average computation time is minimum and with acceptable recognition rate and validates the performance efficiency over the normal model.
LAND USE CLASSIFICATION USING DEEP MULTITASK NETWORKS

Abstract. Updated information on urban land use allows city planners and decision makers to conduct large scale monitoring of urban areas for sustainable urban growth. Remote sensing data and classification methods offer an efficient and reliable way to update such land use maps. Features extracted from land cover maps are helpful on performing a land use classification task. Such prior information can be embedded in the design of a deep learning based land use classifier by applying a multitask learning setup—simultaneously solving a land use and a land cover classification task. In this study, we explore a fully convolutional multitask network to classify urban land use from very high resolution (VHR) imagery. We experimented with three different setups of the fully convolutional network and compared it against a baseline random forest classifier. The first setup is a standard network only predicting the land use class of each pixel in the image. The second setup is a multitask network that concatenates the land use and land cover class labels in the same output layer of the network while the other setup accept as an input the land cover predictions, predicted by a subpart of the network, concatenated to the original input image patches. The two deep multitask networks outperforms the other two classifiers by at least 30% in average F1-score.
Predicting black smoke levels from deposit gauge and SO 2 data to 1 estimate long term exposure in the United Kingdom , 1956-61 2 3

estimate long term exposure in the United Kingdom, 1956-61 2 3 Susan Hodgson, Fu-Meng Khaw, Mark S. Pearce, Tanja Pless-Mulloli 4 5 a Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK 6 b North East Health Protection Unit, Health Protection Agency North East, Newcastle upon Tyne, 7 UK 8 9 e mail addresses of the authors: 10 Susan Hodgson – susan.hodgson@ncl.ac. uk 11 Fu-Meng Khaw f.m. khaw@ncl.ac. uk 12 Mark S. Pearce m.s.pearce@ncl.ac. uk 13 Tanja Pless-Mulloli tanja.pless-mulloli@ncl.ac. uk 14 15 Corresponding author: 16 Dr Susan Hodgson 17 Lecturer in Environmental Epidemiology 18 Institute of Health and Society 19 4th Floor William Leech Building 20 The Medical School 21 Newcastle University 22 Newcastle upon Tyne NE2 4HH 23 24 Tel: +44 (0) 191 222 3823 25 Fax: +44 (0) 191222 8211 26 e mail: susan.hodgson@ncl.ac. uk 27 28
Evaluation of business models of urban IoT-applications for a medium sized city

Digitalization of the public sector is being driven by a number of factors. In particular, the concept of "Smart Cities" has become an important driver of this development. This relies heavily on an intelligent infrastructure including the Internet of Things (IoT). But does it make sense for small and medium-sized municipalities to develop this? Is it justified to invest in IoT? (How) can a mediumsized city benefit from it? This paper presents the application of an evaluation scheme for business models of urban IoT applications to answer these questions. The research question focuses on how best practices of urban IoT applications in general and in particular can be evaluated. In order to establish a concrete practical reference we evaluated ten chosen IoT applications for the German city of Herrenberg.
Design and implementation of multiple output forward converter with Mag-amp and LDO as post regulators for space application

Linear power supplies are commonly used power suppl ies for many applications. They have some drawbacks such as low efficiency, difficulty in thermal management and al so in regulation of the output voltage. Some of the se drawbacks can be overcome by Switch Mode Power Supplies (SMPS). One of the best-suited applications of SMPS is for spac e pplications that require power supplies which are lighter, smaller, more efficient and highly reliable. Multiple-output DC-DC converters are an important topology of SMPS that can be used for spa ce applications. But, in multiple output converters usually, only the master output is regulated and the other outputs are left unregulated and this can result in cross-regulation . In this paper, post regulators such as Magnetic amplifiers (Mag-amp) and Low DropO ut regulator (LDO) are proposed to regulate each ou tput and also to improve load regulation. In addition to this, the i nput voltage feed-forward control technique is prop osed to control the duty cycle of the switch, which is dynamically faster an d provides better line regulation when compared to the voltage feedback controller. Besides, over current protection circu it for the converter is discussed in detail.
Performance Analysis of Different Acoustic Features based on LSTM for Bangla Speech Recognition

In this work a new Bangla speech corpus along with proper transcriptions has been developed; also various acoustic feature extraction methods have been investigated using Long Short-Term Memory(LSTM) neural network to find their effective integration into a state-of-the-art Bangla speech recognition system. The acoustic features are usually a sequence of representative vectors that are extracted from speech signals and the classes are either words or sub word units such as phonemes. The most commonly used feature extraction method, known as linear predictive coding (LPC), has been used first in this work. Then the other two popular methods, namely, the Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) have also been applied. These methods are based on the models of the human auditory system. A detailed review of the implementation of these methods have been described first. Then the steps of the implementation have been elaborated for the development of an automatic speech recognition system (ASR) for Bangla speech.
Differentiable operad, Kuranishi correspondence, and Foundation of topological field theories

In this article the author describes a general framework to establish foundation of various topological field theories. By taking the case of Lagrangian Floer theory as an example, we explain it in a way so that it is applicable to many similar situations including, for example, the case of ‘symplectic field theory’. The results of this article is not really new in the sense that its proof was already written in [33], in detail. However several statements are formulated here, for the first time. Especially the relation to the theory of operad is clarified.