Miracle-dz/UniFAR-FTUs huggingface dataset:
High Energy Physics Calorimeter Detector Simulation Using Generative Adversarial Networks With Domain Related Constraints

Generative Adversarial Networks (GANs) have gained notoriety by generating highly realistic images. The present work explores GAN for simulating High Energy Physics detectors, interpreting detector output as three-dimensional images. The demands and requirements of a scientific simulation are quite stringent, as compared to the domain of visual images. Image characteristics such as pixel intensity and sparsity, for example, have very different distributions. Moreover, detector simulation requires conditioning on physics inputs, and domain knowledge becomes essential. We, therefore, adjust the pre-processing and incorporate physics-based constraints in the loss function. We also introduce a multi-step training process based on transfer learning by breaking up the task complexity. Validation of the results primarily consists of a detailed comparison to full Monte Carlo in terms of several physics quantities where a high level of agreement is found (ranging from a few percent up to 10% across a large particle energy range). In addition, we assess the performance by physics unrelated metrics, thereby proving further the variability and pertinence through diverse standpoints. We have demonstrated that an image generation technique from vision can successfully simulate highly complex physics processes while achieving a speedup of more than three orders of magnitude in comparison to the standard Monte Carlo.
Automated Hypotheses Generation via Combinatorial Causal Optimization

A powerful form of causal inference employed in many tasks, such as medical diagnosis, criminology, root cause analysis, biology, is abduction. Given an effect, it aims at generating a plausible and useful set of explanatory hypotheses for its causes. This article formulates the abductive hypotheses generation activity as an optimization problem, introducing a new class called Combinatorial Causal Optimization Problems (CCOP). In a CCOP, solutions are in the form of cause-effect combinations: algorithms are required to construct hypothetical solutions automatically assessed for plausibility – a mechanism mimicking the human reasoning when he skims the best solutions from a set of hypotheses - and for novelty with respect to already known solutions.The paper presents the CCOP formulation and four real-world benchmark problems from various domains, released along with artefacts to implement, run and properly evaluate algorithms for CCOP solutions. Then, for illustrative purpose, four conventional evolutionary algorithms are customized to solve CCOPs. Their application demonstrates the possibility of generating useful solutions (i.e., novel and realistic hypotheses for a given effect), but also evidences a great margin for improvement in terms of ratio of good vs bad solutions.
Designing Efficient Phase-Gradient Metasurfaces for Near-Field Meta-Steering Systems

We investigate the aptness of various $4^{th}$ order (90°) rotationally symmetric phase-transforming cells for the upper phase-gradient metasurface, which always receives an oblique incidence wave from the lower metasurface in a Near-Field Meta-Steering system. A comprehensive study on the behavior of various phase-transforming cells and corresponding supercells when a rotating oblique plane wave impinges on them is presented. First, we select the supercell with high transmission in the desired output Floquet modes, for both TE and TM input modes, when an oblique incidence wave is rotated. The selected supercell is then optimized using Floquet analysis in conjunction with particle swarm optimization (PSO). All the undesired modes are successfully suppressed below −32 dB in the optimized supercell, and the predicted broadside radiation pattern is free of spurious grating lobes. A Near-Field Meta-Steering system with an aperture diameter of $7.3\lambda _{0}$ (110mm @ 20 GHz) is presented. It has a pair of optimized phase-gradient metasurfaces and a dipole antenna array. A maximum peak directivity of 24.2 dB is achieved when the beam is in the broadside direction. The proposed steering system is capable of scanning a conical range with an apex angle of 126° when a 6 dB reduction in peak directivity is allowed. For a 3 dB variation in the peak directivity, the corresponding apex angle is 103°.
Efficient Detection of Rare Beacon Events in GEO Satellite Communication Systems using Deep Learning

Geosynchronous satellite (GEO) communications are highly susceptible to interference from environments such as rain, clouds, and hail. This exhibits a technical challenge to accurately detect the status of the weak Q-band (39.4 GHz) satellite beacon signals, which is critical for ensuring reliable satellite pointing using the ground station antenna. This paper exploits recent advances in deep learning to cope with this challenge. The proposed approach based on deep neural networks (DNN) and filtering (Filter-DNN) classifies rare events such as cloud, mist, rain as Non-line of sight (NLOS) and ordinary clear skies as Line of sight (LOS). Beacon data from a GEO satellite (Alphasat) ground station under two different attenuation conditions is used for validation. The experimental results show that our method can detect the rare event with an accuracy score of 92% by using only 2000 data sample points, while conventional approaches such as MUSIC require about 100k sample points to maintain detection. These results indicate that the proposed technique could be a promising tool for achieving satisfactory results in space exploration and gain insights into GEO satellite communication problems.
A Survey of Advances in Evolutionary Neural Architecture Search

