Small Number Of Examples Research Articles

Artvin-Ahıska Ağızlarındaki /–(y)AnçA(y/ğ)i/ Biçimbirimi Üzerine

Artvin’den Ahıska’ya uzanan çizgide yer alan ağız bölgesi, her ne kadar Anadolu ağızlarının doğu grubu içinde değerlendirilmekteyse de içinde barındırdığı Oğuzca dışı dil unsurlarıyla bu ağızlardan ayrılmaktadır. Bu makalede ele alınıp incelenen /–(y)Ança(y/ğ)i/ biçimbirimi de bahsedilen unsurlardan birini teşkil etmektedir. Çalışmamızda biçimbirimin evvela tarihî ve çağdaş Türk lehçeleri içinde yayılımı ele alınmış, sonra yapısı ve işlevleri üzerinde durulmuştur. Bu biçimbirimin aynısı veya benzerlerinin tarihî lehçelerden Kıpçak, Harezm ve Çağatay Türkçelerinde; çağdaş lehçelerden ise Kazan Tatar, Karaçay-Malkar, Kumuk, Nogay, Kazak, Karakalpak, Kırgız, Özbek, Türkmen ve Yeni Uygur Türkçelerinde kullanıldığı tespit edilmiştir. Artvin-Ahıska ağızları hariç ölçünlü Türkiye Türkçesi ve Türkiye Türkçesi ağızlarında, Irak, Suriye, Gagauz ve Azerbaycan Türkçesi metinlerinde belirlenememiştir. Ancak az sayıda örnekte Gürcistan-Borçalı Terekeme-Karapapah Türkçesi ile Güney Azerbaycan Türkçesi ağızlarında Artvin-Ahıska ağızlarıyla paralel bir biçim ve kullanımla yer aldığı görülmüştür. Türkmen Türkçesindeki varlığını, Çağatay Türkçesinin tesiriyle açıklamak ve bu biçimbirimin Oğuz grubu lehçelerinde ortak unsur olmadığını iddia etmek doğru olacaktır. Dolayısıyla /–(y)Ança(y/ğ)i/ biçimbiriminin Artvin, Ahıska ile Borçalı Terekeme-Karapapak ve Güney Azerbaycan ağızlarındaki kullanımının ancak bölgedeki Oğuz dışı Türk yerleşimleriyle izah edilmesi mümkün görülmektedir.

Beyond a reference genome: pangenomes and population genomics of underutilized and orphan crops for future food and nutrition security.

Underutilized crops are, by definition, under-researched compared to staple crops yet come with traits that may be especially important given climate change and the need to feed a globally increasing population. These crops are often stress-tolerant, and this combined with unique and beneficial nutritional profiles. Whilst progress is being made by generating reference genome sequences, in this Tansley Review, we show how this is only the very first step. We advocate that going 'beyond a reference genome' should be a priority, as it is only at this stage one can identify the specific genes and the adaptive alleles that underpin the valuable traits. We sum up how population genomic and pangenomic approaches have led to the identification of stress- and disease-tolerant alleles in staple crops and compare this to the small number of examples from underutilized crops. We also demonstrate how previously underutilized crops have benefitted from genomic advances and that many breeding targets in underutilized crops are often well studied in staple crops. This cross-crop population-level resequencing could lead to an understanding of the genetic basis of adaptive traits in underutilized crops. This level of investment may be crucial for fully understanding the value of these crops before they are lost.

Learning Camera Control in Dynamic Scenes from Limited Demonstrations

AbstractIn this work, we present our strategy for camera control in dynamic scenes with multiple people (sports teams). We learn a generic model of the player dynamics offline in simulation. We use only a few sparse demonstrations of a user's camera control policy to learn a reward function to drive camera motion in an ongoing dynamic scene. Key to our approach is the creation of a low‐dimensional representation of the scene dynamics which is independent of the environment action and rewards, which enables learning the reward function using only a small number of examples. We cast the user‐specific control objective as an inverse reinforcement learning problem, aiming to learn an expert's intention from a small number of demonstrations. The learned reward function is used in combination with a visual model predictive controller (MPC). We learn a generic scene dynamics model that is agnostic to the user‐specific reward, enabling reusing the same dynamics model for different camera control policies. We show the effectiveness of our method on simulated and real soccer matches.

