Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating normal metabolic activity for disease quantification via PET/CT images

In this paper, we propose a novel pipeline for conducting disease quantification in positron emission tomography/computed tomography (PET/CT) images on anatomically pre-defined objects. The pipeline is composed of standardized uptake value (SUV) standardization, object segmentation, and disease quantification (DQ). DQ is conducted on non-linearly standardized PET images and masks of target objects derived from CT images. Total lesion burden (TLB) is quantified by estimating normal metabolic activity (TMAn) in the object and subtracting this entity from total metabolic activity (TMA) of the object, thereby measuring the overall disease quantity of the region of interest without the necessity of explicitly segmenting individual lesions. TMAn is calculated with object-specific SUV distribution models. In the modeling stage, SUV models are constructed from a set of PET/CT images obtained from normal subjects with manually delineated masks of target objects. Two ways of SUV modeling are explored, where the mean of mean values of the modeling samples is utilized as a consistent normality value in the hard strategy, and the likelihood representing normal tissue is determined from the SUV distribution (histogram) for each SUV value in the fuzzy strategy. The evaluation experiments are conducted on a separate clinical dataset of normal subjects and a phantom dataset with lesions. The ratio of absolute TLB to TMA is taken as the metric, alleviating the individual difference of volume sizes and uptake levels. The results show that the ratios in normal objects are close to 0 and the ratios for lesions approach 1, demonstrating that contributions on TLB are minimal from the normal tissue and mainly from the lesion tissue.
Energy-efficient Edge Approximation for Connected Vehicular Services

Connected vehicular services depend heavily on communication as they frequently transmit data and AI models/weights within the vehicular ecosystem. Energy efficiency in vehicles is crucial to keep up with the fast-growing demand for vehicular data processing and communication. To tackle this rising challenge, we explore approximation and edge AI techniques for achieving energy efficiency for vehicular services. Focusing on data-intensive vehicular services, we present an experimental case study on the high-definition (HD) map using the model partition approach. Our study compares the AI model energy consumption using multiple approximation ratios over embedded edge devices. Based on experimental insights, we further discuss an envisioned approximate Edge AI pipeline for developing and deploying energy-efficient vehicular services.
Photography Style Analysis using Convolutional Neural Networks

In this paper, we present a novel dataset of photographs annotated in terms of the respective image aesthetics. We also examine the ability of Convolutional Neural Networks (CNNs) to distinguish between the adopted photography style classes. In particular, we have defined five photography style classification tasks, related to the following aesthetic attributes: Color, Depth of Field (DoF), Palette, Composition and Type. We then followed an annotation procedure using on a set of 1832 photos selected from the Unsplash dataset. Multiple annotators have also been used, in order to measure inter-annotator agreement. As soon as the dataset was compiled, we trained and evaluated a Residual Neural Network (ResNet50). The experimental results prove that, despite the imbalanced dataset, our model was able to achieve acceptable classification results. The dataset is openly provided, along with the trained models and Python code to use them.
Integration of Self-Adaptive Physical-Layer Key Distribution and Encryption in Optical Coherent Communication

We propose and experimentally demonstrate a compatible physical-layer secure optical communication (PLSOC) system that integrates self-adaptive physical-layer key distribution (PLKD) and encryption (PLE) in optical coherent communication. Based on bit error rate difference of QAM signals mapped by asymmetric basis state Y-00 protocol, the secret key can be secretly exchanged over public fiber links without the pre-shared keys. Moreover, we perform a parameter self-adaptive strategy for practical and dynamic PLKD. The security of the key is evaluated in the case of a fiber-tapping attack. A secure hash algorithm, SHA3-512, is used to perform privacy amplification to obtain the virtually secure key. An error-free PLKD rate reaches 39.3 Kbits/s over 300 km ultra-low loss fiber. We experimentally enable the integration of the proposed PLKD scheme and quantum noise stream cipher (QNSC) with a single wavelength, same system. Q factor penalty of the integration system compared to the QNSC system is 3.7 dB (optical back-to-back) and 4.8 dB (300 km) respectively. By exploiting a common hardware platform, with the same wavelength, the proposed PLSOC system addresses the problem that PLKD and PLE are separately performed through independent optical fiber links or wavelengths. Since only digital signal processing is used, the scheme does not require extra hardware.
Asymptotic analysis for the 3D primitive equations in a channel

