Large-scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications

In computer vision, many problems such as image segmentation, pixel labelling, and scene parsing can be formulated as binary quadratic programs (BQPs). For submodular problems, cuts based methods can be employed to efficiently solve large-scale problems. However, general nonsubmodular problems are significantly more challenging to solve. Finding a solution when the problem is of large size to be of practical interest, however, typically requires relaxation. Two standard relaxation methods are widely used for solving general BQPs--spectral methods and semidefinite programming (SDP), each with their own advantages and disadvantages. Spectral relaxation is simple and easy to implement, but its bound is loose. Semidefinite relaxation has a tighter bound, but its computational complexity is high, especially for large scale problems. In this work, we present a new SDP formulation for BQPs, with two desirable properties. First, it has a similar relaxation bound to conventional SDP formulations. Second, compared with conventional SDP methods, the new SDP formulation leads to a significantly more efficient and scalable dual optimization approach, which has the same degree of complexity as spectral methods. We then propose two solvers, namely, quasi-Newton and smoothing Newton methods, for the dual problem. Both of them are significantly more efficiently than standard interior-point methods. In practice, the smoothing Newton solver is faster than the quasi-Newton solver for dense or medium-sized problems, while the quasi-Newton solver is preferable for large sparse/structured problems. Our experiments on a few computer vision applications including clustering, image segmentation, co-segmentation and registration show the potential of our SDP formulation for solving large-scale BQPs.Comment: Fixed some typos. 18 pages. Accepted to IEEE Transactions on Pattern Analysis and Machine Intelligenc

Scalable surveillance software architecture

Copyright © 2006 IEEEVideo surveillance is a key technology for enhanced protection of facilities such as airports and power stations from various types of threat. Networks of thousands of IP-based cameras are now possible, but current surveillance methodologies become increasingly ineffective as the number of cameras grows. Constructing software that efficiently and reliably deals with networks of this size is a distributed information processing problem as much as it is a video interpretation challenge. This paper demonstrates a software architecture approach to the construction of large scale surveillance network software and explores the implications for instantiating surveillance algorithms at such a scale. A novel architecture for video surveillance is presented, and its efficacy demonstrated through application to an important class of surveillance algorithms.Henry Detmold, Anthony Dick, Katrina Falkner, David S. Munro, Anton van den Hengel, Ron Morriso

Topology estimation for thousand-camera surveillance networks

Copyright © 2007 IEEESurveillance camera technologies have reached the point whereby networks of a thousand cameras are not uncommon. Systems for collecting and storing the video generated by such networks have been deployed operationally, and sophisticated methods have been developed for interrogating individual video streams. The principal contribution of this paper is a scalable method for processing video streams collectively, rather than on a per camera basis, which enables a coordinated approach to large-scale video surveillance. To realise our ambition of thousand camera automated surveillance networks, we use distributed processing on a dedicated cluster. Our focus is on determining activity topology - the paths objects may take between cameras' fields of view. An accurate estimate of activity topology is critical to many surveillance functions, including tracking targets through the network, and may also provide a means for partitioning of distributed surveillance processing. We present several implementations using the exclusion algorithm to determine activity topology. Measurements reported for the key system component demonstrate scalability to networks with a thousand cameras. Whole-system measurements are reported for actual operation on over a hundred camera streams (this limit is based on the number of cameras and computers presently available to us, not scalability). Finally, we explore how to scale our approach to support multi-thousand camera networks. ©2007 IEEE

Incremental learning of 3D-DCT compact representations for robust visual tracking

Visual tracking usually requires an object appearance model that is robust to changing illumination, pose and other factors encountered in video. Many recent trackers utilize appearance samples in previous frames to form the bases upon which the object appearance model is built. This approach has the following limitations: (a) the bases are data driven, so they can be easily corrupted; and (b) it is difficult to robustly update the bases in challenging situations. In this paper, we construct an appearance model using the 3D discrete cosine transform (3D-DCT). The 3D-DCT is based on a set of cosine basis functions, which are determined by the dimensions of the 3D signal and thus independent of the input video data. In addition, the 3D-DCT can generate a compact energy spectrum whose high-frequency coefficients are sparse if the appearance samples are similar. By discarding these high-frequency coefficients, we simultaneously obtain a compact 3D-DCT based object representation and a signal reconstruction-based similarity measure (reflecting the information loss from signal reconstruction). To efficiently update the object representation, we propose an incremental 3D-DCT algorithm, which decomposes the 3D-DCT into successive operations of the 2D discrete cosine transform (2D-DCT) and 1D discrete cosine transform (1D-DCT) on the input video data. As a result, the incremental 3D-DCT algorithm only needs to compute the 2D-DCT for newly added frames as well as the 1D-DCT along the third dimension, which significantly reduces the computational complexity. Based on this incremental 3D-DCT algorithm, we design a discriminative criterion to evaluate the likelihood of a test sample belonging to the foreground object. We then embed the discriminative criterion into a particle filtering framework for object state inference over time. Experimental results demonstrate the effectiveness and robustness of the proposed tracker.Xi Li, Anthony Dick, Chunhua Shen, Anton van den Hengel, and Hanzi Wan

