2,230 research outputs found
A new strategy to improve proactive route updates in mobile ad hoc networks
This paper presents two new route update strategies for performing proactive route discovery in mobile ad hoc networks (MANETs). The first strategy is referred to as minimum displacement update routing (MDUR). In this strategy, the rate at which route updates are sent into the network is controlled by how often a node changes its location by a required distance. The second strategy is called minimum topology change update (MTCU). In this strategy, the route updating rate is proportional to the level of topology change each node experiences. We implemented MDUR and MTCU on top of the fisheye state routing (FSR) protocol and investigated their performance by simulation. The simulations were performed in a number of different scenarios, with varied network mobility, density, traffic, and boundary. Our results indicate that both MDUR and MTCU produce significantly lower levels of control overhead than FSR and achieve higher levels of throughput as the density and the level of traffic in the network are increased
Flow Matching on General Geometries
We propose Riemannian Flow Matching (RFM), a simple yet powerful framework
for training continuous normalizing flows on manifolds. Existing methods for
generative modeling on manifolds either require expensive simulation, are
inherently unable to scale to high dimensions, or use approximations for
limiting quantities that result in biased training objectives. Riemannian Flow
Matching bypasses these limitations and offers several advantages over previous
approaches: it is simulation-free on simple geometries, does not require
divergence computation, and computes its target vector field in closed-form.
The key ingredient behind RFM is the construction of a relatively simple
premetric for defining target vector fields, which encompasses the existing
Euclidean case. To extend to general geometries, we rely on the use of spectral
decompositions to efficiently compute premetrics on the fly. Our method
achieves state-of-the-art performance on many real-world non-Euclidean
datasets, and we demonstrate tractable training on general geometries,
including triangular meshes with highly non-trivial curvature and boundaries
Long-Term Consequences of Congestion Pricing: A Small Cordon in the Hand Is Worth Two in the Bush
We evaluate and compare the long-term economic effects of three cordon-based road pricing schemes applied to the Washington, DC, metropolitan area. To conduct this analysis, we employ a spatially disaggregated general equilibrium model of a regional economy that incorporates the decisions of residents, firms, and developers, integrated with a spatially disaggregated strategic transportation planning model that features mode, time period, and route choice. We find that all cordon pricing schemes increase welfare of the residents, as well as lead to GDP growth. At the optimum, the larger cordon and a double cordon lead to higher benefits than the small cordon encompassing downtown core. Nevertheless, the small cordon seems to be a safer bet because when the toll charge is set suboptimally, the net benefits from the small cordon compared to the optimum change negligibly, while the net benefits from the larger cordon decline sharply as the charge deviates from the optimal level.traffic congestion, cordon tolls, land use, welfare analysis, road pricing, general equilibrium, simulation, Washington DC
HIV-associated multi-centric Castleman’s disease with multiple organ failure: cuccessful treatment with rituximab
Introduction:
Multicentric Castleman's Disease (MCD), a lymphoproliferative disorder associated with Human Herpes Virus-8 (HHV-8) infection, is increasing in incidence amongst HIV patients. This condition is associated with lymphadenopathy, polyclonal gammopathy, hepato-splenomegaly and systemic symptoms. A number of small studies have demonstrated the efficacy of the anti-CD20 monoclonal antibody, rituximab, in treating this condition.
Case presentation:
We report the case of a 46 year old Zambian woman who presented with pyrexia, diarrhoea and vomiting, confusion, lymphadenopathy, and renal failure. She rapidly developed multiple organ failure following the initiation of treatment of MCD with rituximab. Following admission to intensive care (ICU), she received prompt multi-organ support. After 21 days on the ICU she returned to the haematology medical ward, and was discharged in remission from her disease after 149 days in hospital.
Conclusion:
Rituximab, the efficacy of which has thus far been examined predominantly in patients outside the ICU, in conjunction with extensive organ support was effective treatment for MCD with associated multiple organ failure. There is, to our knowledge, only one other published report of its successful use in an ICU setting, where it was combined with cyclophosphamide, adriamycin and prednisolone. Reports such as ours support the notion that critically unwell patients with HIV and haematological disease can benefit from intensive care
Algorithms to automatically quantify the geometric similarity of anatomical surfaces
We describe new approaches for distances between pairs of 2-dimensional
surfaces (embedded in 3-dimensional space) that use local structures and global
information contained in inter-structure geometric relationships. We present
algorithms to automatically determine these distances as well as geometric
correspondences. This is motivated by the aspiration of students of natural
science to understand the continuity of form that unites the diversity of life.
