Search

The Sentinel-6 satellites are equipped with a dual-constellation GNSS receiver and laser retroreflector arrays (LRAs) to enable tracking GNSS and SLR signals. Integrating the LEO and ground GNSS data at the observation level is reported to be capable of improving both GNSS and LEO orbits. In this study, we developed a method to integrate ground and onboard GNSS and SLR data for Galileo and Sentinel-6A satellite orbits determination, where the Galileo and Sentinel-6A were used as colocations. Meanwhile, three weighting strategies and SLR range bias estimation methods are assessed by the quality of satellites orbits. In the experiments, data over a month is utilized for validation, the results confirmed the Galileo orbits improvement brought by the Sentinel-6A data. And it is also found that the SLR data could improve both Galileo and Sentinel-6A orbits with suitable weighting and range bias estimation strategy. The GNSS/SLR fusion method with colocation onboard also shows the potential to improve the terrestrial reference frame further., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

We study private and robust multi-armed bandits (MABs), where the agent receives Huber's contaminated heavy-tailed rewards and meanwhile needs to ensure differential privacy. We first present its minimax lower bound, characterizing the information-theoretic limit of regret with respect to privacy budget, contamination level and heavy-tailedness. Then, we propose a meta-algorithm that builds on a private and robust mean estimation sub-routine \texttt{PRM} that essentially relies on reward truncation and the Laplace mechanism only. For two different heavy-tailed settings, we give specific schemes of \texttt{PRM}, which enable us to achieve nearly-optimal regret. As by-products of our main results, we also give the first minimax lower bound for private heavy-tailed MABs (i.e., without contamination). Moreover, our two proposed truncation-based \texttt{PRM} achieve the optimal trade-off between estimation accuracy, privacy and robustness. Finally, we support our theoretical results with experimental studies.

We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected cumulative reward subject to a cumulative constraint on the expected utility (cost). In the non-stationary environment, reward, utility functions, and transition kernels can vary arbitrarily over time as long as the cumulative variations do not exceed certain variation budgets. We propose the first model-free, simulator-free RL algorithms with sublinear regret and zero constraint violation for non-stationary CMDPs in both tabular and linear function approximation settings with provable performance guarantees. Our results on regret bound and constraint violation for the tabular case match the corresponding best results for stationary CMDPs when the total budget is known. Additionally, we present a general framework for addressing the well-known challenges associated with analyzing non-stationary CMDPs, without requiring prior knowledge of the variation budget. We apply the approach for both tabular and linear approximation settings.

In this paper, we study the problem of (finite horizon tabular) Markov decision processes (MDPs) with heavy-tailed rewards under the constraint of differential privacy (DP). Compared with the previous studies for private reinforcement learning that typically assume rewards are sampled from some bounded or sub-Gaussian distributions to ensure DP, we consider the setting where reward distributions have only finite $(1+v)$-th moments with some $v \in (0,1]$. By resorting to robust mean estimators for rewards, we first propose two frameworks for heavy-tailed MDPs, i.e., one is for value iteration and another is for policy optimization. Under each framework, we consider both joint differential privacy (JDP) and local differential privacy (LDP) models. Based on our frameworks, we provide regret upper bounds for both JDP and LDP cases and show that the moment of distribution and privacy budget both have significant impacts on regrets. Finally, we establish a lower bound of regret minimization for heavy-tailed MDPs in JDP model by reducing it to the instance-independent lower bound of heavy-tailed multi-armed bandits in DP model. We also show the lower bound for the problem in LDP by adopting some private minimax methods. Our results reveal that there are fundamental differences between the problem of private RL with sub-Gaussian and that with heavy-tailed rewards., Comment: ICML 2023

We consider cross-silo federated linear contextual bandit (LCB) problem under differential privacy, where multiple silos (agents) interact with the local users and communicate via a central server to realize collaboration while without sacrificing each user's privacy. We identify three issues in the state-of-the-art: (i) failure of claimed privacy protection and (ii) incorrect regret bound due to noise miscalculation and (iii) ungrounded communication cost. To resolve these issues, we take a two-step principled approach. First, we design an algorithmic framework consisting of a generic federated LCB algorithm and flexible privacy protocols. Then, leveraging the proposed framework, we study federated LCBs under two different privacy constraints. We first establish privacy and regret guarantees under silo-level local differential privacy, which fix the issues present in state-of-the-art algorithm. To further improve the regret performance, we next consider shuffle model of differential privacy, under which we show that our algorithm can achieve nearly ``optimal'' regret without a trusted server. We accomplish this via two different schemes -- one relies on a new result on privacy amplification via shuffling for DP mechanisms and another one leverages the integration of a shuffle protocol for vector sum into the tree-based mechanism, both of which might be of independent interest. Finally, we support our theoretical results with numerical evaluations over contextual bandit instances generated from both synthetic and real-life data.