High Incidence of Signs of Neuropathy and Self-Reported Substance Use Disorder for Nitrous Oxide in Patients Intoxicated with Nitrous Oxide

Introduction: The number of patients with excessive nitrous oxide (N2O) use and neurological disorders has been rising, indicating an addictive potential of N2O. We studied the incidence of self-reported substance use disorder (SUD)-related symptoms, signs of neuropathy, and the patterns of use in N2O-intoxicated patients. Methods: The Dutch Poisons Information Center (DPIC) provides information by telephone on the management of intoxications to healthcare professionals. Retrospective data on signs of neuropathy and patterns of use were collected for all N2O intoxications reported to the DPIC in 2021 and 2022. Frequent and heavy use were self-reported as "often/frequent/weekly use"and as "use of tanks or >50 balloons/session,"respectively. From this cohort, we included patients with excessive N2O use or signs of neuropathy in a prospective observational cohort study. Online surveys were sent 1 week, 1 month, and 3 months after DPIC consultation. The survey included the drug use disorder questionnaire (validated to measure self-reported substance abuse [SA] and substance dependence [SD] based on Diagnostic and Statistical Manual of Mental Disorders [DSM]-IV-TR criteria) and questions on patterns of use and signs of neuropathy. DSM-IV-TR criteria were translated to DSM-V criteria to score for mild, moderate, or severe SUD, with 2-3, 4-5, or ≥6 symptoms, respectively. Results: We included 101 N2O-intoxicated patients in the retrospective study. Of these, 41% showed signs of neuropathy (N = 41), 53% used N2O tanks to fill balloons (N = 53), 71% used them frequently (N = 72), and 76% used them heavily (N = 77). We included 75 patients in the prospective study and 10 (13%) completed the first survey. All 10 patients fulfilled the criteria for SA and SD (DSM-IV-TR, median number of questions answered "yes"= 10/12), all used N2O tanks to fill balloons, and 90% (N = 9) experienced signs of neuropathy. After 1 and 3 months, 6/7 and 1/1 patients, respectively, continued to fulfill SA and SD criteria. Translating to DSM-V criteria, 1/10 patients fulfilled the criteria for (self-reported) mild SUD, 1/10 patients for moderate SUD, and 8/10 patients for severe SUD, 1 week after consultation. Conclusion: The high proportion of N2O-intoxicated patients reporting frequent and heavy use of N2O indicates an addictive potential of N2O. Although follow-up rate was low, all patients fulfilled self-reported SA, SD (DSM-IV-TR), and SUD (DSM-V) criteria for N2O. Somatic healthcare professionals treating patients with N2O intoxications should be aware of possible addictive behavior in patients. The screening, brief intervention, and referral to treatment approach should be considered to treat patients with self-reported SUD symptoms

The effectiveness of workplace health promotion programs on self-perceived health of employees with a low socioeconomic position: An individual participant data meta-analysis

The aim of the current study was to evaluate whether workplace health promotion programs improve self-perceived health of employees with a low socioeconomic position (SEP), and whether differential effects exist between individuals with a low SEP for gender, marital status or age. Individual participant data from six Dutch intervention studies aiming at promoting healthy behavior and preventing obesity in the work setting, with a total of 1906 participants, were used. The overall intervention effect and interaction effects for gender, marital status and age were evaluated using two-stage meta-analyses with linear mixed regression models. In the first stage effect sizes of each study were estimated, which were pooled in the second stage. Compared to control conditions, workplace health promotion programs did not show an overall improvement in self-perceived health of employees with a low SEP (β0.03 (95%CI: −0.03 to 0.09)). Effects did not differ across gender, marital status and age. Future research could be focused on the determinants of self-perceived health next to health behavior to improve the health of employees with a low SEP

Inflammatory and tolerogenic myeloid cells determine outcome following human allergen challenge

Innate mononuclear phagocytic system (MPS) cells preserve mucosal immune homeostasis. We investigated their role at nasal mucosa following allergen challenge with house dust mite. We combined single-cell proteome and transcriptome profiling on nasal immune cells from nasal biopsies cells from 30 allergic rhinitis and 27 non-allergic subjects before and after repeated nasal allergen challenge. Biopsies of patients showed infiltrating inflammatory HLA-DRhi/CD14+ and CD16+ monocytes and proallergic transcriptional changes in resident CD1C+/CD1A+ conventional dendritic cells (cDC)2 following challenge. In contrast, non-allergic individuals displayed distinct innate MPS responses to allergen challenge: predominant infiltration of myeloid-derived suppressor cells (MDSC: HLA-DRlow/CD14+ monocytes) and cDC2 expressing inhibitory/tolerogenic transcripts. These divergent patterns were confirmed in ex vivo stimulated MPS nasal biopsy cells. Thus, we identified not only MPS cell clusters involved in airway allergic inflammation but also highlight novel roles for non-inflammatory innate MPS responses by MDSC to allergens in non-allergic individuals. Future therapies should address MDSC activity as treatment for inflammatory airway diseases.</p