However,asan update ofonlyoneparameter group depends onalltheparameter groups ordata points, the computation cost is high when the number of the parameters or data points islarge. This paper proposes afast Block Coordinate Descent for Sparse GroupLasso.

Sparse Group Lasso is a method of linear regression analysis that finds sparse parameters in terms of both feature groups and individual features.

Summary: This paper presents a fast block coordinate descent algorithm for the sparse-group lasso problem. Two strategies are proposed to improve the computational efficiency. The first strategy is quickly identifying the groups of inactive features. The idea is to use an easy-to-compute upper bound when checking if the inactive-group condition holds. The second strategy is to select a set of candidate groups and update the feature vectors inside those groups first before iterating over all groups.

Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. However, existing graph anomaly detection algorithms focus on distinguishing individual entities (nodes or graphs) and overlook the possibility of anomalous groups within the graph. To address this limitation, this paper introduces a novel unsupervised framework for a new task called Group-level Graph Anomaly Detection (Gr-GAD). The proposed framework first employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that belong to potential anomaly groups by capturing long-range inconsistencies. Subsequently, group sampling is employed to sample candidate groups, which are then fed into the proposed Topology Pattern-based Graph Contrastive Learning (TPGCL) method. TPGCL utilizes the topology patterns of groups as clues to generate embeddings for each candidate group and thus distinct anomaly groups. The experimental results on both real-world and synthetic datasets demonstrate that the proposed framework shows superior performance in identifying and localizing anomaly groups, highlighting it as a promising solution for Gr-GAD. Datasets and codes of the proposed framework are at the github repository https://anonymous.4open.science/r/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-Detection.

The study of fairness in multiwinner elections focuses on settings where candidates have attributes. However, voters may also be divided into predefined populations under one or more attributes (e.g., "California" and "Illinois" populations under the "state" attribute), which may be same or different from candidate attributes. The models that focus on candidate attributes alone may systematically under-represent smaller voter populations. Hence, we develop a model, DiRe Committee Winner Determination (DRCWD), which delineates candidate and voter attributes to select a committee by specifying diversity and representation constraints and a voting rule. We show the generalizability of our model, and analyze its computational complexity, inapproximability, and parameterized complexity. We develop a heuristic-based algorithm, which finds the winning DiRe committee in under two minutes on 63% of the instances of synthetic datasets and on 100% of instances of real-world datasets. We present an empirical analysis of the running time, feasibility, and utility traded-off. Overall, DRCWD motivates that a study of multiwinner elections should consider both its actors, namely candidates and voters, as candidate-specific "fair" models can unknowingly harm voter populations, and vice versa. Additionally, even when the attributes of candidates and voters coincide, it is important to treat them separately as having a female candidate on the committee, for example, is different from having a candidate on the committee who is preferred by the female voters, and who themselves may or may not be female.

Automated discovery of candidate communities in networks finds a variety of applications in physical and social sciences (biological and biochemical networks, physical and virtual human networks) [1, 2]. Given a graph representing binary relations among nodes, informally and intuitively, a community corresponds to a subgraph, i.e. a subset of nodes, with relatively high edge density among the community members (nodes of the subgraph), and comparatively lower density of edges going outside the community. Defining communities more precisely and what overall community structure may be in various domains, and design of efficient robust algorithms for uncovering such in networks has been the subject of much research [1, 3]. In our use-case, we are interested in the automated discovery and effective presentation of candidate communities comprised of computers (hosts) in an enterprise network. In particular this effort is a component of a tool that provides a user, such as a security administrator of an organization, visibility into their complex network, and importantly helps the user partition the network into groups corresponding to geographic partitions, different departments, and hosts running different applications in the organization. This partitioning and naming of the groups is a necessary step in defining and maintaining network security policies, aka network segmentation: hosts in different groups (segments) can only communicate on a few well-defined and restricted channels. Such policy enforcement severely limits penetration and spread of malware and hackers. This step of grouping hosts and assigning meaningful names/labels to the groups, with the human in the loop, is also highly useful in generating insights, for example in uncovering broad patterns of communications with applications not just for security but also for network optimization.

LAS VEGAS--It's become a common analogy for the use of predictive analysis in business technology: Wayne Gretzky became the best hockey player of his generation not because he skated to where the puck was, but because he skated to where the puck was going. Similarly, financial institutions are hoping to get ahead of the growing and seemingly insurmountable problem of payment card fraud not just by looking at who cyber-attackers are going after currently but who they are likely to defraud in the near future. At the Black Hat USA conference here last week, a pair of researchers -- one from Royal Bank of Canada and the other from a service provider that focuses on dark web intelligence -- presented on their joint effort to use machine learning, predictive analytics and transactional data together to get a handle on which cardholders might be the next victims of cyber-crime. With the vast stores of payment card, transactional, personal, demographic and historical fraud data to work from, it would seem that card-issuing banks already have a lot of information with which to work to help them determine the direction of fraudulent activity. The problem with having so much data is it is hard to find the right information at the right time.

In this work, we target at the problem of offline sketch parsing, in which the temporal orders of strokes are unavailable. It is more challenging than most of existing work, which usually leverages the temporal information to reduce the search space. Different from traditional approaches in which thousands of candidate groups are selected for recognition, we propose the idea of shapeness estimation to greatly reduce this number in a very fast way. Based on the observation that most of hand-drawn shapes with well-defined closed boundaries can be clearly differentiated from non-shapes if normalized into a very small size, we propose an efficient shapeness estimation method. A compact feature representation as well as its efficient extraction method is also proposed to speed up this process. Based on the proposed shapeness estimation, we present a three-stage cascade framework for offline sketch parsing. The shapeness estimation technique in this framework greatly reduces the number of false positives, resulting in a 96.2% detection rate with only 32 candidate group proposals, which is two orders of magnitude less than existing methods. Extensive experiments show the superiority of the proposed framework over state-of-the-art works on sketch parsing in both effectiveness and efficiency, even though they leveraged the temporal information of strokes.

We propose a new sketch recognition framework that combines a rich representation of low level visual appearance with a graphical model for capturing high level relationships between symbols. This joint model of appearance and context allows our framework to be less sensitive to noise and drawing variations, improving accuracy and robustness. The result is a recognizer that is better able to handle the wide range of drawing styles found in messy freehand sketches. We evaluate our work on two real-world domains, molecular diagrams and electrical circuit diagrams, and show that our combined approach significantly improves recognition performance.