Distributed aggregative optimization with quantized communication

summary:In this paper, we focus on an aggregative optimization problem under the communication bottleneck. The aggregative optimization is to minimize the sum of local cost functions. Each cost function depends on not only local state variables but also the sum of functions of global state variables. The goal is to solve the aggregative optimization problem through distributed computation and local efficient communication over a network of agents without a central coordinator. Using the variable tracking method to seek the global state variables and the quantization scheme to reduce the communication cost spent in the optimization process, we develop a novel distributed quantized algorithm, called D-QAGT, to track the optimal variables with finite bits communication. Although quantization may lose transmitting information, our algorithm can still achieve the exact optimal solution with linear convergence rate. Simulation experiments on an optimal placement problem is carried out to verify the correctness of the theoretical results

Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs

Distributed online learning has been proven extremely effective in solving large-scale machine learning problems over streaming data. However, information sharing between learners in distributed learning also raises concerns about the potential leakage of individual learners' sensitive data. To mitigate this risk, differential privacy, which is widely regarded as the "gold standard" for privacy protection, has been widely employed in many existing results on distributed online learning. However, these results often face a fundamental tradeoff between learning accuracy and privacy. In this paper, we propose a locally differentially private gradient tracking based distributed online learning algorithm that successfully circumvents this tradeoff. We prove that the proposed algorithm converges in mean square to the exact optimal solution while ensuring rigorous local differential privacy, with the cumulative privacy budget guaranteed to be finite even when the number of iterations tends to infinity. The algorithm is applicable even when the communication graph among learners is directed. To the best of our knowledge, this is the first result that simultaneously ensures learning accuracy and rigorous local differential privacy in distributed online learning over directed graphs. We evaluate our algorithm's performance by using multiple benchmark machine-learning applications, including logistic regression of the "Mushrooms" dataset and CNN-based image classification of the "MNIST" and "CIFAR-10" datasets, respectively. The experimental results confirm that the proposed algorithm outperforms existing counterparts in both training and testing accuracies.Comment: 21 pages, 4 figure

Defense for Advanced Persistent Threat with Inadvertent or Malicious Insider Threats

In this paper, we propose a game-theoretical framework to investigate advanced persistent threat problems with two types of insider threats: malicious and inadvertent. Within this framework, a unified three-player game is established and Nash equilibria are obtained in response to different insiders. By analyzing Nash equilibria, we provide quantitative solutions to the advanced persistent threat problems with insider threats. Furthermore, optimal defense strategy and defender's cost comparisons between two insider threats have been performed. The findings suggest that the defender should employ more active defense strategies against inadvertent insider threats than against malicious insider threats, despite the fact that malicious insider threats cost the defender more. Our theoretical analysis is validated by numerical results, including an additional examination of the conditions of the risky strategies adopted by different insiders. This may help the defender in determining monitoring intensities and defensive strategies

Distributed Nash equilibrium tracking via the alternating direction method of multipliers

summary:Nash equilibrium is recognized as an important solution concept in non-cooperative game theory due to its broad applicability to economics, social sciences, computer science, and engineering. In view of its importance, substantial progress has been made to seek a static Nash equilibrium using distributed methods. However, these approaches are inapplicable in dynamic environments because, in this setting, the Nash equilibrium constantly changes over time. In this paper, we propose a dynamic algorithm that can track the time-varying Nash equilibrium in a non-cooperative game. Our approach enables each player to update its action using an alternating direction method of multipliers while ensuring this estimated action of each player always converges to a neighborhood of the Nash equilibrium at each sampling instant. We prove that the final tracking error is linearly proportional to the sampling interval, which implies that the tracking error can be sufficiently close to zero when the sampling interval is small enough. Finally, numerical simulations are conducted to verify the correctness of our theoretical results

Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation

3D spatial information is known to be beneficial to the semantic segmentation task. Most existing methods take 3D spatial data as an additional input, leading to a two-stream segmentation network that processes RGB and 3D spatial information separately. This solution greatly increases the inference time and severely limits its scope for real-time applications. To solve this problem, we propose Spatial information guided Convolution (S-Conv), which allows efficient RGB feature and 3D spatial information integration. S-Conv is competent to infer the sampling offset of the convolution kernel guided by the 3D spatial information, helping the convolutional layer adjust the receptive field and adapt to geometric transformations. S-Conv also incorporates geometric information into the feature learning process by generating spatially adaptive convolutional weights. The capability of perceiving geometry is largely enhanced without much affecting the amount of parameters and computational cost. We further embed S-Conv into a semantic segmentation network, called Spatial information Guided convolutional Network (SGNet), resulting in real-time inference and state-of-the-art performance on NYUDv2 and SUNRGBD datasets

