Miracle-dz/UniFAR-FTUs huggingface dataset:
Learned Regularization for Iterative Photoacoustic Reconstruction

Recently, deep learning methods have shown great potential in photoacoustic imaging. In this work a specific network structure is designed to learn the regularize term of traditional iteration method reconstruction. The performance of this method is tested on simulation dataset for sparse view condition. Clinical Relevance— This method will help improving imaging speed and imaging quality of photoacoustic imaging.
QUANTIFYING ACOUSTIC DISTORTIONS FROM HEARING AID GROUP DELAYS

In digital hearing aids, the input sound is processed in different frequency channels, where each channel is manipulated by various algorithms (such as compression, directional microphone, noise reduction, feedback cancellation, and frequency lowering). Processed signals from each channel are then added together to form the final output from the hearing aid. This processing is not instantaneous, but requires time, described by the group or processing delay of the device. Delay is typically not a concern when it is less than 10 ms (Stone and Moore, 1999, 2002) and the hearing aid is worn in a completely occluding mode, where no intentional (from vents) or unintentional leakage occurs. On the other hand, the amount of processing delay does matter when the hearing aid is worn in a non-occluding mode (such as with the use of open-fit or any type of instant-fit eartips), where there is opportunity for the direct unamplified sounds to leak through the vent and mix with the amplified sounds in the ear canal. This interaction between the amplified sound, which is delayed, and the unamplified sound results in a distortion to the combined signal.
Comparing the Effects of Cognitive-Behavioral Therapy and Mindfulness Treatment on Depressive Symptoms in Students with Suicidal Ideation

Author Address 1. PhD Student in General Psychology, Department of Psychology, Karaj Branch, Islamic Azad University, Karaj, Iran; 2. Assistant Professor, Department of Psychology, Karaj Branch, Islamic Azad University, Karaj, Iran; 3. Assistant Professor, Department of Psychology, Roudehen Branch, Islamic Azad University, Roudehen, Iran; 4. Assistant Professor, Department of Clinical Psychology, Faculty of Psychology and Educational Sciences, Kharazmi University, Tehran, Iran. * Corresponding author’s email: mohsen.mansoobifar@kiau.ac. ir
Onboard Artificial Intelligence for Space Situational Awareness with Low-Power GPUs

Onboard processing provides the reduction in latency that is critical to Space Situational Awareness (SSA) applications. By receiving data directly from the sensor and processing it in real-time on board of spacecraft, this technology enables real-time processing and response. Currently, onboard processing is a nascent technology and the capabilities that exist are limited: they are highly-customized, one-off systems typically built for large spacecrafts. This is poised to change drastically in the coming decade, as off-the-shelf computing hardware and algorithms mature while spacecraft operations turn towards more scalable small-sat missions. Within that time period, onboard processing will transform from a research topic to an essential element of most space missions. One of the key functionalities for onboard processing in SSA domain is object classification. Current state of the art classification algorithms are based on Artificial Intelligence (AI) technologies. Due to prohibitively high computational needs for AI applications, their deployment onboard a spacecraft has not been possible to date. However, the rapid advance in AI-oriented computing hardware, especially Graphics Processing Units (GPU), has opened the door to AI in space. In particular, low size, weight, and power (SWaP) GPU devices have been developed that would be ideal for space-based processing. MDA is currently investigating use of state of the art off-the-shelf low-power GPUs for deployment of AI applications essential for real-time object identification as part of the SSA domain. This work is motivated by recent increased focus within the AI community on operationalizing AI methodology. In this paper, we discuss the motivation behind the research, technical details of the implementation and current results. More specifically, three simple neural networks of different sizes trained on MDA Sapphire dataset for space object classification are presented. The classification performance of these algorithms are benchmarked demonstrating that larger models tend to be more robust to added noise in the input image. Effects of computing optimization techniques applied on these models are also presented, which generally show great improvement by several factors in throughput and power efficiency of these algorithms onboard the hardware platform hosting these technologies.
Adaptation in Heterogeneous Mobile SoCs

