Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Novel Starches in Planta: Opportunities and Challenges

In addition to being the main source of energy in the human diet, starch is also used for a wide variety of industrial processes. Potato, maize, wheat and cassava are the major sources for industrial starch. While there are significant species-dependent differences among these starches, in most cases it is necessary to chemically or physically modify the starches in order to meet the various industrial needs. The advent of molecular biology and genetic engineering has provided the requisite technologies to produce starches in planta with improved properties, which may reduce or eliminate the need for in vitro starch modification. In the last decade significant progress in our understanding of starch biosynthesis has been achieved through the use of these technologies. In this review, starch structure, functionality and use in industry are discussed. The recent discoveries in the area of starch biosynthesis are reviewed and several potential plant breeding and/or genetic engineering strategies for the modification of starch synthesis in planta are presented.
The persistence of privacy in teacher professional development online

Two cycles of a professional development course were run for teachers in urban and regional areas of New South Wales, Australia. Delivery and communication tools were facilitated through the Internet. The use of collective learning and mutual problem solving was made transparent to participants prior to participation and was a keystone to the design of the professional development course. In an article by Little (1990) entitled Persistence of Privacy: Autonomy and Initiative in Teachers' Professional Relations, the author argues that common forms of 'collegiality' and hence common configurations of teacher-to-teacher discourse may do more to bolster isolation than diminish it. Using transcripts from semi-structured interviews and electronic communications, participants’ experiences and actions are used to illuminate the issue of privacy in online teacher communication.
BIOLOGICAL CLASSIFICATION FROM A GROOTE EYLANDT ABORIGINE'S POINT OF VIEWI

-This paper reviews the contributions of Berlin, Buhner, Dwyer, Hunn and Randall to the interpretation of folk classification systems. The problems of comparison of folk taxa with scientific taxa, of determining the degree of perception of discontinuity, of assessing the cognitive status of folk taxa and of the nature of folk taxonomies are identified and discussed. Biological classification from the view point of Aborigines on Groote Eylandt in northern Australia is described using Berlin's set of terms as a convenient reference point. Examples given provide additional evidence for the notion of a basic unit of perception within folk classification systems though this unit cannot as yet be satisfactorily defined. It is suggested that folk taxa may be arranged in contrast sets according to purpose and that only those sets required for the purpose would be called on to generate a hierarchical classification.
Incorporating Landscape Attributes into Models for Migratory Grassland Bird Conservation

Abstract: We evaluated the influence of local and landscape attributes on the occurrence and density of seven passerine species in grasslands (n = 380) throughout eastern South Dakota ( U.S.A.). Landscape composition and land cover were quantified at three spatial scales: 400‐, 800‐, and 1600‐m radii from the transect center. Separate habitat models were generated for mixed‐grass and tallgrass prairie regions to depict the way birds respond to geographic variation in local and landscape structure. Sedge Wrens ( Cistothorus platensis) and Clay‐colored Sparrows ( Spizella pallida) in the mixed‐grass and tallgrass regions used both large and small patches if they were embedded in a landscape with a high proportion of grassland habitat. Occupancy rates for these species were higher in small patches within landscapes with high grassland abundance than in large patches within low grassland landscapes. Grasshopper Sparrows (Ammodramus savannarum) and Dickcissels ( Spiza americana) in the mixed‐grass region and Savannah Sparrows ( Passerculus sandwichensis) in the tallgrass region were area‐sensitive species that used large grassland patches regardless of landscape configuration. The occurrence of Savannah Sparrows in both regions and Sedge Wrens, Grasshopper Sparrows, and Western Meadowlarks ( Sturnella neglecta) in the mixed‐grass region was negatively associated with an increase in patch edges with trees. Explained variation in density estimates was associated only with local patch and vegetation variables. Individual species demonstrated scale‐dependent differences in the way they perceived habitat and in their response to regional landscape structure. These findings provide evidence that results from one region may not be extrapolated to others because they may vary with species distribution, vegetation structure, and landscape composition. Assessments of regional grassland bird habitat must be conducted at multiple scales because some species respond only to local conditions within grasslands, whereas others respond to habitat structure at landscape scales.
Revisiting and generalizing the dual iteration for static and robust output‐feedback synthesis

The dual iteration was introduced in a conference paper in 1997 by Iwasaki as an iterative and heuristic procedure for the challenging and non‐convex design of static output‐feedback controllers. We recall in detail its essential ingredients and go beyond the work of Iwasaki by demonstrating that the framework of linear fractional representations allows for a seamless extension of the dual iteration to output‐feedback designs of practical relevance, such as the design of robust or robust gain‐scheduled controllers. In the paper of Iwasaki, the dual iteration is solely based on, and motivated by algebraic manipulations resulting from the elimination lemma. We provide a novel control theoretic interpretation of the individual steps, which paves the way for further generalizations of the powerful scheme to situations where the elimination lemma is not applicable. As an illustration, we extend the dual iteration to a design of static output‐feedback controllers with multiple objectives. We demonstrate the approach with numerous numerical examples inspired from the literature.
Relationships among subspecies of New World octoploid strawberry species, Fragaria virginiana and Fragaria chiloensis, based on simple sequence repeat marker analysis

