90 research outputs found
Purified and Unified Steganographic Network
Steganography is the art of hiding secret data into the cover media for
covert communication. In recent years, more and more deep neural network
(DNN)-based steganographic schemes are proposed to train steganographic
networks for secret embedding and recovery, which are shown to be promising.
Compared with the handcrafted steganographic tools, steganographic networks
tend to be large in size. It raises concerns on how to imperceptibly and
effectively transmit these networks to the sender and receiver to facilitate
the covert communication. To address this issue, we propose in this paper a
Purified and Unified Steganographic Network (PUSNet). It performs an ordinary
machine learning task in a purified network, which could be triggered into
steganographic networks for secret embedding or recovery using different keys.
We formulate the construction of the PUSNet into a sparse weight filling
problem to flexibly switch between the purified and steganographic networks. We
further instantiate our PUSNet as an image denoising network with two
steganographic networks concealed for secret image embedding and recovery.
Comprehensive experiments demonstrate that our PUSNet achieves good performance
on secret image embedding, secret image recovery, and image denoising in a
single architecture. It is also shown to be capable of imperceptibly carrying
the steganographic networks in a purified network. Code is available at
\url{https://github.com/albblgb/PUSNet}Comment: 8 pages, 9 figures, Accepted at CVPR202
On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions
As Federated Learning (FL) has gained increasing attention, it has become
widely acknowledged that straightforwardly applying stochastic gradient descent
(SGD) on the overall framework when learning over a sequence of tasks results
in the phenomenon known as ``catastrophic forgetting''. Consequently, much FL
research has centered on devising federated increasing learning methods to
alleviate forgetting while augmenting knowledge. On the other hand, forgetting
is not always detrimental. The selective amnesia, also known as federated
unlearning, which entails the elimination of specific knowledge, can address
privacy concerns and create additional ``space'' for acquiring new knowledge.
However, there is a scarcity of extensive surveys that encompass recent
advancements and provide a thorough examination of this issue. In this
manuscript, we present an extensive survey on the topic of knowledge editing
(augmentation/removal) in Federated Learning, with the goal of summarizing the
state-of-the-art research and expanding the perspective for various domains.
Initially, we introduce an integrated paradigm, referred to as Federated
Editable Learning (FEL), by reevaluating the entire lifecycle of FL. Secondly,
we provide a comprehensive overview of existing methods, evaluate their
position within the proposed paradigm, and emphasize the current challenges
they face. Lastly, we explore potential avenues for future research and
identify unresolved issues.Comment: 7 pages, 1 figure, 2 tabel
Front-running Attack in Sharded Blockchains and Fair Cross-shard Consensus
Sharding is a prominent technique for scaling blockchains. By dividing the
network into smaller components known as shards, a sharded blockchain can
process transactions in parallel without introducing inconsistencies through
the coordination of intra-shard and cross-shard consensus protocols. However,
we observe a critical security issue with sharded systems: transaction ordering
manipulations can occur when coordinating intra-shard and cross-shard consensus
protocols, leaving the system vulnerable to attack. Specifically, we identify a
novel security issue known as finalization fairness, which can be exploited
through a front-running attack. This attack allows an attacker to manipulate
the execution order of transactions, even if the victim's transaction has
already been processed and added to the blockchain by a fair intra-shard
consensus.
To address the issue, we offer Haechi, a novel cross-shard protocol that is
immune to front-running attacks. Haechi introduces an ordering phase between
transaction processing and execution, ensuring that the execution order of
transactions is the same as the processing order and achieving finalization
fairness. To accommodate different consensus speeds among shards, Haechi
incorporates a finalization fairness algorithm to achieve a globally fair order
with minimal performance loss. By providing a global order, Haechi ensures
strong consistency among shards, enabling better parallelism in handling
conflicting transactions across shards. These features make Haechi a promising
solution for supporting popular smart contracts in the real world. To evaluate
Haechi's performance, we implemented the protocol using Tendermint and
conducted extensive experiments on a geo-distributed AWS environment. Our
results demonstrate that Haechi achieves finalization fairness with little
performance sacrifice compared to existing cross-shard consensus protocols
Prophet: Conflict-Free Sharding Blockchain via Byzantine-Tolerant Deterministic Ordering
Sharding scales throughput by splitting blockchain nodes into parallel
groups. However, different shards' independent and random scheduling for
cross-shard transactions results in numerous conflicts and aborts, since
cross-shard transactions from different shards may access the same account. A
deterministic ordering can eliminate conflicts by determining a global order
for transactions before processing, as proved in the database field.
Unfortunately, due to the intertwining of the Byzantine environment and
information isolation among shards, there is no trusted party able to
predetermine such an order for cross-shard transactions. To tackle this
challenge, this paper proposes Prophet, a conflict-free sharding blockchain
based on Byzantine-tolerant deterministic ordering. It first depends on
untrusted self-organizing coalitions of nodes from different shards to
pre-execute cross-shard transactions for prerequisite information about
ordering. It then determines a trusted global order based on stateless ordering
and post-verification for pre-executed results, through shard cooperation.
