Miracle-dz/UniFAR-FTUs huggingface dataset:
Topographic and kinetic effects of the SF6/O2 rate during a cryogenic etching process of silicon

In order to fabricate the structures with high aspect ratio (depth/width), it is necessary to develop plasma etching processes with a very accurate feature control and improvements in etching rates. We have developed an etching simulator which takes into account the main plasma–surface interactions in a SF6/O2 plasma etching on silicon substrate process. In this article, the role of oxygen on final trench topography and etching rate evolution is discussed. The presented results show that the notion of balance between the passivation regime and the etching processes has great consequences in topographic and kinetic trench characteristics. In particular, a good correlation has been established between the roughness on the trench sidewalls and zones of underpassivation.
Deep Diffusion-Invariant Wasserstein Distributional Classification

In this paper, we present a novel classification method called deep diffusioninvariant Wasserstein distributional classification (DeepWDC). DeepWDC represents input data and labels as probability measures to address severe perturbations in input data. It can output the optimal label measure in terms of diffusion invariance, where the label measure is stationary over time and becomes equivalent to a Gaussian measure. Furthermore, DeepWDC minimizes the 2-Wasserstein distance between the optimal label measure and Gaussian measure, which reduces the Wasserstein uncertainty. Experimental results demonstrate that DeepWDC can substantially enhance the accuracy of several baseline deterministic classification methods and outperforms state-of-the-art-methods on 2D and 3D data containing various types of perturbations (e.g., rotations, impulse noise, and down-scaling).
Applications of Common Entropy for Causal Inference

We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent. The minimum entropy required for such a latent is known as common entropy in information theory. We extend this notion to Rényi common entropy by minimizing the Rényi entropy of the latent variable. To efﬁciently compute common entropy, we propose an iterative algorithm that can be used to discover the trade-off between the entropy of the latent variable and the conditional mutual information of the observed variables. We show two applications of common entropy in causal inference: First, under the assumption that there are no low-entropy mediators, it can be used to distinguish causation from spurious correlation among almost all joint distributions on simple causal graphs with two observed variables. Second, common entropy can be used to improve constraint-based methods such as PC or FCI algorithms in the small-sample regime, where these methods are known to struggle. We propose a modiﬁcation to these constraint-based methods to assess if a separating set found by these algorithms are valid using common entropy. We ﬁnally evaluate our algorithms on synthetic and real data to establish their performance.
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints

Effective intersection control can play an important role in reducing trafﬁc congestion and associated vehicular emissions. This is vitally needed in developing countries, where air pollution is reaching life threatening levels. This paper presents EcoLight intersection control for developing regions, where budget is constrained and network connectivity is very poor. EcoLight learns effective control ofﬂine using state-of-the-art Deep Reinforcement Learning methods, but deploys highly efﬁcient runtime control algorithms on low cost embedded devices that work stand-alone on road without server connectivity. EcoLight optimizes both average case and worst case values of throughput, travel time and other metrics, as evaluated on open-source datasets from New York and on a custom developing region dataset.
A Bandit Learning Algorithm and Applications to Auction Design

We consider online bandit learning in which at every time step, an algorithm has to make a decision and then observe only its reward. The goal is to design efficient (polynomial-time) algorithms that achieve a total reward approximately close to that of the best fixed decision in hindsight. In this paper, we introduce a new notion of ( λ, µ ) -concave functions and present a bandit learning algorithm that achieves a performance guarantee which is characterized as a function of the concavity parameters λ and µ . The algorithm is based on the mirror descent algorithm in which the update directions follow the gradient of the multilinear extensions of the reward functions. The regret bound induced by our algorithm is (cid:101) O ( √ T ) which is nearly optimal. We apply our algorithm to auction design, specifically to welfare maximization, revenue maximization, and no-envy learning in auctions. In welfare maximization, we show that a version of fictitious play in smooth auctions guarantees a competitive regret bound which is determined by the smooth parameters. In revenue maximization, we consider the simultaneous second-price auctions with reserve prices in multi-parameter environments. We give a bandit algorithm which achieves the total revenue at least 1 / 2 times that of the best fixed reserve prices in hind-sight. In no-envy learning, we study the bandit item selection problem where the player valuation is submodular and provide an efficient 1 / 2 -approximation no-envy algorithm.
On the Parameterized Complexity of Clique Elimination Distance