Simple sequence repeat (SSR) markers, developed from Fragaria ×ananassa Duchesne ex Rozier ‘Earliglow’, were used to assess diversity among all of the proposed taxa of Fragaria virginiana Mill. and Fragaria chiloensis (L.) Mill. to facilitate strawberry germplasm utilization and conservation and to help resolve taxonomic relationships. DNA was obtained from 111 individuals representing F. chiloensis subsp. pacifica Staudt, F. chiloensis subsp. lucida (E. Vilm. ex Gray) Staudt, F. virginiana subsp. virginiana Mill. , F. virginiana subsp. glauca (S. Watson) Staudt, F. virginiana subsp. grayana (Vilm. ex J. Gay) Staudt, and F. virginiana subsp. platypetala (Rydb.) Staudt from North America, F. chiloensis subsp. chiloensis f. chiloensis and F. chiloensis subsp. chiloensis f. patagonica from South America, F. chiloensis subsp. sandwicensis (Decne.) Staudt from Hawaii, as well as F. ×ananassa subsp. cuneifolia (Nutt ex Howell), and F. ×ananassa subsp. ananassa. A total of 108 bands were scored among all accessio.
Measurement-device-independent QSDC protocol using Bell and GHZ states on quantum simulator

Secure cryptographic protocols are indispensable for modern communication systems. It is realized through an encryption process in cryptography. In quantum cryptography, Quantum Key Distribution (QKD) is a widely popular quantum communication scheme that enables two parties to establish a shared secret key that can be used to encrypt and decrypt messages. But security loopholes still exist in this cryptographic protocol, as an eavesdropper can in principle still intercept all the ciphertext to perform cryptanalysis and the key may get leaked to the eavesdropper, although it happens very rarely. However, there exists a more secure quantum cryptographic scheme known as Quantum Secure Direct Communication (QSDC) protocol that eliminates the necessity of key, encryption and ciphertext transmission. It is a unique quantum communication scheme where secret information is transmitted directly over a quantum communication channel. We make use of measurement-device-independent (MDI) protocol in this scheme where all the measurements of quantum states during communication are performed by a third party that can be untrusted or even an eavesdropper. This eliminates all loopholes in practical measurement devices. Here, we realize this MDI-QSDC protocol using Bell and GHZ states in the IBM Quantum Experience platform and implement swapping circuits for security check.
A simple bipartite graph projection model for clustering in networks

Graph datasets are frequently constructed by a projection of a bipartite graph, where two nodes are connected in the projection if they share a common neighbor in the bipartite graph; for example, a coauthorship graph is a projection of an author-publication bipartite graph. Analyzing the structure of the projected graph is common, but we do not have a good understanding of the consequences of the projection on such analyses. Here, we propose and analyze a random graph model to study what properties we can expect from the projection step. Our model is based on a Chung-Lu random graph for constructing the bipartite representation, which enables us to rigorously analyze the projected graph. We show that common network properties such as sparsity, heavy-tailed degree distributions, local clustering at nodes, the inverse relationship between node degree, and global transitivity can be explained and analyzed through this simple model. We also develop a fast sampling algorithm for our model, which we show is provably optimal for certain input distributions. Numerical simulations where model parameters come from real-world datasets show that much of the clustering behavior in some datasets can just be explained by the projection step.
Spot the conversation: speaker diarisation in the wild

The goal of this paper is speaker diarisation of videos collected 'in the wild'. We make three key contributions. First, we propose an automatic audio-visual diarisation method for YouTube videos. Our method consists of active speaker detection using audio-visual methods and speaker verification using self-enrolled speaker models. Second, we integrate our method into a semi-automatic dataset creation pipeline which significantly reduces the number of hours required to annotate videos with diarisation labels. Finally, we use this pipeline to create a large-scale diarisation dataset called VoxConverse, collected from 'in the wild' videos, which we will release publicly to the research community. Our dataset consists of overlapping speech, a large and diverse speaker pool, and challenging background conditions.
EFFECTS OF DIFFERENT LEVELS OF EXCESS METHIONINE AND LYSINE ON SOME ORGANS OF BROILER