Numerical analysis of initial imperfection influence on the performance of buckling-restrained brace

Potrebno je razmotriti interakciju između jezgre i vanjskih ograničavajućih elemenata kod spojnice ograničenog izvijanja (buckling-restrained brace - BRB) zbog značajnog učinka na ukupnu performansu spojnica. Mehanizam deformacije elementa jezgre u obliku nekoliko valova, po prvi se puta analizira u ovom istraživanju i predstavlja element jezgre kao savojni valoviti oblik s povećavajućim aksijalnim opterećenjem te pokazuje distribuciju i razvoj dodirne sile između vanjskih i ograničavajućih elemenata jezgre analizom konačnih elemenata. Objektno orijentiran programski jezik Python primijenjen je u ABAQUS parametrijskoj analizi, a također se analiziraju utjecaji inicijalne nesavršenosti jezgre i vanjskih elemenata ograničenja kao i amplitude razmaka na performansu BRB. Rezultati numeričke simulacije pokazuju da je povratno savijanje elementa jezgre rezultiralo iznenadnim izvijanjem kod viših tipova oscilacije kao i sveukupno smanjenje naprezanja u vanjskom ograničavajućem elementu, dok je lokalno naprezanje poraslo s razvojem valovitog oblika deformacije jezgre. Spojnica (BRB) s jezgrom simetrične inicijalne nesavršenosti funkcionirala je lošije od one s jezgrom protu-simetrične početne nesavršenosti u kompresiji. Nadalje, manji početni progib vanjskih ograničavajućih elemenata i amplitude razmaka rezultira manjom dodirnom silom te spojnica može stoga učinkovitije funkcionirati.The interaction between the core and external restraining members of the buckling-restrained brace (BRB) should be considered because of the significant effect it may cause on the overall performance of BRBs. The mechanism of core member multi-wave deformation is studied for the first time in this research, which presents an actual flexural wave-shape development of core member with increasing axial load and reveals the contact force distribution and development between the core and external restraining members by employing the refined finite element (FE) analysis. The object-oriented programming language Python is applied in the ABAQUS parameter analysis, and the influences of initial imperfection of the core and external restraining members, as well as that of the gap amplitude, on BRB performance are also investigated. Numerical simulation results show that the reverse bending of core member triggered sudden buckling in high-order modes, as well as the overall stress decrease in the external restraining member, whereas the local stress increased with the development of the core deformation waveform. The BRB with the core of symmetric initial imperfection performed worse than that with the core of anti-symmetric initial imperfection in compression. Furthermore, less initial deflection of external restraining members and gap amplitude leads to smaller contact force, thus the BRB can perform more effectively

Multivariate analysis and optimal configuration of wind-photovoltaic complementary power generation system

Advantages of wind-solar complementary power generation system to utilize solar and wind energy in the aspect of resource and technical economy have been reviewed tersely. Convenience of entering and exiting generating equipment and load from DC as well as AC bus are interpreted briefly. The factors that affect the electrical power output of the system were analyzed and studied. Based on the law of energy conservation, the energetic matching algorithm was proposed which forms the foundation of optimal configuration of system. Finally, the intelligent control and on-line monitoring of wind-solar complementary power generation system were discussed

An L0_0L1_1-norm compressive sensing paradigm for the construction of sparse predictive lattice models using mixed integer quadratic programming

First-principles based lattice models allow the modeling of ab initio thermodynamics of crystalline mixtures for applications such as the construction of phase diagrams and the identification of ground state atomic orderings. The recent development of compressive sensing approaches for the construction of lattice models has further enabled the systematic construction of sparse physical models without the need for human intuition other than requiring the compactness of effective cluster interactions. However, conventional compressive sensing based on L1-norm regularization is strictly only applicable to certain classes of optimization problems and is otherwise not guaranteed to generate optimally sparse and transferable results, so that the method can only be applied to some materials science applications. In this paper, we illustrate a more robust L0L1-norm compressive-sensing method that removes the limitations of conventional compressive sensing and generally results in sparser lattice models that are at least as predictive as those obtained from L1-norm compressive sensing. Apart from the theory, a practical implementation based on state-of-the-art mixed-integer quadratic programming (MIQP) is proposed. The robustness of our methodology is illustrated for four different transition-metal oxides with relevance as battery cathode materials: Li2xTi2(1-x)O2, Li2xNi2yO2, MgxCr2O4, and NaxCrO2. This method provides a practical and robust approach for the construction of sparser and more predictive lattice models, improving on the compressive sensing paradigm and making it applicable to a much broader range of applications.Comment: 25 pages, 3 figure