Supplementary material?

To fully utilize the abundant spectrum resources in millimeter wave (mmWave), Beam Alignment (BA) is necessary for large antenna arrays to achieve large array gains. In practical dynamic wireless environments, channel modeling is challenging due to time-varying and multipath effects. In this paper, we formulate the beam alignment problem as a non-stationary online learning problem with the objective to maximize the received signal strength under interference constraint. In particular, we employ the non-stationary kernelized bandit to leverage the correlation among beams and model the complex beamforming and multipath channel functions. Furthermore, to mitigate interference to other user equipment, we leverage the primal-dual method to design a constrained UCB-type kernelized bandit algorithm. Our theoretical analysis indicates that the proposed algorithm can adaptively adjust the beam in time-varying environments, such that both the cumulative regret of the received signal and constraint violations have sublinear bounds with respect to time. This result is of independent interest for applications such as adaptive pricing and news ranking. In addition, the algorithm assumes the channel is a black-box function and does not require any prior knowledge for dynamic channel modeling, and thus is applicable in a variety of scenarios. We further show that if the information about the channel variation is known, the algorithm will have better theoretical guarantees and performance. Finally, we conduct simulations to highlight the effectiveness of the proposed algorithm.

In this paper, we study the problem of global reward maximization with only partial distributed feedback. This problem is motivated by several real-world applications (e.g., cellular network configuration, dynamic pricing, and policy selection) where an action taken by a central entity influences a large population that contributes to the global reward. However, collecting such reward feedback from the entire population not only incurs a prohibitively high cost but often leads to privacy concerns. To tackle this problem, we consider differentially private distributed linear bandits, where only a subset of users from the population are selected (called clients) to participate in the learning process and the central server learns the global model from such partial feedback by iteratively aggregating these clients' local feedback in a differentially private fashion. We then propose a unified algorithmic learning framework, called differentially private distributed phased elimination (DP-DPE), which can be naturally integrated with popular differential privacy (DP) models (including central DP, local DP, and shuffle DP). Furthermore, we prove that DP-DPE achieves both sublinear regret and sublinear communication cost. Interestingly, DP-DPE also achieves privacy protection "for free" in the sense that the additional cost due to privacy guarantees is a lower-order additive term. In addition, as a by-product of our techniques, the same results of "free" privacy can also be achieved for the standard differentially private linear bandits. Finally, we conduct simulations to corroborate our theoretical results and demonstrate the effectiveness of DP-DPE., Comment: 55 pages, a short version has been accepted by IEEE WiOpt'22

As one of the most concerned technologies in the field of artificial intelligence in recent ten years, deep learning has achieved excellent results in many applications, but the current learning strategies rely heavily on a large number of labeled data. In many practical problems, it is not feasible to obtain a large number of labeled training data, so it increases the training difficulty of the model. But it is easy to obtain a large number of unlabeled data. Semi-supervised learning makes full use of unlabeled data, provides solutions and effective methods to improve the performance of the model under the condition of limited labeled data, and achieves high recognition accuracy in the task of image classification. This paper first gives an overview of semi-supervised learning, and then introduces the basic ideas commonly used in classification algorithms. It focuses on the comprehensive review of image classification methods based on semi-supervised deep learning framework in recent years, including multi- view training, consistency regularization, diversity mixing and semi-supervised generative adversarial networks. It summarizes the common technologies of various methods, analyzes and compares the differences of experimental results of different methods. Finally, this paper thinks about the existing problems and looks forward to the feasible research direction in the future.