Fast Adaptive Meta-Learning for Few-Shot Image Generation

Generative Adversarial Networks (GANs) are capable of effectively synthesising new realistic images and estimating the potential distribution of samples utilising adversarial learning. Nevertheless, conventional GANs require a large amount of training data samples to produce plausible results. Inspired by the capacity for humans to quickly learn new concepts from a small number of examples, several meta-learning approaches for the few-shot datasets are presented. However, most of meta-learning algorithms are designed to tackle few-shot classification and reinforcement learning tasks. Moreover, the existing meta-learning models for image generation are complex, thereby affecting the length of training time required. Fast Adaptive Meta-Learning (FAML) based on GAN and the encoder network is proposed in this study for few-shot image generation. This model demonstrates the capability to generate new realistic images from previously unseen target classes with only a small number of examples required. With 10 times faster convergence, FAML requires only one-fourth of the trainable parameters in comparison baseline models by training a simpler network with conditional feature vectors from the encoder, while increasing the number of generator iterations. The visualisation results are demonstrated in the paper. This model is able to improve few-shot image generation with the lowest FID score, highest IS, and comparable LPIPS to MNIST, Omniglot, VGG-Faces, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mini</i> ImageNet datasets. The source code is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/phaphuang/FAML</uri> .

Efekti primjene prirodnog gasa pri sagorijevanju čvrstih goriva u letu – reburning tehnologija

The energy transition basically implies a phased and significant reduction in the use of fossil fuels, until the final cessation of use in the near future. Renewable energy sources and alternative fuels are an increasingly important part of the energy transition. At the same time, it is necessary that energy systems increase energy efficiency and environmental acceptability. In this regard, great efforts are being made to expand the portfolio of primary fuels in existing large energy systems, e.g. thermal power plants. Therefore, today in large boilers, in order to reduce NOx emission, staged air supply for combustion is inevitably used as the primary measure in the furnace (air staging). In addition to the previous measure, there are many examples of staged fuel supply to the combustion zone (fuel staging), and still a small number of examples of the use of third or additional fuel, e.g. application of biogas or natural gas in combustion of pulverized solid fuels (reburning technology). General, these measures simultaneously increase the efficiency of primary energy conversion from fuel and reduce the emission of undesirable components into the environment. The results of this paper show the extent to which the effects of the use of natural gas in the co-firing of Bosnian coal and waste wood biomass are expressed. In this process there is an additional reduction of NOx in proportion to the share of gas - at a process temperature of 1350 °C and at 10% of the energy content of gas in combustion with coal, compared to emissions without additional fuel, recorded reduction of NOx emissions by more than 250 mg/mn 3.

Few-Shot Domain Adaptation via Mixup Optimal Transport.

Unsupervised domain adaptation aims to learn a classification model for the target domain without any labeled samples by transferring the knowledge from the source domain with sufficient labeled samples. The source and the target domains usually share the same label space but are with different data distributions. In this paper, we consider a more difficult but insufficient-explored problem named as few-shot domain adaptation, where a classifier should generalize well to the target domain given only a small number of examples in the source domain. In such a problem, we recast the link between the source and target samples by a mixup optimal transport model. The mixup mechanism is integrated into optimal transport to perform the few-shot adaptation by learning the cross-domain alignment matrix and domain-invariant classifier simultaneously to augment the source distribution and align the two probability distributions. Moreover, spectral shrinkage regularization is deployed to improve the transferability and discriminability of the mixup optimal transport model by utilizing all singular eigenvectors. Experiments conducted on several domain adaptation tasks demonstrate the effectiveness of our proposed model dealing with the few-shot domain adaptation problem compared with state-of-the-art methods.

