Miracle-dz/UniFAR-FTUs huggingface dataset:
FusionClock: Energy-Optimal Clock-Tree Reconfigurations for Energy-Constrained Real-Time Systems

Numerous embedded real-time systems have, besides their timing requirements, strict energy constraints that must be satisfied. Examples of this class of real-time systems are implantable medical devices, where knowledge of the worst-case execution time (WCET) has the same importance as of the worst-case energy consumption (WCEC) in order to provide runtime guarantees. The core hardware component of modern system-on-chip (SoC) platforms to configure the tradeoff between time and energy is the system’s clock tree, which provides the necessary clock source to all connected devices (i.e., memory, sensors, transceivers). Existing energy-aware scheduling approaches have shortcomings with regard to these modern, feature-rich clock trees: First, with their reactive, dynamic (re-)configuration of the clock tree, they are not able to provide static guarantees of the system’s resource consumption (i.e., energy and time). Second, they only account for dynamic voltage/frequency scaling of the CPU and thereby miss the reconfiguration of clock sources and clock speed for the other connected devices on such SoCs. Third, they neglect the reconfiguration penalties of frequency scaling and clock/power gating in the presence of the CPU’s sleep modes. In this paper, we present FusionClock , an approach that exploits a fine-grained model of the system’s temporal and energetic behavior. By means of our developed clock-tree model, FusionClock processes time-triggered schedules and finally generates optimized code for a system where offline-determined and online-applied reconfigurations lead to the worst-case–optimal energy demand while still meeting given timing-related deadlines. For statically determining these energy-optimal reconfigurations on task level, FusionClock builds a mathematical optimization problem based on the tasks’ specifications and the system’s resource-consumption model. Specific components like transceivers of SoCs usually have strict requirements regarding the used clock source (e
Online Second Order Methods for Non-Convex Stochastic Optimizations

This paper proposes a family of online second order methods for possibly non-convex stochastic optimizations based on the theory of preconditioned stochastic gradient descent (PSGD), which can be regarded as an enhance stochastic Newton method with the ability to handle gradient noise and non-convexity simultaneously. We have improved the implementations of the original PSGD in several ways, e.g., new forms of preconditioners, more accurate Hessian vector product calculations, and better numerical stability with vanishing or ill-conditioned Hessian, etc. We also have unrevealed the relationship between feature normalization and PSGD with Kronecker product preconditioners, which explains the excellent performance of Kronecker product preconditioners in deep neural network learning. A software package (this https URL) implemented in Tensorflow is provided to compare variations of stochastic gradient descent (SGD) and PSGD with five different preconditioners on a wide range of benchmark problems with commonly used neural network architectures, e.g., convolutional and recurrent neural networks. Experimental results clearly demonstrate the advantages of PSGD in terms of generalization performance and convergence speed.
Optimal Sectionalizer Placement in Radial Distribution System using Mutation Assisted Particle Swarm Optimization

The placement of switches in distribution system is important and an inevitable pre-requisite in the electric power industry. As the distribution system are more prone to disturbances, the availability of supply to the customer is affected and so the cost of electricity and reliability. Therefore, achieving reliability levels by concurrently minimizing the total societal cost that includes utility investment cost and customer interruption cost are important issues. In this dissertation, a value-based reliability planning approach is utilized to determine the number and location of sectionalizers. A solution methodology based on the heuristic Mutation Assisted Particle Swarm Optimization (MAPSO) is proposed to avoid premature convergence of PSO. The mutation operator is directed to come out from local minima to such discrete value problem. The location of circuit-breaker is predetermined in the feeder by default. The optimization is directed to utilize total cost which is the sum of system expected outage cost (ECOST) and switches cost, in association with system average interruption duration index (SAIDI) as two single objective functions. The proposed method is also implemented to solve total cost and SAIDI simultaneously using fuzzy decision making (FDM) approach. The performance of the proposed optimization method has been tested on 12-bus, 33-bus and 47-bus distribution system. The effect of section length on optimal placement of sectionalizer is also studied.
A Bayesian semiparametric approach for trend–seasonal interaction: an application to migration forecasts