Wide-field searches for young stellar objects (YSOs) can place useful constraints on the prevalence of clustered versus distributed star formation. The Spitzer/IRAC Candidate YSO (SPICY) catalog is one of the largest compilations of such objects (~120,000 candidates in the Galactic midplane). Many SPICY candidates are spatially clustered, but, perhaps surprisingly, approximately half the candidates appear spatially distributed. To better characterize this unexpected population and confirm its nature, we obtained Palomar/DBSP spectroscopy for 26 of the optically-bright (G, Accepted for publication in AJ. 22 pages, 9 figures, and 4 tables. Figure sets are available from https://sites.astro.caltech.edu/~mkuhn/SPICY/PaperIII/

This paper investigates the problem of regret minimization in linear time-varying (LTV) dynamical systems. Due to the simultaneous presence of uncertainty and non-stationarity, designing online control algorithms for unknown LTV systems remains a challenging task. At a cost of NP-hard offline planning, prior works have introduced online convex optimization algorithms, although they suffer from nonparametric rate of regret. In this paper, we propose the first computationally tractable online algorithm with regret guarantees that avoids offline planning over the state linear feedback policies. Our algorithm is based on the optimism in the face of uncertainty (OFU) principle in which we optimistically select the best model in a high confidence region. Our algorithm is then more explorative when compared to previous approaches. To overcome non-stationarity, we propose either a restarting strategy (R-OFU) or a sliding window (SW-OFU) strategy. With proper configuration, our algorithm is attains sublinear regret $O(T^{2/3})$. These algorithms utilize data from the current phase for tracking variations on the system dynamics. We corroborate our theoretical findings with numerical experiments, which highlight the effectiveness of our methods. To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.

Inverse synthetic aperture radar (ISAR) imaging of near-field targets is potentially useful in some specific applications, which makes it very important to efficiently produce high-quality image of the near-field target. In this paper, the simplified target model with uniform linear motion is applied to the near-field target imaging, which overcomes the complexity of the traditional near-field imaging algorithm. According to this signal model, the method based on coordinate conversion and image interpolation combined with the range-Doppler (R-D) algorithm is proposed to correct the near-field distortion problem. Compared with the back-projection (BP) algorithm, the proposed method produces better focused ISAR images of the near-field target, and decreases the computation complexity significantly. Experimental results of the simulated data have demonstrated the effectiveness and robustness of the proposed method.

Inverse synthetic aperture radar (ISAR) imaging of the target with the non-rigid body is very important in the field of radar signal processing. In this paper, a motion compensation method combined with the preprocessing and global technique is proposed to reduce the influence of micro-motion components in the fast time domain, and the micro-Doppler (m-D) signal in the slow time domain is separated by the improved complex-valued empirical-mode decomposition (CEMD) algorithm, which makes the m-D signal more effectively distinguishable from the signal for the main body by translating the target to the Doppler center. Then, a better focused ISAR image of the target with the non-rigid body can be obtained consequently. Results of the simulated and raw data demonstrate the effectiveness of the algorithm.

We propose a deep generative approach to nonparametric estimation of conditional survival and hazard functions with right-censored data. The key idea of the proposed method is to first learn a conditional generator for the joint conditional distribution of the observed time and censoring indicator given the covariates, and then construct the Kaplan-Meier and Nelson-Aalen estimators based on this conditional generator for the conditional hazard and survival functions. Our method combines ideas from the recently developed deep generative learning and classical nonparametric estimation in survival analysis. We analyze the convergence properties of the proposed method and establish the consistency of the generative nonparametric estimators of the conditional survival and hazard functions. Our numerical experiments validate the proposed method and demonstrate its superior performance in a range of simulated models. We also illustrate the applications of the proposed method in constructing prediction intervals for survival times with the PBC (Primary Biliary Cholangitis) and SUPPORT (Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments) datasets., 33 pages, 14 figures

We study a stochastic bandit problem with a general unknown reward function and a general unknown constraint function. Both functions can be non-linear (even non-convex) and are assumed to lie in a reproducing kernel Hilbert space (RKHS) with a bounded norm. This kernelized bandit setup strictly generalizes standard multi-armed bandits and linear bandits. In contrast to safety-type hard constraints studied in prior works, we consider soft constraints that may be violated in any round as long as the cumulative violations are small, which is motivated by various practical applications. Our ultimate goal is to study how to utilize the nature of soft constraints to attain a finer complexity-regret-constraint trade-off in the kernelized bandit setting. To this end, leveraging primal-dual optimization, we propose a general framework for both algorithm design and performance analysis. This framework builds upon a novel sufficient condition, which not only is satisfied under general exploration strategies, including \emph{upper confidence bound} (UCB), \emph{Thompson sampling} (TS), and new ones based on \emph{random exploration}, but also enables a unified analysis for showing both sublinear regret and sublinear or even zero constraint violation. We demonstrate the superior performance of our proposed algorithms via numerical experiments based on both synthetic and real-world datasets. Along the way, we also make the first detailed comparison between two popular methods for analyzing constrained bandits and Markov decision processes (MDPs) by discussing the key difference and some subtleties in the analysis, which could be of independent interest to the communities.

