191 research outputs found
Development of Neural Electromagnetic Ontologies (NEMO): Ontology-based Tools for Representation and Integration of Event-related Brain Potentials
We describe a first-generation ontology for
representation and integration of event-related brain potentials (ERPs). The ontology is designed following OBO “best practices” and is augmented with tools to perform ontology-based labeling and annotation of ERP data, and a database that enables semantically based reasoning over these data. Because certain high-level concepts in the ERP domain are illdefined, we have developed methods to support coordinated updates to each of these three components. This approach consists of “top-down” (knowledge-driven) design and implementation, followed by “bottom-up” (data-driven) validation and refinement. Our goal is to build an ERP ontology that is logically valid, empirically sound, robust in application, and transparent to users. This ontology will be used to support sharing and meta-analysis of EEG and MEG data collected within our Neural Electromagnetic Ontologies (NEMO) project
On Black Hole Stability in Critical Gravities
We consider extended cosmological gravities with Ricci tensor and scalar
squared terms in diverse dimensions. These theories admit solutions of Einstein
metrics, including the Schwarzschild-Tangherlini AdS black holes, whose mass
and entropy vanish at the critical point. We perform linearized analysis around
the black holes and show that in general the spectrum consists of the usual
spin-2 massless and ghost massive modes. We demonstrate that there is no
exponentially-growing tachyon mode in the black holes. At the critical point,
the massless spin-2 modes have zero energy whilst the massive spin-2 modes are
replaced by the log modes. There always exist certain linear combination of
massless and log modes that has negative energy. Thus the stability of the
black holes requires that the log modes to be truncated out by the boundary
condition.Comment: 16 pages, minor corrections, further comments and references adde
Active headrest combined with a depth camera-based ear-positioning system
Active headrests can reduce low-frequency noise around ears based on active
noise control (ANC) system. Both the control system using fixed control filters
and the remote microphone-based adaptive control system provide good noise
reduction performance when the head is in the original position. However, their
performance degrades significantly when the head is in motion. In this paper, a
human ear-positioning system based on the depth camera is introduced to address
this problem. The system uses RTMpose model to estimate the two-dimensional
(2D) positions of the ears in the color frame, and then derives the
corresponding three-dimensional (3D) coordinates in the depth frame with a
depth camera. Experimental results show that the ear-positioning system can
effectively track the movement of ears, and the broadband noise reduction
performance of the active headrest combined with the system is significantly
improved when the human head is translating or rotating
Identities in Nonlinear Realizations of Supersymmetry
In this paper, we emphasize that a UV SUSY-breaking theory can be realized
either linearly or nonlinearly. Both realizations form the dual descriptions of
the UV SUSY-breaking theory. Guided by this observation, we find subtle
identities involving the Goldstino field and matter fields in the standard
nonlinear realization from trivial ones in the linear realization. Rather
complicated integrands in the standard nonlinear realization are identified as
total-divergences. Especially, identities only involving the Goldstino field
reveal the self-consistency of the Grassmann algebra. As an application of
these identities, we prove that the nonlinear Kahler potential without or with
gauge interactions is unique, if the corresponding linear one is fixed. Our
identities pick out the total-divergence terms and guarantee this uniqueness.Comment: 15 pages, more discussions added, accepted by Nucl Phys
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding
Online real-time bidding (RTB) is known as a complex auction game where ad
platforms seek to consider various influential key performance indicators
(KPIs), like revenue and return on investment (ROI). The trade-off among these
competing goals needs to be balanced on a massive scale. To address the
problem, we propose a multi-objective reinforcement learning algorithm, named
MoTiAC, for the problem of bidding optimization with various goals.
Specifically, in MoTiAC, instead of using a fixed and linear combination of
multiple objectives, we compute adaptive weights overtime on the basis of how
well the current state agrees with the agent's prior. In addition, we provide
interesting properties of model updating and further prove that Pareto
optimality could be guaranteed. We demonstrate the effectiveness of our method
on a real-world commercial dataset. Experiments show that the model outperforms
all state-of-the-art baselines.Comment: 8 Pages, Extensive Experiment
Gauged Kaluza-Klein AdS Pseudo-supergravity
We obtain the pseudo-supergravity extension of the D-dimensional Kaluza-Klein
theory, which is the circle reduction of pure gravity in D+1 dimensions. The
fermionic partners are pseudo-gravitino and pseudo-dilatino. The full
Lagrangian is invariant under the pseudo-supersymmetric transformation, up to
quadratic order in fermion fields. We find that the theory possesses a U(1)
global symmetry that can be gauged so that all the fermions are charged under
the Kaluza-Klein vector. The gauging process generates a scalar potential that
has a maximum, leading to the AdS vacuum. Whist the highest dimension for
gauged AdS supergravity is seven, our gauged AdS pseudo-supergravities can
exist in arbitrary dimensions.Comment: Latex, 13 pages, typos corrected, version in PL
Learning-based real-time imaging through dynamic scattering media
Imaging through dynamic scattering media is one of the most challenging yet fascinating problems in optics, with applications spanning from biological detection to remote sensing. In this study, we propose a comprehensive learning-based technique that facilitates real-time, non-invasive, incoherent imaging of real-world objects through dense and dynamic scattering media. We conduct extensive experiments, demonstrating the capability of our technique to see through turbid water and natural fog. The experimental results indicate that the proposed technique surpasses existing approaches in numerous aspects and holds significant potential for imaging applications across a broad spectrum of disciplines
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
The conjugate gradient method is a crucial first-order optimization method
that generally converges faster than the steepest descent method, and its
computational cost is much lower than the second-order methods. However, while
various types of conjugate gradient methods have been studied in Euclidean
spaces and on Riemannian manifolds, there has little study for those in
distributed scenarios. This paper proposes a decentralized Riemannian conjugate
gradient descent (DRCGD) method that aims at minimizing a global function over
the Stiefel manifold. The optimization problem is distributed among a network
of agents, where each agent is associated with a local function, and
communication between agents occurs over an undirected connected graph. Since
the Stiefel manifold is a non-convex set, a global function is represented as a
finite sum of possibly non-convex (but smooth) local functions. The proposed
method is free from expensive Riemannian geometric operations such as
retractions, exponential maps, and vector transports, thereby reducing the
computational complexity required by each agent. To the best of our knowledge,
DRCGD is the first decentralized Riemannian conjugate gradient algorithm to
achieve global convergence over the Stiefel manifold
Immune Checkpoint in Glioblastoma: Promising and Challenging
Glioblastoma (GBM) is a severe malignant brain cancer with poor overall survival. Conventional intervention remains dismal to prevent recurrence and deterioration of GBM cell. Recent years have witnessed exciting breakthroughs in novel immune strategies, especially checkpoint inhibitors, some of which have become adjuvant setting after standard of care in melanoma. Several clinical trials of checkpoint inhibitors are ongoing in glioblastoma and other brain carcinomas. Plus, synergistic combinations of checkpoint inhibitors with conventional therapy strategies—radiotherapy, temozolomide, bevacizumab, and corticosteroids are now being exploited and applied in clinical settings. This review highlights the recent developments of checkpoints in GBM immunotherapy to provide a brief and comprehensive review of current treatment options. Furthermore, we will discuss challenges remained, such as unique immune system of central nervous system (CNS), immune-related toxicities, synergies, and adverse interactions of combination therapies
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