In this article, we give an asymptotic expansion, with respect to the viscosity which is considered here to be small, of the solutions of the $3D$ linearized Primitive Equations (EPs) in a channel with lateral periodicity. A rigorous convergence result, in some physically relevant space, is proven. This allows, among other consequences, to confirm the natural choice of the non-local boundary conditions for the non-viscous PEs.
The number of alignments between two DNA sequences

We consider two DNA sequences and compare both sequences. One of the crucial issues in bioinformatics is to measure the similarity of two DNA sequences. To this purpose one has to consider different alignments between both sequences. The number of alignments grows very rapidly with the length of the sequences. In this paper we give exact, explicit and computable formulas for the number of different possible alignments and for some classes of reduced alignments. We provide a new insight into the theory of DNA sequence alignment.
Ion Transport in Electromembrane Systems under the Passage of Direct Current: 1D Modelling Approaches

For a theoretical analysis of mass transfer processes in electromembrane systems, the Nernst–Planck and Poisson equations (NPP) are generally used. In the case of 1D direct-current-mode modelling, a fixed potential (for example, zero) is set on one of the boundaries of the considered region, and on the other—a condition connecting the spatial derivative of the potential and the given current density. Therefore, in the approach based on the system of NPP equations, the accuracy of the solution is significantly affected by the accuracy of calculating the concentration and potential fields at this boundary. This article proposes a new approach to the description of the direct current mode in electromembrane systems, which does not require boundary conditions on the derivative of the potential. The essence of the approach is to replace the Poisson equation in the NPP system with the equation for the displacement current (NPD). Based on the system of NPD equations, the concentration profiles and the electric field were calculated in the depleted diffusion layer near the ion-exchange membrane, as well as in the cross section of the desalination channel under the direct current passage. The NPD system, as well as NPP, allows one to describe the formation of an extended space charge region near the surface of the ion-exchange membrane, which is important for describing overlimiting current modes. Comparison of the direct-current-mode modelling approaches based on NPP and NPD showed that the calculation time is less for the NPP approach, but the calculation accuracy is higher for the NPD approach.
^{1,}-interior regularity for minimizers of a class of variational problems with linear growth related to image inpainting

A modiﬁcation of the total variation image inpainting method is investi-gated. By using DeGiorgi type arguments, the partial regularity results established previously are improved to the C 1 ,α interior diﬀerentiability of solutions of this new variational problem.
Morphology and conductivity of Au films on polydimethylsiloxane using (3-mercaptopropyl)trimethoxysilane (MPTMS) as an adhesion promoter

Dielectric elastomer actuators (DEA) are often referred to as artificial muscles due to their high specific continuous power, which is comparable to that of human skeletal muscles, and because of their millisecond response time. We intend to use nanometer-thin DEA as medical implant actuators and sensors to be operated at voltages as low as a few tens of volts. The conductivity of the electrode and the impact of its stiffness on the stacked structure are key to the design and operation of future devices. The stiffness of sputtered Au electrodes on polydimethylsiloxane (PDMS) was characterized using AFM nanoindentation techniques. 2500 nanoindentations were performed on 10 x 10 μm2 regions at loads of 100 to 400 nN using a spherical tip with a radius of (522 ± 2) nm. Stiffness maps based on the Hertz model were calculated using the Nanosurf Flex-ANA system. The low adhesion of Au to PDMS has been reported in the literature and leads to the formation of Au-nanoclusters. The size of the nanoclusters was (25 ± 10) nm and can be explained by the low surface energy of PDMS leading to a Volmer-Weber growth mode. Therefore, we propose (3-mercaptopropyl)trimethoxysilane (MPTMS) as a molecular adhesive to promote the adhesion between the PDMS and Au electrode. A beneficial side effect of these self-assembling monolayers is the significant improvement of the electrode’s conductivity as determined by four-point probe measurements. Therefore, the application of a soft adhesive layer for building a dielectric elastomer actuator appears promising.
Accelerated Stochastic Mirror Descent Algorithms For Composite Non-strongly Convex Optimization