At present, scientists using physical traits to study evolutionary
relationships among living and extinct animals analyze data extracted from
carefully defined anatomical correspondence points (landmarks). Identifying and
recording these landmarks is time consuming and can be done accurately only by
trained morphologists. This renders these studies inaccessible to
non-morphologists, and causes phenomics to lag behind genomics in elucidating
evolutionary patterns. Unlike other algorithms presented for morphological
correspondences our approach does not require any preliminary marking of
special features or landmarks by the user. It also differs from other seminal
work in computational geometry in that our algorithms are polynomial in nature
and thus faster, making pairwise comparisons feasible for significantly larger
numbers of digitized surfaces. We illustrate our approach using three datasets
representing teeth and different bones of primates and humans, and show that it
leads to highly accurate results.Comment: Changes with respect to v1, v2: an Erratum was added, correcting the
references for one of the three datasets. Note that the datasets and code for
this paper can be obtained from the Data Conservancy (see Download column on
v1, v2
Headache in an HIV positive patient: diagnostic challenges and approach to treatment
Headaches are a common complaint in HIV positive patients attending emergency services. A thorough understanding of the differential diagnoses, initial investigations and empirical management of this presentation is essential for the assessing physician. We discuss a case of a patient with known advanced HIV infection presenting with headache to the emergency department. Given the range of possible diagnoses, broad-spectrum antimicrobial therapy was initially commenced. This was stopped when magnetic resonance imaging confirmed a diagnosis of venous sinus thrombosis. Anticoagulation therapy was started in accordance with current clinical guidelines after discussing the rationale and options for treatment with the patient. Here, we review the guidelines and supporting evidence for management of venous sinus thrombosis, and consider the challenges and strategies for engaging a patient with previous poor attendance in their ongoing care
Neural Conservation Laws: A Divergence-Free Perspective
We investigate the parameterization of deep neural networks that by design
satisfy the continuity equation, a fundamental conservation law. This is
enabled by the observation that any solution of the continuity equation can be
represented as a divergence-free vector field. We hence propose building
divergence-free neural networks through the concept of differential forms, and
with the aid of automatic differentiation, realize two practical constructions.
As a result, we can parameterize pairs of densities and vector fields that
always exactly satisfy the continuity equation, foregoing the need for extra
penalty methods or expensive numerical simulation. Furthermore, we prove these
models are universal and so can be used to represent any divergence-free vector
field. Finally, we experimentally validate our approaches by computing neural
network-based solutions to fluid equations, solving for the Hodge
decomposition, and learning dynamical optimal transport maps
Local syzygies of multiplier ideals
In recent years, multiplier ideals have found many applications in local and
global algebraic geometry. Because of their importance, there has been some
interest in the question of which ideals on a smooth complex variety can be
realized as multiplier ideals. Other than integral closure no local
obstructions have been known up to now, and in dimension two it was established
by Favre-Jonsson and Lipman-Watanabe that any integrally closed ideal is
locally a multiplier ideal. We prove the somewhat unexpected result that
multiplier ideals in fact satisfy some rather strong algebraic properties
involving higher syzygies. It follows that in dimensions three and higher,
multiplier ideals are very special among all integrally closed ideals.Comment: 8 page
Equivariant Polynomials for Graph Neural Networks
Graph Neural Networks (GNN) are inherently limited in their expressive power.
Recent seminal works (Xu et al., 2019; Morris et al., 2019b) introduced the
Weisfeiler-Lehman (WL) hierarchy as a measure of expressive power. Although
this hierarchy has propelled significant advances in GNN analysis and
architecture developments, it suffers from several significant limitations.
These include a complex definition that lacks direct guidance for model
improvement and a WL hierarchy that is too coarse to study current GNNs. This
paper introduces an alternative expressive power hierarchy based on the ability
of GNNs to calculate equivariant polynomials of a certain degree. As a first
step, we provide a full characterization of all equivariant graph polynomials
by introducing a concrete basis, significantly generalizing previous results.
Each basis element corresponds to a specific multi-graph, and its computation
over some graph data input corresponds to a tensor contraction problem. Second,
we propose algorithmic tools for evaluating the expressiveness of GNNs using
tensor contraction sequences, and calculate the expressive power of popular
GNNs. Finally, we enhance the expressivity of common GNN architectures by
adding polynomial features or additional operations / aggregations inspired by
our theory. These enhanced GNNs demonstrate state-of-the-art results in
experiments across multiple graph learning benchmarks
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