Motivation for Heterogeneous Mobile SoCs The reliance of embedded computing systems is increasing to heterogeneous mobile SoCs consisting of different types of cores such as CPU and GPU. An example includes the Samsung Exynos 5422 SoC containing a CPU with four ARM cortex-A15 (big) and four ARM Cortex-A7 (LITTLE) cores, and a GPU with 6 ARM Mali-T628 cores [1]. These architectures provide opportunities to exploit distinct features of CPU and GPU cores to meet performance and energy consumption requirements. Further, the cores in these SoCs support dynamic voltage and frequency scaling (DVFS), which can be used to reduce dynamic power consumption (P ∝ V 2f ). This helps to reduce energy consumption if the extra time taken to run the workload at a lower voltage and frequency can be accounted by sufficient reduction in the power consumption. Open Computing Language (OpenCL) [3] provides an opportunity to write applications that can execute across heterogeneous cores including CPUs and GPUs [6, 8–10]. However, since CPU and GPU cores typically handle task/thread and data level parallelism, respectively, the performance and energy consumption of an application varies when executed onto only CPU, only GPU, or both CPU and GPU cores [17]. The variation depends upon the kind of parallelism dominating the application. Existing studies have shown significant reduction in execution time and energy consumption when an application simultaneously exploits both the CPU and GPU cores with an appropriate partitioning of threads between them [6].
Accuracy Verification of a Rupture Prediction Equation for Unruptured Cerebral Aneurysms

We proposed a cerebral aneurysm rupture prediction equation including hemodynamic and morphological parameters and clinical information using statistical analysis and confirmed its validity on a different patient dataset. The calculated rupture prediction equation (RPE) considering NWSSmax, AR and presence of aneurysmal bleb showed good accuracy when it was applied on a different patient dataset. Clinical Relevance—It may be able to predict the rupture of cerebral aneurysms in advance using the rupture prediction equation proposed in this study.
Experimental investigation of mini Gurney flaps in combination with vortex generators for improved wind turbine blade performance

Abstract. This wind tunnel study investigates the aerodynamic effects of mini Gurney flaps (MGFs) and their combination with vortex generators (VGs) on the performance of airfoils and wind turbine rotor blades. VGs are installed on the suction side aiming at stall delay and increased maximum lift. MGFs are thin angle profiles that are attached at the trailing edge in order to increase lift at pre-stall operation. The implementation of both these passive flow control devices is accompanied by a certain drag penalty. The wind tunnel tests are conducted at the Hermann-Föttinger Institut of the Technische Universität Berlin based on two airfoils that are characteristic of different sections of large rotor blades. Lift and drag are determined using a force balance and a wake rake, respectively, for static angles of attack between −5 and 17∘ at a Reynolds number of 1.5 million. The impact of different MGF heights including 0.25 %, 0.5 % and 1.0 % and a VG height of 1.1 % of the chord length is tested and evaluated. Furthermore, the clean and the tripped baseline cases are considered. In the latter, leading-edge transition is forced with Zig Zag (ZZ) turbulator tape. The preferred configurations are the smallest MGF on the NACA63(3)618 and the medium-sized MGF combined with VGs on the DU97W300. Next, the experimental lift and drag polar data are imported into the software QBlade in order to design a generic rotor blade. The blade performance is simulated with and without the add-ons by means of two case studies. In the first case, the retrofit application on an existing blade mitigates the adverse effects of the ZZ tape. Stall is delayed and the aerodynamic efficiency is partly recovered leading to an improvement of the power curve. In the second case, the new design application allows for the design of a more slender blade while maintaining the rotor power. This alternative blade appears to be more resistant against the adverse effects of forced leading-edge transition.
SIMULATION OF MODIFIED MPPT CONTROL FOR OSCILLATION DAMPING IN GRID CONNECTED SOLAR

Rapid depletion of fossil fuel resources on a worldwide basis has necessitated an urgent search for alternative energy sources to cater to the present days’ demand. In this paper a modified P&O algorithm is developed to increase the tracking efficiency. The approach is to simultaneously reduce the steady state oscillation while minimize the loss due to the losing direction. The key to the success of the algorithm is its ability to accurately detect the occurrence of oscillation and to introduce a boundary condition preventing it from being diverged away uncontrollably from the MPPT. The modified scheme retains the conventional P&O structure, but with a unique technique to dynamically alter the perturbation size. At the same time, a dynamic boundary condition is introduced to ensure that the algorithm will not diverge from its tracking
STRUCTURE-INDEPENDENT FACTORS IN EXPRESSIVE TIMING: A PRELIMINARY STUDY ON VIOLIN SOLO PERFORMANCE