Deep neural networks (DNNs) have been frequently and widely applied for intelligent systems such as object detection, natural language understanding and speech recognition. Given a specific problem, we always aim to construct the most suitable DNN to solve it, which requires choosing the most appropriate model architecture and seeking the best model parameters values. However, most existing works focus on model parameters learning under the assumption that the model architecture can be manually specified as per prior knowledge and/or trial-and-error experimentation. To overcome this problem, evolutionary algorithms (EAs) have been widely used to design model architectures automatically. Further, EAs have been used for neural network optimization for more than 30 years. Therefore, in this paper, we review the evolutionary neural architecture search (ENAS) from the view of the advanced techniques. We hope this work can provide a comprehensive understanding of EAs’ roles for the readers and focus themselves on ENAS.
Monitoring of the Effect of Thermal Shock on Crack Growth in Copper Through-Glass Via Substrates

This work aims at understanding thermo-mechanically induced cracks in through-glass via (TGV) made in Corning® HPFS® Fused Silica glass substrate, as well as to study the effect of thermal shock on the propagation of cracks. Two types of thermo-mechanically induced cracks were observed; radial cracks formed during heating and circumferential cracks that are formed during cooling. These cracks were initiated as a result of high tensile stresses in the circumferential and radial directions respectively. Thermal shock was found to lead to the growth of radial cracks. After 1000 cycles, catastrophic failures occurred due to the networking of radial cracks from adjacent vias. This suggests that TGV pitch dimension is a critical parameter that needs to be considered in order to limit catastrophic failures. On the other hand, no crack growth was observed for circumferential cracks in the in-plane direction, even after 1000 cycles of thermal shock. These results indicate that the prevention of radial and circumferential crack formation is the most critical step in mitigating risk of failure concerns, thus, the need for optimized crack-free metallized TGV solutions. Through TGV shape re-design, control of Cu metallization thickness as well as the use of lower annealing heating rate, these thermo-mechanically driven cracks were successfully mitigated in a previously reported work.
A Miniature Optical Fiber Fabry–Perot Interferometer Temperature Sensor Based on Tellurite Glass

An ultracompact optical fiber temperature sensor with a fiber-tip structure is proposed and demonstrated based on the Fabry–Perot (FP) interference principle. Composing a tellurite glass microcavity and a commercial multimode fiber, the proposed sensor is fabricated using a simple method of dipping. The theoretical analysis and experimental observation demonstrate that the ambient temperature can be detected by monitoring the wavelength shift of the reflection spectrum. The obtained sensitivity is about 62 pm/°C with linearity of 0.99982 in the range of 20 °C–170 °C. This sensor has great fringe contrast, high linearity, good stability, a wide detection range, and a short response time. It avoids the fusion difficulty between tellurite fiber and silica fiber, and provides a possibility for real-time temperature detection in the biological field and harsh industrial environment.
Throughput Maximization in Hybrid NOMA assisted Beyond 5G Heterogeneous Networks

The increasing number of mobile devices and multimedia applications demands enormous capacity that requires innovative network designs. A hybrid non-orthogonal multiple access (NOMA) scheme with user clustering has been considered a promising solution to meet the increasing demand for capacity. Successive interference cancellation (SIC) is applied to minimize interference in NOMA. Also, in addition to this, heterogeneous networks (HetNets) improve network throughput. It is necessary to find optimal resource allocation for throughput maximization. In this paper, a mixed-integer non-linear programming (MINLP) problem has formulated for throughput maximization using a hybrid NOMA scheme with user clustering in a macro base station (MBS) only network and HetNets. MINLP problems are non-deterministic polynomial time-hard (NP-hard) in general. An exhaustive search algorithm with the exponential increase in complexity with the number of users restricts it to use for the problem's solution. The throughput maximization problem is solved using an outer approximation algorithm. The proposed algorithm is evaluated with extensive simulations to show that the proposed solution is effective regarding network throughput and user association. Also, the proposed algorithm compares user admission in clusters, user association with BSs in MBS only network, and HetNets. The results clearly show that HetNet outperforms MBS only network in terms of the network throughput, the number of users admitted in clusters, and the number of users associated.
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