Pyg4ometry: A Python library for the creation of Monte Carlo radiation transport physical geometries

Creating and maintaining computer-readable geometries for use in Monte Carlo Radiation Transport (MCRT) simulations is an error-prone and time-consuming task. Simulating a system often requires geometry from different sources and modelling environments, including a range of MCRT codes and computer-aided design (CAD) tools. Pyg4ometry is a Python library that enables users to rapidly create, manipulate, display, debug, read, and write Geometry Description Markup Language (GDML)-based geometry used in MCRT simulations. Pyg4ometry provides importation of CAD files to GDML tessellated solids, conversion of GDML geometry to FLUKA and conversely from FLUKA to GDML. The implementation of Pyg4ometry is explained in detail in this paper and includes a number of small examples to demonstrate some of its capabilities. The paper concludes with a complete example using most of Pyg4ometry's features and a discussion of possible extensions and future work. Program summaryProgram Title:Pyg4ometryCPC Library link to program files:https://doi.org/10.17632/hngmhmh8cx.1Licensing provisions: GPLv3Programming language: Python, C++External routines/libraries: ANTLR, CGAL, FreeCAD, NumPy, OpenCascade, SymPy, VTKNature of problem: Creating computer-readable geometry descriptions for Monte Carlo radiation transport (MCRT) codes is a time-consuming and error-prone task. Typically these geometries are written by the user directly in the file format used by the MCRT code. There are also multiple MCRT codes available and geometry conversion is difficult or impossible between these simulation tools.Solution method: Create a Python application programming interface for the description and manipulation of Geant4 and FLUKA geometries, with full support for the direct reading and writing of their respective geometry description file formats. Form triangular meshes to represent geometric objects for both visualisation of the geometry and to enable the use of advanced mesh-based geometric algorithms. Triangular mesh processing algorithms allow the loading and use of Standard Triangle Language (STL) and CAD/CAM files. Converting from FLUKA to Geant4 requires algorithms to decompose solids to a set of unions of convex solids. Converting from FLUKA to Geant4 requires a number of steps including the replacement of infinite surfaces with finite solids and the automatic elimination of overlaps.

Towards Visual Semantics

Lexical Semantics is concerned with how words encode mental representations of the world, i.e., concepts. We call this type of concepts, classification concepts. In this paper, we focus on Visual Semantics, namely, on how humans build concepts representing what they perceive visually. We call this second type of concepts, substance concepts. As shown in the paper, these two types of concepts are different and, furthermore, the mapping between them is many-to-many. In this paper we provide a theory and an algorithm for how to build substance concepts which are in a one-to-one correspondence with classifications concepts, thus paving the way to the seamless integration between natural language descriptions and visual perception. This work builds upon three main intuitions: (i) substance concepts are modeled as visual objects, namely, sequences of similar frames, as perceived in multiple encounters; (ii) substance concepts are organized into a visual subsumption hierarchy based on the notions of Genus and Differentia; (iii) the human feedback is exploited not to name objects, but, rather, to align the hierarchy of substance concepts with that of classification concepts. The learning algorithm is implemented for the base case of a hierarchy of depth two. The experiments, though preliminary, show that the algorithm manages to acquire the notions of Genus and Differentia with reasonable accuracy, this despite seeing a small number of examples and receiving supervision on a fraction of them.

Photophysics of Ru(II) complexes with hydroxylated diimine ligands: Photoinduced electron/proton transfer to anthraquinone