Bulian and Dawar [Algorithmica, 2016] introduced the notion of elimination distance in an eﬀort to deﬁne new tractable parameterizations for graph problems and showed that deciding whether a given graph has elimination distance at most k to any minor-closed class of graphs is ﬁxed-parameter tractable parameterized by k [Algorithmica, 2017]. In this paper, we consider the problem of computing the elimination distance of a given graph to the class of cluster graphs and initiate the study of the parameterized complexity of a more general version – that of obtaining a modulator to such graphs. That is, we study the ( η, Clq ) -Elimination Deletion problem (( η, Clq ) -ED Deletion ) where, for a ﬁxed η , one is given a graph G and k ∈ N and the objective is to determine whether there is a set S ⊆ V ( G ) such that the graph G − S has elimination distance at most η to the class of cluster graphs. Our main result is a polynomial kernelization (parameterized by k ) for this problem. As components in the proof of our main result, we develop a k O ( ηk + η 2 ) n O (1) -time ﬁxed-parameter algorithm for ( η, Clq ) -ED Deletion and a polynomial-time factor-min {O ( η · opt · log 2 n ) , opt O (1) } approximation algorithm for the same problem.
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning

In this paper, we present a statistical mechanics framework to understand the effect of sampling properties of training data on the generalization gap of machine learning (ML) algorithms. We connect the generalization gap to the spatial properties of a sample design characterized by the pair correlation function (PCF). In particular, we express generalization gap in terms of the power spectra of the sample design and that of the function to be learned. Using this framework, we show that space-ﬁlling sample designs, such as blue noise and Poisson disk sampling, which optimize spectral properties, outperform random designs in terms of the generalization gap and characterize this gain in a closed-form. Our analysis also sheds light on design principles for constructing optimal task-agnostic sample designs that minimize the generalization gap. We corroborate our ﬁndings using regression experiments with neural networks on: a) synthetic functions, and b) a complex scientiﬁc simulator for inertial conﬁnement fusion (ICF).
Pharmaceutical Stress Testing : Predicting Drug Degradation

Stress Testing: A Predictive Tool. Stress Testing: Relation to the Development Timeline. Stress Testing: Frequently Asked Questions. Stress Testing: Analytical Considerations. Oxidative Susceptibility Testing. Photostability Stress Testing. Comparative Stress Stability Studies for Rapid Evaluation of Manufacturing Changes of Materials from Multiple Sources. The Chemistry of Drug Degradation. The Role of "Mass Balance" in Pharmaceutical Stress Testing. Physical and Chemical Stability Considerations in the Development and Stress Testing of Freeze-Dried Pharmaceuticals. Excipient Compatibility Studies
Response surface methodology for optimization of cyanamide analysis by in situ derivatization and dispersive liquid–liquid microextraction

Cyanamide is widely used for agricultural purposes; therefore, its residues can be found in water. A new method was developed for its quantification using in situ derivatization with 2,6‐dimethyl‐4‐quinolinecarboxylic acid N‐hydroxysuccinimide ester followed by dispersive liquid–liquid microextraction (DLLME) and high‐performance liquid chromatography/fluorescence analysis. Multivariate chemometric techniques were successfully used to obtain the optimum conditions for direct derivatization and DLLME extraction. Derivatization parameters and DLLME extraction conditions were optimized by a two‐step design, 2k factorial design for screening, and central composite design for optimization. Best derivatization conditions were addition of 600 μL of derivatizing reagent, a temperature of 4 ºC, and pH 8.5, whereas for optimum extraction 800 μL of solvent, 30% NaCl conc. w/v, and pH 3.8 were chosen.
Successive Interference Cancellation-Based Weighted Least-Squares Estimation of Carrier and Sampling Frequency Offsets for WLANs

A successive interference cancellation (SIC)-based weighted least-squares (WLS) estimation for the carrier frequency offset (CFO) and the sampling frequency offset (SFO) is presented for wireless local area networks (WLANs) based on orthogonal frequency division multiplexing (OFDM). The proposed SIC-based WLS performs the estimation by exploiting the phase rotation in the frequency domain caused by the CFO and the SFO. SIC-based WLS estimates the CFO and the SFO successively instead of by traditional simultaneous estimation. The estimation of CFO based on the Taylor series is performed first, and then the WLS estimation of SFO based on successive cancellation of the CFO is carried out. The simulation results show that the SIC-based WLS can estimate the CFO and the SFO effectively. Compared to the WLS scheme, a performance improvement of more than 0.6 dB is achieved by SIC-based WLS, and nearly 10 percent of the complexity is reduced.
Parallel Evolutionary Algorithm for Coordinated Tuning of Exciter Controllers and a group of Static Var Compensators

