428 research outputs found
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Exploring the supply chain management of fair trade business:Case study of a fair trade craft company in China
For two decades, fair trade has served as an alternative approach of trading that encourages minimal returns, sustainability, and ethics, by offering producers in developing countries better trading conditions and secured rights. This movement has emerged recently in China, with companies involving domestic trading between richer and poorer regions. However, lack of third-party certification, standardization, process control, public awareness, and brand recognition continue to be challenges. To understand the current fair trade business in China, this paper investigates important decision-making areas from a supply chain management perspective. With the nature of empirical studies, an in-depth case analysis of a fair trade craft company has been conducted along with the purchasing and supplier relationship management, internal operations, and marketing and customer relationship management. This company currently combines the role of fair trade organization and retailer, by implementing an in-house certification system and vertically integrating the supply chain. Findings also highlight risk at each stage of supply chain. Compared with the western society, the unique features of Chinese fair trade business are captured with prioritized areas for improvement. This research contributes to the fair trade literature by providing exploratory study into emerging issues in the supply chain, particularly inside developing countries. The recommendations also create value for policy-makers and practitioners of fair trade companies
The Marginal Value of Momentum for Small Learning Rate SGD
Momentum is known to accelerate the convergence of gradient descent in
strongly convex settings without stochastic gradient noise. In stochastic
optimization, such as training neural networks, folklore suggests that momentum
may help deep learning optimization by reducing the variance of the stochastic
gradient update, but previous theoretical analyses do not find momentum to
offer any provable acceleration. Theoretical results in this paper clarify the
role of momentum in stochastic settings where the learning rate is small and
gradient noise is the dominant source of instability, suggesting that SGD with
and without momentum behave similarly in the short and long time horizons.
Experiments show that momentum indeed has limited benefits for both
optimization and generalization in practical training regimes where the optimal
learning rate is not very large, including small- to medium-batch training from
scratch on ImageNet and fine-tuning language models on downstream tasks
Semantic-Aware Fine-Grained Correspondence
Establishing visual correspondence across images is a challenging and
essential task. Recently, an influx of self-supervised methods have been
proposed to better learn representations for visual correspondence. However, we
find that these methods often fail to leverage semantic information and
over-rely on the matching of low-level features. In contrast, human vision is
capable of distinguishing between distinct objects as a pretext to tracking.
Inspired by this paradigm, we propose to learn semantic-aware fine-grained
correspondence. Firstly, we demonstrate that semantic correspondence is
implicitly available through a rich set of image-level self-supervised methods.
We further design a pixel-level self-supervised learning objective which
specifically targets fine-grained correspondence. For downstream tasks, we fuse
these two kinds of complementary correspondence representations together,
demonstrating that they boost performance synergistically. Our method surpasses
previous state-of-the-art self-supervised methods using convolutional networks
on a variety of visual correspondence tasks, including video object
segmentation, human pose tracking, and human part tracking.Comment: 26 page
Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation
In the upcoming next-generation (5G-Advanced and 6G) wireless networks,
sensing as a service will play a more important role than ever before.
Recently, the concept of perceptive network is proposed as a paradigm shift
that provides sensing and communication (S&C) services simultaneously. This
type of technology is typically referred to as Integrated Sensing and
Communications (ISAC). In this paper, we propose the concept of sensing quality
of service (QoS) in terms of diverse applications. Specifically, the
probability of detection, the Cramer-Rao bound (CRB) for parameter estimation
and the posterior CRB for moving target indication are employed to measure the
sensing QoS for detection, localization, and tracking, respectively. Then, we
establish a unified framework for ISAC resource allocation, where the fairness
and the comprehensiveness optimization criteria are considered for the
aforementioned sensing services. The proposed schemes can flexibly allocate the
limited power and bandwidth resources according to both S&C QoSs. Finally, we
study the performance trade-off between S&C services in different resource
allocation schemes by numerical simulations
A simple and scalable hydrogel-based system for culturing protein-producing cells
Recombinant protein therapeutics have become important components of the modern medicine. Majority of them are produced with mammalian cells that are cultured either through adherent culturing, in which cells are cultured on substrates, or suspension culturing, in which cells are suspended and cultured in agitated cell culture medium in a culture vessel. The adherent cell culturing method is limited by its low yield. In suspension culturing, cells need extensive genetic manipulation to grow as single cells at high density, which is time and labor-consuming. Here, we report a new method, which utilizes a thermoreversible hydrogel as the scaffold for culturing protein-expressing cells. The hydrogel scaffolds not only provide 3D spaces for the cells, but also act as physical barriers to prevent excessive cellular agglomeration and protect cells from the hydrodynamic stresses. As a result, cells can grow at high viability, high growth rate, and extremely high yield even without genetic manipulations. The cell yield in the hydrogels is around 20 times of the suspension culturing. In addition, the protein productivity per cell per day in the hydrogel is higher than the adherent culturing method. This new method is simple, scalable and defined. It will be of great value for both the research laboratories and pharmaceutical industry for producing proteins