This manuscript reports the reaction of the 3MLCT excited states of two luminescent chromophores, [(bpy)2Ru(OHbpy)]2+ and [(bpy)2Ru(OMebpy)]2+ (bpy = 2,2′-bipyridine, OHbpy = 4-hydroxy-2,2′-bipyridine, OMebpy = 4-methoxy-2,2′-bipyridine), with anthraquinone (AQ). A series of luminescence, electrochemical, spectrophotometric and transient absorption studies were done in order to determine free energies for the potential reaction paths between the photoexcited complexes and AQ. For the OMebpy complex, only excited state electron transfer (ET*) from the 3MLCT state of the complex to AQ was possible. However, for the OHbpy complex, the excited state could react with AQ via a variety of pathways including excited state electron transfer, ET*, excited state proton transfer (PT*) and excited state proton coupled electron transfer (PCET*). The thermodynamic analysis revealed that, for the OHbpy complex PT* was very endergonic and not a viable reaction pathway, however both ET* and PCET* could occur. Luminescence quenching studies revealed that both the OHbpy and the OMebpy excited complexes reacted with AQ (kq ~ 109 M−1s−1 for both). Transient absorption analysis showed that, for the OMebpy complex, no photoproducts escaped the encounter complex associated with the quenching reaction. The result is consistent with strong electrostatic association of the 3+/1- encounter complex. For the OHbpy complex transient absorption results clearly show the formation of PCET* products from the encounter complex. The result represents one of a small number of examples of excited states of chromophores reacting via proton coupled electron transfer within an encounter complex.

The Unsupervised Feature Selection Algorithms Based on Standard Deviation and Cosine Similarity for Genomic Data Analysis.

To tackle the challenges in genomic data analysis caused by their tens of thousands of dimensions while having a small number of examples and unbalanced examples between classes, the technique of unsupervised feature selection based on standard deviation and cosine similarity is proposed in this paper. We refer to this idea as SCFS (Standard deviation and Cosine similarity based Feature Selection). It defines the discernibility and independence of a feature to value its distinguishable capability between classes and its redundancy to other features, respectively. A 2-dimensional space is constructed using discernibility as x-axis and independence as y-axis to represent all features where the upper right corner features have both comparatively high discernibility and independence. The importance of a feature is defined as the product of its discernibility and its independence (i.e., the area of the rectangular enclosed by the feature’s coordinate lines and axes). The upper right corner features are by far the most important, comprising the optimal feature subset. Based on different definitions of independence using cosine similarity, there are three feature selection algorithms derived from SCFS. These are SCEFS (Standard deviation and Exponent Cosine similarity based Feature Selection), SCRFS (Standard deviation and Reciprocal Cosine similarity based Feature Selection) and SCAFS (Standard deviation and Anti-Cosine similarity based Feature Selection), respectively. The KNN and SVM classifiers are built based on the optimal feature subsets detected by these feature selection algorithms, respectively. The experimental results on 18 genomic datasets of cancers demonstrate that the proposed unsupervised feature selection algorithms SCEFS, SCRFS and SCAFS can detect the stable biomarkers with strong classification capability. This shows that the idea proposed in this paper is powerful. The functional analysis of these biomarkers show that the occurrence of the cancer is closely related to the biomarker gene regulation level. This fact will benefit cancer pathology research, drug development, early diagnosis, treatment and prevention.

Meta Learning for Few-Shot One-Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

MeshWalker

Most attempts to represent 3D shapes for deep learning have focused on volumetric grids, multi-view images and point clouds. In this paper we look at the most popular representation of 3D shapes in computer graphics---a triangular mesh---and ask how it can be utilized within deep learning. The few attempts to answer this question propose to adapt convolutions &amp; pooling to suit Convolutional Neural Networks (CNNs). This paper proposes a very different approach, termed MeshWalker to learn the shape directly from a given mesh. The key idea is to represent the mesh by random walks along the surface, which "explore" the mesh's geometry and topology. Each walk is organized as a list of vertices, which in some manner imposes regularity on the mesh. The walk is fed into a Recurrent Neural Network (RNN) that "remembers" the history of the walk. We show that our approach achieves state-of-the-art results for two fundamental shape analysis tasks: shape classification and semantic segmentation. Furthermore, even a very small number of examples suffices for learning. This is highly important, since large datasets of meshes are difficult to acquire.

Few-Shot Induction of Generalized Logical Concepts via Human Guidance.