We consider the standard $K$-armed bandit problem under a distributed trust model of differential privacy (DP), which enables to guarantee privacy without a trustworthy server. Under this trust model, previous work largely focus on achieving privacy using a shuffle protocol, where a batch of users data are randomly permuted before sending to a central server. This protocol achieves ($\epsilon,\delta$) or approximate-DP guarantee by sacrificing an additional additive $O\!\left(\!\frac{K\log T\sqrt{\log(1/\delta)}}{\epsilon}\!\right)\!$ cost in $T$-step cumulative regret. In contrast, the optimal privacy cost for achieving a stronger ($\epsilon,0$) or pure-DP guarantee under the widely used central trust model is only $\Theta\!\left(\!\frac{K\log T}{\epsilon}\!\right)\!$, where, however, a trusted server is required. In this work, we aim to obtain a pure-DP guarantee under distributed trust model while sacrificing no more regret than that under central trust model. We achieve this by designing a generic bandit algorithm based on successive arm elimination, where privacy is guaranteed by corrupting rewards with an equivalent discrete Laplace noise ensured by a secure computation protocol. We also show that our algorithm, when instantiated with Skellam noise and the secure protocol, ensures \emph{R\'{e}nyi differential privacy} -- a stronger notion than approximate DP -- under distributed trust model with a privacy cost of $O\!\left(\!\frac{K\sqrt{\log T}}{\epsilon}\!\right)\!$.

Additional file 4: Table S1. Summarized data of clinical and pathological parameters from the eligible studies.

With the blooming of Internet-of-Things (IoT), we are witnessing an explosion in the number of IoT terminals, triggering an unprecedented demand for ubiquitous wireless access globally. In this context, the emerging low-Earth-orbit satellites (LEO-SATs) have been regarded as a promising enabler to complement terrestrial wireless networks in providing ubiquitous connectivity and bridging the ever-growing digital divide in the expected next-generation wireless communications. Nevertheless, the harsh conditions posed by LEO-SATs have imposed significant challenges to the current multiple access (MA) schemes and led to an emerging paradigm shift in system design. In this article, we first provide a comprehensive overview of the state-of-the-art MA schemes and investigate their limitations in the context of LEO-SATs. To this end, we propose a novel next generation MA (NGMA), which amalgamates the grant-free non-orthogonal multiple access (GF-NOMA) mechanism and the orthogonal time frequency space (OTFS) waveform, for simplifying the connection procedure with reduced access latency and enhanced Doppler-robustness. Critical open challenging issues and future directions are finally presented for further technical development., Comment: Accepted by IEEE Network

Supplementary material: details of Eq. (1) and Security proof against coherent attacks

Additional file 6: Appendix T2. The survival analysis ofprognostic genes on HPA for validation.

Additional file 2: Appendix D2. The immune infiltrates inclusters in the TCGA cohort.

Additional file 3: Appendix D3. The correlation of nine UCmetabolism-related gene expression with TMB in diverse cancer types.

We propose a deep generative approach to sampling from a conditional distribution basedon a unified formulation of conditional distribution and generalized nonparametric regression function, using the noise-outsourcing lemma. The conditional generator is estimated nonparametrically, with neural networks by matching appropriate joint distributions using the Kullback-Liebler divergence, and Wasserstein distance. We also propose an approach to nonparametric estimation of, conditional survival and hazards functions with right-censored data as an extension. An appealing, aspect of our method is that it allows either or both the predictor and the response to be highdimensional, and can handle both continuous and discrete type predictors and responses. We show, that the proposed method is consistent in the sense that the conditional generator converges in, distribution to the underlying conditional distribution under mild conditions. Our numerical experiments, with simulated and benchmark image data validate the proposed method and demonstrate, that it outperforms several existing conditional density estimation methods.

Additional file 1: Figure S1. Forest plot for different clinicopathological parameters.

Additional file 3: Figure S3. Forest plot for DFS of different clinicopathological parameters.