The electrical distances between substations are reduced due the dense interconnection of the Power Electric Systems (PES); and, when large contingency occur rapidly affect to neighboring regions producing electromechanical oscillations with low, or negative, damping, followed by a significant degradation of the voltage profile. Then, a coordinated tuning methodology is proposed for simultaneously adjusting exciter controllers of each generator and a specific group of static var compensators belong to a certain multi-machine electric system. The referred static compensators are located into voltage sensitive substations to control the injection of reactive power, for voltage regulating. Exciter controllers, like power system stabilizer and voltage automatic regulator, are located into excitation system of generators to damp electromechanical oscillations. The coordinated tuning problem was considered as an optimization problem, maximizing the minimum damping coefficient of the system, considering at the same time several critical operating conditions. The Genetic Algorithm was used for optimization process and adapted for parallel computing in order to face the large dimensionality of the resulting optimization problem.
A Hardware-Accelerated Qubit Control System for Quantum Information Processing

In this paper we present a flexible, highperformance, integrated qubit control system using modular PXIe equipment. To manipulate qubits, a classical control system is required to generate and acquire a mix of baseband, radio frequency, and/or optical signals, with the specific mix different for each qubit technology. Typically, a quantum system requires many of these signals with a high degree of synchronization and phase coherence, and increasingly needs real-time data processing and on-the-fly sequencing capabilities for feedback experiments. The proposed system is scalable to hundreds of channels, all of them being phase coherent and fully time-synchronized.The system includes a programmable real-time sequencer for precise execution timing and decision making; and open FPGA capabilities for custom Digital Signal Processing (DSP) by hardware. Using the latter, we present FPGA IP to generate one-/two-qubit gates efficiently with the AWG, and to analyze high-frequency signals to measure the qubit states in real time on the digitizers. This IP features very low latencies for Quantum Error Correction (QEC) and Frequency Division Multiplexing (FDM) capabilities, among others.
Voltage Control of High-voltage Distribution System Using Distributed Electric Vehicles

This paper proposes a novel method to reduce the opportunity loss of power generation and maintain the system voltage within the specified range when photovoltaic generation systems are intensively connected to the high-voltage distribution system. The proposed method suppresses the voltage rise caused by surplus power of photovoltaic generation systems by charging electric vehicles. Furthermore, when the system voltage cannot be suppressed by only electric vehicles, power conditioning systems of photovoltaic systems are used cooperatively. The power conditioning system performs active power control. In addition, the proposed method suppresses the voltage drop by discharging electric vehicles. The usefulness of the proposed method is verified by computer simulation. As a result, the opportunity loss of power generation in the photovoltaic generation system was reduced from 18.5% to 5.25% of the total electric energy of all nodes with charging and discharging control of electric vehicles. The power generated by the photovoltaic generation system can be effectively utilized and the voltage in the high-voltage distribution system is maintained within the specified range.
Improving Localization Performance of Robot Using Obstacle Recognition and Probability Model through Image Processing

In this paper, we propose an effective localization method with only a stereo camera that has obstacles using particle filter. When localization with flow planning rather than robot scanned map, the error of localization increases when there is an obstacle. To solve this problem, First, we propose two types of obstacle recognition method: "Image Split Obstacle" and "Obstacle In Image" through image processing using the Opencv contour function. Afterwards, we solve the problems caused by the particle filter weight calculation process through a new sensing model using interval angle. In addition, we propose two probability models that can solve the problem of inconsistency between the number of landmarks of robots and particles. After that, we suggest an effective robot localization method by presenting a probability model that considers obstacles. As a result, the probability model considering obstacles showed an error rate reduction of about 45% compared to the existing model that does not considering obstacles.
Smart Greenhouse Management System based on NB-IoT and Smartphone

In order to realize intelligent agricultural greenhouse management and improve the density and refinement of the sensing and control terminal, a smart greenhouse management system based on NBIoT (Narrow Band Internet of Things) and smartphone is designed. The terminal node supports NB-IoT network and Bluetooth communication. Nodes periodically collect environmental data such as temperature, humidity, light intensity, wind speed and wind direction, and upload data to the cloud platform in real time through the NB-IoT network. The cloud platform that suit for NB-IoT is designed. The platform includes data storage, server-side and web applications for centralized management. The web application implements many functions such as user management, node information management, real-time monitoring, alarm recording and historical information querying. The Android APP is designed for distributed on-sited management of greenhouses. The APP implements real-time monitoring and temporary data storage of a single node. The test results show that the system has stable data transmission, the high packet transmission success rate in the greenhouse environment which meets the real-time management requirements for the greenhouse.
A Alternative Approach for Determining the Conductive Thermal Resistances in Lumped Parameter Thermal Networks of Electric Machines Using Conduction Shape Coefficients

An innovative approach to the calculation of conductive thermal resistances in solids is presented. FEA or CFD programs are used for this approach. The advantages of the new calculation approach are shown in comparison to a commercially used approach. In some cases considerable deviations result due to the neglect of circumferential effects. With the help of the approach shown, a fast calculating LPTN can be set up, which has the accuracy of an FEA simulation.
Multipath-Based HTTP Adaptive Streaming Scheme for the 5G Network

