Mustafa Ozcan, Hamza Ergezer, Mustafa Ayazaoglu
Low-light image enhancement (LLIE) is an ill-posed inverse problem due to the lack of knowledge of the desired image which is obtained under ideal illumination conditions. Low-light conditions give rise to two main issues: a suppressed image histogram and inconsistent relative color distributions with low signal-to-noise ratio. In order to address these problems, we propose a novel approach named FLIGHT-Net using a sequence of neural architecture blocks. The first block regulates illumination conditions through pixel-wise scene dependent illumination adjustment. The output image is produced in the output of the second block, which includes channel attention and denoising sub-blocks. Our highly efficient neural network architecture delivers state-of-the-art performance with only 25K parameters. The method's code, pretrained models and resulting images will be publicly available.
Mustafa Devrim Kaba, Mustafa Gokhan Uzunbas, Ser Nam Lim
We present a Reinforcement Learning (RL) solution to the view planning problem (VPP), which generates a sequence of view points that are capable of sensing all accessible area of a given object represented as a 3D model. In doing so, the goal is to minimize the number of view points, making the VPP a class of set covering optimization problem (SCOP). The SCOP is NP-hard, and the inapproximability results tell us that the greedy algorithm provides the best approximation that runs in polynomial time. In order to find a solution that is better than the greedy algorithm, (i) we introduce a novel score function by exploiting the geometry of the 3D model, (ii) we model an intuitive human approach to VPP using this score function, and (iii) we cast VPP as a Markovian Decision Process (MDP), and solve the MDP in RL framework using well-known RL algorithms. In particular, we use SARSA, Watkins-Q and TD with function approximation to solve the MDP. We compare the results of our method with the baseline greedy algorithm in an extensive set of test objects, and show that we can out-perform the baseline in almost all cases.
Matthew E. Trusheim, Benjamin Pingault, Noel H Wan, Mustafa Gundogan, Lorenzo De Santis, Romain Debroux, Dorian Gangloff, Carola Purser, Kevin C. Chen, Michael Walsh, Joshua J. Rose, Jonas N. Becker, Benjamin Lienhard, Eric Bersin, Ioannis Paradeisanos, Gang Wang, Dominika Lyzwa, Alejandro R-P. Montblanch, Girish Malladi, Hassaram Bakhru, Andrea C. Ferrari, Ian Walmsley, Mete Atature, Dirk Englund
Nov 19, 2018·quant-ph·PDF Solid-state quantum emitters that couple coherent optical transitions to long-lived spin qubits are essential for quantum networks. Here we report on the spin and optical properties of individual tin-vacancy (SnV) centers in diamond nanostructures. Through cryogenic magneto-optical and spin spectroscopy, we verify the inversion-symmetric electronic structure of the SnV, identify spin-conserving and spin-flipping transitions, characterize transition linewidths, measure electron spin lifetimes and evaluate the spin dephasing time. We find that the optical transitions are consistent with the radiative lifetime limit even in nanofabricated structures. The spin lifetime is phononlimited with an exponential temperature scaling leading to $T_1$ $>$ 10 ms, and the coherence time, $T_2$ reaches the nuclear spin-bath limit upon cooling to 2.9 K. These spin properties exceed those of other inversion-symmetric color centers for which similar values require millikelvin temperatures. With a combination of coherent optical transitions and long spin coherence without dilution refrigeration, the SnV is a promising candidate for feasable and scalable quantum networking applications.
Mustafa Gündoğan, Patrick M. Ledingham, Attaallah Almasi, Matteo Cristiani, Hugues de Riedmatten
Jan 19, 2012·quant-ph·PDF We report on the quantum storage and retrieval of photonic polarization quantum bits onto and out of a solid state storage device. The qubits are implemented with weak coherent states at the single photon level, and are stored for 500 ns in a praseodymium doped crystal with a storage and retrieval efficiency of 10%, using the atomic frequency comb scheme. We characterize the storage by using quantum state tomography, and find that the average conditional fidelity of the retrieved qubits exceeds 95% for a mean photon number mu=0.4. This is significantly higher than a classical benchmark, taking into account the Poissonian statistics and finite memory efficiency, which proves that our device functions as a quantum storage device for polarization qubits, even if tested with weak coherent states. These results extend the storage capabilities of solid state quantum memories to polarization encoding, which is widely used in quantum information science.
