Investigating the Impact of Recommendation Agents on E-commerce Ecosystem

The influence of recommendation agents on e-commerce ecosystem is profound. Technological impact of predictive intelligence could be explained more reasonably by taking a collective perspective. However, the ecosystem perspective has only served as a prologue for discussion regarding technological influence. The lack of research development associated with the technological influence on business in the ecological lens has constrained our understanding of the penetration and the role of technology in business ecosystem evolution. This paper therefore focuses on the impact of recommendation agents for online shopping environment on e-commerce ecosystem. Moreover, this paper observes and explains the phenomena that most participants in the e-commerce ecosystem are taking recommendation agents as one of the strategic technological investments towards further development as a common goal

Experimental and theoretical evidence for molecular forces driving surface segregation in photonic colloidal assemblies

Surface segregation in binary colloidal mixtures offers a simple way to control both surface and bulk properties without affecting their bulk composition. Here, we combine experiments and coarse-grained molecular dynamics (CG-MD) simulations to delineate the effects of particle chemistry and size on surface segregation in photonic colloidal assemblies from binary mixtures of melanin and silica particles of size ratio (Dlarge/Dsmall) ranging from 1.0 to similar to 2.2. We find that melanin and/or smaller particles segregate at the surface of micrometer-sized colloidal assemblies (supraballs) prepared by an emulsion process. Conversely, no such surface segregation occurs in films prepared by evaporative assembly. CG-MD simulations explain the experimental observations by showing that particles with the larger contact angle (melanin) are enriched at the supraball surface regardless of the relative strength of particle-interface interactions, a result with implications for the broad understanding and design of colloidal particle assemblies

META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection

For learning-based sound event localization and detection (SELD) methods, different acoustic environments in the training and test sets may result in large performance differences in the validation and evaluation stages. Different environments, such as different sizes of rooms, different reverberation times, and different background noise, may be reasons for a learning-based system to fail. On the other hand, acquiring annotated spatial sound event samples, which include onset and offset time stamps, class types of sound events, and direction-of-arrival (DOA) of sound sources is very expensive. In addition, deploying a SELD system in a new environment often poses challenges due to time-consuming training and fine-tuning processes. To address these issues, we propose Meta-SELD, which applies meta-learning methods to achieve fast adaptation to new environments. More specifically, based on Model Agnostic Meta-Learning (MAML), the proposed Meta-SELD aims to find good meta-initialized parameters to adapt to new environments with only a small number of samples and parameter updating iterations. We can then quickly adapt the meta-trained SELD model to unseen environments. Our experiments compare fine-tuning methods from pre-trained SELD models with our Meta-SELD on the Sony-TAU Realistic Spatial Soundscapes 2023 (STARSSS23) dataset. The evaluation results demonstrate the effectiveness of Meta-SELD when adapting to new environments.Comment: Submitted to DCASE 2023 Worksho

Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS

Non-maximum suppression (NMS) is an essential post-processing module used in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments are conducted using the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time

B cell abnormalities and autoantibody production in patients with partial RAG deficiency

Mutations in the recombination activating gene 1 (RAG1) and RAG2 in humans are associated with a broad spectrum of clinical phenotypes, from severe combined immunodeficiency to immune dysregulation. Partial (hypomorphic) RAG deficiency (pRD) in particular, frequently leads to hyperinflammation and autoimmunity, with several underlying intrinsic and extrinsic mechanisms causing a break in tolerance centrally and peripherally during T and B cell development. However, the relative contributions of these processes to immune dysregulation remain unclear. In this review, we specifically focus on the recently described tolerance break and B cell abnormalities, as well as consequent molecular and cellular mechanisms of autoantibody production in patients with pRD