Fifth-generation (5G) communication has a non-line-of-sight (non-LOS) property, in that it cannot pass through obstacles due to the characteristics of ultra-high frequencies. This can lead to a high packet loss rate, which results in loss-based congestion. As the demand for high-quality video streaming services increases, multipath-based Hypertext Transfer Protocol (HTTP) adaptive streaming has been widely studied. It has the advantage of improving stability using bandwidth aggregation and alternative transmission. The request policies of existing systems degrade bandwidth utilization by the ON-OFF traffic pattern and degrade the quality-of-experience (QoE) due to incorrect bandwidth estimation. In the 5G environment, the above problems are exacerbated by severe network asymmetry, and buffer underflow occurs due to the drastic bandwidth changes in a non-LOS environment. We propose a multipath-based transmission scheme to solve the problem of HTTP adaptive streaming in a 5G environment. The proposed scheme presents a multipath-based collective segment request policy for accurate aggregated bandwidth estimation and bandwidth utilization improvement. The aggregated bandwidth estimation measures both block-based and segment-based bandwidth estimation values to improve network responsiveness and stability. We propose a rate adaptation scheme to improve bandwidth utilization and QoE. We also present a segment scheduler to solve the out-of-order problem in multipath-based transmission, and propose an offloading control scheme based on a partial-segment-based request policy to prevent buffer underflow due to sudden bandwidth reduction in the non-LOS 5G environment. Quantitative results from simulations suggest that our scheme solves the shortcomings of the existing solutions improving both bandwidth utilization and QoE.
Probabilistic Matrix Factorization Recommendation of Self-Attention Mechanism Convolutional Neural Networks With Item Auxiliary Information

To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information. First, the self-attention mechanism is added to convolutional matrix factorization and a probabilistic matrix factorization model, based on a convolutional neural networks with self-attention mechanism, is proposed. Second, after integrating auxiliary information, such as item comment, item name, and item category, probabilistic matrix factorization, based on a self-attention mechanism convolutional neural networks, is used for recommendation. Adding the self-attention mechanism allows convolutional matrix factorization to capture the long-distance dependence between different components of the auxiliary information. Integrating the item comment, name, and category information alleviates the data sparsity of recommendation, and improves the accuracy of rating prediction. Experimental results on the MovieLens-1M and MovieLens-10M datasets show that the probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item comment, name, and category information is superior to existing popular methods, in respect of root mean square error.
Influence of Driver Integration on GaN Enhancement Mode Transistor Performance

GaN components are finding their way into power electronic applications as their increased switching speed allows for augmented switching frequencies, leading to higher power densities. However, raising the switching speed causes parasitic effects degrading part of the components superior performance. The main problem is the gate loop inductance which should be as small as possible. A low inductance can be achieved by integrating the driver and GaN component into the same IC. In this paper, a practical implementation of an enhancement mode GaN device with integrated driver IC and external driver is tested and comparisons between the performance of these devices in a boost converter will be reported. This allows to evaluate and compare efficiency and the influence of parasitic effects in the driver loop of a power converter.
An Optimization Procedure for a Synchronous Reluctance Machine with Fluid Shaped Flux Barriers

A novel procedure to optimize the rotor design of a synchronous reluctance machine is proposed. The rotor geometry is symmetrical and based on “fluid-shaped” barriers. The optimization procedure is founded on the parametrization principle, which performs changes in the rotor configuration by varying position, shape and thickness of the flux barriers. Then, through the communication between MATLAB and Flux 2D, this procedure modifies and analyses different configurations by means of finite element simulations. During the post-processing phase the best design which optimizes average torque, torque ripple, efficiency and power factor is chosen.
Blockchain-Based Data Security for Artificial Intelligence Applications in 6G Networks

The sixth generation (6G) networks are expected to provide a fully connected world with terrestrial wireless and satellite communications integration. The design concept of 6G networks is to leverage artificial intelligence (Ai) to promote the intelligent and agile development of network services. intelligent services inevitably involve the processing of large amounts of data, such as storage, computing, and analysis, such that the data may be vulnerable to tampering or contamination by attackers. in this article, we propose a blockchain-based data security scheme for Ai applications in 6G networks. Specifically, we first introduce the 6G architecture (i.e., a space-air-ground-underwater integrated network). Then we discuss two Ai-enabled applications, indoor positioning and autonomous vehicle, in the context of 6G. Through a case study of an indoor navigation system, we demonstrate the effectiveness of blockchain in data security. The integration of Ai and blockchain is developed to evaluate and optimize the quality of intelligent service. Finally, we discuss several open issues about data security in the upcoming 6G networks.