In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises. Additionally, it is disabled when mapless. To tackle these problems, we leverage deep reinforcement learning which is verified to be effective in self-decision-making, to self-explore in narrow spaces without a map while avoiding collisions. Specifically, based on our Ackermann-steering rectangular-shaped ZebraT robot and its Gazebo simulator, we propose the rectangular safety region to represent states and detect collisions for rectangular-shaped robots, and a carefully crafted reward function for reinforcement learning that does not require the destination information. Then we benchmark five reinforcement learning algorithms including DDPG, DQN, SAC, PPO, and PPO-discrete, in a simulated narrow track. After training, the well-performed DDPG and DQN models can be transferred to three brand new simulated tracks, and furthermore to three real-world tracks., Comment: Plan to redo the experiments in section 4, and will use different evaluation methods due to expert comments

Additional file 4: Appendix S1-S3. Figure S1. The association of intersection prognostic genes with immunity. (A) The correlation of 14 UC metabolism-related genes expression level with immune infiltration level in colon cancer. (B) Comparisons of immune infiltration level among samples with copy number alteration and the normal of 14 UC metabolism-related genes. Figure S2. The consensus clustering analysis of clusters, GSEA of the Cluster 1, and the t-SNE and 3D PCA of risk groups in the TCGA cohort. (A) The consensus matrix legend and the other consensus matrixes of clusters in the TCGA cohort. (B) The CDF plots and the item tracking plot of consensus clustering matrix in the external validation cohort. (C) The GSEA of cluster 1 colon cancer in the TCGA cohort. (D) (E) The t-SNE and 3D PCA of risk groups in the TCGA cohort. Figure S3. Immunohistochemistry of four genes in normal colon tissue and cancerous colon tissue, respectively. (A) The protein expression of upregulated genes (CDKN2A and CLCNKB) in normal colon tissue and cancerous colon tissue respectively. The URL of CDKN2A was https://www.proteinatlas.org/ENSG00000147889-CDKN2A/tissue/colon#img , https://www.proteinatlas.org/ENSG00000147889-CDKN2A/pathology/colorectal+cancer#img . The URL of CLCNKB was https://www.proteinatlas.org/ENSG00000184908-CLCNKB/tissue/colon#img and https://www.proteinatlas.org/ENSG00000184908-CLCNKB/pathology/colorectal+cancer#img . (B) The protein expression of downregulated genes (NAT2 and NOS2) in normal colon tissue and cancerous colon tissue respectively. The URL of NAT2 was https://www.proteinatlas.org/ENSG00000156006-NAT2/tissue/colon#img and https://www.proteinatlas.org/ENSG00000156006-NAT2/pathology/colorectal+cancer#img . The URL of NOS2 was https://www.proteinatlas.org/ENSG00000007171-NOS2/tissue/colon#img and https://www.proteinatlas.org/ENSG00000007171-NOS2/pathology/colorectal+cancer#img .

Additional file 1: Appendix D1. The correlation of 14 UC metabolism-related geneexpression with immune infiltration level in colon cancer.

We study the constrained reinforcement learning problem, in which an agent aims to maximize the expected cumulative reward subject to a constraint on the expected total value of a utility function. In contrast to existing model-based approaches or model-free methods accompanied with a `simulator', we aim to develop the first model-free, simulator-free algorithm that achieves a sublinear regret and a sublinear constraint violation even in large-scale systems. To this end, we consider the episodic constrained Markov decision processes with linear function approximation, where the transition dynamics and the reward function can be represented as a linear function of some known feature mapping. We show that $\tilde{\mathcal{O}}(\sqrt{d^3H^3T})$ regret and $\tilde{\mathcal{O}}(\sqrt{d^3H^3T})$ constraint violation bounds can be achieved, where $d$ is the dimension of the feature mapping, $H$ is the length of the episode, and $T$ is the total number of steps. Our bounds are attained without explicitly estimating the unknown transition model or requiring a simulator, and they depend on the state space only through the dimension of the feature mapping. Hence our bounds hold even when the number of states goes to infinity. Our main results are achieved via novel adaptations of the standard LSVI-UCB algorithms. In particular, we first introduce primal-dual optimization into the LSVI-UCB algorithm to balance between regret and constraint violation. More importantly, we replace the standard greedy selection with respect to the state-action function in LSVI-UCB with a soft-max policy. This turns out to be key in establishing uniform concentration for the constrained case via its approximation-smoothness trade-off. We also show that one can achieve an even zero constraint violation while still maintaining the same order with respect to $T$., Comment: Accepted and Published at the 36th Neural Information Processing Systems (NeurIPS'22). Section J (where different episodes may start from different states) is added in this version

Additional file 5: Appendix T1. The urea cyclemetabolism-related genes.