We investigate the static behavior of individual notes in real-world music performance. Different from the previous works which mostly focused on the dynamical behavior of expressive timing in performance, we assume that expressive timing has a global trend which is independent from musical structure, cadence, and other time-varying factors. An expression-wise study on a violin solo dataset with 11 different music expressions veriﬁes our assumption.
Stress Intensity Factor And Limit Load Handbook

Stress Intensity Factor and Limit Load HandbookFatigue and Fracture MechanicsFatigue and Fracture MechanicsFracture and Fatigue Emanating from Stress ConcentratorsStress-intensity Factor for a Cracked Sheet with Riveted and Uniformly Spaced StringersTool and Manufacturing Engineers Handbook Desk EditionAdvances in Mechanical Behaviour, Plasticity and DamageWide Range Stress Intensity Factor and Crack-Mouth-Opening Displacement Expressions Suitable for Short Crack Fracture Testing with Arc BendChord Suppport SamplesFatigue Limit in MetalsFatigue Assessment of Welded Joints by Local ApproachesFracture MechanicsStructural Dynamics, Volume 3Stress-intensity Factors for Three-point Bend Specimens by Boundary CollocationExperimental evaluation of stress concentration and intensity factorsFundamentals of Structural IntegrityFatigue as a Design CriterionProblems of Fracture Mechanics and FatigueWide Range Stress Intensity Factor Expression for an Edge-Cracked Round Bar Bend SpecimenStress-intensity Factor Solutions for Cracks at Countersunk Rivet Holes Under Uniaxial TensionMixed Mode Stress Intensity Factors for Semielliptical Surface CracksFitness-for-Service Fracture Assessment of Structures Containing CracksStress-intensity Factors for Single-edge-notch Specimens in Bending Or Combined Bending and Tension by Boundary Collocation of a Stress FunctionStress Analysis and Growth of CracksStress-intensity Factors for Corner Cracks at the Edge of a HoleStress-intensity Factors for Circumferential Surface Cracks in Pipes and Rods Under Tension and Bending LoadsDamage Tolerance of Metallic StructuresNumerical Fracture MechanicsFracture Mechanics of CeramicsExperimental Techniques in Fracture MechanicsStress Intensity Factors and Weight FunctionsFatigue Crack Growth Rate and StressIntensity Factor Corrections for Out-of-Plane Crack GrowthElements of Fracture MechanicsPlates and Shells with CracksStress Concentration at NotchesStress-intensity Factors for Small Surface and Corner Cracks in PlatesFatigue Crack Growth Thresholds, Endurance Limits, and DesignFracture Mechanics:fourteenth Symposium Volume 1: Theory and AnalysisImproved Stress-intensity Factors for Semi-elliptical Surface Cracks in Finite-thickness PlatesA Review and Assessment of the Stress-intensity Factors for Surface CracksStress Intensity Factors, T-stresses, Weight Functions
Comparing the Effects of Hope Therapy and Spiritual Therapy on Self-Care, Depression, and HbA1c in Middle-Aged Women with Type 2 Diabetes

Author Address 1. Department of Psychology, Karaj Branch, Islamic Azad University, Karaj, Iran; 2. Department of Psychology, Karaj Branch, Islamic Azad University, Karaj, Iran; 3. Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; 4. Department of Biostatistics and Epidemiology, School of Health, Alborz University of Medical Sciences, Karaj, Iran. *Corresponding author’s email: niloufar.tahmouresi@kiau.ac. ir
Collective Almost Synchronization Modeling Used for Motor Imagery EEG Classification