We model complex trend–seasonal interactions within a Bayesian framework. The contribution divides into two parts. First, it proves, via a set of simulations, that a semiparametric specification of the interplay between the seasonal cycle and the global time trend outperforms parametric and non‐parametric alternatives when the seasonal behaviour is represented by Fourier series of order bigger than 1. Second, the paper uses a Bayesian framework to forecast Swiss immigration, merging the simulations’ outcome with a set of priors derived from alternative hypotheses about the future number of incomers. The result is an effective symbiosis between Bayesian probability and semiparametric flexibility that can reconcile past observations with unprecedented expectations.
On the Factors Controlling the Development of Shallow Convection in Eddy-Diffusivity/Mass-Flux Models

This study addresses key aspects of shallow moist convection, as simulated by a multiplume eddy-diffusivity/mass-flux (EDMF) model. Two factors suggested in the literature to be essential for the development of convective plumes are investigated: surface conditions and lateral entrainment. The model consistently decomposes the subgrid vertical mixing into convective plumes and the nonconvective environment. The modeled convection shows low sensitivity to the surface plume area. The results indicate that plume development in the subcloud layer is controlled by both surface conditions and lateral entrainment. Their impact significantly changes in the cloud layer where the surface conditions are no longer important. The development of shallow convection is dominated by the interactions between the plumes and the large-scale field and is sensitive to the representation of the variability of thermodynamic properties between the plumes. A simple two-layer model of steady-state convection is proposed to help understand the role of these processes in shaping the properties of moist convection.
Adaptive Fuzzy Control for Nontriangular Structural Stochastic Switched Nonlinear Systems With Full State Constraints

The problem of adaptive fuzzy control is investigated for a class of nontriangular structural stochastic switched nonlinear systems with full state constraints in this paper. A remarkable feature of the nontriangular structural nonlinear system is the so-called algebraic loop problem in the existing backstepping-based analysis and design. Properties of fuzzy basis functions are utilized to circumvent this algebraic loop problem. Based on the Barrier Lyapunov function, an adaptive fuzzy stochastic switched control scheme is designed. It is proven that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded with full state constraints. The effectiveness of the proposed control scheme is verified via simulation studies.
Investigation of efficient spin-photon interfaces for the realisation of quantum networks

Quantum networks lie at the heart of distributed quantum computing and secure quantum communication research areas that have seen a strong increase of interest over the last decade. Their basic architecture consist of stationary nodes composed of quantum processors which are linked via photonic channels. The key requirement, and at the same time the most demanding challenge, is the efficient distribution of entanglement between distant nodes. The two ground states of single spins confined in self-assembled InGaAs quantum dots provide an effective two-level system for the implementation of quantum bits. Moreover, they offer strong transition dipole moments with outstanding photonic properties allowing for the realisation of close to ideal, high-bandwidth spin-photon interfaces. These properties are combined with the benefits of working in the solid state, such as scalability and integrability of devices, to form a promising candidate for the implementation of fast entanglement distribution. In this dissertation we provide the first implementation of a unit cell of a quantum network based on single electron spins in InGaAs. We use a probabilistic scheme based on spinphoton entanglement and the erasure of which path information to project the two distant spins into a maximally entangled Bell state. The successful generation of entanglement is verified through a reconstruction of the final two-spin state and we achieve an average fidelity of 61.6±2.3% at a record-high generation rate of 5.8kHz. One of the main constraints to the achieved fidelity is the limited coherence of the electron spin. We show that it can be extended by three orders of magnitude through decoupling techniques and develop a new measurement technique, allowing us to investigate the origins of the decoherence which has previously been obscured by nuclear feedback processes. Our results evidence that further extension of coherence is ultimately limited by intrinsic mechanisms closely related to local strain due to the growth method of self-assembled quantum dots. After establishing the intrinsic limits to the electron coherence we investigate the coherence properties of the single hole spin as an alternative two-level system with the potential for higher coherence times. We show that the hole spin coherence is indeed superior to the one of the electron and realise the first successful dynamic decoupling scheme implemented in these
Miniaturization of rectangular patch microstrip antenna by using complementary split ring resonator

The mobile device miniaturization requires the applied antenna microstrip size be much smaller to reduce the radio front-end load. This paper discusses the rectangular patch microstrip antenna miniaturization by using complementary split ring resonator (CSRR) structure for 3G mobile communication system on 2.1 GHz. The structure is implemented on the microstrip ground plane. Simulation evaluation by using the AWR simulator shows that the CSRR structure is able to reduce antenna dimension about 62.71% from 3192 mm2 (56mm×57mm) to 1190 mm2 (34mm×35mm). Beside miniaturization, VSWR and return loss also experience improvements about 1.05 and -33.83 dB subsequently. Bandwidth increment about 70.37% is also achieved.
Finite element analysis of non linear transversaly isotropic hyperelastic membranes for thermoforming applications