Differential privacy (DP) has been recently introduced to linear contextual bandits to formally address the privacy concerns in its associated personalized services to participating users (e.g., recommendations). Prior work largely focus on two trust models of DP: the central model, where a central server is responsible for protecting users sensitive data, and the (stronger) local model, where information needs to be protected directly on user side. However, there remains a fundamental gap in the utility achieved by learning algorithms under these two privacy models, e.g., $\tilde{O}(\sqrt{T})$ regret in the central model as compared to $\tilde{O}(T^{3/4})$ regret in the local model, if all users are unique within a learning horizon $T$. In this work, we aim to achieve a stronger model of trust than the central model, while suffering a smaller regret than the local model by considering recently popular shuffle model of privacy. We propose a general algorithmic framework for linear contextual bandits under the shuffle trust model, where there exists a trusted shuffler in between users and the central server, that randomly permutes a batch of users data before sending those to the server. We then instantiate this framework with two specific shuffle protocols: one relying on privacy amplification of local mechanisms, and another incorporating a protocol for summing vectors and matrices of bounded norms. We prove that both these instantiations lead to regret guarantees that significantly improve on that of the local model, and can potentially be of the order $\tilde{O}(T^{3/5})$ if all users are unique. We also verify this regret behavior with simulations on synthetic data. Finally, under the practical scenario of non-unique users, we show that the regret of our shuffle private algorithm scale as $\tilde{O}(T^{2/3})$, which matches that the central model could achieve in this case.

The stimulated reservoir volume (SRV) technology extends conventional fracturing technology. Understanding how to effectively and accurately determine modified fracture shape and volume is the key point to evaluating the stimulation effect. Using electromagnetic detection technology can provide a new option for measuring these parameters. By the finite method, the rationality of electromagnetic detection technology to obtain the relevant parameters of reconstruction fracture is testified through forward simulation. This study compared the signals of fractures with different conductivity, volume, and shape collected by electromagnetic detection tool, and the results show that the signals have a specific correspondence with fracture geometric parameters. According to the electromagnetic signal curve of the forward model, the description of propped fractures, including positions and sizes, can be realized.

Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations. Although existing attacks have achieved promising results, it still leaves a long way to go for generating transferable adversarial examples under the black-box setting. To this end, this paper proposes to improve the transferability of adversarial examples, and applies dual-stage feature-level perturbations to an existing model to implicitly create a set of diverse models. Then these models are fused by the longitudinal ensemble during the iterations. The proposed method is termed Dual-Stage Network Erosion (DSNE). We conduct comprehensive experiments both on non-residual and residual networks, and obtain more transferable adversarial examples with the computational cost similar to the state-of-the-art method. In particular, for the residual networks, the transferability of the adversarial examples can be significantly improved by biasing the residual block information to the skip connections. Our work provides new insights into the architectural vulnerability of neural networks and presents new challenges to the robustness of neural networks.

Conditional distribution is a fundamental quantity for describing the relationship between a response and a predictor. We propose a Wasserstein generative approach to learning a conditional distribution. The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution. The conditional generator is estimated by matching a joint distribution involving the conditional generator and the target joint distribution, using the Wasserstein distance as the discrepancy measure for these joint distributions. We establish non-asymptotic error bound of the conditional sampling distribution generated by the proposed method and show that it is able to mitigate the curse of dimensionality, assuming that the data distribution is supported on a lower-dimensional set. We conduct numerical experiments to validate proposed method and illustrate its applications to conditional sample generation, nonparametric conditional density estimation, prediction uncertainty quantification, bivariate response data, image reconstruction and image generation., 34 pages, 8 figures

Satellite communication mode is based on spread spectrum technology, and CDMA mode is adopted as a whole. In satellite communication system, all base stations, base station controllers, mobile switching centers, etc. adopt a common CDMA reference time, namely CDMA system time. The uplink channel is differentiated by PN sequence with orthogonal Walsh code, and is transmitted by the same radio-frequency carrier. The downlink code division physical channel is composed of PN long codes with a length of 242-1, and different phase offsets of the long codes are used to distinguish users. Long codes PNA and PNT are code division channels provided by access channel and downlink traffic channel respectively. The uplink and downlink work in X-band, which meets the speed requirements of 8Kb/s for uplink and 32Kb/s for downlink, and realizes the functions of resisting human interference, narrowband interference, multipath interference, rain attenuation and ranging.

