Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Stochastic Neural Networks for Semi-supervised Learning

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most existing models learn a deterministic classification function, which lack sufficient flexibility to explore better choices in the presence of kinds of imperfect observed data such as the scarce labeled nodes and noisy graph structure. To improve the rigidness and inflexibility of deterministic classification functions, this paper proposes a novel framework named Graph Stochastic Neural Networks (GSNN), which aims to model the uncertainty of the classification function by simultaneously learning a family of functions, i.e., a stochastic function. Specifically, we introduce a learnable graph neural network coupled with a high-dimensional latent variable to model the distribution of the classification function, and further adopt the amortised variational inference to approximate the intractable joint posterior for missing labels and the latent variable. By maximizing the lower-bound of the likelihood for observed node labels, the instantiated models can be trained in an end-to-end manner effectively. Extensive experiments on three real-world datasets show that GSNN achieves substantial performance gain in different scenarios compared with state-of-the-art baselines.
Content Provider Dynamics and Coordination in Recommendation Ecosystems

Recommendation Systems like YouTube are vibrant ecosystems with two types of users: Content consumers (those who watch videos) and content providers (those who create videos). While the computational task of recommending relevant content is largely solved, designing a system that guarantees high social welfare for all stakeholders is still in its infancy. In this work, we investigate the dynamics of content creation using a game-theoretic lens. Employing a stylized model that was recently suggested by other works, we show that the dynamics will always converge to a pure Nash Equilibrium (PNE), but the convergence rate can be exponential. We complement the analysis by proposing an efﬁcient PNE computation algorithm via a combinatorial optimization problem that is of independent interest.
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning. Simple algorithms such as the gradient descent ascent (GDA) are the common practice for solving these nonconvex games and receive lots of empirical success. Yet, it is known that these vanilla GDA algorithms with constant stepsize can potentially diverge even in the convex-concave setting. In this work, we show that for a subclass of nonconvex-nonconcave objectives satisfying a so-called two-sided Polyak-Łojasiewicz inequality, the alternating gradient descent ascent (AGDA) algorithm converges globally at a linear rate and the stochastic AGDA achieves a sublinear rate. We further develop a variance reduced algorithm that attains a provably faster rate than AGDA when the problem has the ﬁnite-sum structure.
Wear Leveling Revisited

Wear leveling – a technology designed to balance the write counts among memory cells regardless of the requested accesses – is vital in prolonging the lifetime of certain computer memory devices, especially the type of next-generation non-volatile memory, known as phase change memory (PCM). Although researchers have been working extensively on wear leveling, almost all existing studies mainly focus on the practical aspects and lack rigorous mathematical analyses. The lack of theory is particularly problematic for security-critical applications. We address this issue by revisiting wear leveling from a theoretical perspective. First, we completely determine the problem parameter regime for which Security Refresh – one of the most well-known existing wear leveling schemes for PCM – works eﬀectively by providing a positive result and a matching negative result. In particular, Security Refresh is not competitive for the practically relevant regime of large-scale memory. Then, we propose a novel scheme that achieves better lifetime, time/space overhead, and wear-free space for the relevant regime not covered by Security Refresh. Unlike existing studies, we give rigorous theoretical lifetime analyses, which is necessary to assess and control the security risk.
Bi-Criteria Approximation Algorithms for Load Balancing on Unrelated Machines with Costs

We study a generalized version of the load balancing problem on unrelated machines with cost constraints: Given a set of m machines (of certain types) and a set of n jobs, each job j processed on machine i requires p i,j time units and incurs a cost c i,j , and the goal is to ﬁnd a schedule of jobs to machines, which is deﬁned as an ordered partition of n jobs into m disjoint subsets, in such a way that some objective function of the vector of the completion times of the machines is optimized, subject to the constraint that the total costs by the schedule must be within a given budget B . Motivated by recent results from the literature, our focus is on the case when the number of machine types is a ﬁxed constant and we develop a bi-criteria approximation scheme for the studied problem. Our result generalizes several known results for certain special cases, such as the case with identical machines, or the case with a constant number of machines with cost constraints. Building on the elegant technique recently proposed by Jansen and Maack [15], we construct a more general approach that can be used to derive approximation schemes to a wider class of load balancing problems with constraints.
A Deterministic-Path Routing Algorithm for Tolerating Many Faults on Very-Large-Scale Network-on-Chip