Following the order, the shards thus orderly execute and commit transactions
without conflicts. Prophet orchestrates the pre-execution, ordering, and
execution processes in the sharding consensus for minimal overhead. We
rigorously prove the determinism and serializability of transactions under the
Byzantine and sharded environment. An evaluation of our prototype shows that
Prophet improves the throughput by and achieves nearly no aborts
on 1 million Ethereum transactions compared with state-of-the-art sharding
Training and Serving System of Foundation Models: A Comprehensive Survey
Foundation models (e.g., ChatGPT, DALL-E, PengCheng Mind, PanGu-)
have demonstrated extraordinary performance in key technological areas, such as
natural language processing and visual recognition, and have become the
mainstream trend of artificial general intelligence. This has led more and more
major technology giants to dedicate significant human and financial resources
to actively develop their foundation model systems, which drives continuous
growth of these models' parameters. As a result, the training and serving of
these models have posed significant challenges, including substantial computing
power, memory consumption, bandwidth demands, etc. Therefore, employing
efficient training and serving strategies becomes particularly crucial. Many
researchers have actively explored and proposed effective methods. So, a
comprehensive survey of them is essential for system developers and
researchers. This paper extensively explores the methods employed in training
and serving foundation models from various perspectives. It provides a detailed
categorization of these state-of-the-art methods, including finer aspects such
as network, computing, and storage. Additionally, the paper summarizes the
challenges and presents a perspective on the future development direction of
foundation model systems. Through comprehensive discussion and analysis, it
hopes to provide a solid theoretical basis and practical guidance for future
research and applications, promoting continuous innovation and development in
foundation model systems
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation
We present ArtiGrasp, a novel method to synthesize bi-manual hand-object
interactions that include grasping and articulation. This task is challenging
due to the diversity of the global wrist motions and the precise finger control
that are necessary to articulate objects. ArtiGrasp leverages reinforcement
learning and physics simulations to train a policy that controls the global and
local hand pose. Our framework unifies grasping and articulation within a
single policy guided by a single hand pose reference. Moreover, to facilitate
the training of the precise finger control required for articulation, we
present a learning curriculum with increasing difficulty. It starts with
single-hand manipulation of stationary objects and continues with multi-agent
training including both hands and non-stationary objects. To evaluate our
method, we introduce Dynamic Object Grasping and Articulation, a task that
involves bringing an object into a target articulated pose. This task requires
grasping, relocation, and articulation. We show our method's efficacy towards
this task. We further demonstrate that our method can generate motions with
noisy hand-object pose estimates from an off-the-shelf image-based regressor.Comment: 3DV-2024 camera ready. Project page:
https://eth-ait.github.io/artigrasp
Compassion, Discrimination, and Prosocial Behaviors: Young Diasporic Chinese During the COVID-19 Pandemic
The coronavirus disease 2019 (COVID-19) pandemic has fueled anti-Asian, especially anti-Chinese sentiments worldwide, which may negatively impact diasporic Chinese youths\u27 adjustment and prosocial development. This study examined the association between compassion, discrimination and prosocial behaviors in diasporic Chinese youths during the COVID-19 pandemic. 360 participants participated and completed the multi-country, cross-sectional, web-based survey between April 22 and May 9, 2020, the escalating stage of the pandemic. This study found compassion as prosocial behaviors\u27 proximal predictor, while discrimination independently predicted participation in volunteering, and could potentially enhance the association between compassion and charitable giving. These findings suggest that prosociality among young people is sensitive to social context, and that racial discrimination should be considered in future prosocial studies involving young members of ethnic and racial minorities
Mechanism, structural and functional insights into nidovirus-induced double-membrane vesicles
During infection, positive-stranded RNA causes a rearrangement of the host cell membrane, resulting in specialized membrane structure formation aiding viral genome replication. Double-membrane vesicles (DMVs), typical structures produced by virus-induced membrane rearrangements, are platforms for viral replication. Nidoviruses, one of the most complex positive-strand RNA viruses, have the ability to infect not only mammals and a few birds but also invertebrates. Nidoviruses possess a distinctive replication mechanism, wherein their nonstructural proteins (nsps) play a crucial role in DMV biogenesis. With the participation of host factors related to autophagy and lipid synthesis pathways, several viral nsps hijack the membrane rearrangement process of host endoplasmic reticulum (ER), Golgi apparatus, and other organelles to induce DMV formation. An understanding of the mechanisms of DMV formation and its structure and function in the infectious cycle of nidovirus may be essential for the development of new and effective antiviral strategies in the future
Improving hindlimb locomotor function by non-invasive AAV-mediated manipulations of propriospinal neurons in mice with complete spinal cord injury
After complete spinal cord injury, spinal segments below the lesion maintain inter-segmental communication via the intraspinal propriospinal network. Here, the authors show that neurons in these circuits can be chemogenetically modulated to improve locomotor function in mice after spinal cord injury
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