We consider the problem of minimizing the sum of the average function consisting of a large number of smooth convex component functions and a general convex function that can be non-differentiable. Although many methods have been proposed to solve the problem with the assumption that the sum is strongly convex, few methods support the non-strongly convex cases. Adding a small quadratic regularization is the common trick used to tackle non-strongly convex problems; however, it may worsen certain qualities of solutions or weaken the performance of the algorithms. Avoiding this trick, we extend the deterministic accelerated proximal gradient methods of Paul Tseng to randomized versions for solving the problem without the strongly convex assumption. Our algorithms achieve the optimal convergence rate $O(\nicefrac{1}{k^2})$. Tuning involved parameters helps our algorithms get better complexity compared with the deterministic accelerated proximal gradient methods. We also propose a scheme for non-smooth problem.
BIFURCATION WITHOUT PARAMETERS IN CIRCUITS WITH MEMRISTORS: A DAE APPROACH

Bifurcations without parameters describe qualitative changes in the local dynamics of nonlinear ODEs when normal hyperbolicity of a manifold of equilibria fails. Non-isolated equilibrium points are systematically exhibited by nonlinear circuits with memristors; a memristor is a nonlinear device recently introduced in circuit theory and which is expected to play a key role in electronics in the near future. In this communication we provide a graph-theoretic analysis of the transcritical bifurcation without parameters in memristive circuits, owing to the presence of a locally active memristor. The results are crucially based on the use of differential-algebraic circuit models.
Design of a Multi-Core Compatible Linux Bootable 64-bit Out-of-Order RISC-V Processor Core

We present the design of a 64-bit Out-of-Order Superscalar processor core based on open-source RISC-V instruction set architecture. It implements RV64IMAFDC extension and optional supervisory and user mode privilege levels, making it capable of booting Linux. A cache-coherent memory subsystem using MESI write-invalidate snooping protocol helps in extending the core to multi-core processors. An SoC is designed around the processor core by adding standard peripherals, and a custom embedded Linux system is designed and booted in SoC implemented in the Xilinx Virtex-7 XC7VX485T FPGA based VC707 development board. The design achieved an operating frequency of 32 MHz in FPGA. Multi-core compatibility of the processor core is demonstrated by designing a dual-core processor and an SoC around it. Dual-core SoC is implemented on the Xilinx Kintex UltraScale KU085 FPGA based board and could achieve a speed improvement of around 1.6 times compared to the single-core processor for the test applications.
A Complementary Filter for High-fidelity Attitude Estimation based on Decoupled Linear/Nonlinear Properties of Inertial Sensors

脚ロボットや飛翔ロボット,海洋ロボットなどの,広い空間を 不規則に運動する移動ロボットを制御する上で,ロボットの姿 勢を高精度に計測する技術は重要である. 身体上に計測基準点 を持たない移動ロボットの姿勢は,傾斜計や加速度センサ,角 速度センサなどのいわゆる慣性センサを用いて推定する必要が ある. これらのうち,傾斜計,加速度センサは,準静的な状態で 重力方向を検出することが可能だが,動的な状態ではセンサ自 身のダイナミクスによる遅れや並進加速度の影響が無視できな くなる. 角速度センサは,動的な姿勢変化から比較的良好な精 度で角速度を計測できるものだが,そこから姿勢を得るには出 力値の積分が要求され,わずかな誤差が累積し推定値の発散を 招く. このように,それぞれのセンサには一長一短があり,い ずれか一種類のセンサのみで姿勢を推定するのは難しい. 単体 で高精度な姿勢計測が行えるとして航空機やロケットで用いら れる,光ファイバジャイロ,リングレーザージャイロは,高価 かつ破損しやすく,今のところ広く利用できるものではない.
Quantum Computing for Self-Driving Cars and Pedestrian Detection