Classification based on feature extraction is a crucial technique to develop Brain Computer Interface (BCI) systems. The human brain can be considered as a dynamical system, and its behavior measured by EEG signals can be modeled by a group of nonlinear oscillators. Exploring the dynamical nature of EEG signals along with model based approach may improve classification accuracy in BCI. This study proposes a novel feature extraction method for the classification of Motor Imagery (MI) EEG using a dynamical network model operating in a special collective state, so called Collective Almost Synchronization (CAS). The CAS, the nonlinear oscillators set to operate in a weakly coupled regime, can be used to model an EEG. Purpose of this study is to investigate the performance of the CAS model to identify features for the classification of MI states. To achieve this goal, a linear regression method is used and linear coefficients are extracted as feature vectors. Our approach boils down to identifying patterns in the MI-EEG by associating them to the coefficients of a linear regression (or weights of an output function) constructed to model the MI-EEG signals from simulated time-series generated by a dynamical neural network. The dataset 2b from BCI Competition-IV was used to evaluate the performance of the proposed method. Results indicate that the CAS-based classification method is more robust in extracting distinguishable features from EEG signals as compared with other state-of-the-art methods. The proposed method achieved better performance on two-class MI classification. Moreover, the method developed in this study for MI classification across subjects is effective with 74.03% of the accuracy.
Photovoltaic Panels + Air Conditioners + Multi- Objective Evolutionary Algorithms = A win-win situation

Electricity peak demand represents a serious threat to the power infrastructure and can lead to blackouts when the lines cannot cope with t he required load. As the use of commercial and residential air conditioning (AC) in creases, these peaks can only become higher. Connecting a set of these devices to photovoltaic panels (PV) can help mitigate this problem under the proper management. This paper models a group of airconditioned rooms, whose ACs are partly powered by photovoltaic panels. A multiobjective evolutionary algorithm (MOEA) is used to quantify trade-offs amongst peak demand, energy cost and thermal comfort. We model and study a scenario with 8 air conditioners connected to different configurations of photovoltaic panels and compare our findings against the case of having all ACs wor king independently, irrespective of the renewable generation.
Wireless Indoor localization using fingerprinting and Trilateration

Summary Indoor positioning has gained more interest as one of the upcoming applications due to its use in a variety of services. Multiple technologies such as Bluetooth, Wi-Fi, RFID. However, Wi-Fi based localization in indoor environment offers significant advantages utilizing installed wireless infrastructures and good performances with low cost. With this study, we aim to provide a compromise between accurate positioning and feasibility of the system for practical applications. For this purpose, we minimize the fluctuations of Wi-Fi received signal strength (RSS) by filtering and we combine two approaches to locate a mobile user. At first, we implemeted the traditional fingerprinting technique that uses a real time matching of pre-recorded received signal strength (RSS) from the location data of the user transmitted to nearby access points (AP). Secondly, we used distance-based trilateration technique which determines positions using three known access points. The combination of the two methods provides enhancement of accuracy and wide indoor locating coverage. Regardless the locating data number, experiment confirmed a significant and a consistent performance in term of execution time and accuracy.
IMPROVEMENT OF STRUCTURE DETERMINING QUALITATIVE CHARACTERISTICS OF HYDROPHOBIZED VELOUR

The paper studies optimization of formulation of the alkenmalein-acrylsyntane composition in manufacturing of hydrophobized nutria fur velour with high structural and defining characteristics. A modified McLean and Anderson method was used to synthesize the experimental design. Mathematical modeling approach as well as a hierarchical technique of multi-objective optimization led to the identification the optimal formulation of the filling and hydrophobicizing composition. Based on the experimental results, an adequate non-linear mathematical model “formulation of the alkenmaleinacrylsyntane composition vs properties of fur velour” was constructed. The hierarchical method of successive concessions (epsilon constrained method) allowed to optimize the composition formulation. The optimal composition comprises 37.7 % m/m of alkenmalein polymer, 34.0 % m/m of polyacrylic emulsion Melio Resin A-821, and 28.3 % m/m of BNS synthane tannin. Developed technology ensures high consumer properties of nutria fur velour. The studies of water resistance have shown a significant increase in resistance of fur velour to the action of water in dynamic conditions. The developed technology has the great benefit of increasing the yield of fur velour area by 6.7% in comparison with the intermediate product of chrom-aluminum retanning. Taking into account considerable porosity and heterogeneity of the fiber structure of the raw material, an increase in yield is significant. The optimized formulation of the filling and hydrophobicizing composition can be used effectively in the technologies of manufacturing limed hide and leather materials of high water resistance. With the combination of properties the obtained hydrophobized nutria velour is suitable for the production of pickled leather products, which will be operated in extreme conditions.
NEURAL DRUM ACCOMPANIMENT GENERATION

