143 research outputs found
Strong Revenue (Non-)Monotonicity of Single-parameter Auctions
Consider Myerson's optimal auction with respect to an inaccurate prior, e.g.,
estimated from data, which is an underestimation of the true value
distribution. Can the auctioneer expect getting at least the optimal revenue
w.r.t. the inaccurate prior since the true value distribution is larger? This
so-called strong revenue monotonicity is known to be true for single-parameter
auctions when the feasible allocations form a matroid. We find that strong
revenue monotonicity fails to generalize beyond the matroid setting, and
further show that auctions in the matroid setting are the only downward-closed
auctions that satisfy strong revenue monotonicity. On the flip side, we recover
an approximate version of strong revenue monotonicity that holds for all
single-parameter auctions, even without downward-closedness. As applications,
we get sample complexity upper bounds for single-parameter auctions under
matroid constraints, downward-closed constraints, and general constraints. They
improve the state-of-the-art upper bounds and are tight up to logarithmic
factors
Thermal ageing and its impact on charge trap density and breakdown strength in ldpe LDPE
Low-density polyethylene (LDPE) has been widely used as power cable insulation, because of its good electrical performance and stable chemical characteristics. However, in recent years, with the rise of large-capacity and long-distance HVDC transmission systems, the effect of space charge has a significant impact on the insulation selection and design. Furthermore, the change in the electrical performance of insulation after ageing is also required to be understood. It has been reported that ageing leads to an increase in charge trap density. The increase of trap density in LDPE makes the transport of charge carriers between traps easier. Consequently, the electrical breakdown strength will also be affected. This paper focuses on the LDPE films with different degrees of thermal ageing and studies its impact on charge trap density and change in electrical breakdown strength. The ageing degrees of sample were characterized using Fourier-Transform Infrared (FTIR). Space charge dynamics were measured using the pulsed electroacoustic (PEA) technique. In addition, electrical breakdown strength of the aged samples was measured and breakdown data were processed using the Weibull distribution. The change in characteristic breakdown strength is related to the change in charge trap density. The results suggest that the change in charge trap density of an insulating material can be used to characterize electrical performance of the material, therefore, the ageing status
Contrastive Masked Autoencoders for Self-Supervised Video Hashing
Self-Supervised Video Hashing (SSVH) models learn to generate short binary
representations for videos without ground-truth supervision, facilitating
large-scale video retrieval efficiency and attracting increasing research
attention. The success of SSVH lies in the understanding of video content and
the ability to capture the semantic relation among unlabeled videos. Typically,
state-of-the-art SSVH methods consider these two points in a two-stage training
pipeline, where they firstly train an auxiliary network by instance-wise
mask-and-predict tasks and secondly train a hashing model to preserve the
pseudo-neighborhood structure transferred from the auxiliary network. This
consecutive training strategy is inflexible and also unnecessary. In this
paper, we propose a simple yet effective one-stage SSVH method called ConMH,
which incorporates video semantic information and video similarity relationship
understanding in a single stage. To capture video semantic information for
better hashing learning, we adopt an encoder-decoder structure to reconstruct
the video from its temporal-masked frames. Particularly, we find that a higher
masking ratio helps video understanding. Besides, we fully exploit the
similarity relationship between videos by maximizing agreement between two
augmented views of a video, which contributes to more discriminative and robust
hash codes. Extensive experiments on three large-scale video datasets (i.e.,
FCVID, ActivityNet and YFCC) indicate that ConMH achieves state-of-the-art
results. Code is available at https://github.com/huangmozhi9527/ConMH.Comment: This work is accepted by the AAAI 2023. 9 pages, 6 figures, 6 table
Learning Transferable Spatiotemporal Representations from Natural Script Knowledge
Pre-training on large-scale video data has become a common recipe for
learning transferable spatiotemporal representations in recent years. Despite
some progress, existing methods are mostly limited to highly curated datasets
(e.g., K400) and exhibit unsatisfactory out-of-the-box representations. We
argue that it is due to the fact that they only capture pixel-level knowledge
rather than spatiotemporal commonsense, which is far away from cognition-level
video understanding. Inspired by the great success of image-text pre-training
(e.g., CLIP), we take the first step to exploit language semantics to boost
transferable spatiotemporal representation learning. We introduce a new pretext
task, Turning to Video for Transcript Sorting (TVTS), which sorts shuffled ASR
scripts by attending to learned video representations. We do not rely on
descriptive captions and learn purely from video, i.e., leveraging the natural
transcribed speech knowledge to provide noisy but useful semantics over time.
Furthermore, rather than the simple concept learning in vision-caption
contrast, we encourage cognition-level temporal commonsense reasoning via
narrative reorganization. The advantages enable our model to contextualize what
is happening like human beings and seamlessly apply to large-scale uncurated
video data in the real world. Note that our method differs from ones designed
for video-text alignment (e.g., Frozen) and multimodal representation learning
(e.g., Merlot). Our method demonstrates strong out-of-the-box spatiotemporal
representations on diverse video benchmarks, e.g., +13.6% gains over VideoMAE
on SSV2 via linear probing
Synthesis and optical and electrochemical properties of polycyclic aromatic compounds based on bis(benzothiophene)-fused fluorene
Cubic phase nanoparticles for sustained release of ibuprofen formulation characterization and enhanced bioavailability study
Identification of novel benzothiopyranone compounds against Mycobacterium tuberculosis through scaffold morphing from benzothiazinones
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Germline-Encoded TCR-MHC Contacts Promote TCR V Gene Bias in Umbilical Cord Blood T Cell Repertoire.
T cells recognize antigens as peptides bound to major histocompatibility complex (MHC) proteins through T cell receptors (TCRs) on their surface. To recognize a wide range of pathogens, each individual possesses a substantial number of TCRs with an extremely high degree of variability. It remains controversial whether germline-encoded TCR repertoire is shaped by MHC polymorphism and, if so, what is the preference between MHC genetic variants and TCR V gene compatibility. To investigate the "net" genetic association between MHC variations and TRBV genes, we applied quantitative trait locus (QTL) mapping to test the associations between MHC polymorphism and TCR β chain V (TRBV) genes usage using umbilical cord blood (UCB) samples of 201 Chinese newborns. We found TRBV gene and MHC loci that are predisposed to interact with one another differ from previous conclusions. The majority of MHC amino acid residues associated with the TRBV gene usage show spatial proximities in known structures of TCR-pMHC complexes. These results show for the first time that MHC variants bias TRBV gene usage in UCB of Chinese ancestry and indicate that germline-encoded contacts influence TCR-MHC interactions in intact T cell repertoires
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