Very-large-scale network-on-chip (VLS-NoC) has become a promising fabric for supercomputers, but this fabric may encounter the many-fault problem. This article proposes a deterministic routing algorithm to tolerate the effects of many faults in VLS-NoCs. This approach generates routing tables offline using a breadth-first traversal algorithm and stores a routing table locally in each switch for online packet transmission. The approach applies the Tarjan algorithm to degrade the faulty NoC and maximizes the number of available nodes in the reconfigured NoC. In 2D NoCs, the approach updates routing tables of some nodes using the deprecated channel/node rules and avoids deadlocks in the NoC. In 3D NoCs, the approach uses a forbidden-turn selection algorithm and detour rules to prevent faceted rings and ensures the NoC is deadlock-free. Experimental results demonstrate that the proposed approach provides fault-free communications of 2D and 3D NoCs after injecting 40 faulty links. Meanwhile, it maximizes the number of available nodes in the reconfigured NoC. The approach also outperforms existing algorithms in terms of average latency, throughput, and energy consumption.
Learning Mutational Semantics

In many natural domains, changing a small part of an entity can transform its semantics; for example, a single word change can alter the meaning of a sentence, or a single amino acid change can mutate a viral protein to escape antiviral treatment or immunity. Although identifying such mutations can be desirable (for example, therapeutic design that anticipates avenues of viral escape), the rules governing semantic change are often hard to quantify. Here, we introduce the problem of identifying mutations with a large effect on semantics, but where valid mutations are under complex constraints (for example, English grammar or biological viability), which we refer to as constrained semantic change search (CSCS). We propose an unsupervised solution based on language models that simultaneously learn continuous latent representations. We report good empirical performance on CSCS of single-word mutations to news headlines, map a continuous semantic space of viral variation, and, notably, show unprecedented zero-shot prediction of single-residue escape mutations to key inﬂuenza and HIV proteins, suggesting a productive link between modeling natural language and pathogenic evolution. 1
"Mixed" β-peptides. A unique helical secondary structure in solution. Preliminary communication

beta-Hexapeptides and a beta-dodecapeptide with sequences contg. two different types of beta-amino acids, aliph. proteinaceous side-chains in 2- or 3-position, were studied with CD and NMR measurements. With one exception, the secondary structures formed by these beta-peptides differ from those of isomers studied previously. Detailed NMR anal. of the beta-hexapeptide with alternating beta2,beta3-building blocks, and mol. -dynamics simulations revealed a min. energy conformation which is described as a novel irregular helix contg. 10- and 12-membered H-bonded rings.
Development of an Integrated Tracking Control Algorithm for Digging Operations of an Excavator

The Excavator is one of the key equipment utilized for earthmoving tasks at construction sites. This paper aims to provide an integrative tracking control strategy comprising of position, contour, and force tracking controls for excavation tasks. A non-linear proportional-integral controller was applied for position control of hydraulic actuators and the contour control strategy was added to create an optimal path of the bucket tip minimizing contour errors. The force control was finally considered to compensate for the ground resistive force. A multiphysics simulation model was developed for an evaluation of the designed controller’s performance through co-simulation. Experimental results obtained from a test platform show that the developed control algorithms provide good tracking performance for soil digging.
Constrained Signal-to-Noise Ratio Stabilization and Tracking for SITO systems

In the present work, motivated by the continuous reduction in sensor costs and wireless communications, we consider the control of single-input two-outputs (SITO) linear time invariant (LTI) systems, either stable or unstable, over an additive white noise (AWN) channel located over the control path (between the controller and the plant model). We characterize the stability of the closed-loop by means of the channel Signal-to-Noise Ratio (SNR). We consider first the case of stable SITO models and propose a design for closed-loop stability that achieves the user desired channel SNR level. We then extend the result to include performance as tracking of constant setpoints. We consider second the case of unstable SITO models for which we find an infimal channel SNR level for stability. Below this value no stability is possible, above this value stability is achieved. We then extend the channel SNR result for unstable SITO plant models to include the same closed-loop performance, stated as stability and tracking of constant setpoints. In this case performance is achieved by designing a complementary sensitivity that satisfies the interpolation conditions needed to avoid the cancellation by the controller of any unstable poles in the plant model. We illustrate the obtained results with examples offering insight into the interplay of the different elements of these same results.
Bode envelope bounds for linear time-invariant systems affected by semialgebraic parametric uncertainty

In this paper we address the computation of the Bode plots for linear time-invariant systems affected by semi-algebraic parameteric uncertainty. The problem of computing Bode magnitude and phase bounds is formulated in terms of non-convex semialgebraic optimization and solved by means of semidefinite (SDP) relaxation techniques. Simulation examples are provided to illustrate the effectiveness of the proposed technique.
Analytical Formulation and Minimization of Voltage THD in Staircase Modulated Multilevel Inverters With Variable DC Ratios