Quantum computing and self-driving cars are at the forefront of modern-day technological progress. One of the most crucial tasks for the safety of self-driving cars is pedestrian detection, which must be quick and accurate. Due to the enormous amounts of data involved, quantum computers are ideal as they provide exponential speedup. The benefits of quantum are twofold. First, the speed and accuracy of object detection is significantly improved. Second, groups of self-driving cars can communicate effectively with a centralized quantum computer, which leads to path selection efficiency. This paper presents a novel implementation of a k nearest neighbors classification algorithm, in both classical and quantum versions. The experimental procedure is outlined in four steps: data collection, classical nearest neighbor model, quantum nearest neighbors, and a wireless networking framework. The data consists of square grayscale images from the PennFudan (Shi, 2007) and DaimlerMono (Gavrila., 2012) datasets. Both models were tested by running them on testing data independent of training data. The classical model achieved fifty percent accuracy rate, and the quantum model ninety five percent. Due to the unavailability of real quantum computers, runtime was not tested, but theoretical constructs estimate the speedup to be on the order of millions for moderate input sizes. The networking framework was qualitatively analyzed to be feasible and sufficient. The successful implementation of pedestrian detection serves as a proof of concept and can be extended to a broader range of detections. The results demonstrate quantum computers as a possible solution to safe self-driving car technology.
An Efficient Method for Available Transfer Capability Calculation Considering Cyber-Attacks in Power Systems

At this work, the impact of cyberattacks in power system Available Transfer Capability (ATC) solution is investigated using DIgSILENT Power factory software. Based on the literature, there reported several power network blackouts that caused by hackers and the Stuxnet worm in Ukraine, which exhibited that power systems are quite vulnerable to widespread and integrated cyberattacks. As a result, power market clearing mechanism and ATC solution can no longer be reliable if hackers possess enough information to manipulate input data. Instead of using complex methods to detect information manipulation in state estimation programs, this paper uses the weakness of the program when the correct information is entered. To end the evaluations, the proposed cyber-aware ATC algorithm is tested on a realistic utility network. Based on the simulation results, in spite of improvement in state estimation execution time, the quickness of ATC calculation is somewhat heightened when applying the proposed method of weighted least squares.
Differential Evolution Optimization of Microwave Focused Hyperthermia Phased Array Excitation for Targeted Breast Cancer Heating

Microwave hyperthermia using the phased array applicator is a non-invasive treatment modality for breast cancer. Hyperthermia treatment planning (HTP) is critical to accurately treating breast cancer and avoiding damage to the patient’s healthy tissue. A global optimization algorithm, differential evolution (DE) algorithm, has been applied to optimize HTP for breast cancer and its ability to improve the treatment effect was proved by electromagnetic (EM) and thermal simulation results. DE algorithm is compared to time reversal (TR) technology, particle swarm optimization (PSO) algorithm, and genetic algorithm (GA) in HTP for breast cancer in terms of convergence rate and treatment results, such as treatment indicators and temperature parameters. The current approaches in breast cancer microwave hyperthermia still have the problem of hotspots in healthy tissue. DE enhances focused microwave energy absorption into the tumor and reduces the relative energy of healthy tissue during hyperthermia treatment. By comparing the treatment results of each objective function used in DE, the DE algorithm with hotspot to target quotient (HTQ) as the objective function has outstanding performance in HTP for breast cancer, which can increase the focused microwave energy of the tumor and decrease the damage to healthy tissue.
Unequal edge inclusion probabilities in link-tracing network sampling with implications for Respondent-Driven Sampling

