268 research outputs found
Judging Online Peer-To-Peer Lending Behavior: An Integration of Dual System Framework and Two-Factor Theory
The past decade has witnessed a growing number of business models that facilitate economic exchanges between individuals with limited institutional mediation. One of the important innovative business models is online peer-to-peer (P2P) lending, which has received widely attention from government, industry, investors, and researchers. Based on dual system framework and two-factor theory, this research proposes a research model to investigate the role of various signals from the P2P platform in affecting lender’s investment decisions. With data collected from PPDAI, a popular Chinese P2P lending site, we test the proposed model with logistic regression and hierarchical linear model. The results reveal that most of the factors perform significantly in lenders’ decision making. We also find the specific information of an auction itself is more important than borrower’s characteristics to a large degree. Finally, the research emphasizes that bid number performs well in moderating most of the relationships between variables
Optimized Cartesian -Means
Product quantization-based approaches are effective to encode
high-dimensional data points for approximate nearest neighbor search. The space
is decomposed into a Cartesian product of low-dimensional subspaces, each of
which generates a sub codebook. Data points are encoded as compact binary codes
using these sub codebooks, and the distance between two data points can be
approximated efficiently from their codes by the precomputed lookup tables.
Traditionally, to encode a subvector of a data point in a subspace, only one
sub codeword in the corresponding sub codebook is selected, which may impose
strict restrictions on the search accuracy. In this paper, we propose a novel
approach, named Optimized Cartesian -Means (OCKM), to better encode the data
points for more accurate approximate nearest neighbor search. In OCKM, multiple
sub codewords are used to encode the subvector of a data point in a subspace.
Each sub codeword stems from different sub codebooks in each subspace, which
are optimally generated with regards to the minimization of the distortion
errors. The high-dimensional data point is then encoded as the concatenation of
the indices of multiple sub codewords from all the subspaces. This can provide
more flexibility and lower distortion errors than traditional methods.
Experimental results on the standard real-life datasets demonstrate the
superiority over state-of-the-art approaches for approximate nearest neighbor
search.Comment: to appear in IEEE TKDE, accepted in Apr. 201
Generating Human-Centric Visual Cues for Human-Object Interaction Detection via Large Vision-Language Models
Human-object interaction (HOI) detection aims at detecting human-object pairs
and predicting their interactions. However, the complexity of human behavior
and the diverse contexts in which these interactions occur make it challenging.
Intuitively, human-centric visual cues, such as the involved participants, the
body language, and the surrounding environment, play crucial roles in shaping
these interactions. These cues are particularly vital in interpreting unseen
interactions. In this paper, we propose three prompts with VLM to generate
human-centric visual cues within an image from multiple perspectives of humans.
To capitalize on these rich Human-Centric Visual Cues, we propose a novel
approach named HCVC for HOI detection. Particularly, we develop a
transformer-based multimodal fusion module with multitower architecture to
integrate visual cue features into the instance and interaction decoders. Our
extensive experiments and analysis validate the efficacy of leveraging the
generated human-centric visual cues for HOI detection. Notably, the
experimental results indicate the superiority of the proposed model over the
existing state-of-the-art methods on two widely used datasets
Federated Class-Incremental Learning with Prompting
As Web technology continues to develop, it has become increasingly common to
use data stored on different clients. At the same time, federated learning has
received widespread attention due to its ability to protect data privacy when
let models learn from data which is distributed across various clients.
However, most existing works assume that the client's data are fixed. In
real-world scenarios, such an assumption is most likely not true as data may be
continuously generated and new classes may also appear. To this end, we focus
on the practical and challenging federated class-incremental learning (FCIL)
problem. For FCIL, the local and global models may suffer from catastrophic
forgetting on old classes caused by the arrival of new classes and the data
distributions of clients are non-independent and identically distributed
(non-iid).
In this paper, we propose a novel method called Federated Class-Incremental
Learning with PrompTing (FCILPT). Given the privacy and limited memory, FCILPT
does not use a rehearsal-based buffer to keep exemplars of old data. We choose
to use prompts to ease the catastrophic forgetting of the old classes.
Specifically, we encode the task-relevant and task-irrelevant knowledge into
prompts, preserving the old and new knowledge of the local clients and solving
the problem of catastrophic forgetting. We first sort the task information in
the prompt pool in the local clients to align the task information on different
clients before global aggregation. It ensures that the same task's knowledge
are fully integrated, solving the problem of non-iid caused by the lack of
classes among different clients in the same incremental task. Experiments on
CIFAR-100, Mini-ImageNet, and Tiny-ImageNet demonstrate that FCILPT achieves
significant accuracy improvements over the state-of-the-art methods
6-Benzyl-6,7-dihydro-5H-pyrrolo[3,4-b]pyridine-5,7-dione
In the title compound, C14H10N2O2, the dihedral angle between the heterocyclic ring system and the phenyl ring is 45.8 (5)°. Weak intermolecular C—H⋯N hydrogen bonding is present in the crystal structure
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