Medical datasets are usually imbalanced, where negative cases severely outnumber positive cases. Therefore, it is essential to deal with this data skew problem when training machine learning algorithms. This study uses two representative lung cancer datasets, PLCO and NLST, with imbalance ratios (the proportion of samples in the majority class to those in the minority class) of 24.7 and 25.0, respectively, to predict lung cancer incidence. This research uses the performance of 23 class imbalance methods (resampling and hybrid systems) with three classical classifiers (logistic regression, random forest, and LinearSVC) to identify the best imbalance techniques suitable for medical datasets. Resampling includes ten under-sampling methods (RUS, etc.), seven over-sampling methods (SMOTE, etc.), and two integrated sampling methods (SMOTEENN, SMOTE-Tomek). Hybrid systems include (Balanced Bagging, etc.). The results show that class imbalance learning can improve the classification ability of the model. Compared with other imbalanced techniques, under-sampling techniques have the highest standard deviation (SD), and over-sampling techniques have the lowest SD. Over-sampling is a stable method, and the AUC in the model is generally higher than in other ways. Using ROS, the random forest performs the best predictive ability and is more suitable for the lung cancer datasets used in this study. The code is available at https://mkhushi.github. io/
A Review of Memory Effects in AlGaN/GaN HEMT Based RF PAs

This work is an overview of the current understanding of memory effects arising from AlGaN/GaN HEMT based RF power amplifiers. It reviews the most widely accepted physical sources of those memory effects and their impact on the amplifier's linearizability, to then discuss corresponding circuit design approaches to minimize the phenomena. This is illustrated by results of a power amplifier whose short-term and long-term nonlinear memory is compensated by an analog assisted digital pre-distortion scheme.
A Novel Monocular SLAM Algorithm for High Real-Time Based on Kalman Filter

Simultaneous Localization and Mapping (SLAM) has been a hot research direction in the field of mobile robots since it was proposed. ORB-SLAM is a typical algorithm in feature point SLAM system. However, because the ORB-SLAM can only provide sparse map construction, it can only be used for positioning. Just using ORB-SLAM to handle the positioning problem make the calculation complexity higher and reduce the real-time performance. Because of the complicated calculation, the ORB-SLAM is not practical to the embedded devices with low computing power. Therefore, we propose a monocular SLAM algorithm with high real-time performance based on Kalman filter. Our proposed algorithm reduces the time consumption of the system by modifying the posture optimization algorithm. Our algorithm provides accurate and efficient positioning for upper-level applications. We use Kalman filter modeling to optimize the pose of the camera. The experimental results show that compared with traditional algorithm, our proposed algorithm can effectively improve the real-time performance and reduces the time consumption of the system.
Research on Wireless Resource Management and Scheduling for 5G Network Slice

Network slicing is a key technology in 5G. Generally, 5G networks employ slicing technology to provide the isolated and customizable network services for different scenarios (e.g., different vertical industries, different customers, different businesses etc.) in the form of virtual industry private networks. 5G slicing has the potential to meet the individual requirements of users and services, in terms of bandwidth, delay, reliability, and mobility. This paper gives an overall introduction to network slicing management, end-to-end processes, and wireless slicing capabilities. Then, this paper carries on algorithm research for wireless RB resource reservation and QoS scheduling. On one hand, the proposed algorithm clarifies the specific scheme of wireless RB resource reservation. On the other hand, the algorithm provides QoS scheduling parameter configuration. This lays a solid foundation for the implementation of slice differentiation capabilities in 5G wireless networks.
EMI Filter Re-Design in a SMPS with Inductor in Saturation

This paper analyzes the design issues of the input EMI (Electro-Magnetic Interference) filter's in a switching mode power supply where the power inductor is operated in saturation. Starting with a SMPS equipped with linear inductor, the input filter is firstly designed to comply with Standards. Then, a new inductor with a smaller core size is employed in the same SMPS to exploit saturation. The EMI filter is re-designed, taking into account the increase of EMI introduced by the non-linear operation of the inductor. Finally, the reduction of the inductor's size and cost is compared with the increased size and cost of the EMI filter.
Efficient and Accurate Selection of Optimal Collective Communication Algorithms Using Analytical Performance Modeling

The performance of collective operations has been a critical issue since the advent of Message Passing Interface (MPI). Many algorithms have been proposed for each MPI collective operation but none of them proved optimal in all situations. Different algorithms demonstrate superior performance depending on the platform, the message size, the number of processes, etc. MPI implementations perform the selection of the collective algorithm empirically, executing a simple runtime decision function. While efficient, this approach does not guarantee the optimal selection. As a more accurate but equally efficient alternative, the use of analytical performance models of collective algorithms for the selection process was proposed and studied. Unfortunately, the previous attempts in this direction have not been successful. We revisit the analytical model-based approach and propose two innovations that significantly improve the selective accuracy of analytical models: (1) We derive analytical models from the code implementing the algorithms rather than from their high-level mathematical definitions. This results in more detailed and relevant models. (2) We estimate model parameters separately for each collective algorithm and include the execution of this algorithm in the corresponding communication experiment. We experimentally demonstrate the accuracy and efficiency of our approach using Open MPI broadcast and gather algorithms and two different Grid’5000 clusters and one supercomputer.
Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