Staircase Modulation (SCM) is a commonly employed switching strategy in Multilevel Inverters (MLI) with a high number of voltage levels (<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>). The main challenges in SCM are adjusting the Modulation Index (MI) to the desired value while minimizing the Total Harmonic Distortion (THD). This is achieved by solving an objective function for its optimum variables. These variables are the Switching Angles (SA) in the case of an MLI with Equal DC voltage Supply (EDCS), or both the SAs and the DC voltage supply Ratios (DCR), in the case of an MLI with Unequal DC voltage Supply (UDCS). Such an approach, which is referred to as Optimum Minimization of THD (OMTHD), relies on the accuracy of the THD expression used for the objective function. This paper reveals generic analytical closed-form formulations of both Phase-voltage THD (PTHD) for single-phase (<inline-formula> <tex-math notation="LaTeX">$1\phi$ </tex-math></inline-formula>) MLIs and Line-voltage THD (LTHD) for three-phase (<inline-formula> <tex-math notation="LaTeX">$3\phi$ </tex-math></inline-formula>) MLIs. The revealed THD expressions apply to SCM based MLIs of any topology, with either EDCS or UDCS voltage source configurations, which may be fixed or varying, and arbitrary value of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> (odd and even). Given an arbitrary <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> value, the proposed unified formulations can be used to generate symbolic N-level PTHD or LTHD expressions, which are analytical functions of the SA and (optionally) DCR variables. The proposed THD expressions were used to form a generic OMTHD problem and solve for optimum SA and DCR variables, ensuring PTHD or LTHD minimization, subject to a set of optional constraints, such as the target MI, tolerable Modulation Error (ME), and the Maximum DCR (MDCR). Outcomes of the proposed OMTHD algorithm were successfully verified by Controller + Hardware-in-Loop (C-HIL) based experiments and compared against results of previous works, raveling significant improvement in THD accuracy and MLI’s performance. A downloadable supplemental file containing Maple and MATLAB functions of the proposed THD expressions, as well as pre-calculated sets of optimum SAs and DCRs tables for 13 different values of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$4\le N \le16$ </tex-math></inline-formula>), is provided for readers’ convenience.
Hybrid Neural Network Based on GRU with Uncertain Factors for Forecasting Ultra-short-term Wind Power

The non-stationarity and stochastic nature of wind power bring difficult challenges to large-scale grid-connected of wind power. The ultra-short-term forecasting of wind power is used for balancing load and the optimal optimization of spinning reserve, which has high requirements for prediction accuracy. The neural network can solve the problem of feature selection, but in the task of wind power prediction, it is of great concern to find the optimal input features and model structure by mining physical correlation among features. Inspired by the physical formula for wind power, an uncertain factor is calculated, which caused by both environmental disturbance and wind turbine state changes. This paper proposes a method to predict ultra-short-term wind power, which using the features associated with wind power and the uncertain factors. Time series features are predicted through the Gated Recurrent Unit (GRU) Neural Network, and finally all the features were fused to form a hybrid neural network. The effectiveness of the proposed method has been confirmed on the real datasets derived from a wind field. Compared with the conventional time series dependent methods, our proposed method shows more reasonable results in terms of accuracy and availability.
An Aggregated Model for Power Electronic System Based on Multi-Port Network Reduction Method

An aggregated model is proposed to reduce the dimension of the nodal admittance matrix in a converter-based power system application using a multi-port network reduction method. Further, the eigenvalues of the submatrices of the nodal admittance matrix can be used to predict the terminal behavior of the aggregated model. Case studies validate theoretical analysis with experimental results.
Calculation of Planar Transformer Capacitance Based on the Applied Terminal Voltages

In order to calculate the parasitic capacitors of a transformer, it is important to apply the correct transformer terminal voltages such that only the desired lumped capacitors are charged. This paper shows the correct transformer terminal voltages that match measurement scenarios that are widely used to extract lumped capacitor values. The parasitic capacitors are calculated based on the stored energy in the transformer windings for different measurement scenarios. A 5kW heavily interleaved high turns-ratio planar transformer is constructed, and its parasitic capacitance is measured. Lumped capacitor simulations are performed using Ansys Maxwell 3D. The simulations are verified through comparison with the measurements and the difference from the measurements is between 1.55-3.76%. The calculation method has a difference of 9.92-10.38% compared to the simulated model, which primarily is caused by an assumption of overlapping turns which is different from the simulated model.
Model Predictive Control Design for Tilt-Rotor Aircraft at Low Speed Flight*