Recent advances have been made to simulate plastic blow-moulding and thermoforming processes. However, most of the work done is focused on thermoplastic materials assumed to obey an isotropic behaviour. The present paper deals with free and confined inflation of heat-softened fibers-reinforced membranes in order to simulate moulding processes of composite thermoplastic parisons. The material is assumed to be hyperelastic and incompressible. To take into account the anisotropy induced by aligned fibers, the material symmetry class is considered transversely isotropic. A general expression of the strain energy function in terms of tensorial invariants is developed and governing equations of the inflation problem are solved by a total Lagrangian finite element method for space discretization and an explicit finite-difference algorithm for time integration. In order to illustrate our approach, both free and confined inflation examples are examined.
Signatures of the symmetry breaking phenomenon in pre-seismic electromagnetic emissions

By analyzing pre-seismic electromagnetic emissions in the MHz band we find that their evolution over time presents a striking similarity, both in qualitative and quantitative terms, with the evolution of a thermal system (3D-Ising model) as the temperature drops. The phase transition from the symmetric phase (critical state) to a low symmetry phase, that is the symmetry breaking phenomenon through a second order phase transition, can be monitored in real-time in the form of distinct steps during the preparation of a seismic event at the stage that such signals are emitted.
Particle filter-based monitoring scheme for simulated bio-ethylene production process

ABSTRACT The use of biomass has been promoted to meet the need for sustainable production of ethylene, which is the most used petroleum-derived in the polymer industry. Ethanol is an alternative feedstock to yield the so-called bio-ethylene through catalytic dehydration in fixed-bed reactors. As the reaction system is strongly endothermic, it is very important to know accurately the reactor temperature to assure the process performance. However, in the industrial context, the process measurements are often uncertain and not all variables can be directly measured online. In this regard, this paper analyses the mathematical modelling and numerical simulation of the ethanol catalytic dehydration and contributes with a monitoring scheme using the Bayesian method known as particle filter. Numerical simulations helped understanding the process behaviour and locating the best position for the temperature sensor in the reactor. From temperature measurements, the proposed inferential tool estimates hidden state variables and unmeasured disturbances, using Sequential Importance Resampling algorithm for the particle filter. The proposal is investigated according to the number of particles and the criterion total error reduction. The results show that the monitoring scheme is able to estimate satisfactorily the process variable profiles, as the temperature and chemical conversion, along the reactor length.
Hematobiochemical, serological, and molecular detection of Anaplasma marginale in dromedary camels (Camelus dromedarius) in Al-Najaf desert, Iraq

Background and Aim: Anaplasmosis, an underestimated disease transmitted by ticks, is widespread in ruminants, such as the Arabian camel (dromedary camel). This study aimed to examine the presence of Anaplasma marginale in dromedary camels in the Al-Hiadyia region of the Al-Najaf desert, Iraq, using serological and molecular tests. Moreover, hematological and biochemical changes in infected animals were compared with those in healthy controls. Materials and Methods: The study was conducted on 30 healthy and 260 infected camels with severe anemia, pale mucus membranes, and progressive emaciation to investigate antibodies against A. marginale using indirect enzyme-linked immunosorbent assay, followed by polymerase chain reaction for selected positive samples targeting a specific region of A. marginale major surface protein 5 (MSP5). In addition, hematological and biochemical parameters were measured to indicate the effect of the disease on blood profile, mineral status, and liver and kidney functions. Results: Enzyme-linked immunosorbent assay analysis and microscopic examination revealed that 115/260 (44.23%) and 87 (33.46%) camels were positive for Anaplasma spp. , respectively. The MSP5 gene, which is unique to A. marginale, was amplified. The results of hematological analysis indicated a significant decrease in total red blood cells, hemoglobin, and packed cell volume and a significant increase in mean corpuscular volume in infected camels, but no difference in mean corpuscular hemoglobin concentration. Moreover, there was a significant increase in total white blood cells count, lymphocytes, erythrocyte sedimentation rate, and platelets. The results of biochemical analysis indicated a significant increase in the levels of aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase, blood urea nitrogen, creatinine, and iron and a decrease in copper in infected camels. Cholesterol and triglyceride showed no significant variations between healthy and diseased camels. Conclusion: To the best of our knowledge, this is the first molecular study to demonstrate the presence of A. marginale in dromedary camels in Iraq. The MSP5 gene is a valuable and unique diagnostic target for identifying A. marginale.
SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and Learned Image Models

Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called penalized weighted-least squares (PWLS). Due to the underlying limitations of the post-log statistical model, the PWLS reconstruction quality is often degraded in low-dose scans. This paper investigates a shifted-Poisson (SP) model based likelihood function that uses the pre-log raw measurements that better represents the measurement statistics, together with a data-driven regularizer exploiting a Union of Learned TRAnsforms (SPULTRA). Both the SP induced data-fidelity term and the regularizer in the proposed framework are nonconvex. The proposed SPULTRA algorithm uses quadratic surrogate functions for the SP induced data-fidelity term. Each iteration involves a quadratic subproblem for updating the image, and a sparse coding and clustering subproblem that has a closed-form solution. The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.
An experimental analysis of Hager’s exchange methods in conjunction with heuristics for bandwidth and profile reductions applied to accelerate the ICCG method

This paper evaluates the original Hager’s adjacent exchange methods in conjunction with heuristics for bandwidth and profile reductions with the objective of reducing computing times of the zero–fill incomplete Cholesky–preconditioned conjugate gradient method. The numerical results obtained in this computational experiment show that the original Hager’s exchange methods, although capable of reducing the profile of the instances, are not useful when reducing processing times of the zero–fill incomplete Cholesky–preconditioned conjugate gradient method.
Exploring Moral Bio-enhancement through Psilocybin-Facilitated Prosocial Effects

Biographies Victor Lange is a PhD-fellow at the Section for Philosophy and the Centre for Neuroscience at the University of Copenhagen. His PhD-project investigates person-level metacognitive control, as it is studied in clinical and performance psychology, with a perspective on the philosophical dimensions of agency, attention, and introspection. He has previously done research in bioethics (also at the University of Copenhagen). In addition, he currently works as a meditation teacher and editor at the platform Regnfang.
Distributed Multi Scale Hashing on Efficient Nearest Keyword Set Search for peer-to-peer Multidimensional Dataset Model

— The growing rise of peer-to-peer (P2P) multidimensional dataset models has boosted demand for effective nearest keyword set search approaches. In this paper, we suggest a novel approach termed Distributed Multi-Scale Hashing (DMSH) to overcome the issues associated with keyword-based search in P2P systems. DMSH combines the advantages of multi-scale hashing and distributed computing to accomplish scalable and accurate nearest keyword set retrieval. DMSH's distributed architecture efficiently distributes the computational burden across numerous nodes, ensuring efficient and parallel processing. The multi-scale hashing algorithm offers effective indexing and retrieval of multidimensional datasets, reducing search space and enhancing search efficiency. We illustrate the effectiveness and scalability of DMSH in comparison to existing techniques using extensive experiments on real-world datasets. The study contributes to the area by offering a distributed method for effective nearest keyword set search in P2P multidimensional dataset models. The suggested DMSH algorithm has higher performance, allowing users to efficiently extract relevant data from large-scale dispersed datasets.
Short-term load forecasting for microgrids based on DA-SVM

Purpose The study aims to accomplish the short-term load forecasting for microgrids. Short-term load forecasting is a vital component of economic dispatch in microgrids, and the forecasting error directly affects the economic efficiency of operation. To some extent, short-term load forecasting is more difficult in microgrids than in macrogrids. Design/methodology/approach This paper presents the method of Dragonfly Algorithm-based support vector machine (DA-SVM) to forecast the short-term load in microgrids. This method adopts the combination of penalty factor C and kernel parameters of SVM which needs to be optimized as the position of dragonfly to find the solution. It takes the forecast accuracy calculated by SVM as the current fitness value of dragonfly and the optimal position of dragonfly obtained through iteration is considered as the optimal combination of parameters C and s of SVM. Findings DA-SVM algorithm was used to do short-term load forecast in the microgrid of an offshore oilfield group in the Bohai Sea, China and the forecasting results were compared with those of PSO-SVM, GA-SVM and BP neural network models. The experimental results indicate that the DA-SVM algorithm has better global searching ability. In the case of study, the root mean square errors of DA-SVA are about 1.5 per cent and its computation time is saved about 50 per cent. Originality/value The DA-SVM model presented in this paper provides an efficient and effective method of short-term load forecasting for a microgrid electric power system.