Mustafa Gündoğan, Margherita Mazzera, Patrick M. Ledingham, Matteo Cristiani, Hugues de Riedmatten
Jan 14, 2013·quant-ph·PDF We report on coherent and multi-temporal mode storage of light using the full atomic frequency comb memory scheme. The scheme involves the transfer of optical atomic excitations in Pr3+:Y2SiO5 to spin-waves in the hyperfine levels using strong single-frequency transfer pulses. Using this scheme, a total of 5 temporal modes are stored and recalled on-demand from the memory. The coherence of the storage and retrieval is characterized using a time-bin interference measurement resulting in visibilities higher than 80%, independent of the storage time. This coherent and multimode spin-wave memory is promising as a quantum memory for light.
Mustafa Mustafa, Jan Balewski, Jérôme Lauret, Jefferson Porter, Shane Canon, Lisa Gerhardt, Levente Hajdu, Mark Lukascsyk
As HPC facilities grow their resources, adaptation of classic HEP/NP workflows becomes a need. Linux containers may very well offer a way to lower the bar to exploiting such resources and at the time, help collaboration to reach vast elastic resources on such facilities and address their massive current and future data processing challenges. In this proceeding, we showcase STAR data reconstruction workflow at Cori HPC system at NERSC. STAR software is packaged in a Docker image and runs at Cori in Shifter containers. We highlight two of the typical end-to-end optimization challenges for such pipelines: 1) data transfer rate which was carried over ESnet after optimizing end points and 2) scalable deployment of conditions database in an HPC environment. Our tests demonstrate equally efficient data processing workflows on Cori/HPC, comparable to standard Linux clusters.
Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Zarija Lukić, Rami Al-Rfou, Jan M. Kratochvil
Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively computationally expensive. The application of deep learning techniques to generative modeling is renewing interest in using high dimensional density estimators as computationally inexpensive emulators of fully-fledged simulations. These generative models have the potential to make a dramatic shift in the field of scientific simulations, but for that shift to happen we need to study the performance of such generators in the precision regime needed for science applications. To this end, in this work we apply Generative Adversarial Networks to the problem of generating weak lensing convergence maps. We show that our generator network produces maps that are described by, with high statistical confidence, the same summary statistics as the fully simulated maps.
M. A. El-Damcese, Abdelfattah Mustafa, B. S. El-Desouky, M. E. Mustafa
In this paper we propose a new lifetime model, called the odd generalized exponential linear failure rate distribution. Some statistical properties of the proposed distribution such as the moments, the quantiles, the median, and the mode are investigated. The method of maximum likelihood is used for estimating the model parameters. An applications to real data is carried out to illustrate that the new distribution is more flexible and effective than other popular distributions in modeling lifetime data.
M. A. El-Damcese, Abdelfattah Mustafa, B. S. El-Desouky, M. E. Mustafa
In this paper we propose a new lifetime model, called the odd generalized exponential gompertz distribution, We obtain some of its mathematical properties. Some structural properties of the new distribution are studied. The method of maximum likelihood method is used for estimating the model parameters and the observed Fisher's information matrix is derived. We illustrate the usefulness of the proposed model by applications to real data.
Mikhail Lemeshko, Mustafa Mustafa, Sabre Kais, Bretislav Friedrich
By invoking supersymmetry, we found a condition under which the Stark effect problem for a polar and polarizable molecule subject to nonresonant electric fields becomes exactly solvable. The exact solvability condition for the interaction parameters involved yields exact wavefunction for the "stretched" states, $|J=m,m>$, and for the $|1,1>$ state in the case of a purely induced-dipole interaction. The analytic expressions for the eigenenergy, the space-fixed dipole moment, the alignment cosine, and the expectation value of the angular momentum allow to readily reverse-engineer the problem of finding the values of the interaction parameters required for creating quantum states with preordained characteristics.