Transformer and its variants have demonstrated state-of-the-art performance on various tasks, including music generation, and yet existing works have not explored this model for drum accompaniment generation. In this work, a transformer model is designed to generate an accompanying symbolic drum pattern conditioned on an input melodic track. We propose a data representation scheme for symbolic music, in which silences are also considered. It can be easily scaled to an arbitrary number of instruments, unlike the popular serialized grid, thus eliminating the need of sampling. We propose a loss function accounting for imbalance in the occurrence of the different percussion instruments and report performance on the Lakh Pianoroll dataset via two musically relevant evaluation metrics.
DeepBO: Deep Neural-Network Bayesian Optimization of Polaritonic Metasurfaces in Continuous Space

Thermophotovoltaics (TPVs) rely on selective thermal emitters to tailor the blackbody radiation at high temperatures into band-matching emission for photovoltaic cells, resulting in power-conversion efficiencies surpassing the Shockley-Queisser limit. The selectivity of the thermal emitter must cover three orders of magnitude range of wavelengths, spreading from visible to the far infrared, which requires the superposition of multiple transformation theories of optics and degrees of freedom anisotropic geometries. It is extremely challenging to realize such high-dimensional complex metasurface design using conventional computational photonics. Here we develop a deep neural network-based Bayesian optimization (DeepBO) framework to screen a 16-dimensional design space of 10 candidates, and realize a record-high spectral efficiency of 69% for the TPV emitter. We show that the neural network combined with Bayesian linear regression is an efficient and robust surrogate model which scales linearly with the size of data. We also reveal the underlying physical mechanisms of the geometric design of the TPV emitters using principal component analysis (PCA). We anticipate the DeepBO framework is a useful tool for data-intensive complex geometric design for photonics research community.
FLAPPY BIRD IMPLEMENTATION USING AI

- This paper presents a minimal training strategy based on genetic algorithm and reinforcement learning where an agent is capable of playing the Flappy Bird game itself using NEAT algorithm. Here artificial agents were trained to take the most favorable action at each game instant. And the machine learns to better itself at a particular task through repetitive iterations. NEAT Algorithm uses a ANN (artificial neural network) along with the fitness function to maximize the score of the current generation and replace the old population with this newly generated population for better performance . The idea of actual biological evolution is implemented here based on Darwinian Natural Selection which consists of three properties (Heredity, Variation & Selection) here some members of population have a chance to pass on their genetic information for better results. It is also referred as „survival of the fittest‟. Hence, using these strategies to achieve low complexity and better performance.
Comparing the Effects of Emotion-Focused Couple Therapy and Choice Theory-Based Couple Therapy on Relationship Quality in Couples With Marital Conflicts

Author Address 1. PhD. Student of General Psychology, Department of Psychology, Tehran Center Branch, Islamic Azad University, Tehran, Iran; 2. PhD in Clinical Psychology, Department of Psychology, Tehran Center Branch, Islamic Azad University, Tehran, Iran; 3. PhD in Consouling, Department of Psychology, Tehran Center Branch, Islamic Azad University, Tehran, Iran; 4. PhD in Psychology, Department of Psychology, Tehran Center Branch, Islamic Azad University, Tehran, Iran. *Corresponding author’s email: far.keshavarzi@gmail.com
Identification of Aerosol Sources and Its Character ization at Varanasi: An Air Mass Based Study

Atmospheric Research Laboratory, Department of Phys ics, Department of Geophysics, Department of Chemical Engineering, Indian Institute of Technology, DST Mahamana Centre of Excellence in Climate Chan ge Research, Banaras Hindu University, Varanasi, India. Graduate School of Environmental Studies, Nagoya Un iversity, Japan. Indian Institute of Tropical Meteorology (Branch), Prof Ramnath Vij Marg, New Delhi, India. India Meteorological Department, Lodhi Road, New De lhi, India.