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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?”
On rotational surfaces in 3 dimensional de Sitter space with Weingarten condition
In this article, we study spacelike and timelike rotational surfaces in a 3--dimensional de Sitter space $\mathbb{S}^3_1$ which are the orbit of a regular curve under the action of the orthogonal transformation of 4--dimensional Minkowski space $\mathbb{E}^4_1$ leaving a spacelike, a timelike or a degenerate plane pointwise fixed. We determine the profile curve of such Weingarten rotational surfaces parameterized by the principal curvature. Then, we classify spacelike and timelike Weingarten rotational surface in $\mathbb{S}^3_1$ with the principal curvatures $κ$ and $λ$ satisfying $κ=aλ+b$ or $κ=aλ^m$ for special cases of constants $a, b$ and $m$.
The Science of Rejection: A Research Area for Human Computation
We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.
Timelike Loxodromes on Lorentzian Helicoidal Surfaces in Minkowski n--Space
In this paper, we examine timelike loxodromes on three kinds of Lorentzian helicoidal surfaces in Minkowski $n$--space. First, we obtain the first order ordinary differential equations which determine timelike loxodromes on the Lorentzian helicoidal surfaces in $\mathbb{E}^n_1$ according to the causal characters of their meridian curves. Then, by finding general solutions, we get the explicit parametrizations of such timelike loxodromes. In particular, we investigate the timelike loxodromes on the three kinds of Lorentzian right helicoidal surfaces in $\mathbb{E}^n_1$. Finally, we give an example to visualize the results.
Photometric Mapping of Carbonaceous/Siliceous Dust and Water Ice in the ISM with JWST: Applications to the Dense Sightlines
We introduce a new photometric mapping method for the James Webb Space Telescope (JWST) to measure the spatial distribution of carbonaceous dust, siliceous dust and water ice by using absorption features arising from the grains in the dense interstellar medium (ISM). Employing NIRCam and MIRI imaging filters, low-resolution spectroscopic data can be obtained to measure the optical depths of the 3.0-$μ$m water ice -OH feature, the 3.4-$μ$m aliphatic hydrocarbon -CH feature, and the 10-$μ$m silicate -SiO feature for large fields of view. This method provides extensive statistical data of the grains across wide fields in the ISM at minimal observing cost. In this study, we present its application on observational data from the literature to validate the measured optical depths and simulations to assess the accuracy of the method under various conditions. We showed that the photometric method can be employed to obtain reasonably accurate measurements of optical depth. We demonstrate that JWST optical depth maps enable the independent exploration of abundance distributions of major grain components across a wide spatial coverage in the ISM.
Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?
We examine the syntactic properties of BabyLM corpus, and age-groups within CHILDES. While we find that CHILDES does not exhibit strong syntactic differentiation by age, we show that the syntactic knowledge about the training data can be helpful in interpreting model performance on linguistic tasks. For curriculum learning, we explore developmental and several alternative cognitively inspired curriculum approaches. We find that some curricula help with reading tasks, but the main performance improvement come from using the subset of syntactically categorizable data, rather than the full noisy corpus.
Analysis of a model of the Calvin cycle with diffusion of ATP
The dynamics of a mathematical model of the Calvin cycle, which is part of photosynthesis, is analysed. Since diffusion of ATP is included in the model a system of reaction-diffusion equations is obtained. It is proved that for a suitable choice of parameters there exist spatially inhomogeneous positive steady states, in fact infinitely many of them. It is also shown that all positive steady states, homogeneous and inhomogeneous, are nonlinearly unstable. The only smooth steady state which could be stable is a trivial one, where all concentrations except that of ATP are zero. It is found that in the spatially homogeneous case there are steady states with the property that the linearization about that state has eigenvalues which are not real, indicating the presence of oscillations. Numerical simulations exhibit solutions for which the concentrations are not monotone functions of time.
MedSyn: Enhancing Diagnostics with Human-AI Collaboration
Clinical decision-making is inherently complex, often influenced by cognitive biases, incomplete information, and case ambiguity. Large Language Models (LLMs) have shown promise as tools for supporting clinical decision-making, yet their typical one-shot or limited-interaction usage may overlook the complexities of real-world medical practice. In this work, we propose a hybrid human-AI framework, MedSyn, where physicians and LLMs engage in multi-step, interactive dialogues to refine diagnoses and treatment decisions. Unlike static decision-support tools, MedSyn enables dynamic exchanges, allowing physicians to challenge LLM suggestions while the LLM highlights alternative perspectives. Through simulated physician-LLM interactions, we assess the potential of open-source LLMs as physician assistants. Results show open-source LLMs are promising as physician assistants in the real world. Future work will involve real physician interactions to further validate MedSyn's usefulness in diagnostic accuracy and patient outcomes.
On Space-like Class $\mathcal A$ Surfaces in Robertson-Walker Space Times
In this article, we consider space-like surfaces in Robertson-Walker Space times $L^4_1(f,c)$ with comoving observer field $\frac{\partial}{\partial t}$. We study some problems related to such surfaces satisfying the geometric conditions imposed on the tangential part and normal part of the unit vector field $\frac{\partial}{\partial t}$ naturally defined. First, we investigate space-like surfaces in $L^4_1(f,c)$ satisfying that the tangent component of $\frac{\partial}{\partial t}$ is an eigenvector of all shape operators, called class $\mathcal A$ surfaces. Then, we get a classification theorem of space-like class $\mathcal A$ surfaces in $L^4_1(f,0)$. Also, we examine minimal space-like class $\mathcal A$ surfaces in $L^4_1(f,0)$. Finally, we give the parametrizations of space-like surfaces in $L^4_1(f,0)$ when the normal part of the unit vector field $\frac{\partial}{\partial t}$ is parallel.
Bilevel Planning with Learned Symbolic Abstractions from Interaction Data
Intelligent agents must reason over both continuous dynamics and discrete representations to generate effective plans in complex environments. Previous studies have shown that symbolic abstractions can emerge from neural effect predictors trained with a robot's unsupervised exploration. However, these methods rely on deterministic symbolic domains, lack mechanisms to verify the generated symbolic plans, and operate only at the abstract level, often failing to capture the continuous dynamics of the environment. To overcome these limitations, we propose a bilevel neuro-symbolic framework in which learned probabilistic symbolic rules generate candidate plans rapidly at the high level, and learned continuous effect models verify these plans and perform forward search when necessary at the low level. Our experiments on multi-object manipulation tasks demonstrate that the proposed bilevel method outperforms symbolic-only approaches, reliably identifying failing plans through verification, and achieves planning performance statistically comparable to continuous forward search while resolving most problems via efficient symbolic reasoning.
A systematic review of transfer learning based approaches for diabetic retinopathy detection
Cases of diabetes and related diabetic retinopathy (DR) have been increasing at an alarming rate in modern times. Early detection of DR is an important problem since it may cause permanent blindness in the late stages. In the last two decades, many different approaches have been applied in DR detection. Reviewing academic literature shows that deep neural networks (DNNs) have become the most preferred approach for DR detection. Among these DNN approaches, Convolutional Neural Network (CNN) models are the most used ones in the field of medical image classification. Designing a new CNN architecture is a tedious and time-consuming approach. Additionally, training an enormous number of parameters is also a difficult task. Due to this reason, instead of training CNNs from scratch, using pre-trained models has been suggested in recent years as transfer learning approach. Accordingly, the present study as a review focuses on DNN and Transfer Learning based applications of DR detection considering 38 publications between 2015 and 2020. The published papers are summarized using 9 figures and 10 tables, giving information about 22 pre-trained CNN models, 12 DR data sets and standard …
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.