We introduce Tuna, a static analysis approach to optimizing deep neural network programs. The optimization of tensor operations such as convolutions and matrix multiplications is the key to improving the performance of deep neural networks. Many deep learning model optimization mechanisms today use dynamic analysis, which relies on experimental execution on a target device to build a data-driven cost model of the program. The reliance on dynamic profiling not only requires access to target hardware at compilation time but also incurs significant cost in machine resources. We introduce an approach that profiles the program by constructing features based on the target hardware characteristics in order. We use static analysis of the relative performance of tensor operations to optimize the deep learning program. Experiments show that our approach can achieve up to 11x performance compared to dynamic profiling based methods with the same compilation time.

In order to investigate the factors affecting the release of vitamin C (VC) in gummy jelly during oral processing, four levels of gelatin (3%, 6%, 9% and 12%) were selected and a bionic chewing robot was employed in chewing experiments. Textural profile analysis showed the hardness of gummy jelly increased from 91.7 N to 198.8 N when the gelatin content was raised from 3 to 12%. Single factor chewing experiment showed that the release of VC was positively correlated with chewing frequency (F(chew)) and chewing strain (ST(chew)), while negatively correlated with gelatin content, chewing speed (SP(chew)) and volume of saliva (V(saliva)). Orthogonal chewing experiment showed that the priority of the factors affected the release of VC was following: ST(chew) > F(chew) > SP(chew) > V(saliva). Multivariate linear regression analysis proposed a model to predict the amount of VC released and the goodness-of-fit of the model was 95.7%. The amount of VC released was the highest under the combination of F(chew) 12 times, ST(chew) 100%, V(saliva) 2 ml and SP(chew) 40 times/min. The optimal amount of gelatin addition was 6%. This study provided an effective reference for the formula design and chewing mode optimization of gummy jelly.

Supplement 1

In this paper, we consider the Gaussian process (GP) bandit optimization problem in a non-stationary environment. To capture external changes, the black-box function is allowed to be time-varying within a reproducing kernel Hilbert space (RKHS). To this end, we develop WGP-UCB, a novel UCB-type algorithm based on weighted Gaussian process regression. A key challenge is how to cope with infinite-dimensional feature maps. To that end, we leverage kernel approximation techniques to prove a sublinear regret bound, which is the first (frequentist) sublinear regret guarantee on weighted time-varying bandits with general nonlinear rewards. This result generalizes both non-stationary linear bandits and standard GP-UCB algorithms. Further, a novel concentration inequality is achieved for weighted Gaussian process regression with general weights. We also provide universal upper bounds and weight-dependent upper bounds for weighted maximum information gains. These results are of independent interest for applications such as news ranking and adaptive pricing, where weights can be adopted to capture the importance or quality of data. Finally, we conduct experiments to highlight the favorable gains of the proposed algorithm in many cases when compared to existing methods.

In this note, we apply Stein's method to analyze the performance of general load balancing schemes in the many-server heavy-traffic regime. In particular, consider a load balancing system of $N$ servers and the distance of arrival rate to the capacity region is given by $N^{1-\alpha}$ with $\alpha > 1$. We are interested in the performance as $N$ goes to infinity under a large class of policies. We establish different asymptotics under different scalings and conditions. Specifically, (i) If the second moments linearly increase with $N$ with coefficients $\sigma_a^2$ and $\nu_s^2$, then for any $\alpha > 4$, the distribution of the sum queue length scaled by $N^{-\alpha}$ converges to an exponential random variable with mean $\frac{\sigma_a^2 + \nu_s^2}{2}$. (3) If the second moments quadratically increase with $N$ with coefficients $\tilde{\sigma}_a^2$ and $\tilde{\nu}_s^2$, then for any $\alpha > 3$, the distribution of the sum queue length scaled by $N^{-\alpha-1}$ converges to an exponential random variable with mean $\frac{\tilde{\sigma}_a^2 + \tilde{\nu}_s^2}{2}$. Both results are simple applications of our previously developed framework of Stein's method for heavy-traffic analysis in \cite{zhou2020note}., Comment: arXiv admin note: text overlap with arXiv:2003.06454

In this note, we apply Stein's method to analyze the steady-state distribution of queueing systems in the traditional heavy-traffic regime. Compared to previous methods (e.g., drift method and transform method), Stein's method allows us to establish stronger results with simple and template proofs. In particular, we consider discrete-time systems in this note. We first introduce the key ideas of Stein's method for heavy-traffic analysis through a single-server system. Then, we apply the developed template to analyze both load balancing problems and scheduling problems. All these three examples demonstrate the power and flexibility of Stein's method in heavy-traffic analysis. In particular, we can see that one appealing property of Stein's method is that it combines the advantages of both the drift method and the transform method.