This study aimed to investigate the effect of different levels of lysine and methionine on head, brain, lungs, testes, kidneys, notarium, crop, proventriculus and pancreas in male Ross 308 broilers. The experiments included 9 treatments and 3 replications/treatment based on completely randomized design as a 3 × 3 factorial arrangement. All management process, vaccination, ventilation and other environmental conditions, was quite similar for all treatments. Methionine in this experiment consisted of 0, 10 and 20% more than the recommended level by guide catalogue of Ross 308, and lysine levels in this experiment consisted of 0, 10 and 20% more than the recommended level of guide catalogue of Ross 308. The obtained results of statistical analysis showed that the inclusion of lysine and also methionine as much as 10 and 20% more than the guide catalogue of Ross 308 had not significant effect on weights of head, brain, lungs, testes, kidneys, notarium, crop, proventriculus and pancreas. Nine studied treatments had significant difference for lungs weight and also pancreas weight (P≤0.05), so both lysine and methionine as much as 20% more than recommended level by guide catalogue of Ross 308 had the highest lungs weight significantly (16.057 and 6.043 g respectively). Meanwhile, nine studied treatments had significant difference for notarium weight (P≤0.05), so lysine as much as recommended level by guide catalogue of Ross 308 and methionine as much as 20% more than recommended level by guide catalogue of Ross 308 had the highest notarium weight significantly (40.090 g).
Deep brain state classification of MEG data

Neuroimaging techniques have shown to be useful when studying the brain's activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), in combination with various deep artificial neural network models to perform brain decoding. More specifically, here we investigate to which extent can we infer the task performed by a subject based on its MEG data. Three models based on compact convolution, combined convolutional and long short-term architecture as well as a model based on multi-view learning that aims at fusing the outputs of the two stream networks are proposed and examined. These models exploit the spatio-temporal MEG data for learning new representations that are used to decode the relevant tasks across subjects. In order to realize the most relevant features of the input signals, two attention mechanisms, i.e. self and global attention, are incorporated in all the models. The experimental results of cross subject multi-class classification on the studied MEG dataset show that the inclusion of attention improves the generalization of the models across subjects.
An encoder-decoder-based method for COVID-19 lung infection segmentation

The novelty of the COVID-19 disease and the speed of spread has created a colossal chaos, impulse among researchers worldwide to exploit all the resources and capabilities to understand and analyze characteristics of the coronavirus in term of the ways it spreads and virus incubation time. For that, the existing medical features like CT and X-ray images are used. For example, CT-scan images can be used for the detection of lung infection. But the challenges of these features such as the quality of the image and infection characteristics limitate the effectiveness of these features. Using artificial intelligence (AI) tools and computer vision algorithms, the accuracy of detection can be more accurate and can help to overcome these issues. This paper proposes a multi-task deep-learning-based method for lung infection segmentation using CT-scan images. Our proposed method starts by segmenting the lung regions that can be infected. Then, segmenting the infections in these regions. Also, to perform a multi-class segmentation the proposed model is trained using the two-stream inputs. The multi-task learning used in this paper allows us to overcome shortage of labeled data. Also, the multi-input stream allows the model to do the learning on many features that can improve the results. To evaluate the proposed method, many features have been used. Also, from the experiments, the proposed method can segment lung infections with a high degree performance even with shortage of data and labeled images. In addition, comparing with the state-of-the-art method our method achieves good performance results.
Relative importance of plastic vs genetic factors in adaptive differentiation: geographical variation for stress resistance in Drosophila melanogaster from eastern Australia

Summary 1 Heritable clinal patterns for stress resistance traits have been described in a number of invertebrate species but patterns are usually characterized on populations reared under constant conditions. Here we examined the impact of simulated seasonal variation in temperature/photoperiod as well as constant conditions on stress resistance in eight Drosophila melanogaster populations from eastern Australia across a latitude range of 27 degrees. 2 Desiccation resistance was relatively higher under summer compared with winter/constant conditions, but this trait and starvation resistance did not exhibit clinal variation. Winter conditions increased cold resistance as measured by chill coma recovery time, and decreased heat resistance as measured by time to knock down in a vial. 3 Clinal patterns were evident for the thermal resistance traits regardless of conditions, and involved increased heat resistance and decreased cold resistance in tropical populations. Latitudinal patterns were steeper for cold resistance than for heat resistance. 4 To compare the relative impact of plastic vs genetic changes along the cline, differences in trait means were expressed relative to differences between populations from cline ends. For cold and heat resistance, differences between environmental conditions were approximately 1·5× greater than the heritable differences.
Power fluctuations in a finite-time quantum Carnot engine

Stability is an important property of small thermal machines with fluctuating power output. We here consider a finite-time quantum Carnot engine based on a degenerate multilevel system and study the influence of its finite Hilbert space structure on its stability. We optimize in particular its relative work fluctuations with respect to level degeneracy and level number. We find that its optimal performance may surpass those of nondegenerate two-level engines or harmonic oscillator motors. Our results show how to realize high-performance, high-stability cyclic quantum heat engines.
Feature Extraction on Synthetic Black Hole Images