The demand for collaborative robots is growing in industrial environments due to their versatility and low prices. Thus, more collaborative solutions are emerging for industrial scenarios. However, implementing scenarios where robots work autonomously while synchronizing their operations in a safe industrial environment with shopfloor workers is not easy. To fill the gap existing in the safe implementation of industrial collaborative scenarios, this manuscript presents a review based on five identified challenges that gathers the primary vital aspects to bear in mind while developing applications for them. Thus, a four-level classification is proposed, which collects the identified challenges and the previous developments in the field of human-robot interaction. The five identified challenges pretends to be the missing enabling key for implementing industrial collaborative scenarios in modern industrial plants. Lastly, a discussion and conclusion are exposed to analyze the degree of development in the field and its potential growth.
Development Of Optimization Procedure For The Expression Of The Membrane Protein Aquaporin In Rhodobacter Sphaeroides

Membrane proteins are the new targets for many researchers in the field of biotechnology and medicine. One of the problems regarding their study is low abundance in vivo and limited availability of functional protein for in vitro studies. Rhodobacter sphaeroides is being developed as a highly effective production organism for the expression of biotechnologically relevant membrane proteins. R. sphaeroides is being used developed as a membrane protein platform due to the organism’s large intracellular membrane level and potential for high biomass yields (>4 gDW/L) in scalable anaerobic photoheterotrophic systems. The production of the water-transport membrane protein Aquaporin was studied, quantified and analyzed through the use fluorescent fusion and poly-histidine tags. Correlations between culture fluorescence and Western blot densitometry were validated as a simple screening method for rapidly predicting and quantifying the membrane protein levels. The values for fluorescence and Western Blotting were correlated to each other and shown to correlate with standard pure protein values. Fluorescence per optical density (OD) therefore provides a rapid measurement of membrane protein expression per cell. To demonstrate the utility of this correlation for optimizing the protein expression, temperature effects were studied as a simple control parameter in our system. Room and optimal temperatures conditions (25°C and 32°C, respectively) for Rhodobacter sphaeroides were studied under the anaerobic photoheterotrophic conditions required for protein expression. The results from the experiments indicated that the production of protein per cell showed similar trend at both temperatures, though the total productivity was much higher at 32°C due to faster growth. This work also identifies several opportunities for improving this screening method for membrane expression. Although the fluorescent fusion proteins served as a simple and effective method for determining the protein concentration in vivo, the use of obligate aerobic fluorescent proteins ii presented a significant experimental inconvenience for the measurement of the fluorescent readings due to the use of the anaerobic conditions. The maturation (folding and optimal fluorescence) requires oxygen exposure after sampling. The observed maturation time of more than 40 hours undermined the goal of rapid screening. Future research will seek to overcome this limitation by using a new generation of facultative anaerobic fluorescent proteins such as riboflavin-binding proteins.
On the Deadline Miss Probability of Various Routing Policies in Wireless Sensor Networks

Moving data across communication networks is often subject to deadline requirements. An example is early warning of disasters of natural origin, where sensor measurements at the disaster location must be communicated across a network within a predefined maximum delay in order for a consequent warning to be timely. In this work, we present a probabilistic model that allows for characterizing the delay experienced by sensor measurements in a wireless sensor network from source to sink depending upon the routing metric used for forwarding the data through the network. Using link delay probability distributions and the probabilities of following different paths to the sink, source-to-sink delay distributions are found for routing policies based on minimum hop-count, minimum mean delay and the Joint Latency (JLAT) protocol. An algorithm for calculating the end-to-end source to sink delay probability density function (PDF) is presented for the general case of networks that use routing tables whose input for routing decisions is the remaining time-to-deadline. The work provides a general tool for routing delay analysis, allowing for comparison of the deadline miss probability between different routing policies. An improved form of JLAT is proposed. Its deadline miss probability is found using the presented algorithm and compared to the ones determined for minimum hop-count, minimum mean delay and JLAT by means of an example.
Automated Development of Latent Representations for Optimization of Sequences Using Autoencoders

In this paper, we propose an automated method for the development of new representations of sequences. For this purpose, we introduce a two-way mapping from variable length sequence representations to a latent representation modelled as the bottleneck of an LSTM (long short-term memory) autoencoder. Desirable properties of such mappings include smooth fitness landscapes for optimization problems and better evolvability. This work explores the capabilities of such latent encodings in the context of optimization of 3D structures. Various improvements are adopted that include manipulating the autoencoder architecture and its training procedure. The results of evolutionary algorithms that use different variants of automatically developed encodings are compared.