Synthesizing Diverse Human Motions in 3D Indoor Scenes
We present a novel method for populating 3D indoor scenes with virtual humans
that can navigate the environment and interact with objects in a realistic
manner. Existing approaches rely on high-quality training sequences that
capture a diverse range of human motions in 3D scenes. However, such motion
data is costly, difficult to obtain and can never cover the full range of
plausible human-scene interactions in complex indoor environments. To address
these challenges, we propose a reinforcement learning-based approach to learn
policy networks that predict latent variables of a powerful generative motion
model that is trained on a large-scale motion capture dataset (AMASS). For
navigating in a 3D environment, we propose a scene-aware policy training scheme
with a novel collision avoidance reward function. Combined with the powerful
generative motion model, we can synthesize highly diverse human motions
navigating 3D indoor scenes, meanwhile effectively avoiding obstacles. For
detailed human-object interactions, we carefully curate interaction-aware
reward functions by leveraging a marker-based body representation and the
signed distance field (SDF) representation of the 3D scene. With a number of
important training design schemes, our method can synthesize realistic and
diverse human-object interactions (e.g.,~sitting on a chair and then getting
up) even for out-of-distribution test scenarios with different object shapes,
orientations, starting body positions, and poses. Experimental results
demonstrate that our approach outperforms state-of-the-art human-scene
interaction synthesis frameworks in terms of both motion naturalness and
diversity. Video results are available on the project page:
https://zkf1997.github.io/DIMOS
Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence
Open-source software (OSS) supply chain enlarges the attack surface, which
makes package registries attractive targets for attacks. Recently, package
registries NPM and PyPI have been flooded with malicious packages. The
effectiveness of existing malicious NPM and PyPI package detection approaches
is hindered by two challenges. The first challenge is how to leverage the
knowledge of malicious packages from different ecosystems in a unified way such
that multi-lingual malicious package detection can be feasible. The second
challenge is how to model malicious behavior in a sequential way such that
maliciousness can be precisely captured. To address the two challenges, we
propose and implement Cerebro to detect malicious packages in NPM and PyPI. We
curate a feature set based on a high-level abstraction of malicious behavior to
enable multi-lingual knowledge fusing. We organize extracted features into a
behavior sequence to model sequential malicious behavior. We fine-tune the BERT
model to understand the semantics of malicious behavior. Extensive evaluation
has demonstrated the effectiveness of Cerebro over the state-of-the-art as well
as the practically acceptable efficiency. Cerebro has successfully detected 306
and 196 new malicious packages in PyPI and NPM, and received 385 thank letters
from the official PyPI and NPM teams
Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild
While 6D object pose estimation has wide applications across computer vision
and robotics, it remains far from being solved due to the lack of annotations.
The problem becomes even more challenging when moving to category-level 6D
pose, which requires generalization to unseen instances. Current approaches are
restricted by leveraging annotations from simulation or collected from humans.
In this paper, we overcome this barrier by introducing a self-supervised
learning approach trained directly on large-scale real-world object videos for
category-level 6D pose estimation in the wild. Our framework reconstructs the
canonical 3D shape of an object category and learns dense correspondences
between input images and the canonical shape via surface embedding. For
training, we propose novel geometrical cycle-consistency losses which construct
cycles across 2D-3D spaces, across different instances and different time
steps. The learned correspondence can be applied for 6D pose estimation and
other downstream tasks such as keypoint transfer. Surprisingly, our method,
without any human annotations or simulators, can achieve on-par or even better
performance than previous supervised or semi-supervised methods on in-the-wild
images. Our project page is: https://kywind.github.io/self-pose .Comment: Project page: https://kywind.github.io/self-pos
A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle‐adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteries.
The safety assurance is very important for the unmanned aerial vehicle lithium ion batteries, in which the state of charge estimation is the basis of its energy management and safety protection. A new equivalent modeling method is proposed for the mathematical expression of different structural characteristics, and an improved reduce particle-adaptive Kalman filtering model is designed and built, in which the incorporate multiple featured information is absorbed to explore the optimal representation by abandoning the redundant and abnormal information. And then, the multiple parameter identification is investigated that has the ability of adapting the current varying conditions, according to which the hybrid pulse power characterization test is accommodated. As can be known from the experimental results, the polynomial fitting treatment is carried out by conducting the curve fitting treatment and the maximum estimation error of the closed-circuit-voltage is 0.48% and its state of charge estimation error is lower than 0.30% in the hybrid pulse power characterization test, which is also within 2.00% under complex current varying working conditions. The iterate calculation process is conducted for the unmanned aerial vehicle lithium ion batteries together with the compound equivalent modeling, realizing its adaptive power state estimation and safety protection effectively
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