Major Depressive Disorder (MDD) is a prevalent and severe mental disease. Functional Magnetic Resonance Imaging (fMRI)-based diagnostic methods, which analyze Functional Connectivity (FC) to identify abnormal functional connections, have shown promise as biomarker-based approaches for diagnosing depression. However, the high costs of fMRI data result in small sample sizes, hindering the effective identification of abnormal FC patterns. Moreover, existing methods often overlook the potential benefits of incorporating domain knowledge into their models. In this paper, we propose KnowMDD, a novel knowledge-guided cross contrastive learning framework for MDD diagnosis. By incorporating domain knowledge and employing data augmentation, KnowMDD addresses data sparsity while improving robustness and interpretability. Specifically, multiple atlases are used to construct complementary brain graph representations. The default mode network, closely associated with depression, is introduced into the contrastive learning paradigm for diverse subgraph augmentations, while an attention mechanism captures global semantic relationships between brain regions. Based on them, a cross contrastive learning is designed to learn robust representations for accurate diagnosis. Extensive experiments demonstrate the effectiveness, robustness, and interpretability of KnowMDD, which outperforms state-of-the-art methods. We also develop a demonstration system to show its practical application.

In recent years, the burgeoning urban population, coupled with expanding urban dimensions and various sociodemographic factors, has led to an alarming escalation in criminal activities within urban centers. This escalation has further exacerbated the existing strain on police resources, rendering them increasingly inadequate for effective law enforcement. Concurrently, police patrol operations have emerged as a pivotal instrument in the ongoing battle against violent criminal activities. The judicious planning of patrol routes has the potential to markedly enhance the efficiency of police patrolling endeavors, thereby bolstering the overall security infrastructure within the jurisdiction while simultaneously conserving invaluable police resources. The formulation of an efficient patrol strategy within the intricate and ever-evolving landscape of urban regions replete with crime hotspots represents an intellectually taxing challenge. To address this exigent problem, this paper proffers a novel real-time patrol route planning algorithm tailored to dynamic environments, employing the principles of deep reinforcement learning, specifically the Integrated Double Q-Network (IDQN) method. Subsequently, the efficacy of the proposed method is empirically substantiated through experimentation, attesting to its practical viability and utility in the field of computer science and urban security management.

Content feed, a type of product that recommends a sequence of items for users to browse and engage with, has gained tremendous popularity among social media platforms. In this paper, we propose to study the diversity problem in such a scenario from an item sequence perspective using time series analysis techniques. We derive a method calledsliding spectrum decomposition (SSD) that captures users' perception of diversity in browsing a long item sequence. We also share our experiences in designing and implementing a suitable item embedding method for accurate similarity measurement under long tail effect. Combined together, they are now fully implemented and deployed in Xiaohongshu App's production recommender system that serves the main Explore Feed product for tens of millions of users every day. We demonstrate the effectiveness and efficiency of the method through theoretical analysis, offline experiments and online A/B tests.

Accurate depth estimation is an essential part of most light field applications. Previous approach Spinning Parallelogram Operator (SPO) achieves robust depth estimation results in noisy and occluded scenes by fitting lines in Epipolar Plane Images (EPIs). However, only using histogram distances, SPO cannot handle complex occlusions and is sensitive to different parameters. In this letter, we propose enhanced Spinning Parallelogram Operator utilizing color constraint and histogram integration (SPO-CH) to address the above problems. The color consistency between different views is first introduced to exclude occlusions when measuring the histogram distances of occluded areas. We then design a novel Gaussian integration histogram that combines information from other adjacent bins, which improves the performances of noisy and texture-less scenes. Experimental results show that the proposed method effectively improves the depth estimation results. Comparisons with other state-of-the-art approaches show that the proposed method achieves better results for both synthetic and real-world images.

Estimating parameters of performance models from empirical measurements is a critical task, which often has a major influence on the predictive accuracy of a model. This tutorial presents the problem of parameter estimation in queueing systems and queueing networks. The focus is on reliable estimation of the arrival rates of the requests and of the service demands they place at the servers. The tutorial covers common estimation techniques such as regression methods, maximum-likelihood estimation, and moment-matching, discussing their sensitivity with respect to data and model characteristics. The tutorial also demonstrates the automated estimation of model parameters using new open source tools.

Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application.

Workload measurements in enterprise systems often lead to observe a dependence between the number of requests running at a resource and their mean service requirements. However, multiclass performance models that feature these dependences are challenging to analyze, a fact that discourages practitioners from characterizing workload dependences. We here focus on closed multiclass queueing networks and introduce QD-AMVA, the first approximate mean-value analysis (AMVA) algorithm that can efficiently and robustly analyze queue-dependent service times in a multiclass setting. A key feature of QD-AMVA is that it operates on mean values, avoiding the computation of state probabilities. This property is an innovative result for state-dependent models, which increases the computational efficiency and numerical robustness of their evaluation. Extensive validation on random examples, a cloud load-balancing case study and comparison with a fluid method and an existing AMVA approximation prove that QD-AMVA is efficient, robust and easy to apply, thus enhancing the tractability of queue-dependent models.

Performance modelling of web applications involves the task of estimating service demands of requests at physical resources, such as CPUs. In this paper, we propose a service demand estimation algorithm based on a Markov Chain Monte Carlo (MCMC) technique, Gibbs sampling. Our methodology is widely applicable as it requires only queue length samples at each resource, which are simple to measure. Additionally, since we use a Bayesian approach, our method can use prior information on the distribution of parameters, a feature not always available with existing demand estimation approaches. The main challenge of Gibbs sampling is to efficiently evaluate the conditional expression required to sample from the posterior distribution of the demands. This expression is shown to be the equilibrium solution of a multiclass closed queueing network. We define a novel approximation to efficiently obtain the normalising constant to make the cost of its evaluation acceptable for MCMC applications. Experimental evaluation based on simulation data with different model sizes demonstrates the effectiveness of Gibbs sampling for service demand estimation.