The Event Horizon Telescope (EHT) recently released the first horizon-scale images of the black hole in M87. Combined with other astronomical data, these images constrain the mass and spin of the hole as well as the accretion rate and magnetic flux trapped on the hole. An important question for EHT is how well key parameters such as spin and trapped magnetic flux can be extracted from present and future EHT data alone. Here we explore parameter extraction using a neural network trained on high resolution synthetic images drawn from state-of-the-art simulations. We find that the neural network is able to recover spin and flux with high accuracy. We are particularly interested in interpreting the neural network output and understanding which features are used to identify, e.g., black hole spin. Using feature maps, we find that the network keys on low surface brightness features in particular.
Web-scale, Schema-Agnostic, End-to-End Entity Resolution

Entity Resolution lies at the core of data integration, with a bulk of research focusing on both its effectiveness and time efficiency. Initially, most relevant works were crafted for structured, relational data that are described by a schema of well-known quality and meaning. With the advent of Big Data, though, these early schema-based approaches became inapplicable, as the scope of Entity Resolution moved to Web Data collections, which abound in noisy, semi-structured, voluminous and highly heterogeneous information. To address these inherent challenges of Web Data, recent works on Entity Resolution adopt a novel, schema-agnostic functionality that emphasizes scalability and robustness to noise. In this tutorial, we take a close look on this line of research, organizing the state-of-the-art in the field into a scalable, schemaagnostic end-to-end workflow that consists of 4 steps. The first two focus on improving time efficiency through blocking, while the last two steps are dedicated to effectiveness: (i) Block Building clusters similar entities into blocks so as to restrict the originally quadratic complexity to comparing just pairs of entities that are highly likely to be matching. (ii) Block Processing further cuts down on the computational cost by discarding pairwise comparisons that are repeated or lack sufficient evidence for producing duplicates. (iii) Entity Matching carries out all comparisons in the final set of blocks, creating a similarity graph with a node for every entity and a weighted edge for every pair of compared entities. (iv) Entity Clustering partitions the nodes of the similarity graph into equivalence clusters such that every cluster contains all resources that correspond to the same real-world object. Special care is taken to highlight recent works that take the efficiency of these steps to the next level through massive parallelization, which is typically based on the MapReduce paradigm. The tutorial concludes with a hands-on session that involves our publicly available reference toolbox for Entity Resolution. This will allow the participants to put in practice all the topics discussed in theory, examining the relative performance of the main state-of-the-art techniques over established benchmark datasets. ACM Reference Format: George Papadakis and Themis Palpanas. 2017. Web-scale, Schema-Agnostic, End-to-End Entity Resolution. In Proceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/
Immunochemical sexing of living yellowfin tuna, Thunnus albacares (Bonnaterre), using a vitellogeninlike protein

Sexing of living yellowfin tuna, Thunnus albacares (Bonnaterre), was carried out based on immunochemical detection of vitellogenin (VTG), a female-specific protein. Tissue samples were taken from swimming fish in a sea pen by a hand-made apparatus. Recently fertilized eggs were also collected before tissue sample trials. A double immunodiffusion pattern showed that the egg homogenate of T. albacares reacted with rabbit antibody against the purified VTG of greater amberjack, Seriola dumerili (Risso). This suggests that the VTG of T albacares has an antigenicity similar to that of S, dumerili and that this antibody can be used to detect T albacares VTG. Immunodot-blotting analysis revealed that, out of the two fish tested, the tissue sample from one, but not the other, reacted with this antibody, suggesting that the former was a mature female, and the latter a male or immature female. Examination of the fish gonads revealed an ovary and a testis, respectively. Immunological and morphological results corresponded completely. Therefore, the method used in the present study is available for discriminating the sexes of T albacares.
AUTOMATED HYDROPONIC SYSTEM FOR DEEP WATER CULTURE TO GROW TOMATO USING ATMEGA328

This paper presents a way to automate the growth of plants with hydroponics in deep water culture using a simple algorithm to maintain the pH level in water and supply nutrients to the nutrientsolution when necessary.Despite hydroponic crops being highly nutritious and free from pest, it is difficult to grow hydroponic crops since it requires constant attention to maintain the suitable conditions for the plants and requires knowledge about the hydroponic system to grow them. A sensor is used to log the data from the pH meter and electrical conductivity meter. This data is then used to identify different phenomena and notification is sent to a person.The automated system is designed to take the required action when a certain phenomenon occurs. Index Terms Hydroponics, Automation, Soil-less culture, pH, Nutri-culture