Control design in the longitudinal-vertical channel is studied for tilt-rotor aircraft at low speed flight. The famous XV-15 tilt-rotor aircraft is used as a prototype. A model predictive control (MPC) design is developed based on a state-space model in a cascaded architecture. The MPC algorithm considers a terminal cost in its objective function so as to ensure close-loop stability, and an error-rate term is introduced in order to improve transient performance. By transforming constraints, the controller designed based on a linearized model at single operating point is applied to the whole stage at low speed flight. It means that there is not switching as gain scheduling in the realization of the longitudinal control. The simulation results show that the flight control system presents satisfied performance and robustness.
An Event-Triggered χ2-Detector for Cyber-Physical Systems under False Data Injection Attacks

Attacks on the cyber-physical systems (CPSs) are increasing recently, which makes the research of CPSs’ security issues more urgent. χ2-detector, the most common attack detector, has a fatal defect, that it requires all the covariance matrices of observation residual be reversible. In this paper, we propose a novel method to construct an attack detection variable obeying χ2 distribution without special requirements, and develop an event-triggered mechanism to reduce the adverse impact of the detection error. Observer-based state feedback controller is applied and a sufficient condition of stability on the alarm probability of the detector and the parameter in the event-triggered mechanism are further given. Finally, a simulation example is given to verify the effectiveness of the proposed attack detector.
Research on Cyberpunk Images in the Visual Digital Media

Cyberpunk is a kind of literature school that appeared in 1960s and 1970s. Artists gradually transform the literary concept into a visual style. This paper discusses the spiritual characteristics and cultural connotation of cyberpunk, and summarizes the visual characteristics formed in the application of digital media, and analyzes the aesthetic and cultural functions of cyberpunk images. In view of the impact on the visual digital media, from the aspects of culture, art and design practical cases, this paper summarizes the expression and application value of cyberpunk images. They have provided a new form of expression for the contemporary visual digital media.
Prediction of Abnormal Body Fat Percentage by Anthropometrics Parameters Using Receiver Operating Characteristic Curve

The World Health Organization has defined obesity ‟as the abnormal or excessive fat accumulation that represents a risk to health". Although obesity is characterized by an excessive amount of body fat, it is commonly measured using body mass index which is unable to differentiate between elevated body fat content and increased lean mass. The indicator that best predicts obesity is the one that quantify adipose tissue and, therefore, the estimation of body fat percentage (BFP). Skinfolds have been used to measure the BFP, based on the Siri and Brozec formula. There are no official cut-off points for BFP, as the associated data is relatively insufficient worldwide. Studies agreed that fewer than 25% in men and 30% in women are commonly used as normal BFP. The aim of this study is to evaluate the capability of the anthropometrics variables to discriminate subjects with abnormal BFP. A database of 1053 subjects with 28 anthropometrics measures was used. Area under the receiver operating characteristic curves (AUCROC), sensibility (SEN), specificity (SPE) and negative predictive value (NPV) was calculated to evaluate the predictive ability of anthropometric variables measured. Three circumferences (Arm, waist and hip) and four skinfolds (calf, suprailiac, abdominal and thigh) were the variables with the best abnormal BFP detection capability, with an AUCROC>0.800 (SEN>0.760 and SPE>0.673). Having a high probability of detecting subjects with normal BFP (NPV>0.970). Easier variables to acquire, such as waist, arm, and hip circumferences, could be used in low-income countries where it is not easy to have a plicometer.
Determining Cluster Distance when Designing High - Density WI-FI

This article examines the issues and problems of building and organizing a wireless network with a high density of clients. Nowadays, when the number of devices that one person has with him at a time is constantly growing, the load on the network equipment used by the Wi-Fi network also increases. Already at the design stage, it is important to take into account the fact that the projected network will have a segment where a large share of traffic will be concentrated. The article describes the process of calculating a network at the design stage, which helps to calculate the optimal distance to the access point at which the loss of signal power from the point will be minimal. For the calculation, the network was presented as a system of clusters, where an access point to a wireless network was taken as the center of attraction (cluster), and user devices were taken as objects of attraction that form a group around the cluster. The article shows graphs of the dependence: delay time on the number of subscribers at the access point, signal level depending on the distance to the cluster. And also, a graph of the distribution of the free loss factor in space and a graph of the optimal ratio of gain and free loss factor.