We consider the problem of learning generalized first-order representations of concepts from a small number of examples. We augment an inductive logic programming learner with 2 novel contributions. First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization. Second, we leverage richer human inputs in the form of advice to improve the sample efficiency of learning. We prove that the proposed distance measure is semantically valid and use that to derive a PAC bound. Our experiments on diverse learning tasks demonstrate both the effectiveness and efficiency of our approach.

Synthesis and Evaluation of a Macrocyclic Actinium-225 Chelator, Quality Control and In Vivo Evaluation of 225 Ac-crown-αMSH Peptide.

Targeted alpha-therapy (TAT) has great potential for treating a broad range of late-stage cancers by delivering a focused and lethal radiation dose to tumors. Actinium-225 (225 Ac) is an emerging alpha emitter suitable for TAT; however, the availability of chelators for Ac remains limited to a small number of examples (DOTA and macropa). Herein, we report a new Ac macrocyclic chelator named 'crown', which binds quantitatively and rapidly (<10 min) to Ac at ambient temperature. We synthesized 225 Ac-crown-αMSH, a peptide targeting the melanocortin 1 receptor (MC1R), specifically expressed in primary and metastatic melanoma. Biodistribution of 225 Ac-crown-αMSH showed favorable tumor-to-background ratios at 2 h post injection in a preclinical model. In addition, we demonstrated dramatically different biodistrubution patterns of 225 Ac-crown-αMSH when subjected to different latency times before injection. A combined quality control methodology involving HPLC, gamma spectroscopy and radioTLC is recommended.

Unsupervised evaluation of multiple node ranks by reconstructing local structures

A problem that frequently occurs when mining complex networks is selecting algorithms with which to rank the relevance of nodes to metadata groups characterized by a small number of examples. The best algorithms are often found through experiments on labeled networks or unsupervised structural community quality measures. However, new networks could exhibit characteristics different from the labeled ones, whereas structural community quality measures favor dense congregations of nodes but not metadata groups spanning a wide breadth of the network. To avoid these shortcomings, in this work we propose using unsupervised measures that assess node rank quality across multiple metadata groups through their ability to reconstruct the local structures of network nodes; these are retrieved from the network and not assumed. Three types of local structures are explored: linked nodes, nodes up to two hops away and nodes forming triangles. We compare the resulting measures alongside unsupervised structural community quality ones to the AUC and NDCG of supervised evaluation in one synthetic and four real-world labelled networks. Our experiments suggest that our proposed local structure measures are often more accurate for unsupervised pairwise comparison of ranking algorithms, especially when few example nodes are provided. Furthermore, the ability to reconstruct the extended neighborhood, which we call HopAUC, manages to select a near-best among many ranking algorithms in most networks.

Looking Back to Lower-Level Information in Few-Shot Learning

Humans are capable of learning new concepts from small numbers of examples. In contrast, supervised deep learning models usually lack the ability to extract reliable predictive rules from limited data scenarios when attempting to classify new examples. This challenging scenario is commonly known as few-shot learning. Few-shot learning has garnered increased attention in recent years due to its significance for many real-world problems. Recently, new methods relying on meta-learning paradigms combined with graph-based structures, which model the relationship between examples, have shown promising results on a variety of few-shot classification tasks. However, existing work on few-shot learning is only focused on the feature embeddings produced by the last layer of the neural network. The novel contribution of this paper is the utilization of lower-level information to improve the meta-learner performance in few-shot learning. In particular, we propose the Looking-Back method, which could use lower-level information to construct additional graphs for label propagation in limited data settings. Our experiments on two popular few-shot learning datasets, miniImageNet and tieredImageNet, show that our method can utilize the lower-level information in the network to improve state-of-the-art classification performance.

