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1CONN Creative Reklam Ajansı
Sektörünüzle ilgili genel ve yerel bilgileri büyük bir titizlikle araştırıyor, sizin için gerçekleştireceğimiz çalışmalarımıza bu bilgiler ışığında yön veriyoruz. İlk düşündüğümüz; “Biz, bu markaya ne katabiliriz?”
Design and Implementation of Interactive Videos in Undergraduate Transnational Education
The growing development of online educational video resources over the past two decades, has significantly reshaped university students' self-regulated study approaches and influenced diverse ways of learning. Within an environment of abundantly and publicly available online resources, educators cannot always control which materials students choose to supplement their study. Consequently, considerable deviations are likely to be present between the taught content by the educator and an arbitrary online educational video the student watches, in terms of terminology, notation and methods, especially in mathematical sciences. These deviations can be even more considerable when Transnational Education (TNE) students incorporate native language resources to comprehend the material. This paper presents the design, creation and implementation of interactive video resources produced in alignment with established pedagogical frameworks to support an undergraduate TNE mathematics module.
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge. Parti treats text-to-image generation as a sequence-to-sequence modeling problem, akin to machine translation, with sequences of image tokens as the target outputs rather than text tokens in another language. This strategy can naturally tap into the rich body of prior work on large language models, which have seen continued advances in capabilities and performance through scaling data and model sizes. Our approach is simple: First, Parti uses a Transformer-based image tokenizer, ViT-VQGAN, to encode images as sequences of discrete tokens. Second, we achieve consistent quality improvements by scaling the encoder-decoder Transformer model up to 20B parameters, with a new state-of-the-art zero-shot FID score of 7.23 and finetuned FID score of 3.22 on MS-COCO. Our detailed analysis on Localized Narratives as well as PartiPrompts (P2), a new holistic benchmark of over 1600 English prompts, demonstrate the effectiveness of Parti across a wide variety of categories and difficulty …
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language models (e.g. T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model. Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, without ever training on COCO, and human raters find Imagen samples to be on par with the COCO data itself in image-text alignment. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models. With DrawBench, we compare Imagen with recent methods including VQ-GAN+CLIP, Latent Diffusion Models, and DALL-E 2, and find that human raters prefer Imagen over other models in side-by-side comparisons, …
A method for mapping the aliphatic hydrocarbon content of interstellar dust towards the Galactic Centre
In the interstellar medium, the cosmic elemental carbon abundance includes the total carbon in both gas and solid phases. The aim of the study was to trial a new method for measuring the amount and distribution of aliphatic carbon within interstellar dust over wide fields of view of our Galaxy. This method is based on measurement of the 3.4 $μ$m absorption feature from aliphatic carbonaceous matter. This can readily be achieved for single sources using IR spectrometers. However, making such measurements over wide fields requires an imaging IR camera, equipped with narrow-band filters that are able to sample the spectrum. While this cannot produce as good a determination of the spectra, the technique can be applied to potentially tens to hundreds of sources simultaneously, over the field of view of the camera. We examined this method for a field in the centre of the Galaxy, and produced a map showing the variation of 3.4 $μ$m optical depth across it.
All finite solvable groups satisfy the Taketa inequality
In this paper, we prove that all finite solvable groups satisfy the Isaacs-Seitz conjecture namely the derived lenght of a finite solvable group G is less than or equal to the number of distinct irreducible complex character degrees of G.
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing
Semantic parsing provides a way to extract the semantic structure of a text that could be understood by machines. It is utilized in various NLP applications that require text comprehension such as summarization and question answering. Graph-based representation is one of the semantic representation approaches to express the semantic structure of a text. Such representations generate expressive and adequate graph-based target structures. In this paper, we focus primarily on UCCA graph-based semantic representation. The paper not only presents the existing approaches proposed for UCCA representation, but also proposes a novel self-attentive neural parsing model for the UCCA representation. We present the results for both single-lingual and cross-lingual tasks using zero-shot and few-shot learning for low-resource languages.
Joint PoS Tagging and Stemming for Agglutinative Languages
The number of word forms in agglutinative languages is theoretically infinite and this variety in word forms introduces sparsity in many natural language processing tasks. Part-of-speech tagging (PoS tagging) is one of these tasks that often suffers from sparsity. In this paper, we present an unsupervised Bayesian model using Hidden Markov Models (HMMs) for joint PoS tagging and stemming for agglutinative languages. We use stemming to reduce sparsity in PoS tagging. Two tasks are jointly performed to provide a mutual benefit in both tasks. Our results show that joint POS tagging and stemming improves PoS tagging scores. We present results for Turkish and Finnish as agglutinative languages and English as a morphologically poor language.
Android Security using NLP Techniques: A Review
Android is among the most targeted platform by attackers. While attackers are improving their techniques, traditional solutions based on static and dynamic analysis have been also evolving. In addition to the application code, Android applications have some metadata that could be useful for security analysis of applications. Unlike traditional application distribution mechanisms, Android applications are distributed centrally in mobile markets. Therefore, beside application packages, such markets contain app information provided by app developers and app users. The availability of such useful textual data together with the advancement in Natural Language Processing (NLP) that is used to process and understand textual data has encouraged researchers to investigate the use of NLP techniques in Android security. Especially, security solutions based on NLP have accelerated in the last 5 years and proven to be useful. This study reviews these proposals and aim to explore possible research directions for future studies by presenting state-of-the-art in this domain. We mainly focus on NLP-based solutions under four categories: description-to-behaviour fidelity, description generation, …
Flat minimal quantizations of Stackel systems and quantum separability
In this paper, we consider the problem of quantization of classical Stäckel systems and the problem of separability of related quantum Hamiltonians. First, using the concept of Stäckel transform, all considered systems are expressed by flat coordinates of related Euclidean configuration space. Then, the so-called flat minimal quantization procedure is applied in order to construct an appropriate Hermitian operator in the respective Hilbert space. Finally, we distinguish a class of Stäckel systems which remains separable after any of admissible flat minimal quantizations.
Mapping the aliphatic hydrocarbon content of interstellar dust in the Galactic plane
We implement a new observational method for mapping the aliphatic hydrocarbon content in the solid phase in our Galaxy, based on spectrophotometric imaging of the 3.4 $μ$m absorption feature from interstellar dust. We previously demonstrated this method in a field including the Galactic Centre cluster. We applied the method to a new field in the Galactic centre where the 3.4 $μ$m absorption feature has not been previously measured and we extended the measurements to a field in the Galactic plane to sample the diffuse local interstellar medium, where the 3.4 $μ$m absorption feature has been previously measured. We have analysed 3.4 $μ$m optical depth and aliphatic hydrocarbon column density maps for these fields. Optical depths are found to be reasonably uniform in each field, without large source-to-source variations. There is, however, a weak trend towards increasing optical depth in a direction towards $b=0^{\circ}$ in the Galactic centre. The mean value of column densities and abundances for aliphatic hydrocarbon were found to be about several $\rm \times 10^{18} \, cm^{-2}$ and several tens $\times 10^{-6}$, respectively for the new sightlines in the Galactic plane. We …
1Klinik CMS Web Yönetim Sistemi
Yeni Nesil Klinik Deneyimi. Bilim, teknoloji ve estetiği modern hasta deneyimiyle bir araya getiriyoruz. Dijital planlama, kişiye özel tedavi yaklaşımı ve güçlü hasta iletişimi.
Dentastik Klinik Yönetimi
Kliniğinizin tüm dünyası Tek bir akış. İlk temastan son kontrole. Satıştan tedaviye. Birbirine bağlı her an, tek ekosistemde.