This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee. In stochastic bandit systems, the rewards may refer to the users' activities, which may involve private information and the users may not want the agent to know. However, in many cases, the agent needs to know these activities to provide better services such as recommendations and news feeds. To handle this dilemma, we adopt differential privacy and study the regret upper and lower bounds for MAB algorithms with a given LDP guarantee. In this paper, we prove a lower bound and propose algorithms whose regret upper bounds match the lower bound up to constant factors. Numerical experiments also confirm our conclusions.

Applications in cloud platforms motivate the study of efficient load balancing under job-server constraints and server heterogeneity. In this paper, we study load balancing on a bipartite graph where left nodes correspond to job types and right nodes correspond to servers, with each edge indicating that a job type can be served by a server. Thus edges represent locality constraints, i.e., each job can only be served at servers which contained certain data and/or machine learning (ML) models. Servers in this system can have heterogeneous service rates. In this setting, we investigate the performance of two policies named Join-the-Fastest-of-the-Shortest-Queue (JFSQ) and Join-the-Fastest-of-the-Idle-Queue (JFIQ), which are simple variants of Join-the-Shortest-Queue and Join-the-Idle-Queue, where ties are broken in favor of the fastest servers. Under a "well-connected" graph condition, we show that JFSQ and JFIQ are asymptotically optimal in the mean response time when the number of servers goes to infinity. In addition to asymptotic optimality, we also obtain upper bounds on the mean response time for finite-size systems. We further show that the well-connectedness condition can be satisfied by a random bipartite graph construction with relatively sparse connectivity., Comment: 30 pages, 6 figures

The three lowest-lying $��$ states, i.e. $��(1S)$, $��(2S)$, and $��(3S)$, composed of $b\bar b$ pairs and below the $B\bar B$ threshold, provide a good platform for the researches of hadronic physics and physics beyond the Standard Model. They can be produced directly in $e^+e^-$ colliding experiments, such as CLEO, Babar, and Belle, with low continuum backgrounds. In these experiments, many measurements of the exclusive $��(1S)$ and $��(2S)$ decays into light hadrons, which shed light on the "80\% rule" for the Okubo-Zweig-Iizuka suppressed decays in the bottomonium sector, were carried out. Meanwhile, many studies of the charmonium and bottomonium productions in $��(1S,2S,3S)$ decays were performed, to distinguish different Quantum Chromodynamics (QCD) models. Besides, exotic states and new physics were also extensively explored in $��(1S,2S,3S)$ decays at CLEO, BaBar, and Belle. The $��(1S,2S,3S)$ states can also be produced in $pp$ collisions and in collisions involving heavy ions. The precision measurements of their cross sections and polarizations at the large hadron collider (LHC), especially in the CMS, ATLAS, and LHCb experiments, help to understand $��$ production mechanisms in $pp$ collisions. The observation of the sequential $��$ suppression in heavy ion collisions at CMS is of great importance for verifying the quark-gluon plasma predicted by QCD. In this article, we review the experimental results on $��(1S,2S,3S)$ at $e^+e^-$ colliders and the LHC, and summarize their prospects at Belle II and the LHC., 42 pages, 40 figures; revised version, accepted by Frontiers of Physics

The applications of a-InGaZnO thin-film transistors (TFTs) to logic circuits have been limited owing to the intrinsic n-channel operation. In this paper, we demonstrated a hybrid inverter constructed by p-channel low-temperature poly-silicon (LTPS) TFTs and n-channel amorphous-indium–gallium–zinc–oxide (a-IGZO) TFTs. Hydrogenated LTPS TFTs and a-IGZO TFTs have been successfully fabricated on the same panel, followed by a rapid thermal annealing treatment to remove the hydrogens in the a-IGZO TFTs. The resulted hybrid inverter exhibits large noise margin closed to ${V}_{\textsf {DD}}/\textsf {2}$ and a high voltage gain as 68.3. Due to the complementary configurations in the static state, the inverter shows small current and thus consumes low power in hundreds of picowatts. As all the fabrication processes are compatible with conventional techniques, the reported results may open new opportunities in circuit design and applications for oxide TFTs.

Aiming at the security problems of mobile storage devices, a software which can flexibly control the read and write permissions of mobile storage devices is designed and developed based on file filtering driving technology. The functional tests show that the software is easy to use and can effectively prevent file network-leakage, and enhance the security of the use of mobile storage device.