: Respondent-Driven Sampling (RDS) is a widely adopted link- tracing sampling design used to draw valid statistical inference from samples of populations for which there is no available sampling frame. RDS estimators rely upon the assumption that each edge (representing a relationship between two individuals) in the underlying network has an equal probability of being sampled. We show that this assumption is violated in even the simplest cases, and that RDS estimators are sensitive to the violation of this assumption.
Short-term power load forecasting based on PSO-GRU

In order to improve the accuracy of short-term power load forecasting, a short-term power load forecasting model (PSO-GRU) based on gated recurrent unit (GRU) neural network optimized by particle swarm optimization (PSO) is proposed. For the problem of difficult selection of GRU model parameters, PSO is used to optimize model parameters, which avoids the disadvantages of manual parameter adjustment The PSO-GRU prediction model is optimized on the basis of the GRU model, and is better at mining the characteristic information among non-linear and time-series data. The results of a case simulation analysis using power load data from a power company show that the PSO algorithm optimises the GRU model with higher forecasting accuracy compared to the GRU model and the LSTM model.
Estimation of distributed parameters by multiresolution optimization

This dissertation proposes, develops and evaluates multiresolution optimization methods for estimation of distributed parameters of mathematical models. The methods are based on the assumption that the distributed parameters are continuous almost everywhere in the defined field. The main idea in employing multiresolution optimization is to give priority to large-scale characteristics of the parameter distribution over smaller-scale ones in the estimation process. This allows the overall structure of the distribution to be found more quickly, which results in rapid approach of the estimation to the true distribution. This in turn allows more reliable search for the details. The multiresolution optimization method consists of a local search method and a multiresolution scheme that controls the resolution of the search. This dissertation employs the conjugate gradient method as a local search method, and the discrete Fourier transform and the Haar wavelet transform as a multiresolution scheme. Since the coefficients of these transforms are inherently sorted in frequency or scale, multiresolution estimation can be realized by estimating the transform coefficients with some controlled weights and inversely transforming the coefficients back to the parameter distribution. Two methods of controlling the resolution are devised. One is the "step scheme" in which the threshold frequency for a low-pass filter steps up every time the search converges. The other method is the "weight scheme" in which fixed weights are assigned so that the coefficients of low frequencies get larger weights than the coefficients of higher frequencies. This dissertation gives a proof that estimating the transform coefficients in either scheme is equivalent to directly estimating the desired parameter distribution by using the gradient that is filtered by the transform. In other words, we can realize a multiresolution optimization by simply filtering the gradient in a multiresolution manner to control the resolution of the search direction. The developed methods are evaluated in simulation of the so-called electrical impedance tomography, which is one of many potential applications. It is shown that the multiresolution optimization yields better estimates more rapidly than the conventional single-resolution method.
Exploring the Role of Mining Wireless Framework in Identifying Human Privacy Vulnerabilities in Internet of Things Networks

One of the most promising technologies with global impact is the Internet of Things (IoT). As IoT systems continue to mature and be implemented, they have the potential to profoundly alter how human societies produce goods, conduct business, and live their daily lives. The number of active Internet of Things gadgets and software has increased exponentially during the last few years. People appreciate IoT's advantages, but its security and privacy concerns are not being addressed at the same rate as the technology itself is progressing. Our research shows that even when wireless IoT communication is encrypted, sensitive user data may be leaked. Using a wireless sniffer stationed outside the user's home, we may infer the user's activities while at home. In this research, we offer a strategy for wireless eavesdropping that may be used too many forms of user privacy. Initially, we identify IoT events by extracting characteristics from the packet sequence. Then, we deduce the user's actions and, in certain cases, their emotional state based on the identified IoT occurrence. In addition, we may deduce the user's lifestyle, and IoT apps by studying the wireless context, which is uncovered by mining thedependencies of the IoT. By disclosing this information, the user's privacy is violated, and the attack surface that an attacker may exploit to take control of the smart home is widened. We use the SmartThings platform to test our eavesdropping methodology and apply it. The results show that our eavesdropping method is both practical and efficient.