Mustafa Mustafa
During RHIC 2010 run, STAR has collected a large amount of minimum-bias, central and high $p_{T}$ trigger data in Au+Au collisions at $\sqrt{s_{NN}} = 39$, 62.4 and 200 GeV with detector configuration for minimum photonic conversion background. In this article we report on a new high precision measurement of non-photonic electron mid-rapidity invariant yield, improved nuclear modification factor and $v_{2}$ in Au+Au collisions at $\sqrt{s_{NN}} = 200$ GeV. We also present measurements of mid-rapidity invariant yield at $\sqrt{s_{NN}} = 62.4$ and $v_{2}$ at $\sqrt{s_{NN}} = 39$ and 62.4 GeV.
Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu
While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim to predict turbulent flow by learning its highly nonlinear dynamics from spatiotemporal velocity fields of large-scale fluid flow simulations of relevance to turbulence modeling and climate modeling. We adopt a hybrid approach by marrying two well-established turbulent flow simulation techniques with deep learning. Specifically, we introduce trainable spectral filters in a coupled model of Reynolds-averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES), followed by a specialized U-net for prediction. Our approach, which we call turbulent-Flow Net (TF-Net), is grounded in a principled physics model, yet offers the flexibility of learned representations. We compare our model, TF-Net, with state-of-the-art baselines and observe significant reductions in error for predictions 60 frames ahead. Most importantly, our method predicts physical fields that obey desirable physical characteristics, such as conservation of mass, whilst faithfully emulating the turbulent kinetic energy field and spectrum, which are critical for accurate prediction of turbulent flows.
Mustafa Mustafa
The Solenoidal Tracker at RHIC (STAR) experiment utilizes its excellent mid-rapidity tracking and particle identification capabilities to study the emergent properties of Quantum Chromodynamics (QCD). The STAR heavy-ion program at vanishingly small baryon density is aimed to address questions about the quantitative properties of the strongly-interacting Quark Gluon Plasma (QGP) matter created in high energy collisions ($η/s$, $\hat{q}$, chirality, transport parameters, heavy quark diffusion coefficients ...). At finite baryon density, the questions concern the phases of nuclear matter (the QCD phase diagram) and the nature of the phase transition, namely: what is the onset collision energy for the formation of QGP? What is the nature of phase transition in heavy-ion collisions? Are there two phase transition regions? If yes, where is the critical point situated? At Quark Matter 2015, the STAR collaboration has presented a wealth of new experimental results which address these questions. In these proceedings I highlight a few of those results.
Mikhail Lemeshko, Mustafa Mustafa, Sabre Kais, Bretislav Friedrich
We made use of supersymmetric (SUSY) quantum mechanics to find a condition under which the Stark effect problem for a polar and polarizable closed-shell diatomic molecule subject to collinear electrostatic and nonresonant radiative fields becomes exactly solvable. The condition, $Δω= \frac{ω^2}{4 (m+1)^2}$, connects values of the dimensionless parameters $ω$ and $Δω$ that characterize the strengths of the permanent and induced dipole interactions of the molecule with the respective fields. The exact solutions are obtained for the $|\tilde{J}=m,m;ω,Δω>$ family of "stretched" states. The field-free and strong-field limits of the combined-fields problem were found to exhibit supersymmetry and shape-invariance, which is indeed the reason why they are analytically solvable. By making use of the analytic form of the $|\tilde{J}=m,m;ω,Δω>$ wavefunctions, we obtained simple formulae for the expectation values of the space-fixed electric dipole moment, the alignment cosine, the angular momentum squared, and derived a "sum rule" which combines the above expectation values into a formula for the eigenenergy. The analytic expressions for the characteristics of the strongly oriented and aligned states provide a direct access to the values of the interaction parameters required for creating such states in the laboratory.
Samal Mukhtar, Yinghua Yao, Zhu Sun, Mustafa Mustafa, Yew Soon Ong, Youcheng Sun
Software vulnerability detection (SVD) is a critical challenge in modern systems. Large language models (LLMs) offer natural-language explanations alongside predictions, but most work focuses on binary evaluation, and explanations often lack semantic consistency with Common Weakness Enumeration (CWE) categories. We propose VulReaD, a knowledge-graph-guided approach for vulnerability reasoning and detection that moves beyond binary classification toward CWE-level reasoning. VulReaD leverages a security knowledge graph (KG) as a semantic backbone and uses a strong teacher LLM to generate CWE-consistent contrastive reasoning supervision, enabling student model training without manual annotations. Students are fine-tuned with Odds Ratio Preference Optimization (ORPO) to encourage taxonomy-aligned reasoning while suppressing unsupported explanations. Across three real-world datasets, VulReaD improves binary F1 by 8-10% and multi-class classification by 30% Macro-F1 and 18% Micro-F1 compared to state-of-the-art baselines. Results show that LLMs outperform deep learning baselines in binary detection and that KG-guided reasoning enhances CWE coverage and interpretability.