ОСОБЛИВОСТІ ВІДТВОРЕННЯ РЕАЛІЙ У ПЕРЕКЛАДАХ СУЧАСНОЇ АНГЛОМОВНОЇ ПРОЗИ (НА ПРИКЛАДАХ РОМАНІВ «БІЛІ ЗУБИ» З. СМІТ ТА «ЗАПРОДАНЕЦЬ» П. БЕЙТІ)

The article, based on the elaboration of theoretical sources, clarifies the ways of reproduction of realia in translations of modern English prose. For the conduction of more detailed analysis, there were chosen 400 realia from novels «White Teeth» by the modern British author Z. Smith and «The Sellout» by the modern American writer P. Beatty. The peculiarities of the transfer of both the substantive meaning of realia and its connotation (national and historical coloration) are analyzed. The factors influencing the choice of the way of reproduction of realia are studied. It was found that the most common way to reproduce realia is transcription, due to the presence of a large number of geographical realia. Combined renomination took second place in terms of frequency of use in translations of the works «White Teeth» and «The Sellout». This is primarily due to the large proportion of polynomial realia, the reproduction of which requires the simultaneous application of different approaches, such as transcription, calquing, addition, extraction, situational equivalent, and assimilation method. The third most popular method in the novel «White Teeth» turned out to be transliteration, which often served to convey the meaning of realia that denoted geographical objects, and in the work “The Sellout” – calquing, which was used to reproduce the names of educational institutions, authorities, various organizations. Among the least common ways of reproducing realities in the works “White Teeth” and “The Sellout” were hyperonymic renaming, descriptive paraphrase, situational equivalent, method of assimilation and direct inclusion. The translation of the novel «White Teeth» also features a small number of examples of the use of transposition at the connotative level and extraction. The low prevalence of the above-mentioned methods can be explained by the risk of losing an important coloration of realia. In most cases, preference was given to those methods that helped to convey the meaning of realia as accurately as possible, and at the same time, helped to preserve the coloration of such national and cultural units.

Unsustainable Science

Unsustainable Science

Synthesizing symmetric lenses

Lenses are programs that can be run both "front to back" and "back to front," allowing updates to either their source or their target data to be transferred in both directions. Since their introduction by Foster et al., lenses have been extensively studied, extended, and applied. Recent work has also demonstrated how techniques from type-directed program synthesis can be used to efficiently synthesize a simple class of lenses---so-called bijective lenses over string data---given a pair of types (regular expressions) and a small number of examples. We extend this synthesis algorithm to a much broader class of lenses, called simple symmetric lenses, including all bijective lenses, all of the popular category of "asymmetric" lenses, and a rich subset of the more powerful "symmetric lenses" proposed by Hofmann et al. Intuitively, simple symmetric lenses allow some information to be present on one side but not the other and vice versa. They are of independent theoretical interest, being the largest class of symmetric lenses that do not rely on persistent internal state. Synthesizing simple symmetric lenses is substantially more challenging than synthesizing bijective lenses: Since some of the information on each side can be "disconnected" from the other side, there will, in general, be many lenses that agree with a given example. To guide the search process, we use stochastic regular expressions and ideas from information theory to estimate the amount of information propagated by a candidate lens, generally preferring lenses that propagate more information, as well as user annotations marking parts of the source and target data structures as either irrelevant or essential. We describe an implementation of simple symmetric lenses and our synthesis procedure as extensions to the Boomerang language. We evaluate its performance on 48 benchmark examples drawn from Flash Fill, Augeas, the bidirectional programming literature, and electronic file format synchronization tasks. Our implementation can synthesize each of these lenses in under 30 seconds.

Screening designs based on weighing matrices with added two-level categorical factors

Screening designs are used widely in industrial experimentation. They are used to identify factors with the most potential among a large number of factors. In a recent article, Jones and Nachtsheim proposed a new class of designs called definitive screening designs (DSDs), and they later showed how a few categorical factors could be introduced in those designs. In this article, we provide a new general method that can use weighing matrices to construct screening designs with some two-level qualitative factors. The methodology is illustrated through a number of small examples. The generated designs are compared to the designs in the literature and their advantages and disadvantages are discussed.