194 research outputs found
A two-stage probability based, conservatism reduction methodology for traditional Minimax robust control system design
A two-stage, probability-based controller design methodology is proposed to reduce the conservatism from traditional robust minimax controller design method, by relaxing the norm-bounded parameter uncertainty constraint and incorporating uncertain parameters' probabilistic information.Ph.D
Enhanced Multimodal Representation Learning with Cross-modal KD
This paper explores the tasks of leveraging auxiliary modalities which are
only available at training to enhance multimodal representation learning
through cross-modal Knowledge Distillation (KD). The widely adopted mutual
information maximization-based objective leads to a short-cut solution of the
weak teacher, i.e., achieving the maximum mutual information by simply making
the teacher model as weak as the student model. To prevent such a weak
solution, we introduce an additional objective term, i.e., the mutual
information between the teacher and the auxiliary modality model. Besides, to
narrow down the information gap between the student and teacher, we further
propose to minimize the conditional entropy of the teacher given the student.
Novel training schemes based on contrastive learning and adversarial learning
are designed to optimize the mutual information and the conditional entropy,
respectively. Experimental results on three popular multimodal benchmark
datasets have shown that the proposed method outperforms a range of
state-of-the-art approaches for video recognition, video retrieval and emotion
classification.Comment: Accepted by CVPR202
Acid Sphingomyelinase Regulates the Localization and Trafficking of Palmitoylated Proteins
In human, loss of Acid Sphingomeylinase (ASM/SMPD1) causes Niemann-Pick Disease, type A. ASM hydrolyzes sphingomyelins to produce ceramides but protein targets of ASM remain largely unclear. ... See full text for complete abstract
The application of artificial intelligence in glaucoma diagnosis and prediction
Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups
Applying DTN Routing for Reservation-Driven EV Charging Management in Smart Cities
Charging management for Electric Vehicles (EVs) on-the-move (moving on the road with certain trip destinations) is becoming important, concerning the increasing popularity of EVs in urban city. However, the limited battery volume of EV certainly influences its driver’s experience. This is mainly because the EV needed for intermediate charging during trip, may experience a long service waiting time at Charging Station (CS). In this paper, we focus on CS-selection decision making to manage EVs’ charging plans, aiming to minimize drivers’ trip duration through intermediate charging at CSs. The anticipated EVs’ charging reservations including their arrival time and expected charging time at CSs, are brought for charging management, in addition to taking the local status of CSs into account. Compared to applying traditionally applying cellular network communication to report EVs’ charging reservations,we alternatively study the feasibility of applying Vehicle-to-Vehicle (V2V) communication with Delay/Disruption Tolerant Networking (DTN) nature, due primarily to its flexibility and cost-efficiency in Vehicular Ad hoc NETworks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V for reservation reporting is promisingly cost-efficient in terms of communication overhead for reservation making, while achieving a comparable performance in terms of charging waiting time and total trip duration
Mobile Charging as a Service: A Reservation-Based Approach
This paper aims to design an intelligent mobile
charging control mechanism for Electric Vehicles (EVs), by
promoting charging reservations (including service start time,
expected charging time, and charging location, etc.). EV mobile
charging could be implemented as an alternative recharging solution, wherein charge replenishment is provided by economically
mobile plug-in chargers, capable of providing on-site charging
services. With intelligent charging management, readily available
mobile chargers are predictable and could be efficiently scheduled
towards EVs with charging demand, based on updated context
collected from across the charging network. The context can
include critical information relating to charging sessions as well
as charging demand, etc. Further with reservations introduced,
accurate estimations on charging demand for a future moment
are achievable, and correspondingly, optimal mobile chargersselection can be obtained. Therefore, charging demands across
the network can be efficiently and effectively satisfied, with the
support of intelligent system-level decisions. In order to evaluate
critical performance attributes, we further carry out extensive
simulation experiments with practical concerns to verify our
insights observed from the theoretical analysis. Results show
great performance gains by promoting the reservation-based
mobile charger-selection, especially for mobile chargers equipped
with suffice power capacity
Redundancy-Adaptive Multimodal Learning for Imperfect Data
Multimodal models trained on complete modality data often exhibit a
substantial decrease in performance when faced with imperfect data containing
corruptions or missing modalities. To address this robustness challenge, prior
methods have explored various approaches from aspects of augmentation,
consistency or uncertainty, but these approaches come with associated drawbacks
related to data complexity, representation, and learning, potentially
diminishing their overall effectiveness. In response to these challenges, this
study introduces a novel approach known as the Redundancy-Adaptive Multimodal
Learning (RAML). RAML efficiently harnesses information redundancy across
multiple modalities to combat the issues posed by imperfect data while
remaining compatible with the complete modality. Specifically, RAML achieves
redundancy-lossless information extraction through separate unimodal
discriminative tasks and enforces a proper norm constraint on each unimodal
feature representation. Furthermore, RAML explicitly enhances multimodal fusion
by leveraging fine-grained redundancy among unimodal features to learn
correspondences between corrupted and untainted information. Extensive
experiments on various benchmark datasets under diverse conditions have
consistently demonstrated that RAML outperforms state-of-the-art methods by a
significant margin
Towards Autonomy: Cost-effective Scheduling for Long-range Autonomous Valet Parking (LAVP)
Continuous and effective developments in Autonomous Vehicles (AVs) are happening on daily basis. Industries nowadays, are interested in introducing less costly and highly controllable AVs to public. Current so-called AVP solutions are still limited to a very short range (e.g., even only work at the entrance of car parks). This paper proposes a parking scheduling scheme for long-range AVP (LAVP) case, by considering mobility of Autonomous Vehicles (AVs), fuel consumption and journey time. In LAVP, Car Parks (CPs) are used to accommodate increasing numbers of AVs, and placed outside city center, in order to avoid traffic congestions and ensure road safety in public places. Furthermore, with positioning of reference points to guide user-centric long-term driving and drop-off/pick-up passengers, simulation results under the Helsinki city scenario shows the benefits of LAVP. The advantage of LAVP system is also reflected through both analysis and simulation
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