Mustafa Erkovan
Different thicknesses of cobalt thin films were growth by magnetron sputtering deposition techniques. The films thicknesses were determinated with X ray Photoelectron Spectroscopy (XPS) and Quartz Crystal Monitoring (QCM). XPS is also used to determinate the films quality. The films magnetic properties were determinated by Ferromagnetic Resonance (FMR) technique.
M. S. Shikakhwa, M. Mustafa
Jan 20, 2010·quant-ph·PDF The angular part of the Schrodinger equation for a central potential is brought to the one-dimensional 'Schrodinger form' where one has a kinetic energy plus potential energy terms. The resulting polar potential is seen to be a family of potentials characterized by the square of the magnetic quantum number m. It is demonstrated that this potential can be viewed as a confining potential that attempts to confine the particle to the xy-plane, with a strength that increases with increasing m. Linking the solutions of the equation to the conventional solutions of the angular equation, i.e. the associated Legendre functions, we show that the variation in the spatial distribution of the latter for different values of the orbital angular quantum number l can be viewed as being a result of 'squeezing' with different strengths by the introduced 'polar potential'.
M. Mustafa, S. Kais
Supersymmetry, shape invariance, exact solubility, and the factorization method are often studied together in the literature. At the dawn of these topics confusion was present in regards to their scope of applicability and the relation among them. Considerable work have been put to study and resolve the relation among two or more of these topics. These works are scattered over the literature. While looking at the literature, one can not overlook the number of places where authors confuse these terms, and concluding implications depending on wrong assumptions of the relation between two or more of these topics. In this letter we define supersymmetry, and shape invariance, and show the relations which connects them to exact solubility and the factorization method, referring to the literature for the respective detailed work and proofs. At last we conclude our letter with a Venn diagram which illustrates those relations.
Mustafa Mohammed Mustafa
We propose a command-filter backstepping controller that integrates a disturbance observer and a high-gain observer (HGO) to handle unknown internal and external disturbances acting on a quadrotor. To build the controller, we first define tracking errors between the measured and desired quadrotor outputs, which allow the system to be rewritten in a new set of state variables. Using this transformed model, we apply Lyapunov theory to derive a backstepping control law. To avoid repeated differentiation of states and virtual controls, a first-order command filter is introduced, and a nonlinear disturbance observer is added to provide disturbance estimates. Each state in the controller and observer is replaced with its estimate from the HGO. The resulting control law enables the quadrotor to follow its path despite internal and external disturbances, with each subsystem allowed its own disturbance type for realism. A new state transformation and Lyapunov-based derivation prevent the usual explosion of complexity, while the HGO reconstructs unmeasured states and their rates for output feedback. The nonlinear disturbance observer attenuates constant and nonlinear disturbances as well as band-limited white noise. The method reduces dependence on high-precision sensors and mitigates wind, model error, and rotor noise effects during flight. Unlike previous studies that treat either disturbance rejection or partial sensing, this work combines the command filter, disturbance observer, and HGO to address both challenges simultaneously while avoiding the complexity growth typical of backstepping designs.
Mustafa Erkovan, Melek Türksoy Öcal, Osman Öztürk
We experimentally investigated disordered PtxCo1-x (here x: 0.4, 0.5 and 0.6) alloy thin films magnetic properties which depended on Pt content. The magnetic properties of PtCo films were described with two effects, one of them is the hybridization between Co 3d and Pt 5d energy levels and it causes Pt magnetic polarization. The second one is the high spin orbit coupling constant of Pt which increases the ratio of magnetic orbital moment to spin moment. We investigated magnetic properties considering these effects by vibrating sample magnetometer (VSM) and ferromagnetic resonance (FMR) techniques.