OR-051 Exploration of Potential Integrated Biomarkers for Sports Monitoring Based on Metabolic Profiling

Objective Metabolomic analysis is extensively applied to identify sensitive and specific biomarkers capable of reflecting pathological processes and physical responses or adaptations. Exercise training leads to profound metabolic changes, manifested as detectable alterations of metabolite levels and significant perturbations of metabolic pathways in sera, urine, and rarely, in saliva. Several metabolites have been exploited as biomarkers for generally evaluating physical states in almost all sports. However, alterations of metabolic profile caused by specific sports would be heterogeneous. Thus, developments of new techniques are eagerly required to identify characteristic metabolites as unique biomarkers for specifically accessing training stimulus and sports performances. In the present work, we conducted both metabolic profiling and a binary logistic regression model (BRM) of biological fluids derived from rowing ergometer test with the following aims: 1) to examine changes of metabolite profiles and identify characteristic metabolites in the samples of sera, urine, and saliva; 2) to screen out potential integrated biomarkers for sports-specific monitoring. Methods A total of 11 rowers (6 male, 5 female; aged 15±1 years; 4±2 years rowing training) underwent an indoor 6000m rowing ergometer test. Samples of sera, urine and saliva were collected before and immediately after the test. 1D 1H NMR spectra were recorded with a Bruker Avance III 650 MHz NMR spectrometer. NMR spectra were processed and aligned, resonances of metabolites were assigned and confirmed, and metabolite levels were calculated based on NMR integrals. Multivariate statistical analysis was carried out using partial least-squares discrimination analysis (PLS-DA) to distinguish metabolic profiles between the groups. The validated PLS-DA model gave the variable importance in the projection (VIP) for a given metabolite. Moreover, inter-group comparisons of metabolite levels were quantitatively conducted using the paired-sample t-test. Then, we identified characteristic metabolites with VIP>1 in PLS-DA and p<0.05 in t-test. Furthermore, we screened out potential biomarkers based on the characteristic metabolites identified from the three types of biological fluids using the BRM (stepwise). Results The rowing training induced profound changes of metabolic profiles in serum and saliva samples rather than in urine samples. Totally, 44 metabolites were assigned in which 19, 20, and 19 metabolites were identified from serum, urine and saliva samples, respectively. Seven metabolites were shared by the three types of samples. Moreover, five characteristic metabolites (pyruvate, lactate, succinate, N-acetyl-L-cysteine, and acetone) were identified from the serum samples. The elevated levels of pyruvate, lactate and succinate suggested that, the rowing training evidently promoted both oxidative phosphorylation and glycolysis pathways. Furthermore, three characteristic metabolites (tyrosine, formate, and methanol) were identified from the saliva samples. Given that tyrosine is the precursor of dopamine, the increased level of salivary tyrosine in all rowers experiencing the test, suggesting that salivary tyrosine could be explored as a potential indicator closely related to nervous fatigue in the test. On the other hand, PLS-DA did not show observable distinction of metabolic profiles between the urine samples before and immediately after the test. Moreover, 20 urinary metabolites did not display detectable altered levels. We then established the BRM with the identified characteristic metabolites, from which we selected one optimal regression model based on serum pyruvate and salivary tyrosine (adjusted R square was 0.935, P<0.001), indicating that the two selected metabolites would efficiently reflect the metabolic alterations in the test. Conclusions As far as the 6000m rowing ergometer test is concerned, serum samples could be a preferred resource for assessing the changes of energy metabolism in the test, while urine samples might have a relatively lower sensitivity to exercise-induced metabolic responses. Even though metabolite levels in saliva samples are generally lower than those in serum and urine samples, some salivary metabolites potentially have higher sensitivities to exercise-induced metabolic responses. Thus, the integration of multiple biomarkers identified from different type of species could potentially provide more sensitive and specific manners to monitor physical states in sports and exercise. This work may be of benefit to the exploration of integrated biomarkers for sports-specific monitoring